CNLS Smart Grid Seminar Series
Sponsored by CNLS, IS&T, Energy Institutes at LANL
& LDRD DR on ``Optimization and Control Theory for Smart Grids"
CNLS conference room, Tues, 10:30-12:00
| Ensemble control involves the manipulation of an uncountably in nite collection of structurally identical or similar dynamical systems, which are indexed by a parameter set, by applying a common control without using feedback. This subject is motivated by compelling problems in quantum control, sensorless robotic manipulation, and neural engineering, which involve ensembles of linear, bilinear, or nonlinear oscillating systems, for which analytical control laws are infeasible or absent. My focus is on constructive control design methods for practical ensemble control problems. The fi rst result is an efficient numerical method based on the singular value decomposition (SVD) for the synthesis of minimum norm ensemble controls for time-varying linear systems. This method is extended to iterative techniques to accommodate bounds on the control amplitude, and to synthesize ensemble controls for unitary bilinear systems. Example ensemble systems include harmonic oscillators, quantum transport, and quantum spin transfers on the special orthogonal group SO(n), in particular the Bloch system on SO(3). Another result involves the control of ensembles of nonlinear oscillators, which occur in neuroscience and electrochemistry. The ability to optimally manipulate such systems provides insight into treatments for disorders such as Parkinson's disease and epilepsy. A key phenomenon is entrainment, which refers to the dynamic synchronization of an oscillating system to a periodic input. Phase coordinate transformation, formal averaging, and the calculus of variations are used to derive minimum energy and minimum mean time controls that entrain ensembles of non-interacting oscillators to a harmonic or subharmonic target frequency, and establish desired dynamical structures. |
| During the last years a fundamental transformation of the electric power and integrated energy systems has been initiated in Europe and other industrialized countries. These new developments will drastically change the structure of these systems and the way they are operated. One can identify two main driving forces in this process. First, a massive introduction of distributed renewable power sources, i.e. mostly wind power and photo voltaics (PV), requires new system solutions. Since these sources are fluctuating and uncertain new methods for planning, managing, and operating the system must be developed and introduced. Second, information and communication technologies (ICT) offer new possibilities with regard to system control in general and management of distributed power sources and demand side response in particular. This presentation will give an overview of the current developments in this field. The work at ETHZ concerning modeling of future energy systems will be presented. In particular the energy hub and power node will be described and their use in system analysis exemplified. Simulations from real system will be presented. The role of storage devices and demand side response will be elaborated. |
| Power systems are undergoing an unprecedented period of change driven by increased penetration of renewable generation, electric vehicles, and increased take-up of Demand Side Response facilitated by smart metering. All those changes mean that the traditional mode of power system operation and control based on centralised control, deterministic (N-1)-based dispatch and generation following demand will have to change. A new paradigm is needed to facilitate distributed and stochastic control. This change of paradigm will require a significant interdisciplinary effort combining the skills of engineers, mathematicians, economists, social scientists and politicians. The talk will give examples of some of the research projects that address those challenges. |
| Future smart grids will likely support bi-directional flow of electricity and include power production from multiple, disparate, and uncontrollable sources due to a high penetration of distributed renewable energy resources. Some of the more challenging problems for the future grid include maximizing the use and efficiency of renewable resources, and realizing optimal demand and power production responses that can complement renewable intermittency. Integration of renewables together with energy storage systems has been motivated by the increasing attention to feature renewable energies from not only solar and wind power but also the excess generation from many customers. Effective use of renewable resources using battery systems can be realized by balanced distribution of such distributed energy resources (DERs) with complementary demand and dispatchable generation responses. The spatial distribution, intermittency, and uncontrollability of most renewable resources, however, make stable and reliable electricity transmission and distribution difficult especially with high renewable market penetration in large-scale complex power networks. In order to use energy storage systems effectively to optimize DERs as well as realize a reliable and sustainable future grid, we present an autonomous distributed management model that can realize optimum power flow control together with demand and power response, which especially integrates Kirchhoff’s core theory and autonomous agent systems. |
| In recent years, UAVs been have extensively used as relays in disaster management applications. By the dynamic nature of UAVs as they traverse rugged terrains, the problem of determining the interconnections among UAVs is important from the view point of power consumption, maximum number of communication links and robust connectivity. We pose this network synthesis problem as a Mixed Integer Semi-Definite Program (MISDP) with the smallest non-zero eigenvalue of the weighted networks’s Laplacian matrix as the measure for robustness. Solving this MISDP is a difficult optimization problem because of its non-linear objective coupled with the possibility that the number of feasible solutions combinatorially explode with the size of the network. In this presentation, we discuss novel algorithms based on cutting plane methods to obtain optimal solutions and upper bounds for problems of moderate sizes. Also, based on the spectrum of connected networks, we develop efficient neighbourhood search heuristics which can be applied for large UAV networks. |
| The equilibrium operation of power grid requires that the generation meets the demand at each time instant and any deviation raises critical stability concerns. However with ever increasing load, more so with the imminent release of plug-in electric vehicles en masse, the grids are under greater pressure. On the other hand, the growing penetration of renewable energy sources provides an excellent opportunity to meet the increased electricity demand, but the challenge remains to mitigate the uncertainties associated with renewable generation. The challenge here is to ensure seamless integration of newer forms of generation and load, while maintaining a stable and satisfactory grid-level performance. Specifically in this talk, I will be covering how we propose to model the aggregate dynamics of a large group of flexible (or “time deferrable”) electric loads, such as plug-in electric vehicle chargers, thermostat-controlled heating/cooling loads, etc. using a hysteresis-based approach, and control their aggregate electricity demand to mitigate fluctuations in renewable generation. It will be shown that, often this population dynamics exhibits interesting nonlinear behavior, such as period adding cascade, and thus need to be understood well to ensure safe electrical grid operations. Finally I would briefly discuss some of the more recent techniques that concern with analyzing the power systems stability from a Lyapunov stability perspective, which is often very complex because of the system’s complexity and high dimensionality. However a recent advancement shows promising results by using sum-of-squares techniques to compute the system’s Lyapunov function. |
| We consider the problem of synthesizing optimal linear feedback policies subject to arbitrary convex constraints on the feedback matrix. This is known to be a hard problem in the usual formulations (H2; H1; LQR) and previous works have focused on characterizing classes of structural constraints that allow ef?cient solution through convex optimization or dynamic programming techniques. In this paper, we propose a new control objective and show that this formulation makes the problem of computing optimal linear feedback matrices convex under arbitrary convex constraints on the feedback matrix. This allows us to solve problems in decentralized control (sparsity in the feedback matrices), control with delays and variable impedance control. Although the control objective is nonstandard, we present theoretical and empirical evidence that it agrees well with standard notions of control. We also present an extension to nonlinear control af?ne systems. We present numerical experiments validating our approach. |
| Due to the potential for finding globally optimal solutions, significant research interest has focused on the application of semidefinite optimization techniques to problems in the field of electric power systems. This seminar discusses a semidefinite relaxation of the non-convex AC optimal power flow (OPF) problem, which seeks to minimize the operating cost of an electric power system subject to both engineering inequality and network equality constraints. The convex semidefinite relaxation is capable of finding globally optimal solutions to many OPF problems. By exploiting power system sparsity, semidefinite relaxations of practically sized OPF problems are computationally tractable. The semidefinite relaxation is “tight” for many but not all OPF problems. For practical problems where the semidefinite relaxation is not tight, results show small active and reactive power mismatches at the majority of load buses while only small subsets of the network exhibit significant mismatch. This suggests that the relevant non-convexities in these problems are isolated in small subsets of the network. Examination of the feasible spaces for small test cases illustrates such non-convexities and explains the semidefinite relaxation’s lack of tightness. Finally, preliminary results from the application of higher-order “moment” semidefinite relaxations show promise in obtaining globally optimal solutions to these small test cases. |
| There is an emerging consensus that the nation's electricity grid is vulnerable to cyber attacks. This vulnerability arises from an increasing reliance on transmitting remote measurement data over legacy data networks to system operators who make critical decisions based on available data. Data integrity attacks are a class of cyber attacks that involve a compromise of information that is processed by the grid operator. These attacks have consequences only when the system operator responds to compromised data, for example, by re-dispatching generation under normal or contingency protocols. These consequences include (a) financial losses from sub-optimal economic dispatch to service loads, (b) robustness/resiliency losses from placing the grid at operating points that are at greater risk from contingencies, and (c) systemic losses resulting from cascading failures induced by poor operational choices. In this talk we compute the worst case economic consequence of an unobservable data integrity attacks. This serves as an effective metric to assess the importance of various attacks. |
| Energy Systems Integration is a methodology for deliberate and objective energy system planning, operations, and optimization across multiple scales, domains, and time resolutions. Dr. Kroposki will discuss the vision for the future energy system to develop, demonstrate, and operate highly integrated, flexible, scalable, and efficient systems that provide integration of clean energy sources while maintaining reliability and resiliency at an affordable cost. Dr. Kroposki will also discuss NREL’s recently opened Energy Systems Integration Facility (ESIF). At 182,500ft2, the ESIF is the largest R&D facility on NREL’s campus and focuses on research and development of clean energy technologies such as variable renewable generation, smart load controls, and electric vehicles that are being deployed in the electric power system at an increasing rate. ESIF research seeks to connect the simulation environments with demonstration of technology at scale through hardware-in-the-loop testing. |
| Nowadays, the world is facing several major challenges that include air and water pollution, global warming and the rising market prices of the primary energy resources, among others. Natural gas, as an energy source, offers several advantages in comparison with other non-renewable energy sources to overcome these problems. For example, natural gas is a cleaner fossil fuel than oil or coal, i.e., it emits a lower percentage of carbon dioxide than gasoline, diesel or coal. Natural gas is also more economically attractive than gasoline despite that its listing on the financial sector has been increasing in recent years (which represents better profits for the industrial sector). Since natural gas has become a good candidate for being one of the preferential supplies of primary energy, the natural gas industry has had to quickly expand its transmission networks in order to satisfy the increasing demand of the gas consumption. Hence, this presentation aims at integrating mathematical models and solution approaches for tackling various optimization problems in natural gas transport via pipeline systems. Mainly, three challenging problems and their underlying optimization methods are addressed: (1) The fuel cost minimization problem -formulated as a non-linear programming (NLP) model- for which three different solution methodologies are proposed, namely, (a) a heuristic method that includes a non-sequential dynamic programming technique, (b) a tree decomposition technique and a dynamic programming algorithm, which is proposed to overcome dense network instances, and (c) an adaptive discretization (multi-local search) heuristic to enhance the application of the dynamic programming. (2) Natural gas transport problems with variable specific gravity and compressibility factor. Here, an enhanced mathematical model is proposed to account for more accurate estimates in maximum flows on steady-state transmission network systems. This problem arises since traditional approaches in steady-state flow problems assume the gas specific gravity and compressibility factor as universal constants, thus leading to misleading results. Due to the non-convexity of the suggested model, a heuristic algorithm based on an iterative scheme is proposed in which a simpler NLP model is solved. (3) The line-packing problem. Here, a mathematical model is proposed to optimize the short-term storage and transport of natural gas in pipelines for a given planning horizon. The proposed model adopts all characteristics of a mixed-integer non-linear programming (MINLP) model. A thorough computational evaluation based on a global optimizer is conducted to assess the computability of the model. Empirical evidence over a wide set of problem instances illustrate the usefulness and positive impact of the proposed strategies resulting in cconsiderably high-quality solutions when compared to existing approaches and commercial methods. |
| Energy microgrids fully integrated with buildings and with the smart-grid are a promising concept for accelerating the introduction of distributed energy generation. When fully implemented, this concept will provide a large number of benefits ranging from a wider use of renewable resources to improved energy efficiency, power quality and reliability. In order to bring this concept to market, there are a large number of technologies and systems integration concepts that need to be mature alongside with the development of a strong business case for the involved stakeholders. These technologies include microgrid energy and power management systems, alongside with many other enabling technologies such as smart meters, power electronics, communications between stakeholders and microgrid components, implementation of cyber-security, etc. The energy management system (or supervisory system) is responsible for decisions relative to supply and demand energy flows and set-points based on operating costs, customer preferences, utility requests and operational constraints, and communicates these decisions by dispatching set-points to the local controllers. The power management system requires a much higher bandwidth, and needs to provide system stability, coordination between multiple microgrid components, and synchronous connection and disconnection with the grid; alongside with the capability to provide other power services such as power factor and power quality correction. The seminar to be presented at Los Alamos National Lab will introduce some of the power and energy management systems that United Technologies Research Center (UTRC) has been developing and demonstrating, as part of its integrated building solutions portfolio. UTRC has been working with concepts based on model predictive controls and stochastic programming formulation (SPF) to address the uncertainty of load and weather profiles and the dynamic nature of energy storage and renewable resources. Besides, power algorithms for seamless transition with the external-grid, providing multiple ancillary and power services have been demonstrated in real buildings and will be presented. |
| Modern society critically depends on the services electric power provides. Power systems rely on a network of power lines and transformers to deliver power from the sources of power (generators) to the consumers (loads). However, when power lines fail (for example through lightning or natural disasters) the network is often NOT able to fulfill all of the demand for power. To mitigate these failures, increasingly, new devices such as FACTS devices have been deployed on power systems. A FACTS device allows power grid operators to adjust the impedance parameters of power lines, thereby redistributing flow in the network and potentially increasing the amount of power that is supplied. Here we develop new approaches for determining the optimal parameter settings for supplying the maximal amount of power. |
| Power system operators make real time control decisions based on the real time state estimate. This state includes not only continuous variables (complex voltage at each bus) but also discrete topological state variables. Previous work has developed uncertainty quantification methods that provide bounds on the continuous state variables which hold at specific confidence levels. I will present an extension of this work that allows for estimation of discrete topological variables. The objective of this project is to provide a topological model bank that the true topology must lie in. Furthermore, we can assure that the true topology falls within this model bank at a specific certainty. |
| Demand response (DR) is increasing being viewed as an alternative method for balancing generation and load on the power system. To this end, HVAC loads, which incorporate a significant amount of thermal storage, could potentially provide this service with a minimal investment in additional infrastructure. This research looks at a large commercial building that has been equipped for DR and tests the system dynamics under various control inputs. The results suggest that building energy use and efficiency are significantly impacted by the type of control signal applied, implying that care must be taken to reduce transient losses in a DR application. |
