Energy Markets and Responsive Grids

Power industry is facing revolutionary changes. The direction of the US Government to low carbon footprint and, as a consequence, high penetration of renewable energy resources and smart grid technologies are completely transforming planning and operational patterns for electric grid. As more and more variable and demand response resources being integrated into the electric grid, the grid operation is experiencing increasing level of uncertainties. The decision-making process under such environment becomes more challenging. The grid architecture and control also become more and more decentralized requiring new control paradigms and reliability metrics to be investigated in order to achieve much higher level of flexibility and resilience. These changes are disruptive enough to cause even transformations in utility business dealing with completely unknown situations. On the other hand, the evolution in computing; generation, transmission, and distribution technologies; and mathematical methods creates opportunities for innovation in power system design and control. New mathematical models for power system analysis and operation are being developed to address above challenges. We will discuss the need for new power system control and electricity market design directions while managing grid complexity.

The push toward competition, market pricing, and less regulation in the electricity industry embraces the logic and elegance of markets. It means that participants are exposed to more price risk than in the past, and it represents a narrowing of both the notion of the public interest and the government’s role in protecting that interest. But electricity markets can never resemble the idealized markets of economic theory that have become so popular in conservative policy discourse. This chapter explores why that is. More specifically, it (i) reviews the work of economic thinkers whose work shapes the conservative challenge to regulation and the push for further deregulation, (ii) explores why the economist’s goal of allocative efficiency does not subsume elements of fairness and risk management that are important to voters and policymakers and why economic models continue to have trouble incorporating important lessons from behavioral research, and (iii) explains why these lessons are important to understanding the operation of electricity markets and to an understanding of the problem of ensuring a reliable, reasonably priced energy supply.

Many electricity markets around the world have implemented capacity markets (e.g., PJM, New England, New York, UK, Colombia), and many more are in the process of finalizing capacity market designs. In this chapter we will review the rationale behind capacity markets, the basic traits of the most popular designs and some outstanding design issues.

This paper surveys the different approaches in use today to ensure grid reliability and incentivize new resources. Market challenges are surveyed, as well as empirical findings that suggest that current market approaches do not provide proper incentives. It is argued that the primary problem is that organized capacity markets today do not consider risks and uncertainty over the proper time frame – decades instead of months or years. Because of this, the analyses ignore risks and other factors that are key to making optimal investment decisions. Solutions are proposed based in part on concepts from traditional resource planning.

The need for flexible service provision in electric power systems has dramatically increased due to the growing penetration of variable energy resources, as has the need to ensure fair access and compensation for this provision. A swing contract facilitates flexible service provision with appropriate compensation because it permits multiple services to be offered together in bundled form with each service expressed as a range of possible values rather than as a single point value. This paper discusses a new swing-contract market design for electric power systems that permits swing contracts to be offered by any dispatchable resource. An analytical optimization formulation is developed for the clearing of a swing-contract day-ahead market that can be implemented using any standard mixed-integer linear programming solver. The practical feasibility of the optimization formulation is demonstrated by means of a numerical example.

The current transformation towards a cyber-enabled power grid consists of two dominant features, one of which is a high penetration of distributed energy resources (DER) and the other is an increasing participation of demand response (DR), the concept of adjustable power consumption. As much of the DERs are renewable energy resources, their increased penetration introduces intermittencies and uncertainties, which encompass a large range of timescales. DR-compatible devices bring in new degrees of freedom that lead to decision-making over multiple timescales as well. Both of these features necessitate the need for revisiting the electricity market structure, its mechanisms and its overall coupling with the physical power grid. In this paper, we propose a dynamic framework for market mechanisms in the wholesale electricity market at fast timescales. In particular, we propose a dynamic market mechanism and a dynamic regulation market mechanism for the operation of a real-time market and a regulation market, respectively. Taking into account various physical constraints for generation, transmission and consumption, we design these mechanisms so that efficient market equilibrium can be realized. Performance metrics that reflect the social cost as well as physical costs of frequency regulation and area control errors are taken into account. Both market mechanisms are shown to be implementable and exhibit good performance through case studies on a modified IEEE 118-bus system and a three-area system where each of the areas is a modified IEEE 300-bus system.

Real-time electricity markets are the main transaction platforms for providing necessary balancing services, where the market clearing (nodal or zonal prices depending on markets) is very close to real-time operations of power systems. We present single and multiple time period decentralized market clearing models based on the DC power flow model. The electricity market we study consists of a set of generation companies (GenCos) and a set of Distribution System Operators (DSOs). The Independent System Operator (ISO) determines the market clearing generation and demand levels by coordinating with the market participants (GenCos and DSOs). We exploit the problem structure to obtain a decomposition of the market clearing problem where the GenCos and DSOs are decoupled. We propose a novel semismooth Newton algorithm to compute the competitive equilibrium. Numerical experiments demonstrate that the algorithm can obtain several orders of magnitude speedup over a typical subgradient algorithm with no modification to the existing communication protocol between the ISO and market participants.

