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, bi-weekly
Coordinated by M. Anghel, M. Chertkov, R. Gupta
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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. |
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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. |
| 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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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. |
| 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. |
| 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. |
| 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. |
| 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. |
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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 |
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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:
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:
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:
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 systemfs 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. |
| 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. |
| 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 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. |
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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: 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: 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: 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. |
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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. |