| The electric power grid is undergoing rapid changes driven by demand growth, rising energy costs, concerns about energy security and the desire to integrate more renewable energy sources. In order to facilitate these changes a greater understanding of how they will affect both the stability and performance of the power system is required. For example, the addition of inherently intermittent renewable energy sources such as solar and wind power will affect the power balance on the grid. The nature of these resources also has the potential to make the power system more distributed through the addition of numerous small wind and solar plants. This talk illustrates the use of control and optimization based methods to provide insight into a few example problems related to the design, operation and management of the envisioned new power system. First, we discuss the use of storage to provide greater system flexibility and to mitigate the inherent variability of renewable sources. We then extend this idea to investigate the factors that drive optimal storage sizing and siting in a transmission network. The second part of the talk will briefly introduce two complementary problems. The first examines how increasing amounts of distributed generation will affect power system efficiency. In particular, we evaluate the losses associated with synchronizing a power system after a transient disturbance or in maintaining synchrony in the face of ongoing disturbances and their relationship to the network properties. Finally, we address the question of how wind farm placement affects system damping and offers control strategies to drive the frequency response of the integrated system to a desired shape. The array of problems discussed represent results and analysis for a small subset of stability and performance issues related to grid efficiency and are meant to demonstrate the fact that achieving the full potential of “smart” and clean power systems is a multifaceted problem that will require a combination of strategies. |
| In this tutorial I will summarize recent developments on the convex relaxations of optimal power flow (OPF) problems. OPF is a fundamental problem that underlies many power system operations. It can be formulated as a nonconvex quadratically constrained quadratic program (QCQP). Recently several convex relaxations have been developed based on semidefinite programming, chordal extension, and second-order cone programming in both bus injection model and branch flow model. I will explain the relations among these relaxations, and the various sufficient conditions in the literature that guarantee the exactness of these relaxations. |
| Continual growth in the size and peak power consumption of high performance computing (HPC) platforms is increasing the stress on local power distribution systems. In particular, the large, fast and random transitions in HPC power consumption create large and uncertain voltage swings and power loss, unless proper control is designed and performed. We consider three types of control devices, i.e., fixed capacitor, switchable capacitor and FACTS device, installed on the HPC load side of the power distribution system. Though these devices have different control logics and response characteristics, they can jointly provide the reactive power compensation required by HPC to regulate the voltage and decrease power loss (improve energy efficiency). We formulate the minimum power loss objective and the voltage safe bound as a chance-constrained optimization problem for HPC power system control. Moreover, supposing that the optimal control is always performed and considering different prices of control devices, we formulate an optimal control devices sizing problem. Based on the statistics of HPC power transitions observed from LANL's Ceilo platform, we find the structures of both the control and sizing problems which make them tractable to solve. This is a joint work with Misha Chertkov, Scott Backhaus and Steven Low. |
| Natural gas is used to heat homes and to power gas-turbines in power plants which produce electricity. Sources of natural gas are often separated by great distances from the loads. As a result, there are major gas pipelines that run across states and across countries. Laws of physics that govern the steady-state flow through these pipelines dictate that the square flow is proportional to the difference in square pressure between the ends of a pipe and inversely to the length of the pipe. We consider networks with tree structures, which closely resemble the structure of major interstate pipelines in the US. Given a fixed input flow, the remaining flows on the tree are uniquely determined based on the loads. Since it is not uncommon for pipeline lengths to exceed 1,000 miles, to prevent pressure from dropping too much it is necessary to install compressor stations along the pipe which locally boost the pressure, making it feasible to transport the gas over such long distances. However, there is an operational cost associated with running the compressors that depends on their compression ratios: the ratio of outlet to inlet pressure at the compressor. Different configurations of compressor ratios might lead to feasible pressures that support the flows, but some are more expensive than others. The goal is to find an optimal configuration that minimizes the total cost of running the compressors while maintaining feasible pressures. We propose two ways to solve this optimization problem efficiently. The first method is based on reformulation of the problem as a geometric program, and the second is based on a well-known dynamic programming approach. We apply both these methods to the Belgium gas network and to the US Transco pipeline, which runs from the Gulf of Mexico up to Pennsylvania, and discuss the results. |
| The contribution of renewable resources to the energy portfolio across the world has been steadily increasing over the past few years. Several studies predict the continuation of this trend in the future leading to large scale integration of renewable resources into energy networks. A principal challenge associated with this is the intermittency and non-dispatchability of the renewable sources. This necessitates the need to incorporate faster reserves, storage devices and similar services operating alongside the slow ramping conventional generators in the energy network. To maintain the robustness of such a network, there are proposals to require hourly planning for some resources, and sub-hourly planning for others: an hourly scale may be used for conventional generator production levels and a sub-hourly scale for renewable generator levels and/or storage and transmission network utilization. The talk will present a multiple time scale stochastic programming formulation of the economic dispatch problem and algorithmic frameworks to tackle it. The first approach highlights the difference between hourly and sub-hourly planning of economic dispatch and uses the two-stage Stochastic Decomposition(SD) algorithm. The second framework combines three principal components: optimization, dynamic control and simulation. The conventional generator decisions are obtained iteratively by solving a regularized linear problem in the first stage of SD. For these first stage decisions, a policy for recommending the dispatch decisions is identified using an Approximate Dynamic Programming based controller. A vector auto-regression based simulator is used to provide the sub-hourly wind generation scenarios. The performance of these algorithms was tested on the IEEE model networks and the Illinois network. The insights gained regarding the benefits of sub-hourly planning and role of operating reserves/storage in energy network with high renewable penetration will be presented. |
| Inter-area oscillations in bulk power systems are typically poorly controllable by means of local decentralized control. Recent research efforts have been aimed at developing wide-area control strategies that involve communication of remote signals. In conventional wide-area control, the control structure is fixed a priori typically based on modal criteria. In contrast, here we employ the recently-introduced paradigm of sparsity- promoting optimal control to simultaneously identify the optimal control structure and optimize the closed-loop performance. To induce a sparse control architecture, we regularize the standard quadratic performance index with an L1-penalty on the feedback matrix. The quadratic objective functions are inspired by the classic slow coherency theory and are aimed at imitating homogeneous networks without inter-area oscillations. We briefly review the slow coherency theory and illustrate it with different examples. Next, we use the New England power grid model to demonstrate that the proposed combination of the sparsity-promoting control design with the slow coherency objectives performs almost as well as the optimal centralized control while only making use of a single wide-area communication link. In addition to this nominal performance, we also demonstrate that our control strategy yields favorable robustness margins and that it can be used to identify a sparse control architecture for control design via alternative means. |
| In this presentation we review modeling of modern power markets, its applications, methods, software tools and challenges faced by typical users of these tools. Special emphasis is placed on the ability of modeling tools to replicate the multi-cycle decision logic underlying operations of real power markets spanning over multiple time scales ranging from minutes to days and weeks. We will review the implementation of such decision logic in the Power System Optimizer (PSO). Next, we discuss the opportunities provided by commercial cloud computing in modeling power markets and overview the architecture of pCloud, a power market simulation environment utilizing the PSO engine and implemented on the Windows Azure and Amazon commercial clouds. |
| We formulate an optimal load control (OLC) problem in power networks where the objective is to minimize the aggregate cost of tracking an operating point subject to power balance over the network. We prove that the swing dynamics and the branch power ?ows, coupled with frequency-based load control, serve as a distributed primal-dual algorithm to solve OLC. Even though the system has multiple equilibrium points, we prove that it nonetheless converges to an optimal point. This result implies that the local frequency deviations at each bus convey exactly the right information about the global power imbalance for the loads to make individual decisions that turn out to be globally optimal. It allows a completely decentralized solution without explicit communication among the buses. Simulations show that the proposed OLC mechanism can resynchronize bus frequencies with signi?cantly improved transient performance. |
| With the increasing penetration of renewable energies, the power system is being stressed by the great uncertainty from the supply side. On the other hand, with the popularity of electric vehicles and the expansion of data centers, the power system will witness a significant increase of demand in the next decade. Either of these two stresses alone can make the conventional power system collapsed easily. Together, they imply smart grid is a crucial task in the next decade. A naive approach to solve the problems is to directly ask the demand to follow the supply in a centralized control fashion. However, this may not work in practice since each entity in the system pursues its own interest. This motivates us to study the deregulated electricity market for the smart grid from a network economic approach. To ensure a successful market, we investigate two key challenges: efficiency and fairness. To understand the first challenge, we study the ancillary service market. One important challenge with wind DG units is to provide low-cost and fast-responding reactive power compensation of the wind turbine's inductive load to ensure a stable voltage profile in the system. Though STATCOMs have fast enough response time, they are usually expensive and may not be a feasible solution for large-scale deployment of wind DG units. We look at an alternative approach to compensate reactive power of wind DG units: to utilize the the inverter circuits in the charger of PEVs. We consider a scenario where a wind DG unit is co-located with a PEV charging station, and we use game theoretic model to ensure adequate incentives to the PEV owners to actively participate in the market. Our incentive design can achieve the same optimal performance as the centralized control does. To tackle the second challenge - the fairness issue, our goal is to identify and to assess the market power in the deregulated electricity market. This is challenging because congestion fragments the transmission system into smaller zones, behind bottleneck interconnects, and the markets within these zones may be highly concentrated even when the whole transmission system seems competitive. We introduce a novel functional approach to measuring long term market power that unifies a variety of popular market power indices. Our functional approach naturally defines a family of superadditive market power measures and can serve as the guidance for the evolution of the power system. |
| For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of these stochastic fluctuations near a codimension 1 saddle-node bifurcation, we explicitly calculate the autocorrelation function of the state vector and show how its behavior explains the phenomenon of critical slowing-down often observed for power systems on the threshold of blackout. We also estimate the collapse probability/mean clearing time for the power system and construct a new indicator function signaling the proximity to a large scale collapse. The new indicator function is easy to estimate in real time using PMU data feeds as well as SCADA information about fluctuations of power load on the nodes of the power grid. We discuss control strategies leading to the minimization of the collapse probability. |
| An imminent release of plug-in electric vehicles en masse will add substantial load to electrical power grids that are already operating near limits. Coordinated control of vehicle charging, however, can eliminate the need for expensive overhauls of grid infrastructure. Furthermore, the growing penetration of renewable energy sources provides an excellent opportunity to meet the increased electricity demand, but the challenge remains to tackle the variability and intermittency associated with renewable energy. Our research focuses on identifying and analyzing key issues regarding interactions between renewable generation, vehicle charging, and the power grid. In order to address these issues, we are designing control schemes that ensure seamless integration of newer forms of generation and load, while achieving satisfactory grid-level performance in areas such as loss minimization, voltage regulation, generation balancing and valley filling. We show how hysteresis-based control strategies can be utilized to model and control a large number of electrical loads, e.g. thermostatic loads, plug-in electric vehicle chargers. Our study shows that such load aggregations often display rich non-linear dynamic behavior such as period-multiplying bifurcations. We also look at how a synchronized response of vehicle chargers can impact the resiliency of electrical grid. Another interesting issue that we are looking at is optimal control of reactive power output from photovoltaic inverters on a radial distribution feeder, based upon only locally available measurements. |
| This talk gives an overview of energy research at NICTA, including residential load scheduling, randomized load control, fault location and restoration, and power flow equations. |
| Critical transmission outages often cause line overloading and voltage degradation. Without corrective action, eventually overloaded lines may trip and/or voltage collapse may ensue. Importantly, these secondary effects evolve relatively slowly, allowing sufficient time for corrective controls to be enacted. This talk will present receding horizon model predictive control (MPC) strategies that capture the relevant dynamics governing the thermal behaviour of overloaded transmission lines. The controls available to MPC include generation set-points, energy storage and load regulation. MPC determines the optimal use of those resources, subject to a variety of constraints that include rate limits and resource availability. The proposed corrective control strategies will be illustrated using a system of around 100 nodes. |
| The modernization of electricity networks to accommodate increasing renewable energy targets and new technologies, such as electric vehicles and demand management, leads to system control and planning challenges and so analytical challenges. To name two, we will have to deal with much greater uncertainty and scale in computations. For example, the latter arises from the increasing granularity of modeling and control devices across transmission and distribution all the way to households. This presentation is based on a Future Grid (FG) project in Australia funded by the CSIRO and four universities. It takes a long-term view out to 2050. Advanced modeling and analytical techniques will be developed to provide a suite of tools to help understand and design future grids. This is being done by following a tree structure of alternative scenarios according to technology and policy changes, probabilistic modeling of generation sites and outputs, use of enhanced Monte Carlo methods with learning, network science methods, automated scanning tools for system properties such as stability and multi-objective stochastic optimization on networks for planning. One way or another, grids must become more adaptive and resilient to changing power supply and demand, failures and attacks through coordinated planning and control more than ever before. The talk will introduce some key ideas and preliminary results from the FG project. |
| We consider a scenario where multiple classes of requests arrive at a dispatcher at time-varying rates for processing by various resources. This scenario is typical in data centers where each server hosts multiple classes of applications. It is well documented that data centers consume a phenomenal amount of energy while their servers are under-utilized. The main challenge is in managing time-variability and uncertainty. We have formulated approaches for smoothing so that requests receive time-stable performance, and control algorithms can be developed for energy efficiency. Our objective is to develop strategies to: (i) assign classes to servers, (ii) determine the number of servers to be powered on, (iii) route requests from the dispatcher to appropriate servers, and (iv) create a procedure for speed scaling. The goal is to develop the aforementioned strategies under: (a) a distributed setting where real-time information is not exchanged between the sub-systems, i.e. servers and the dispatcher; (b) a requirement for time-stable performance; (c) a preference for simplified operations while maintaining cost-effectiveness and high performance. We show that in the asymptotic regime where we scale the arrival rates, number of classes and the number of servers, the aforementioned objectives and goals can be met. |
| Before the functional unbundling and deregulation of electricity markets, system expansion planning was a centralized process for relatively self-sufficient concentrated territories served by vertically integrated utility companies. Since deregulation, generation supply has been de-centralized to competitive markets, driven by power purchase agreements and/or the establishment of capacity markets. Meanwhile, the responsibility for transmission planning remains primarily with regulated utilities, often coordinated in regional transmission organizations. Existing system expansion planning methodology fails to properly measure locational aspects of resource adequacy and for reliability tradeoff between generation and transmission options. This and the lack of coordination in generation and transmission planning lead to inefficient investment decisions. The formulation of the generation and transmission expansion problem which incorporates resource adequacy assessment into the stochastic optimization framework will be presented. The system expansion problem is considered as an auction resolved through a stochastic mixed integer linear problem in which generation and transmission expansion offers are explicitly identified on the electrical grid modeled using linearized DC SCOPF. A novel formulation of transmission topology control using flow cancelling transactions will be introduced. Locational resource adequacy indicators are derived as dual variables: shadow prices for reliability limiting transmission facilities and Locational Stochastic Reliability Prices (LSRPs). The main result demonstrates that the auction can identify an optimal mix of generation and transmission expansion offers which guarantees resource adequacy of the system at least costs. Moreover, the problem of transmission cost allocation is unambiguously resolved through the market pricing mechanism |