Recent regulatory initiatives, technological advancements, and public policies such as New York’s Reforming the Energy Vision (REV) and California’s Energy Storage Mandate have incentivized the development of smaller, cleaner, and more distributed energy resources. As part of the day-ahead market clearing process, the ISOs/RTOs today have to solve more computationally intensive mixed integer programming (MIP)-based security-constrained unit commitment (SCUC) models to accommodate these small-scale resources within a short time window. This chapter will discuss the MIP solution performance challenges in dealing with the increasing penetration of small resources in the ISO/RTO day-ahead market in terms of both practicality and theory aspects.

We discuss how multi-grid computing schemes can be used to design hierarchical coordination architectures for energy systems. These hierarchical architectures can be used to manage multiple temporal and spatial scales and mitigate fundamental limitations of centralized and decentralized architectures. We present the basic elements of a multi-grid scheme, which includes a smoothing operator (a high-resolution decentralized coordination layer that targets phenomena at high frequencies) and a coarsening operator (a low-resolution centralized coordination layer that targets phenomena at low frequencies). For smoothing, we extend existing convergence results for Gauss-Seidel schemes by applying them to systems that cover unstructured domains. This allows us to target problems with multiple timescales and arbitrary networks. The proposed coordination schemes can be used to guide transactions in decentralized electricity markets. We present a storage control example and a power flow diffusion example to illustrate the developments.

We review new ideas and the first results from the application of the graphical models approach, which originated from statistical physics, information theory, computer science, and machine learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems.

This work demonstrates that there is an analytical relationship between nodal price signals and the optimal allocation and operation of distributed energy storage systems (ESSs) in alternating current (AC) power networks. The results are based on a semidefinite relaxation of a multi-period optimal power flow (OPF) with storage problem in which the ESSs provide both real and reactive power to the grid. Strong duality is exploited to define a storage operator subproblem that is used to elucidate the natural duality between minimizing system costs and maximizing the profits of the storage operator in purely competitive markets. We illustrate these theoretical relationships, which require strong duality to hold, and discuss their impact on siting decisions using case studies based on an augmented IEEE benchmark system. We focus on how the provision of reactive power in combination with traditional grid services changes both the ESS allocation strategy and the overall performance of the simulated power network. Our results highlight the tight connections between market design and the financial viability of large-scale storage integration in AC power systems.

The past few years have witnessed a steady shift in the nature of power generation worldwide. While the share of renewable-based distributed generation has been on the rise, there has also been a decline in the conventional synchronous-based generation. The renewable-based power generation interfaced to the grid via power-electronic converters, however, does not provide rotational inertia, an inherent feature of synchronous machines. This absence of inertia has been highlighted as the prime source for the increasing frequency violations and severely impacting grid stability. As a countermeasure, virtual or synthetic inertia and damping emulated by advanced control techniques have been proposed. In this chapter, we study the optimal placement and tuning of these devices. We discuss two approaches based on the control notion of \(\mathcal H_2\) system gain characterizing the amplification of a disturbance and the spectral notion of pole-placement. A comprehensive analysis accompanied by iterative gradient-based algorithms is presented for both the approaches and validated on a three-area test case for comparison.

This chapter develops a timescale-based hierarchy of microgrid models that can be utilized in analysis and control design tasks. The focus is on microgrids with distributed generation interfaced via grid-forming inverters. The process of developing the model hierarchy involves two key stages: (1) the formulation of a microgrid high-order model using circuit and control laws, and (2) the systematic reduction of this high-order model to several reduced-order models using singular perturbation techniques. The timescale-based hierarchy of models is comprised of the aforementioned microgrid high-order model (μHOm), along with three reduced-order models: microgrid reduced-order model 1 (μROm1), microgrid reduced-order model 2 (μROm2), and microgrid reduced-order model 3 (μROm3). A numerical validation of all the models is also presented.

To enable greater penetration of renewable energy, there is a need to move away from the traditional form of ensuring electric grid reliability through fast-ramping generators and instead consider an active role for flexible and controllable distributed energy resources (DERs), e.g., plug-in electric vehicles (PEVs), thermostatically controlled loads (TCLs), and energy storage systems (ESSs) at the consumer level. However, in order to facilitate consumer acceptance of this type of load coordination, DERs need to be managed in a way that avoids degrading the consumers’ quality of service (QoS), autonomy, and privacy. This work leverages a probabilistic packetized approach to energy delivery that draws inspiration from random access, digital communications. Packetized energy management (PEM) is an asynchronous, bottom-up coordination scheme for DERs that both abides by the constraints of the transmission and distribution grids and does not require explicit knowledge of specific DER’s local states or schedules. We present a novel macro-model that approximates the aggregate behavior of packetized DERs and is suitable for estimation and control of available flexible DERs to closely track a time-varying regulation signal. PEM is then implemented in a transmission/distribution system setting, validated with realistic numerical simulations, and compared against state-of-the-art load coordination schemes from industry.