| Renewable generation penetration levels are expected to rise dramatically in the near future. Due to the variability of renewable generation, high penetration levels will require the system operator to maintain the system at finer time scales. In turn, this requires fast state estimation that can accurately track the state of the system. The most accurate, non-linear state estimators are executed infrequently. This is because they are computationally time consuming and they require many measurements, some of which may be sampled infrequently. Between executions, the state is updated using less accurate estimators (often linear estimators). We developed a state estimation technique that can be computed quickly, using only a few measurements that are sampled frequently. This technique bounds the current state of the system. Generally, few measurements result in loose bounds. However, by selectively placing a few frequently sampled measurements we can find tight bounds on specific 'important' state variables. This state estimation technique allows the system operator to accurately track a few "important" state variables on a fine time scale. |
| This talk is about design of feedback gains that strike a balance between the quadratic performance of distributed systems and the sparsity of the controller. Our approach consists of two steps. First, we identify sparsity patterns of the feedback gains by incorporating sparsity-promoting penalty functions into the optimal control problem, where the added terms penalize the number of communication links in the distributed controller. Second, we optimize the feedback gains subject to the structural constraints determined by the identified sparsity patterns. In the first step, we identify sparsity patterns of the feedback gains using the alternating direction method of multipliers, which is a powerful algorithm well-suited to large optimization problems. This method alternates between optimizing the sparsity and optimizing the closed-loop performance, which allows us to exploit the structure of the corresponding objective functions. In particular, we take advantage of the separability of the sparsity-promoting penalty functions to decompose the minimization problem into sub-problems that can be solved analytically. In the second step, we develop Newton's method in conjunction with the conjugate gradient scheme to efficiently compute the sparse feedback matrix. Several examples are provided to illustrate the effectiveness of the developed approach |
| Abstract: Wind power is one of the most attractive sources of clean and renewable energy due to its vast potential and availability. Despite the valuable efforts to date, fundamental problems related to the flow/structure interaction, scale dynamics, power maximization, structural reliability, multivariable control, flow control strategies, environmental assessments, among others, remain unsolved/unclear. Properly addressing these issues will allow us to widespread these sources of power. In this talk I will present some of the latest insights obtained from wind tunnel experiments carried out at the St. Anthony Falls Laboratory using different sizes and number of model wind turbines. The focus is placed on understanding the complex mechanisms of the flow/structure turbulent interaction; tip vortices stability, drag reduction - flow control, scalability of the problem, thermal stratification effects and the relevance of the wind farm layout in both total power and turbulence loads. These studies provide valuable information of the turbulent flow/structure interaction needed to improve the design of wind turbines and wind farms. This information is being used to test and guide the development of improved parameterizations of wind turbines in high-resolution numerical models, such as large-eddy simulations (LES). |
| As the penetration of wind and solar energy into the electric grid continues to grow, there will be an increasing need to evolve demand-side solutions capable of compensating the inherent variability in power supply from such resources. Today, demand is largely treated as inelastic. However, the power requirements of many commercial and residential loads are such that a fraction of power demand at any given moment is inherently deferrable in time subject to a deadline constraint on the total energy supplied. In this talk, I'll discuss some limitations of spot pricing mechanisms (e.g., real-time pricing) as a means of inducing responsive demand and suggest a novel forward contracting mechanism for deadline-differentiated pricing of deferrable energy to alleviate these difficulties. Essentially, consumers who are willing to defer their consumption further in time will receive a more favorable per-unit price for energy. The supply side is modeled stochastically to capture variability in renewable power supply. Using a general model for consumer preferences to capture the effect of consumption deferral on utility, we prove the existence of a competitive equilibrium and provide a characterization of deadline-differentiated prices yielding such an equilibrium. I'll also discuss provably optimal online scheduling algorithms to dynamically allocate the variable supply to a bundle of deadline-differentiated energy tasks. |
| Abstract: Smart Grid applications are imposing challenging requirements of privacy, security and reliability on the N-way communication infrastructure being designed to support multiple grid applications. These challenges stem from the increasing incorporation of distributed renewable energy sources on to the grid, the rising deployment of electric vehicles, and active consumer participation into power grid operations, all of which communicate with the utility control center with varying degrees of priority and security. To address these challenging requirements, we propose SeDAX, a SEcure Data-centric Application eXtensible middleware for Smart Grid applications. SeDAX im- plements scalable, resilient and secure data delivery and data sharing in a wide area network. The platform can scalably handle high volumes of data generated by both applications and sensors and support secure data-centric (or information-centric) group communication. The primary goals of this platform are to support communication resilience, data availability and privacy preserving secure data analytics. The talk will describe the design details of the SeDAX platform and illustrate its operation in the context of implementing demand response. |
| The California state legislature has set aggressive Green House Gas emissions reduction goals of 60% by 2030 and 80% by 2050. Achieving these goals requires combining strategies that significantly rethink how we plan and operate our power networks. In this talk I address how to rethink the dispatch of conventional generation, how to design and operate demand shaping strategies and the challenges to manage autonomous distribution system clusters in the presence of significant penetration of renewable generation. I present Risk Limiting Dispatch that significantly reduces integration cost of wind power compared to current operation procedures, without requiring ample redesign of markets. I then discuss demand shaping program targeting opportunities based on smart meter and appliance load data. I conclude the talk introducing two measurement and automation platforms for distribution side cluster management based on integrated power sensing. |
| The physical security of a power system is usually assessed using the N-1 criterion. Operators like this criterion because it give simple answers, its implementation is straightforward and compliance can be demonstrated unambiguously. However, it does not guarantee that the power system will be operated at a constant risk level, even less an optimal level of risk. This presentation will discuss work that has been done to assess more accurately the level of physical security in a power system, particularly as it relates to the risk of blackouts. In particular, the interactions between the power system's "heavy electrical" infrastructure and its associated information infrastructure will be analyzed. Daniel Kirschen received his mechanical and electrical engineer's degree from the Free University of Brussels (Belgium) and his MS and PhD degrees in electrical engineering from the University of Wisconsin-Madison. From 1985 to 1994 he worked for Control Data Corporation and Siemens on the development of software for power system operation. From 1994 till 2010 he taught at The University of Manchester (UK). He is now Close Professor of Electrical Engineering at the University of Washington in Seattle. |
| Concerns regarding global warming, the finite nature of conventional energy reserves, energy security and rising costs are driving the need to find more efficient and renewable energy sources and systems. Unfortunately, renewable power sources, such as solar cells or wind farms, differ significantly from conventional power plants and integrating them into the power grid poses a number of challenges. This talk examines strategies for mitigating these challenges through case studies using wind power. The first presents a simple framework to study the use of storage combined with fast-ramping spinning reserves (back-up generation) to mitigate the inherent variability of renewable sources. This idea is then extended to address the question of how to place storage resources in order to simultaneously mitigate risks and minimize costs given different network topologies. Finally, an examination of the complementary problem of wind farm placement and its effect on system damping is carried out. Achieving the full potential of “smart” and clean power systems will require a combination of different generation schemes, storage, ancillary services and other energy assets as well as an understanding of how to best coordinate and distribute these resources. |
| The optimal PMU locations to collect voltage phase angle measurements for detecting line outages in wide-area transmission networks are investigated. We consider two optimization criteria: a) minimum distance among the voltage phase angle signatures of the outages, and b) percentage of indistinguishable outage pairs. The problems are formulated as nonlinear integer programs. Based on greedy heuristics and convex relaxation, we develop branch and bound algorithms to find the optimal PMU locations. Using the proposed algorithms, the optimal tradeoff between the number of PMUs and the outage detection performance is characterized for IEEE 14, 24 and 30 bus systems. The algorithm is shown to find the globally optimal PMU locations in a small number of iterations. It is observed that it is sufficient to have roughly one third of the buses providing PMU measurements in order to achieve the same outage detection performance as with all the buses providing PMU measurements. |
| Fault-Induced Delayed Voltage Recovery (FIDVR) is an observed phenomenon in electric power system that is driven by the stalling of compressor loads. Under certain conditions following a successfully cleared fault, the voltage will depressed for up to a minute after which it will rise to a level exceeding its pre-fault level. This behavior is consistent with the cascaded stalling of compressor motor loads (air conditioners) during the fault, and subsequent unsynchronized tripping of components on local thermal protection. The concern about FIDVR events is that they may lead to larger system-wide cascading outages. In this talk we will review some of the data associated with events and for laboratory tests of air conditioner stall characteristics. We then consider the question of how many compressor motors need to stall before all of them on a feeder must stall. We perform a bifurcation analysis to determine whether there exist solutions for 1, 2, or more stalled motors, leaving the rest running normally. As we will show, a low percentage of motor stalls will necessitate that all will. This makes it difficult to mitigate FIDVR events in the short term. As part of our attempt to construct complete bifurcation diagrams it is necessary to compute all the solutions to the power system equations. These equations may be viewed as a set of coupled multi-variable quadratic equations. The task of finding all solutions scales poorly with the size of the system. We show that a long-standing practical approach to finding all the real-valued solutions to the power flow solutions is flawed. This remains an open question for research. |
| Abstract: The linearized DC model has become a ubiquitous tool in electrical power systems, especially in planning and operational settings. In recent years, it has been used in an increasing number of applications, including line switching, power restoration, and vulnerability analysis. However, there is a lack of understanding regarding the accuracy and feasibilty of its solutions in these contexts. This talk presents a systematic analysis of the linearized DC model and proposes a number of techniques to overcome its limitations. In particular, the talk shows how to use smart load and generation scheduling to compensate for its inaccuracies in power restoration applications. It also shows how to approximate line losses and apparent power in cold- and hot-start models. Experimental results on standard IEEE benchmarks and on power restoration benchmarks using the infrastructure of the United States demonstrate the benefits and practicability of the proposed enhancements. |
| Abstract: Broad smart-grid implementation could enable the deployment of flexible and adaptive transmission networks, thus allowing for the transmission topology to be optimized depending on electricity demand and other system conditions. One suite of technologies that would facilitate real-time transmission optimization includes Flexible Alternating Current Transmission Systems (FACTS), which use power-electronic switching to enact fast changes to the electrical network topology. FACTS technologies could be centrally controlled by system operators, as is the currently the case with transmission assets, or they could be dispatched alongside generation in hourly electricity markets. We formulate the profit-maximizing objective of FACTS device owners and the cost-minimization objective of transmission system operators as a multi-level Mathematical Program with Equilibrium Constraints, and characterize equilibrium in such a market on small systems. Bidding by FACTS devices introduces non-convexities into the market that can lead to so-called “Nash Traps” under which local optima are mis-identified when computing market equilibria. We examine strategic bidding behavior under multiple compensation mechanisms by FACTS device owners. When FACTS devices are compensated using a clearing price-mechanism, the owners of such devices have incentives to relieve congestion in transmission-constrained systems. When FACTS devices are compensated based on locational price differentials, device owners no longer have the same incentives. FACTS devices may be characterized as transmission-type assets, but in a market design context should be treated more like generation assets. |
| Abstract: This presentation discussed some approaches to simulate uncertainties associated with system load and variable generation and their application to practical problems that needs to be addressed by operations and planning engineers. Another example reflected in this presentation addresses uncertainties in power system dynamic behavior as they are reflected by synchrophasor measurements. |
| Abstract: The LBNL study is the first to identify frequency response limitations and verify that frequency response metrics are useful for planning and operating the bulk-power system reliably in the context of integrating new resources. The approach builds on existing industry practices for controlling frequency after the unexpected loss of a large amount of generation. The study also introduces a set of metrics and tools for measuring the adequacy of frequency response within an interconnection. Primary frequency response is the main metric used in this study to assess the adequacy of primary frequency control reserves, which are necessary to ensure reliable power system operation. Primary frequency response measures what is needed to arrest frequency decline (i.e., to form a frequency nadir) at a frequency higher than the highest set point for under-frequency load shedding within an interconnection. The frequency response metrics introduced by the report can be used to maintain the reliable operation of an interconnection under changing circumstances and to guide and gauge the extent and success of reliable integration of any new resource into an interconnection. The metrics can also be used to plan a path forward when existing resource mixes undergo major changes, such as when conventional plants are retired or de-rated or when new forms of generation are added such as variable renewable generation. The study tested and validated frequency response metrics through simulation studies of the generation and transmission infrastructures that power system operators expect to have in place in 2012. Wind is expected to be a major new source of renewable generation for each of the U.S. interconnections in the near term. Wind generation creates challenges for reliable operation of the electric power system in part because the electricity generated from wind is more variable than electricity generated from conventional sources. The purpose of the study was to specifically determine and validate metrics that can be used to assess and plan for reliable integration of any amount of variable renewable resources. |
| Abstract: Seasonal hurricanes and ice storms often cause significant damage to the power system infrastructure in the United States. These disasters can cause power outages that last several days or even weeks in extreme cases. This talk will introduce a comprehensive disaster preparation and recovery algorithm that can be used to provide decision support to policy makers and mitigate blackout effects. We formulate the power restoration planning and recovery problem as a stochastic optimization problem which takes into consideration the physical properties of the power network and the vehicle routing of repair crews. The talk will be organized into four sections, formulation of the recovery problem, linear approximations of the power systems, the preparation phase, and recovery phase. These sections will span topics including, power system modeling, stochastic programming, hybrid optimization, mixed integer programing, constraint programming, and local search. No previous knowledge of these topics is required. |
| Abstract: Renewable energy sources and plug-in hybrid electric vehicles (PHEVs) are environmentally friendly technologies that are currently being advocated for the nation's power grid. It has been shown that some of the roadblocks for adoption of these technologies can be overcome by providing control and interaction to renewables and PHEVs. A comprehensive model for optimally locating PHEV battery exchange stations has been proposed in recent research, but some critical research questions still remain open. This research focuses on modeling and solving a multi-stage planning problem for locating the charging stations and deciding when to open them. Indeed, the number of exchange stations needs to grow over time to accommodate the growing demand. In addition, new exchange stations are viewed as further encouragement for driving a PHEV, thus driving the demand even higher. Two different modeling approaches for this multi-stage problem are presented, and computational comparison results are given. |
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Abstract:
Optimal Transmission Switching for electric power networks is a research topic of great interest for both researchers and electric power engineers. Transmission switching is commonly considered a corrective mechanism tool for overloaded networks; however in recent years it has been sought as a means to reduce power loss in the network. Ultimately the objective of this analysis is to provide smart tools for power and economic dispatch planning, allowing utilities and engineers to utilize existing infrastructure as a means of automatic control.
The optimization problem is a complex multi-objective search including criteria such as system power loss, equality/non-equality constraints, islanding management, and radial topology configuration in the case of distribution networks. Many optimization methods have been adopted for the transmission switching problem in past years using various heuristic search algorithms. The scope of methodology extends from tried and true methods such as simulated annealing to more modern methods like neural network optimization. Solution benchmarks under consideration commonly include solution state accuracy, computational burden and solution time.
The objective of this presentation is to provide a comprehensive account of development in transmission switching methodologies. Benchmarking and testing strategies will be discussed as well as application of the Transmission Expansion Planning Tool in finding transmission switching solutions. Limited Discrepancy Search (LDS) and Probe Discrepancy Search (PDS) Algorithms are compared with more classic optimization strategies, as seen in defining literature and publications in this research field. Kunaal Verma received his B.S. in Electrical Engineering and is currently pursuing a M.S. from Michigan State University. He is a Graduate Research Assistant at LANL, D-4 for the Summer of 2011 and interned at LANL during the Summer of 2010 as a post-Bac student. His interests include smart grid research, electric power control and energy policy analysis. He is mentored by G. Loren Toole of D-4. |
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Abstract:
This presentation describes an effective algorithm for optimizing distribution system
operation in a smart grid, from cost and system stability points of view. The proposed algorithm
mainly aims at controlling the power available from different sources such that they satisfy the
load demand with the least possible cost while giving the highest priority to renewable energy
sources. Moreover, a smart battery charger was designed to control the batteries in such a way
that they are allowed to discharge only when there is no very big load predicted within an
immediate future period. This will make such a storage available to act as a buffer for the
predicted large load to increase the stability of the system and reduce voltage dips. In addition,
batteries are used to serve another purpose from an economic point of view, which is peak shifting
during the day in order to avoid the relatively high prices of grid power during peak
periods. Since this algorithm is mainly dependent on forecasted data of the power available from
different renewable energy sources as well as the load demand, a full attention has been paid to
the forecasting process. Hence, a non-linear regression technique was applied to build accurate
forecasting models for different sources and for the load. These models help in monitoring and
predicting the total power generation and demand online. Furthermore, a fuzzy controller was
utilized to make use of the forecasted data of the coming peak period then decide dynamically
the amount of power that should be taken out of energy storage. Different case studies were
investigated to verify the validity of the proposed algorithm and define the system behavior
under several conditions. The presentation will also describe efforts currently underway on the development of a wide area measurement (WAMS) system for smart grid applications. This system is based on synchronized phasor measurement technology with the access of a broadband communication capability. The purpose is to increase the overall system efficiency and reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with improved monitoring, protection, and control capabilities of the power network. An example of consisting of a 50 kW generation station, 20 kW wind turbine, three transformers, four circuit breakers, four buses, two short transmission lines, and two 30 kW loads is presented. The communication layer consists of three PMUs, located at generation and load buses, and one Phasor data concentrator (PDC) collecting the data received from remote PMUs and send it to the control center for analysis and control actions. The power system status can be easily monitored and controlled in real time by using the measured bus values online which improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the validity of the proposed WAMS technology for smart grid applications. Bio: Dr. Osama A. Mohammed received his M.S. and Ph.D. degrees in Electrical Engineering from Virginia Tech He is currently a Professor of Electrical Engineering at Florida International University. He has more than 30 years of teaching, research and industrial consulting experience. He authored and coauthored more than 300 technical papers in the archival literature and in National and International Conference records in addition to several book Chapters and numerous technical and project reports. Professor Mohammed specializes in Electrical Energy Systems especially in areas related to alternate and renewable energy systems and smart grid applications. He is also interested in design optimization of electromagnetic devices, Artificial Intelligence Applications to Energy Systems as well as Electromagnetic Field Computations in Nonlinear Systems for these energy system applications. He has current interest in Shipboard power systems and integrated motor drives. Dr. Mohammed has been successful in obtaining a number of research contracts and grants from industries and Federal government agencies. He has a current active and funded research programs in several areas funded by the office of Naval Research and the US Department of Energy. Professor Mohammed is a Fellow of IEEE and is a Fellow of the Applied Computational Electromagnetic Society. He is an Editor of IEEE Transactions on Energy Conversion, IEEE Transactions on Magnetics, Power Engineering Letters and also an Editor of COMPEL. He received many awards for excellence in research, teaching and service to the profession. Professor Mohammed has been General Chair of several major IEEE conferences including, IEEE IEMDC, IEEE CEFC, IEEE ISAP and the COMPUMAG Conferences. Professor Mohammed was the chair of the IEEE PES Electric Machinery Committee and was a member of the PES Governing Board and the Chair of the PES Constitution and Bylaws Committee. He is currently the Chair of the International Steering Committee for IEEE IEMDC conference and the IEEE CEFC Conference and is a member of several other PES committees subcommittees and working groups. |
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Abstract:
The first part of the talk is devoted to a research topic I conducted last year. We consider a Cournot oligopoly model where multiple suppliers (oligopolists) compete by choosing quantities. We compare the social welfare achieved at a Cournot equilibrium to the maximum possible, for the case where the inverse market demand function is convex. We establish a lower bound on the efficiency of Cournot equilibria in terms of a scalar parameter extracted from the inverse demand function. Through the methodology used in our social welfare analysis, we then construct a framework to compare the social welfare, consumer surplus, and supplier profit realized at a Cournot Equilibrium (CE), a Social Optimum (SO) where the social welfare is maximized, and a Monopoly Output (MO) where the aggregate profit of suppliers is maximized. We derive a lower bound on the ratio of the aggregate profit earned by all suppliers at a CE to the maximum possible aggregate profit, that is, the profit that would have been achieved if the suppliers were to collude at a MO. Our results provide nontrivial quantitative bounds on the loss of social welfare and aggregate profit for several inverse demand functions that appear in the economics literature. In the second part of the talk, we study the scheduling problem for PHEVs to maximize social welfare. If the decision is made by a centralized operator, the centralized PHEV scheduling problem can be formulated as a dynamic programming problem. The difficulty of solving the formulated dynamic programming problem is that the state space grows exponentially with the number of vehicles. The issue on computation complexity can be addressed through an approximate dynamic programming approach. We use limited look ahead policies where the heuristics can be obtained through greedy algorithms, or by solving a simplified dynamic programming problem with an aggregated state space. For the case where each vehicle makes its decision to maximize its own benefit, we construct a dynamic game theoretical model to study the decentralized PHEV scheduling problem. |
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Abstract:
It is well recognized that distributed energy sources such as solar and wind are intermittent, that their presence will shift the operation of power system from the current mode of regulated utilities to a competitive generation provision, and that a high penetration level of these sources demands new regimes of measurement and estimation, communication, control, protection, security, and operation. On the other hand, contemporary sensing and communication networks enable real-time collection and subscription of geographically-distributed information and such information can be used to significantly enhance the performance of electric power systems at the levels of generation, transmission and distribution. Through a shared sensing/communication network, distributed generation can now be controlled to operate autonomously and robustly as micro grids, and operation of these micro grids can exhibit cooperative behaviors to enhance voltage stability of distribution networks. By incorporating dynamic game and cooperative control algorithms, pooled generation/consumption of micro-grids can be automatically optimized to enhance both energy dispatch and transient stability of the overall power system.
The talk illustrates analysis and design tools for the so-called cooperative networked systems among which information exchanges are local and intermittent, their changing patterns are not known a priori, and may have significant latencies. Canonical forms and designs of distributed cooperative control are introduced to ensure cooperative stability and design cooperative control for networked linear and nonlinear systems. Formulations and distributed algorithms of estimation, optimization and dynamic games are also illustrated.
Based on these methodologies and tools, innovative algorithms of distributed control and optimization can be implemented to enable robust, intelligent and efficient operations for power systems with distributed and intermittent power generation sources. As an illustration, the following three-layered control-optimization-control structure is discussed: (i) A cooperative control algorithm that enables micro-grids to form autonomously and to evolve as distributed generation changes over time; (ii) Each of the self-evolving micro-grids negotiates with the main grid to determine the best operating conditions by following the incentive (and limit) specified by the main grid and by maximizing the group energy output; and (iii) In the event of a major disturbance or fault, distributed generations and their inverter-based controls provide transient controls that maintain voltage stability of distribution networks; (iv) Dynamic game algorithms enable multi-level optimization that improves both energy dispatch and transient stability of the overall power system. Sample results from the on-going DoE SEGIS and NSF projects will be presented to illustrate their effectiveness. Bio: Dr. Qu received his Ph.D. degree in Electrical Engineering at the Georgia Institute of Technology in June 1990. Since then, he has been with the University of Central Florida and is currently the SAIC Endowed Professor at UCF and a Professor of Electrical Engineering. Dr. Qu's areas of expertise are nonlinear systems and control, energy and power systems, and robotics. His recent research activities in controls have been cooperative control of heterogeneous dynamical systems as well as control of nonholonomic systems. In energy systems, his current research covers such subjects as dynamic stability of distributed power systems, anti-islanding control and protection, distributed generation and load sharing control, distributed VAR compensation, and distributed optimization. Dr. Qu is the author of three books: Robust Tracking Control of Robot Manipulators by IEEE Press (1996), Robust Control of Nonlinear Uncertain Systems by John Wiley & Sons (1998), and Cooperative Control of Dynamical Systems with Applications to Autonomous Vehicles by Springer Verlag (2009). His research has been supported by governmental agencies (NSF, Army, AFOSR, ONR, NASA, Oak Ridge, DoE) and industry (Lockheed, L-3, SAIC). Dr. Qu is a Fellow of IEEE, and is serving or served on Board of Governors of IEEE Control Systems Society and as Associate Editor for Automatica, IEEE Transactions on Automatic Control, and International Journal of Robotics and Automation. |
| Abstract: We discuss solution techniques for the new class of Integrated Network Design and Scheduling problems. Motivating applications for this problem class include infrastructure restoration after an extreme event and plug-in hybrid electric vehicle (PHEV) battery charging and discharging within a smart grid. Infrastructures, such as power grids and transportation systems, can be modeled as networks. Network managers must coordinate repairs or operational decisions using limited resources in order to maximize performance. Selecting which components to repair or utilize (i.e. downed power lines) can be viewed as network design decisions. Traditional network design decisions only focus on the end performance of the design, i.e., the network operation after all components are repaired. Clearly, in infrastructure restoration the success of the efforts depend on how well the services come back online. Therefore, it is important to allocate resources, such as work groups, to implement network design decisions. This resource allocation can be viewed as scheduling decisions. This novel model incorporating the combination of decisions occurring simultaneously does increase the problem difficulty, which motivates the need for both exact and approximate solution methods. I will present complexity results on the problem class under standard network performance metrics, exact and approximate solution methods, and case studies based on real-life data sets representing the infrastructure systems of lower Manhattan and New Hanover county, NC. |
| Abstract: As the penetration level of transmission-scale time-intermittent renewable generation resources increases, control of flexible resources will become important to mitigating the fluctuations due to these new renewable resources. Flexible resources may include new or existing synchronous generators as well as new energy storage devices. The addition of energy storage, if needed, should be done optimally to minimize the integration cost of renewable resources, however, optimal placement and sizing of energy storage is a difficult optimization problem. The fidelity of such results may be questionable because optimal planning procedures typically do not consider the effect of the time dynamics of operations and controls. Here, we use an optimal energy storage control algorithm to develop a heuristic procedure for energy storage placement and sizing. We generate many instances of intermittent generation time profiles and allow the control algorithm access to unlimited amounts of storage, both energy and power, at all nodes. Based on the activity of the storage at each node, we restrict the number of storage node in a staged procedure seeking the minimum number of storage nodes and total network storage that can still mitigate the effects of renewable fluctuations on network constraints. The quality of the heuristic is explored by comparing our results to seemingly "intuitive" placements of storage. Joint work with S. Backhaus and M. Chertkov. |
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Abstract:
On the distribution side of a power system, there exist many distributed energy resources (DERs) that can be potentially used to provide ancillary services to the grid they are connected to. An example is the utilization of power electronics grid interfaces commonly used in distributed generation to provide reactive power support. While the primary function of these power electronics-based systems is to control active power flow, when properly controlled, they can also be used to provide reactive power support. Another example is the utilization of plug-in-hybrid vehicles (PHEV) for providing active power for up and down regulation. For instance, such resources could be utilized for energy peak-shaving during peak hours and load-leveling at night. Proper coordination and control of DERs is key for enabling their utilization for ancillary services provision. One solution to this problem can be achieved through a centralized control strategy where each DER is commanded from a central controller located, for example, at the substation that interconnects the distribution network and the transmission/subtransmission network. In this talk, we propose an alternative approach to this centralized control. The alternative approach rely on a distributed control strategy where each DER can exchange information with a number of other ``close-by" DERs, and subsequently make a local control decision based on this available information. Collectively, the local control decisions made by the DERs should have the same effect as the centralized control strategy. Such a solution could rely on inexpensive and simple communication protocols, e.g., ZigBee technology, that would provide the required local exchange of information for the distributed control approach to work. We provide algorithms that solve this coordination/cooperation problem when i) there is no limit on the amount of active or reactive power that each DER can provide (though some notion of fair distribution of the contribution of active or reactive power among DERs might be imposed); and ii) the maximum amount of active or reactive power each DER can provide is limited, which is a more realistic case. We will provide a careful analysis of the applicability capabilities and limitations of each of these strategies. Speaker's bio: Alejandro Dominguez-Garcia is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Illinois, Urbana, where he is affiliated with the Power and Energy Systems area. His research interests lie at the interface of system reliability theory and control, with special emphasis on applications to electric power systems and power electronics. Dr. Dominguez-Garcia received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, MA, in 2007 and the degree of Electrical Engineer from the University of Oviedo (Spain) in 2001. After finishing the Ph.D., he spent some time as a post-doctoral research associate at the Laboratory for Electromagnetic and Electronic Systems of the Massachusetts Institute of Technology. Prior to joining MIT as a graduate student, Dr. Dominguez-Garcia was with the Department of Electrical Engineering of the University of Oviedo where he held the position of Assistant Professor. Dr. Dom\'{i}nguez-Garc\'{i}a received the NSF CAREER Award in 2010. |
| Abstract: We discuss the modeling and synchronization problem for structure-preserving power system models with either frequency-dependent or linear load models. The latter load model leads to the network-reduced model of the generator swing dynamics. We exploit the relationship between the considered power network models and the well-known Kuramoto model of coupled oscillators. Extending methods from transient stability analysis, synchronization theory, and consensus protocols, we establish static synchronization conditions for the dynamic power network and coupled-oscillator models. First, we focus on a network of coupled first-order Kuramoto oscillators and derive purely algebraic conditions for synchronization. Our conditions are necessary and sufficient for a complete and homogeneous network, they are sufficient for a topological network with heterogeneous coupling, and they improve upon previously-available tests for the Kuramoto model. Second, we discuss the extension of these synchronization conditions to the second-order coupled-oscillator models arising in power networks. This extension from first-order to second-order dynamics can be made rigorous by means of topological conjugacy arguments, by a singular perturbation analysis, or by strict-mechanical Lyapunov functions. In the end, we are able to state concise and purely algebraic conditions that relate synchronization in a power network to the underlying network state, parameters, and topology. Third, we analyze the network-reduction process relating the network-reduced and the more detailed structure-preserving power system model. The network reduction process, termed Kron reduction, is characterized by iterative Schur complementation of the admittance matrix. A detailed algebraic and graph-theoretic analysis of the Kron reduction process allows us to extend the synchronization conditions obtained for the network-reduced model to the structure-preserving model. In the end, we are able to state one spectral and one resistance-based condition for synchronization. Time permitting, we briefly touch upon other networked-control approaches to power network problems. |
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Abstract:
The major subject of this talk is the introduction and testing of a new operating paradigm necessary for sustainable performance of the changing electric power industry. We first show that it is very difficult with today's software tools to balance power system supply and demand with large amount of variable resources due to their hard-to-forecast nature. This creates the need for fundamentally new algorithms in support of short-term forecast and flexible operation. We then propose a distributed look-ahead dispatch concept, which leverages (1) the near-term forecasts of intermittent wind generation, (2) flexibility in price responsive demand, and (3) the storage capabilities such as Plug-in Hybrid Electric Vehicles to participate into real-time energy balancing and frequency regulation services. Through an IEEE Reliability Test System example, we show that the proposed dispatch will lead to an overall cost-effective and environmentally benign utilization of the electric energy system portfolio in electricity markets. The proposed approach provides a theoretical framework for systematic integration of sustainable energy resources, such as wind and solar, with quantifiable performances.
Bio: Le Xie is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University, College Station, Texas, where he is affiliated with the Electric Power and Power Electronic Group. He received his B.E. in Electrical Engineering from Tsinghua University, Beijing, China in 2004. He received S.M. in Engineering Sciences from Harvard University in June 2005. He obtained his Ph.D. from Electric Energy Systems Group (EESG) in the Department of Electrical and Computer Engineering at Carnegie Mellon University in 2009. His industry experience includes an internship in 2006 at ISO-New England and an internship at Edison Mission Energy Marketing and Trading in 2007. His research interest includes modeling and control of large-scale complex systems, smart grid applications in support of variable energy integration, and electricity markets. He also serves as the founding faculty advisor to Texas A&M Energy Club, a university-wide student-run organization focusing on energy. |
| Abstract: The goal of the transmission network expansion planning (TNEP) is to determine the optimal plan for power grid expansion. The plan must specify the number of new power lines to be installed in each transmission corridors and the number of new control components added at each bus. The problem of long-term transmission system planning based on the so-called DC model is considered. Due to constraints imposed by physical laws of the power flows, the resulting optimization problem is a nonlinear mixed-integer problem (NLMIP) with high complexity, especially for large-scale and real-world problems. Many of the solution algorithms for TNEP problem are constructive heuristics that are developed based on specific (approximate) models of power flows. The goal is to develop adaptive optimization algorithms that can be applied for solving TNEP for a wide variety of power models. Inspired by recent metaheuristics, such as the variable neighborhood search (VNS) and the iterated local search (ILS), several neighborhood structures on the TNEP problem solution space are investigated. |
| Abstract: As the transfer from non-renewable to renewable energy resources has become increasingly widespread, certain assumptions regarding power grid efficiency have changed. Power generators have become cheaper to build and install, but may be situated large distances from demand, as for instance in the case of wind power. We consider the problem of optimal transmission line placement and conductance assignment, given the combined costs of resistive power loss and line construction. We adopt the DC model of Johnson and Chertkov, with a single generator and multiple loads, all at known locations. Transmission line construction costs are made up of a fixed cost for each line present, as well as variable costs proportional to each line's conductance, leading to a nonconvex optimization problem. We have developed a novel two-part discrete and continuous hybrid algorithm for this problem. The discrete method, genetic algorithms, is used to sample over the space of grid topologies. As the topologies are sampled, they are sent to a continuous optimization procedure that uses Newton's method to determines the optimal line conductance values for a related convex optimization problem. We have studied, implemented and tested this hybrid algorithm, obtaining results compatible with those of Johnson and Chertkov. We will discuss possible modifications to the algorithm that may improve upon these results. |
| Abstract: Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this talk, after a briefly review of the motivation and the main issues in demand response design, we will first discuss an abstract market model for designing demand response to match power supply. We characterize the resulting equilibria in competitive as well as oligopolistic markets, and propose distributed demand response algorithms to achieve the equilibria. We then discuss another market model for designing demand response to shape power demand. We consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute these optimal prices and demand schedules. Numerical experiments show that it is effective in reducing the peak load, smoothing the entire demand profile, and saving significant generation costs. |
| Abstract: As significant variable generation is added to the power grid, operators have to make decisions in the presence of power output uncertainties at multiple locations throughout the network. The cumulative effects of all the uncertainties on the system are non-obvious, and incorrect assessment of the cumulative effect could affect the reliability and stability of the power system. This paper proposes a method of combining confidence intervals of short term (one to eight hours ahead) wind forecast errors with the existing unit commitment on the system to determine possible operational impacts. The output is a visualization that can alert system operators to the potential for transmission line overloads and indicate whether changes to the existing commitment schedule should be considered. In addition, a modified confidence interval estimation method has been developed that, based on simulation results, outperforms existing methods for short term wind forecasts. |
| Abstract: In 2003 blackout in the large portion of the eastern national power grid an environmental uncertainty - falling of a tree branch on a power line -caused a disturbance that propagated dynamically at a rapid pace through the grid causing one power plant after another to fail. The possibility of such events occurring frequently becomes large when one starts thinking about the scenario of a power grid with subcomponents providing wildly fluctuating amounts of power and storage capacity as would be the case if current thinking on the issues such as co-generation and alternative power sources plays a substantial role in the future power generation network. We are interested in elucidating core causes of instabilities leading to large disturbances and failure of catastrophic proportions. It turns out that it is the coupling of architecture and dynamics of the system that matter the most. If two parts of the system are completely separated from each other, a big disturbance in one will, of course, not influence the other. But, if the subsystems are connected, even weakly, and the dynamics is resonant, a small disturbance in one subsystem can grow, spill to the other part and cause the whole system to fail. This is true even if there are controls in place attempting to stabilize one side - the phenomenon is of the emergent kind, and the only way to control it is to act early at the root cause or provide system-wide regulation that prevents catastrophes. I will discuss the technical aspects of this phenomenon that in the context of power grid we named a "Coherent Swing Instability" (CSI). A simple ring architecture will be presented first, followed by more complex New England Grid model. In order to treat such more complex, large-scale models, we needed to develop new tools, drawing from an operator-theoretic point of view, that also incorporates, in a strong way, the geometric point of view that is so fruitful in low dimensions. This approach leads to a new proposal for model reduction that is rooted in the dynamics of the system rather than in energy-minimization arguments (like in POD). We named the modes that appear in such reduction the "Koopman modes". I will show how this leads to extraction of single-frequency, spatial modes embedded in non-stationary data of short-term,nonlinear swing dynamics, and provides a novel technique for identification of coherent swings and machines. In addition, I will present a technique for identifying CSI by using Koopman modes, by providing a precursor signal based on their interaction. The set of techniques that we have developed also enables analysis of uncertain and stochastic systems - where initial conditions and/or parameter values are not known exactly - within the same framework. Most of the tools apply equally to discontinuous systems. |
|
Abstract:
For many utilities around the world, there has been
considerable pressure to increase power flows over existing
transmission corridors, partly due to economic incentives (a
trend towards deregulation and competition) and partly due
to practical difficulties of obtaining authorization to build
power plants and transmission lines (environmental
concerns). This consistent pressure has prompted the
requirement for extending EMS to take account of dynamic
security assessment (DSA) and control. Such extension,
however, is a rather difficult task and requires several breakthroughs
in measurement systems, analysis tools,
computation methods and control schemes. Indeed, on-line DSA, concerned with power system stability/instability after contingencies, requires the handling of a large set of nonlinear differential equations in addition to the nonlinear algebraic equations involved in the static security assessment. The computational efforts required in on-line DSA is roughly three magnitudes higher than that for the SSA (static security assessment). This explains why dynamic security assessment and control has long remained in off-line activity. Hence, current power system operating environments have prompted the need to significantly enhance time-domain stability analysis programs to meet new requirements. In addition, it is becoming advantageous to move transient stability analysis from the off-line planning area into the on-line operating environment. There are significant financial benefits expected from this movement. It appears that there is always significant incentive to find superior approaches for stability analysis and control. The PJM Interconnection has successfully designed and implemented a Transient Stability Analysis & Control (TSA&C) system at its energy management system (EMS). EMS provides a real time snapshot including power flow case data from the state estimator, dynamic data and contingency list to the TSA&C application. For each real time snapshot, TSA&C performed a transient stability assessment against the list of 3000 contingencies, calculated stability limits on key transfer interfaces. TEPCO-BCU was selected as the leading fast screening tool for improving the performance of the PJM TSA system. The TEPCO-BCU software was evaluated for three months in the PJM TSA Test System environment. The three-month evaluation period was chosen to include the scheduled outage season and the summer peak load season. This period historically encountered a wide range of real time scenarios that can happen on the PJM power system. During this evaluation period, both the PJM TSA software and the TEPCO-BCU software were run periodically in parallel, every 15 minutes in the PJM TSA Test System environment. The goal was to evaluate TEPCO-BCU in a real time environment as a transient stability analysis screening tool. This talk will present a comprehensive evaluation of the dynamic contingency screening function of TEPCO-BCU and an overview of the solution methodologies behind TEPCO-BCU. Requirements for an on-line screening and ranking tool for PJM systems are presented and evaluated. This evaluation study is the largest in terms of system size, 14,500-bus, 3000 generators, for a practical application of direct methods. The total number of contingencies involved in this evaluation is about 5.3 million. The integrated package TEPCO-BCU is based on the theory of the controlling UEP method, BCU method, energy function method and BCU-guided time-domain method The controlling UEP method and the boundary of stabilityregion- based controlling UEP (BCU) method will be described at a level sufficient to comprehend the concept of a transient-stability screening program for TSA systems. |
| Abstract: Smart electric meters are arguably the most well developed aspect of the smart grid to date. With increased economic stimulus for smart grid pilots and new vendors constantly entering the market, smart meters are poised to become ubiquitous within the decade. As with any emerging system, their security should be well understood at both the architectural and implementation levels before standardization and deployment ramp up. We present a first step in this direction through the use of systematic penetration testing to discover security and privacy problems in commercially available smart meters. To address the challenges of pen-testing a diverse set of metering equipment, we use attack trees to reason about potential architectural and implementation flaws in smart meters. Our results show basic shortcomings in the security of metering systems from two different vendors. |
| Abstract: Recent advances in the wide-area measurement system (WAMS) technology using phasor measurement units (PMU) have given a new impetus to control-oriented research in large-scale electric power systems. One of the main challenges in the dynamic analysis and control of power systems is the development of analytical tools from limited measurement data. In this talk I will address this problem and develop methods for model identification, dynamic stability assessment and controller designs for large power systems using PMU measurements. The discussion will be broadly divided into two parts. In Part-1, I'll present several novel coherency-based algorithms for constructing dynamic equivalents of different classes of radial power systems, with and without intermediate voltage control. In Part-2, I'll address the application of these equivalent models in wide-area monitoring and distributed damping control of multi-machine power systems using a novel control inversion approach. A brief note on how PMU's should be placed optimally in the network for generating the most accurate control strategies, especially when the PMU data are noisy and unreliable, will also be discussed. The overall motivation of the talk would be to understand how the WAMS technology can help us in gaining valuable insight about the physical behavior of the North American grid, which is becoming more expansive, and, hence, more chaotic day by day. |
| Abstract: This talk will concentrate on presenting and discussing various novel methodologies for the inclusion of stability constraints in optimal power flow (OPF) models to better represent power system security in OPF applications. Comparisons of the proposed methods with respect to classical security constrained OPF techniques typically used in energy auctions will be presented, concentrating on discussing the effect that the proposed new models can have on electricity prices as well as system security in the context of competitive electricity markets. |
|
Abstract:
The smart grid efforts are focused, among other tasks, on getting better use
of abundant data coming from substations, feeders/transmission lines, loads and
generators equipped with advanced intelligent electronic devices (IEDs) such as
digital protective relays, digital fault recorders, remote terminal units of SCADA,
phasor measurement units, smart meters, equipment monitors, etc. As the
amount of the smart grid data increases in the future, it becomes critical that the
data integration and conversion to information be performed automatically. A
major challenge remains to convert information into actionable knowledge. This
requires full understanding of the cause-effect relationship between data and
knowledge, often termed “Translational Knowledge”. This lecture focuses on
better understanding the features of the data recording issues such as front-end
data processing, accuracy, time synchronization, and eventually the conversion
of data to information. Several smart grid applications enabled by the
translational knowledge approach are discussed and open issues are mentioned.
[slides of the first part of Prof. Kezunovic presentation on ``Smart Energy Center at TAMU".]
Bio: Dr. Mladen Kezunovic is a Professor at the Department of Electrical and Computer Engineering at Texas A&M University (TAMU) and holds the Eugene E. Webb Endowed Professorship. He worked for Westinghouse Electric in the U.S.A. as a Systems Engineer on development of the first all-digital substation during 1979-1980 and for Energoinvest Company in Europe as the Technical Lead for substation automation development during 1980-86. He also spent sabbaticals at EdF’s Research Centre in Clamart, France in 1999/2000 and at the University of Hong Kong in the fall of 2007. Dr. Kezunovic served as a consultant to over 50 utilities and vendors worldwide. He is TAMU Site Director of the Power Systems Engineering Research Center (PSerc), and Deputy Director of the PHEV/BEV Center for Transportation and Electricity Convergence (CTEC), both Industry/University Cooperative Research Centers (I/UCRCs) of the National Science Foundation. Dr. Kezunovic acted as a PI on close to 100 R&D projects covering analysis of faults and other disturbances. His current research activity includes new concepts for substation automation, new approaches to condition-based asset management, and new applications in relaying and control. Dr. Kezunovic has published more than 400 journal and conference papers and has given over 100 invited lectures, short courses and seminars around the world. He is a Fellow of the IEEE and a member of CIGRE-Paris. He is a registered Professional Engineer in the State of Texas. Dr. Kezunovic is listed as the IEEE Distinguished Speaker. |
|
Abstract:
A wide area measurement system (WAMS) consists of advanced measurement technology, information tools, and operational infrastructure that facilitate the understanding and management of the increasingly complex behavior exhibited by large power systems. Synchro-phasors or Phase Measurement Units (PMUs) are a technology that offers absolute time-stamped voltage phase measurements and even more detailed voltage profiles at buses in the electricity grid. The first WAMS was installed in 2000 by the Bonneville Power Administration. Only 200 PMUs are already installed in North America. In 2009, the U.S. government announced an investment of 3.4 B $ in energy grid modernization. This investment will include the installation of more than 850 PMUs that will monitor the complete U.S. electric grid. Static-state estimation is a well-known and widely used technique for determining optimal estimates of phase angles ? from noisy real power P, reactive power Q, and voltage magnitude V measurements at generator and large substation buses. This technique permits monitoring the relative phase angles between adjacent generators. Large changes in phase angle between two generators is an early indicator of transient stability problems. Phasor Measurement Units sense the relative phase angle between generators directly and transmit them to a data aggregator. It is crucial to identify any attacks that change PMU measurements since PMU data is used directly and critically for monitoring the power system. In this talk I will begin with a brief survey of cyber security for physical systems. I will then present a taxonomy of cyber-attacks on PMU systems. Following this, I will briefly review WAMS systems and applications. Then, I will present some preliminary research ideas on how to detect integrity attacks on the devices. These attack detection algorithms are based on checking for consistency of the [possibly] corrupted data against the underlying physical models that constrain the phase measurements. The consistency checks are based on static state estimation. I will offer some synthetic simulation results, and close with a discussion of computational and implementation issues that require further exploration. Bio: Annarita received her Laurea (Master) degree in Mathematics from the Universitá di Pisa, Italy and a Ph.D in Computer Engineering at Thayer School of Engineering at Dartmouth College, Hanover, NH. There she participated to the Process Query System (PQS), project, sponsored by the Advanced Research and Development Activity (ARDA). She graduated with a dissertation on computer security, anomaly tracking and cognitive attacks. She is currently a postdoctoral fellow at the Department of Electrical Engineering and Computer Science at the University of California at Berkeley working with Professor Kameshwar Poolla and Professor Shankar Sastry. She is part of the Team for Research in Ubiquitous Secure Technology (TRUST) project, sponsored by the National Science Foundation, Science and Technology Center. |
|
Abstract:
Critical infrastructures are complex physical and cyber-based systems that form the lifeline of modern society, and their reliable, secure, and safe operations are of paramount importance to national security and economic vitality. The electric power grid, one of the key critical infrastructures, is a highly automated network that uses a variety of sensors, information/control systems, and communication networks (collectively known as SCADA, EMS, WAMS, DMS) for the purpose of sensing, monitoring, and controlling the physical grid. The recent findings, as documented in federal reports and in the literature, indicate the growing threat of cyber-based attacks in numbers and sophistication on the nation's electric grid and other critical infrastructure systems. Therefore, cyber security of the power grid-encompassing attack prevention, detection, mitigation, and resilience-is among the most important research issues today and in the emerging smart grid. This talk will provide a brief taxonomy of potential cyber attacks on the power grid, and present a cyber-physical systems framework for risk modeling and mitigation of cyber attacks on the power grid that accounts for dynamics of the physical system, as well as the operational aspects of the cyber-based control system. In particular, the talk will focus on risk modeling of intrusion-based attacks on the substation automation system and data integrity attacks on the wide-area control network. The core of the modeling lies in the integration of cyber attack/defense modeling with physical system simulation capabilities, which makes it possible to quantify the potential consequences of a cyber attack on the power grid in terms of load loss, stability violations, equipment damage, or economic loss. Finally, the talk will conclude with discussing the experience in building a SCADA cyber security testbed and its operational capabilities. Biography: Dr. Manimaran Govindarasu is currently an Associate Professor and Director for Student Professional Development in the Department of Electrical and Computer Engineering at Iowa State University; he also is affiliated to industry-funded Electric Power Research Center (EPRC) and NSF-funded Information Assurance Center (IAC) at Iowa State. He received his Ph.D. in Computer Science and Engineering from Indian Institute of Technology, Madras, India, in 1998 and joined Iowa State in 1999. At Iowa State, he received the Young Engineering Research Faculty Award in 2003 and the Outstanding Engineering Faculty Award in 2009. His research expertise is in the areas of real-time systems, cyber security, and cyber security of power grid. He has published over 100 peer-reviewed research papers of which 50 are in archival journals. He is co-author of the text "Resource Management in Real-Time Systems and Networks," MIT Press, 2001. He has given tutorials in reputed conferences and delivered industry short courses on the subject of cyber security. He has served in leadership roles in many IEEE conferences, symposiums, and workshops. He contributed to the DoE NASPInet Specification project, and is currently chairing the Cyber Security Task Force at IEEE Power and Energy Systems CAMS Subcommittee. |
| Abstract: |
| Abstract: Large blackouts are usually the result of a cascade of failures of various components. A power grid being made of millions of components, occasionally a few of these components do not perform their function as desired putting additional burden on the working components, causing them to misbehave, and thus leading to a cascade of failures. The complexity of the power grid makes it difficult to model each and every individual component and study the stability of the entire system. We therefore construct an abstract model which is computationally tractable and a reasonable approximation to the power grid. We theoretically analyze this model and perform simulations which confirm the theory. (Joint work with S. Kadloor) Improvements to make the simulations much more realistic will be discussed. Reasonably good data on the transmission grid is needed to achieve further realism. I will talk about some of my recent attempts to gather openly available transmission grid data. The resulting incomplete data set is now available. Some results from analyzing the grid data (such as degree distributions/power-law) will be presented. |
|
Abstract:
Cascading failures contribute disproportionately to power system blackout risk due
to the sizes of the blackouts that can result. Providing information to electricity
decision makers about risk is crucial for both real-time operations and long-term
decision making. This talk will discuss results from the analysis of two approaches
to blackout risk analysis in electric power systems. In the first analysis, we
compare two topological (graph-theoretic) methods for finding vulnerable locations
in a power grid, to a simple model of cascading outage. This comparison indicates
that topological models can lead to misleading conclusions about vulnerability. In
the second analysis, we describe preliminary results indicating that both a simple
dynamic power system model and frequency data from the August 10, 1996 disturbance
in North America show evidence of critical slowing down as the system approaches a
failure point. In both data sets, autocorrelation in the time-domain signals
(frequency and phase angle), significantly increases before reaching the critical
point. These results indicate that critical slowing down could be a useful indicator
of increased blackout risk. Bio: Paul Hines is an Assistant Professor in the School of Engineering at the University of Vermont. He is also a member of the Carnegie Mellon Electricity Industry Center Adjunct Research Faculty and a commissioner for the Burlington Electric Department. He received the Ph.D. in Engineering and Public Policy from Carnegie Mellon U. in 2007 and the M.S. degree in Electrical Engineering from the U. of Washington in 2001. Formerly he worked at the US National Energy Technology Laboratory, where he participated in Smart Grid research, the US Federal Energy Regulatory Commission, where he studied interactions between nuclear power plants and power grids, Alstom ESCA, where he developed load forecasting software, and for Black and Veatch, where he worked on substation design projects. His main research interests are in the areas of complex systems and networks, the control of cascading failures in power systems and energy security policy. |
| Abstract: Electricity and the electric grid will play a crucial role in our transition to a sustainable energy infrastructure. Integrating a significant percentage of renewable energy sources into the nation's energy mix, and delivering it through electric transmission and distribution systems is a major research challenge since these sources are less controllable than the fossil-fuel-based generation they will displace. Simultaneous with these changes is the need to make the electric grid even more resilient in order to insure maximum continuity of electric service, even during severe system disturbances. This talk will focus on the modeling and simulation aspects of these problems. [slides of WECC Frequency Contour for Opening Palo Verde 1 and 2 6 Seconds of Simulation] |
| Abstract: When wind turbines are deployed in large arrays, their ability to extract kinetic energy from the flow decreases due to complex interactions among them and the atmospheric boundary layer. In order to improve our understanding of the vertical transport of momentum and kinetic energy across a boundary layer flow with wind turbines, Large Eddy Simulations and wind-tunnel experimental studies are undertaken. A suite of LES, in which wind turbines are modeled using the classical `drag disk' concept, are performed for various wind turbine arrangements, turbine loading factors, and surface roughness values. In the wind tunnel studies, the boundary layer flow includes a 3 by 3 array of lightly loaded model wind turbines. The results of both the simulations and experiments are used to shed light on the vertical turbulent transport of momentum and kinetic energy across the boundary layer. The results are also used to develop improved models for effective roughness length scales and to obtain new optimal spacing distances among wind turbines in a large farm. This work is a collaboration with M. Calaf, J. Meyers, R. Cal, J. Lebron, H.S. Kang, and L. Castillo, and is supported by the National Science Foundation. |
|
Abstract:
As electric generation and transmission systems have become more complex and congested, new supply patterns are pushing transmission systems to the limits resulting in reduced margins and a significant challenge to system reliability. At the same time, system planners are seeing more volatile dispatch patterns. This trend will continue as market prices and new initiatives into Smart Grids affect the demand for power in a competitive market. The growth in "destabilizing" renewable resources presents challenges that were not realized just a few years ago. All of these factors point to the need for increased accuracy in modeling, and greater productivity in system planning. GE Positive Sequence Load Flow Software (PSLF) can help utilities achieve these goals. This full-scale program is designed to provide comprehensive and accurate load flow, dynamic simulation and short circuit analysis. PSLF provides for analysis of transfer limits while performing economic dispatch. PSLF is a suite of analytical tools that can simulate large-scale power systems up to 80,000 buses. Since PSLF has its own fully configured programming language (EPCL), users can build new models that interact with models within the program, perform post-processing and construct macros that automate execution of repetitive simulations and generate reports. |
| Abstract: The talk will discuss catastrophic failures in power systems. Some of the past failures will be described, and the reasons for their occurrence explained. The focus of this discussion will be on the protection system used on power systems, and the reasons for their inappropriate operations under stressed power system states. One of the developments which offers interesting countermeasures for power system failures is the synchrophasor technology. The lecture will go over the origin of this technology, and explain why this technology offers superior capabilities for making black-outs less frequent and less intense. |
| Abstract: This talk presents the application of the continuous Newton's method to the power flow problem. This method consists in formulating the power flow problem as a set of autonomous ordinary differential equations. Based on this formal analogy, an entire family of numerically efficient algorithms for solving ill-conditioned or badly-initialized power flow cases is proposed. |
| Abstract: I will describe and demonstrate how to use the Global Energy Observatory |
|
Abstract:
Seven Independent System Operators and Regional Transmission Organizations (ISO/RTOs) operate regional electricity markets and manage bulk power system reliability operations. However, they differ in important ways in terms of the functions they perform. Some establish and operate capacity markets to support meeting regional adequacy standards. Others limit their involvement to provide planning services to assist other entities, such as state public service commissions, determine and oversee the provision of capacity requirements. Most operate some form of real-time energy market to balance supply with demand, but the structure of those markets (using auctions versus accommodating bi-lateral arrangements) vary considerably. All are actively involved in operating the system dynamically to ensure that North American Electric Reliability Council (NERC) standards are met, but the mechanics of how that responsibility is achieved are not uniform.
One market structure does not fit all. Understanding the differences is important for comparing market performance. It is also important to characterize how retail markets interface with ISO/RTO market operations. Those interfaces between wholesale and retail markets are important for achieving a high degree of overall market performance. In some regions, ISO/RTO interfaces have influenced the way retail consumers buy electricity. In other regions there is little or no relationship between retail tariffs and wholesale market transactions. Drs. Neenan and Flaim will address the extent to which ISO/RTO market structure has influenced the development of efficient retail electricity prices and services needed to achieve the efficiency gains upon which electricity market restructuring was premised. A prognosis will be offered as to the extent to which Smart Meter and Smart Grid initiatives will improve the integration of wholesale and retail markets. |
| Abstract: This talk will present new methods to model and control the aggregated power demand from a population of thermostatically controlled loads. The control objective is to produce relatively short time scale responses (hourly to sub-hourly) for ancillary services such as load following and regulation. The control signal is applied by manipulation of temperature set points. The methods leverage the existence of system diversity and use physically-based load models to inform the development of a new theoretical model that accurately predicts - even when the system is not in equilibrium - changes in load resulting from changes in thermostat temperature set points. Insight into the transient dynamics that result from set point changes is developed by deriving a new exact solution to a well-known hybrid state aggregated load model. A straightforward minimum variance control law is developed and it is shown that the high frequency components of the output of a wind plant can be followed with very small changes in the nominal thermostat temperature set points. |
| Abstract: This talk describes a new method for solving inverse modeling problems in systems modeled by conservation laws, with applications to highway traffic flow estimation. The state of the system is written in the form of a scalar Hamilton-Jacobi (HJ) partial differential equation (PDE), for which the solution is fully characterized by a Lax-Hopf formula. Using the properties of the solution, we prove that when the data of the problem is prescribed in piecewise affine form, the constraints of the model are convex. This property enables us to identify a class of inverse modeling problems that can be formulated using convex programs. The inverse modeling algorithms presented in this talk are part of the Mobile Millennium traffic information system, launched recently by UC Berkeley, Nokia and Navteq. The purpose of this system is to use GPS data generated by the smartphones of the driving public of Northern California to estimate the state of traffic on highways and secondary roads. The algorithms presented in this talk are used to estimate the state of traffic (data assimilation and data reconciliation) in Mobile Millennium, detect some sensor failures (in particular inductive loop detector failures), give guaranteed bounds on some traffic parameters (for instance travel time), and detect cyber attacks. |
| Abstract: Many jurisdictions worldwide are greatly increasing the amount of wind production, with the expectation that increasing renewables will cost-effectively reduce greenhouse emissions. We discuss the interaction of increasing wind, transmission constraints, renewable credits, wind and demand correlation, intermittency, carbon prices, and electricity market prices using the particular example of the Electric Reliability Council of Texas (ERCOT) market. |
| Abstract: We propose a visual and interactive method for discovering distinct groups of nodes in a network using a user-selected set of node properties computed from the network structure. The user's input on the visual separation of nodes in random 2D projections of a high-dimensional node property space is systematically analyzed to divide the nodes into distinct groups, the number of which is selected by the user interactively. The discovered groups are then examined to reveal their distinguishing characteristics. Our method is capable of discovering communities structures, k-partite structures, or any other structures in which the groups can be distinguished by a combination of node properties. We demonstrate that our method can effectively find and characterize a variety of group structures in model and real-world networks. Joint work with A. Motter. |
| Abstract: The ISO market design has evolved over time using traditions, economic theory, power system operations heuristics and operational experience. Early market design made simplifying assumptions and approximations often due to the inability to solve the more detailed design. Complicating features in market design such as reactive power, unit commitment, switching of transmission assets, renewable generation, storage and demand participation make the market non-convex and add new uncertainties that have little empirical information. We discuss the need and ability to solve more complex market models faster. |
| Abstract: Everyday at noon, the french transport system operator has to forecast the next 36 hours of the french electricity consumption for the electricity balance. We will present the motivation of the work and the solution we proposed using IBR (iterative bias correction) R package. |
|
Abstract: We discuss the synchronization problem for the network-reduced
model of a power system with non-trivial transfer conductances. Our key
insight is to exploit the relationship between the power network model
and
a first-order model of coupled oscillators. Assuming overdamped
generators
(possibly due to local excitation controllers), a singular perturbation
analysis shows the equivalence between the classic swing equations
and a non-uniform Kuramoto oscillator model. Here, non-uniform Kuramoto
oscillators are characterized by multiple time constants, non-
homogeneous
coupling, and non-uniform phase shifts. Extending methods from
transient
stability, synchronization theory and consensus protocols, we establish
sufficient conditions for synchronization of non-uniform Kuramoto
oscillators. These conditions reduce to and improve upon
previously-available tests for standard Kuramoto model. Combining our
singular perturbation and Kuramoto analyses, we derive concise and
purely
algebraic conditions that relate synchronization and transient
stability of
a power network to the underlying system parameters and initial
conditions.
Biosketch: Francesco Bullo received the Laurea degree in Electrical Engineering from the University of Padova in 1994, and the Ph.D. degree in Control and Dynamical Systems from the California Institute of Technology in 1999. From 1998 to 2004, he was affiliated with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He is currently a Professor with the Mechanical Engineering Department at the University of California, Santa Barbara. His students' papers were finalists for the Best Student Paper Award at the IEEE Conference on Decision and Control (2002, 2005, 2007), and the American Control Conference (2005, 2006). He is the coauthor of the book "Geometric Control of Mechanical Systems" (Springer, 2004) and of the book "Distributed Control of Robotic Networks" (Princeton, 2009). His main research interest is multi-agent networks with application to robotic coordination, distributed computing and power networks. He is also interested in vehicle routing, geometric control, and motion planning problems. |
| Abstract: Random Matrix Theory is useful in the study of complex networks such as electric grids. These transmission systems can be modeled as complex networks, with high-voltage lines the edges that connect nodes representing power plants and substations. We draw upon established literature of complex systems theory and introduce new methods from nuclear and statistical physics to identify new characteristics of these networks. We show that most grids can be characterized by the Gaussian Orthogonal Ensemble, an indicator of chaos in many complex systems. Under certain circumstances, however, grids may be described by Poisson statistics, an indicator of regularity. |
| Abstract: The dynamic behavior of wind turbines is dominated by interactions between continuous and discrete processes. Care must be taken to ensure appropriate modeling of such hybrid dynamics. The talk will discuss various issues that arise in both large- and small-disturbance analysis of wind turbine systems. We will show that industry-standard models may exhibit deadlock and Zeno-type behavior, and consider implications for eigenvalue analysis. We will explore model revisions that establish well-defined solution concepts. |
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Abstract:
The smart grid is a vision for the electric system of the future that goes far beyond simply generating, transmitting and delivering power more reliability and more efficiently. Part of the vision includes bringing the behavior of individual loads and energy consumption to bear on the processes that govern large-scale system phenomena.
This talk gives an elementary account of how a statistical thermodynamic approach can help us understand certain economic and physical behaviors of the smart grid. We will discuss how entropy measures the flexibility of a system and how an efficient distribution market can choose the state that maximizes entropy.
When two separate power systems are connected they freely exchange power, the total power remains constant, but the constraints on individual exchanges are lifted and the price of energy changes. The result is a transfer of benefit from one system to another that increases the combined systems entropy to a state which an efficient market will always find, but a flawed market may not find.
This observation allows us to derive a number of useful aggregate system parameters that can be observed and perhaps used to monitor and control market-based systems. Beyond the observed relation between entropy maximization and market efficiency, we can also identify a parameter analogous to thermodynamic temperature, T, that is associated with the net flow of benefits from the market with higher T to the one with lower T; and an analog to electrochemical potential, M, that is associated with the net transfer of control from the system with higher M to the one with lower M.
While this approach differs from traditional methods used to study power markets, the results appear consistent with them but may provide the opportunity for useful insights into the behavior of the future smart grid as it matures.
Biography: David Chassin is a staff scientist at Pacific Northwest National Laboratory, where he has worked in energy systems modeling, diagnostics, and control system research and development since 1992. Prior to that, he was Vice-President of Development at Image Systems Technology, where he led the commercialization of his thesis work on image processing for computer aided design systems, which is marketed today by Autodesk as gCAD Overlayh. Today, he is the principle investigator for the development of GridLAB-D, DOEfs next generation smart grid simulator. |
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Abstract:
This talk describes how the mobile internet is changing the face of traffic monitoring at a rapid pace. In the last five years, cellular phone technology has bypassed several attempts to construct dedicated infrastructure systems to monitor traffic. Today, GPS equipped smartphones are progressively morphing into an ubiquitous traffic monitoring system, with the potential to provide information almost everywhere in the transportation network. Traffic information systems of this type are one of the first instantiations of participatory sensing for large scale cyberphysical infrastructure systems.
However, while mobile device technology is very promising, fundamental challenges remain to be solved to use it to its full extent, in particular in the fields of modeling and data assimilation. The talk will present a new system, called Mobile Millennium, launched recently by UC Berkeley, Nokia and Navteq, in which the driving public in Northern California can freely download software into their GPS equiped smartphones, enabling them to view traffic in real time and become probe vehicles themselves.
The smartphone data is collected in a privacy-by-design environment, using spatially aware sampling. Using data assimilation, the probe data is fused with existing sensor data, to provide real time estimates of traffic. The data assimilation scheme relies on the appropriate use of Ensemble Kalman Filtering on networked hyperbolic first order partial differential equations, and the construction of lower-semicontinuous viability solutions to Moskowitz Hamilton-Jacobi equations.
Results from experimental deployments in California and New York will be presented, as well as preliminary results from a pilot field operational test in California, with already more than 5,000 downloads.
Additional applications will be discussed, in particular how to use smartphones interfaced with sensors to monitor chemical agents which might be transported by air, social networks user generated content (such as twitter from the phone) for emergency response (for example earthquakes or attacks).
Biography: Alexandre Bayen received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in July 1998, the M.S. degree in aeronautics and astronautics from Stanford University in June 1999, and the Ph.D. in aeronautics and astronautics from Stanford University in December 2003. He was a Visiting Researcher at NASA Ames Research Center from 2000 to 2003. Between January 2004 and December 2004, he worked as the Research Director of the Autonomous Navigation Laboratory at the Laboratoire de Recherches Balistiques et Aerodynamiques, (Ministere de la Defense, Vernon, France), where he holds the rank of Major. He has been an Assistant Professor in the Department of Civil and Environmental Engineering at UC Berkeley since January 2005. He is the recipient of the Ballhaus Award from Stanford University, 2004. His project Mobile Century received the 2008 Best of ITS Award for ‘Best Innovative Practice’, at the ITS World Congress, and a TRANNY Award from the California Transportation Foundation. He is the recipient of a CAREER award from the National Science Foundation, 2009. |
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Abstract:
This decade marks the beginning of a process of renewal of the electric energy industry. Widespread deployment of new, diverse and distributed generation assets has begun, and it has become increasingly evident that significant additions need to be made to the transmission and distribution (T&D) system. At the same time, new sensing and control technologies are being deployed system-wide. Consequently, it will become necessary to be considerably more prudent in planning for expansion than we have been in the past. Responsible, long-term strategies will become necessary, and the planning process will become more complex. It will become important to take into account the impacts on controllability, stability and reliability.
The speaker will share some of his research experience with strategic planning of distribution assets and distributed generators, particularly in the area of reliability-centered design and planning for optimal expansion of distribution systems, in both customer-driven and utility-driven scenarios. He will present applications of both evolutionary and traditional methods in reliability-centered expansion planning. He will discuss ways of adapting and applying some of these approaches to the bulk power system. The speaker hopes to stimulate discussion on directions for future research on long-term planning for tomorrow’s cyber-enabled power system, and on opportunities for collaborative work in this emerging field.
Biography: Joydeep Mitra is Associate Professor of Electrical Engineering and Faculty Associate of the Institute of Public Utilities at Michigan State University, East Lansing. He received his B.Tech. (Hons.) degree in Electrical Engineering from the Indian Institute of Technology, Kharagpur, and his Ph.D. degree, also in Electrical Engineering, from Texas A&M University, College Station. Dr. Mitra’s experience includes five years in industry and nine in academia. His research interests include power system reliability, distributed energy resources, and power system planning. His research has been funded by the National Science Foundation, Sandia National Laboratories, Bonneville Power Administration, and Ottertail Power Company. Dr. Mitra is a Senior Member of the IEEE, and recipient of a Career Award from the National Science Foundation, USA. |
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Abstract:
Traditional security constrained economic dispatch of electricity resources treats the transmission network as a fixed static topology while optimizing deployment of generation assets. However, it is well known that the redundancy build into the grid in order to handles the multitude of contingencies over a long planning horizon can in the short run create congestion and necessitate costly out of merit dispatch. While it is quite common for operators to occasionally open lines that reach their thermal limit, such practices are employed on an ad hoc basis and are not driven by cost considerations. The objective of our work is to explore, from an economic perspective, the potential of treating the grid as a flexible topology that can be co-optimized along with generation dispatch, subject to reliability constraints, so as to minimize the cost of serving load.
This talk will review recent work by the authors demonstrating that optimizing the network topology with generation unit commitment and dispatch can significantly improve the economic operations while maintaining the traditional “N-1 reliability” standard. Our analysis also provides an assessment of potential economic gains from smart grid technologies that will enable of the N-1 reliability standard in favor of new reliability concepts such as “just in time N-1 reliability’. Test results based on a DC OPF analysis are presented for the IEEE 118 bus model, the IEEE RTS 96 system and the ISO-NE 5000 bus electric grid.
(Based on joint work with Kory Hedman, Emily Bartholomew Fisher, Richard P. O’Neill and Michael C. Ferris.)
Biography: Shmuel S. Oren is the Earl J. Isaac Chair Professor in the Science and Analysis of Decision Making in the Industrial Engineering and Operations Research department at the University of California, Berkeley. He is the Berkeley site director of PSERC – a multi-university Power System Engineering Research Center sponsored by the National Science Foundation and industry members. His academic research focuses on planning and scheduling of power systems and on various aspects of electricity market design and regulation. He has been a consultant to various private and government organizations in the US and abroad and is currently a Senior Adviser to the Market Oversight Division of the Public Utility Commission of Texas (PUCT), and a consultant to the Energy Division of the California Public Utility Commission (CPUC). He holds B.Sc. and M.Sc. degrees in Mechanical Engineering from the Technion in Israel and also M.S. and Ph.D. degrees in Engineering Economic Systems in 1972 from Stanford University. He is a Fellow of the IEEE and of INFORMS. |
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Abstract: Federal programs are subsidizing deployments of smart grid elements. But for these initial deployments to grow, the smart grid needs to become self sustaining. This will require modifications in system operations that create a level field for both reliable and interruptible power. One such modification is proposed, founded on the concept of risk-limiting dispatch, and realized in a way that permits incremental deployment. The current practice of worst-case dispatch assumes reliable power sources and limited information. Risk-limiting dispatch relies on information—from sensors, generators and customers—to coordinate interruptible power sources and demand response.
Biography: Pravin Varaiya is Nortel Networks Distinguished Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. From 1975 to 1992 he was also Professor of Economics at Berkeley. His current research interests include transportation networks, electric power systems, and hybrid systems. His honors include a Guggenheim Fellowship, three Honorary Doctorates, the Field Medal and Bode Prize of the IEEE Control Systems Society, and the Richard E. Bellman Control Heritage Award. He is a Fellow of IEEE, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Science |
| In this talk we describe ongoing work with new methodologies for two classes of problems: (1) vulnerability analysis of large-scale transmission systems, and (2) algorithms for throughput maximization in transmission systems. Vulnerability analysis, in particular the so-called "N-k" problem and derivatives, are well-known. As mathematical problems these are quite difficult. We first present results with a mixed-integer programming formulation that addresses a standard version of the problem. We then present results with an indirect approach that proves vastly more scalable and informative while at the same time being able to address a more realistic version of the problem, including 'noise' and model uncertainty. Concerning (2) we describe a primal-dual approach which combines techniques from convex programming, linear programming and branching techniques. |
| Information technology is expected to have a central role in the future of electric power delivery. In this presentation, I will discuss techniques for modeling and simulation of future power systems that have classical electro-mechanical elements which interact with communication networks, software agents, real-time price signals, and other discrete event systems. Applications and supporting simulation technology will be discussed in the context of a notionally emergency load shedding system and a study of the impact that market clearing time and communication delay has on the stability of electric power markets. |
| We consider the management of electric vehicle (EV) loads within a market-based Electric Power System Control Area. EV load management achieves cost savings in both (i) EV battery charging and (ii) the provision of additional regulation service required by wind farm expansion. More specifically we develop a decision support method for an EV Load Aggregator/Energy Service Company (ESCo) that controls EV battery charging. At the beginning of each period in a 24 hour cycle, the ESCo purchases firm energy from the real-time wholesale market and bids for a non-firm block of energy which the ESCo commits to allow the Independent System Operator (ISO) to schedule up or down for regulation service over 5 second intervals. The ESCO’s regulation service bid may or may not be accepted depending on the clearing of the real time regulation service market. The ESCo is also assumed to have access to information about local distribution network congestion constraints, namely the maximal additional load that may be applied along a specific low voltage distribution network feeder without stressing transformer and other distribution hardware tolerances. This retail-transactions-market information is employed together with wholesale market information on expected wind farm generation and clearing prices to make optimal feasible decisions regarding the quantity and bid prices for firm and non-firm energy nominations. Note that the quantity committed to regulation service must be backed by additional battery charging capacity allowing response to an up regulation service command, and, moreover, by setting aside sufficient unused feeder capacity so as to accommodate a commensurate increase in consumption that may be requested by the ISO, even momentarily, in conjunction with the regulation service bid. Wind farm generation forecasts affect the clearing price and the demand for regulation service. This is crucial to the ESCo’s decision on how much non-firm regulation service capacity and at what price it is profitable to bid for. A hierarchical decision making methodology is proposed for hedging in the day-ahead market and for playing the real-time market in a manner that yields regulation service revenues and allows for negotiated discounts on the use-of-distribution-network payments. The proposed methodology employs a rolling horizon look-ahead stochastic dynamic programming algorithm solved approximately by linear programming. Its implementation and the observed numerical/computational experience are also reported. |
| As deployment rates for new energy technologies rapidly increase, there is growing emphasis on infrastructure and systems operations upgrades that will be needed to accommodate the unique operating characteristics of new renewable, efficiency, and end-use technologies. To meet these integration challenges, the National Renewable Energy Laboratory (NREL) is implementing a comprehensive systems approach to its R&D and engineering efforts on integration. Through its new center – The Electricity, Resources, and Building Systems Integration Center – NREL is looking across electricity system interfaces to explore the impacts of new technology introduction on the whole system. As the centerpiece for these efforts, the U.S. Department of Energy has commissioned the design and construction of a state-of-the-art laboratory facility – the Energy Systems Integration Facility (ESIF). The ESIF will be constructed to enable complex systems research and development that fully integrates the most advanced simulation, data analysis, engineering, and evaluation techniques to enable optimal deployment of advanced energy technologies. This presentation will overview the ESIF’s role in NREL’s approach to addressing large-scale renewable and efficiency technology integration issues, and discuss specific capabilities and efforts in technology development and integration in generating, transmission, distribution, and end-use systems. |
| Blackouts of the electric power transmission infrastructure are complicated cascading events in a huge network with diverse, interacting failures. In these cascading failures, a series of dependent failures successively weaken the system and making further failures more likely. The cascading causes power law and criticality phenomena in blackout statistics. One contention is that we should study not arbitrary networks, but engineered networks, and we outline a complex systems simulation approach to generate "engineered" data when this data is not otherwise available. We model cascading in a bulk statistical fashion as initial failures propagating probabilistically according to a branching process. We estimate branching process parameters from data and hence estimate the probability of cascading failures of various sizes. Initial testing of these methods on real and simulated data open the possibility that the probabilities of large blackouts could be practically estimated from power system observations or non-exhaustive simulation runs. |
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Much has changed in the electric power industry. However, the basic theoretic problem formulations used have remained unchanged.
Unfortunately, these no longer lend themselves to the future needs. In this talk we start by summarizing monitoring, estimation and control problem formulation for the traditional electric power industry. Simple simulations are provided to point out the key issues and room for improvement. We next consider a representative future electric energy systems architecture and contrast its objectives with the objectives of the old industry. The emphasis is on multi-layered industry capable of integrating distributed resources close to the end users. A possible problem posing for this industry architecture is presented. The problem posing is used to illustrate challenges, opportunities and open questions. It is described how the new problem lends itself to the distributed model-predictive control for network systems. State-of-art sufficient conditions for predictable performance of such systems is discussed in light of what must be relaxed to be used and useful for future energy systems. Possible solutions must go beyond competitive decentralized system assumptions, and have to rely on digitally enabled cooperation among the industry participants. Finally, implications of such novel problem formulation on facilitating technically feasible integration of intermittent resources such as wind and PVs, in cooperation with small hydro, demand side management and small storage are illustrated. |
| In this talk, I will review problems arising in the modeling, monitoring, and controlling large electrical grids. A summary of current approaches to such problems is provided as well as an introduction to various control approaches for general dynamical systems. The talk will conclude by presenting an array of open problems in light of the recent movement to a smarter and more robust electrical grid. |
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A wide variety of analysis problems in large-scale power
systems have historically relied upon partitioning the network into a
number of distinct clusters or zones, each of which is analyzed
independently. Examples include reliability or deliverability
assessments and tests for "load pockets" where some generators in
competitive markets are likely to possess market power. In regionally
integrated systems such as Regional Transmission Organizations (RTOs),
the network has been subdivided into zones based on historical ownership
of transmission assets or some other geographic criteria.
Recent work in the science of networks has drawn connections between network structure and network performance, or the vulnerability of networks to certain types of failures. Devising structural measures for power systems is difficult because a power grid's structure contains both topological and electrical dimensions. However, defining network partitions based on structural information rather than asset ownership or historical affiliation has the potential to improve the utility of planning procedures that are based on zone boundaries. This talk presents some ongoing work on using structural information for network partitioning, based on the concept of electrical centrality proposed by Hines and Blumsack [1]. The electrical centrality measure is used to define a set of quality metrics for network partitions. We use these metrics to build a fitness function that is used to solve a network clustering problem with explicit reliability or economic objectives. Preliminary results from an exploratory study of the PJM Interconnection by Blumsack, et al. [2] demonstrate the feasibility of the proposed clustering approach, and suggest that structural partitioning can be a useful tool for improving planning assessments in power systems. [1] P. Hines and S. Blumsack. A Centrality Measure for Electrical Networks. Proc. of the 41st Hawaii International Conference on System Scienes, Waikoloa HI, 2008. [2] S. Blumsack, P. Hines, M. Patel, C. Barrows and E. Cotilla Sanchez. Defining Power Network Zones by Measures of Electrical Distance. Forthcoming in Proc. of the IEEE Power Engineering Society General Meeting, Calgary AB, 2009. |
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About 25% of primary energy is consumed in the production of electricity. After conversion losses, about half of this is delivered to consumers over the global electricity infrastructure. Most predict that this percentage will increase substantially in the foreseeable future, particularly with growing interest in electric-drive vehicles.
While power grids are generally robust to small failures, and thus provides a fairly high level of reliability, they are notably vulnerable to large, often spectacular, cascading failures. Single component failures rarely impede the ability of a power grid to serve its customers. But larger sets concurrent outages can produce blackouts with sizes that are highly improbable from the perspective of Gaussian statistics. Because of the number of components in a power grid it is impossible to plan for and mitigate all sets of failures.
Maintaining a high level of reliability in the midst of this risk is challenging. As market forces, variable sources (eg. wind and solar power) and new loads (eg. electric cars) increase stress on power grids, the challenge of managing reliability and costs will certainly increase. Therefore we need strategies that enable the most important services that depend on electricity infrastructure to continue in the midst of risks. This talk will focus on two strategies for enabling the most important services that depend on electricity continue in the midst of significant systemic vulnerability. The first, as proposed by Talukdar et al. [1] is survivability, in which backup electricity sources provide a very high level of reliability for services that are economically and socially vital. The second, as proposed by Hines et al. [2], is Reciprocal Altruism, under which agents that manage the infrastructure are encouraged to align personal goals with those of the system as a whole. Results from simulated reciprocally altruistic agents indicate that this approach can substantially reduce the size and costs of large blackouts. [1] S.N. Talukdar, J. Apt, M. Ilic, L.B. Lave, and M.G. Morgan. Cascading failures: survival versus prevention. The Electricity Journal, 2003. [2] P. Hines and S. Talukdar. Reciprocally altruistic agents for the mitigation of cascading failures in electrical power networks. In Proc. of the International Conference on Infrastructure Systems, Rotterdam, 2008. |