A Markov decision process (MDP) framework is adopted to represent ensemble control of devices with cyclic energy consumption patterns, e.g., thermostatically controlled loads. Specifically we utilize and develop the class of MDP models previously coined linearly solvable MDPs, that describe optimal dynamics of the probability distribution of an ensemble of many cycling devices. Two principally different settings are discussed. First, we consider optimal strategy of the ensemble aggregator balancing between minimization of the cost of operations and minimization of the ensemble welfare penalty, where the latter is represented as a KL-divergence between actual and normal probability distributions of the ensemble. Then, second, we shift to the demand response setting modeling the aggregator’s task to minimize the welfare penalty under the condition that the aggregated consumption matches the targeted time-varying consumption requested by the system operator. We discuss a modification of both settings aimed at encouraging or constraining the transitions between different states. The dynamic programming feature of the resulting modified MDPs is always preserved; however, “linear solvability” is lost fully or partially, depending on the type of modification. We also conducted some (limited in scope) numerical experimentation using the formulations of the first setting. We conclude by discussing future generalizations and applications.

Inexpensive energy from the wind and the sun comes with unwanted volatility, such as ramps with the setting sun or a gust of wind. Controllable generators manage supply-demand balance of power today, but this is becoming increasingly costly with increasing penetration of renewable energy. It has been argued since the 1980s that consumers should be put in the loop: “demand response” will help to create needed supply-demand balance. However, consumers use power for a reason and expect that the quality of service (QoS) they receive will lie within reasonable bounds. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability. The goal of this chapter is to describe an emerging science for demand dispatch that will create virtual energy storage from flexible loads. By design, the grid-level services from flexible loads will be as controllable and predictable as a generator or fleet of batteries. Strict bounds on QoS will be maintained in all cases. The potential economic impact of these new resources is enormous. California plans to spend billions of dollars on batteries that will provide only a small fraction of the balancing services that can be obtained using demand dispatch. The potential impact on society is enormous: a sustainable energy future is possible with the right mix of infrastructure and control systems.

An electricity distribution network’s efficiency and reliability can be improved using real-time knowledge of the total consumption/production of different load/generator types (e.g., air conditioning loads, lighting loads, photovoltaic generation) within the network. This information could be gathered from additional device-level sensors and communication infrastructure. Alternatively, this information can be inferred using existing network measurements and some knowledge of the underlying system. This work applies two online learning algorithms, dynamic mirror descent (DMD) and dynamic fixed share (DFS), to separate (or disaggregate), in real-time, feeder-level active demand measurements into two components: (1) the demand of a population of residential air conditioners and (2) the demand of the remaining loads served by the feeder. The online learning algorithms include models of the underlying load types, which are generated using historical building-level or device-level data. We develop methods to incorporate model prediction error statistics into the algorithms, develop connections between DMD and Kalman filtering, adapt the algorithms for the energy disaggregation application, and present case studies demonstrating that the algorithms perform disaggregation effectively.

Electric utilities typically offer demand-side management (DSM) programs in order to reduce peak demand and to shift supply risks. These same programs engender a new business model, that of the energy aggregators. Energy aggregators seek to harvest the DSM incentives by strategically deferring the loads under their control. Examples of deferrable loads are electric vehicles (EVs) and heating, ventilation, and air-conditioning (HVAC) systems. To choose appropriately from a utility’s menu of programs, the aggregator must forecast both temperature and load and should also estimate the uncertainties associated with these forecasts. Further, the aggregator can work to mitigate these uncertainties by managing flexible loads under their control.

We propose a formulation that unifies the various kinds of deferrable loads and explicitly balances the trade-off between user discomfort and monetary costs. Our main contribution comes from incorporating the uncertainty of temperature and load forecasts into the optimal choice of DSM program selection.

This contribution presents an approach to improve the resilience of electricity distribution networks (DNs) to a class of cyber-physical failures by way of optimal allocation of distributed energy resources (DERs). The approach is motivated by the need to adapt the well-known security-constrained optimal power flow problem to DNs with remotely controllable (and, hence, vulnerable) distributed generation sources or loads. To this end, we model the interaction between the system operator (SO) and an external adversary as a three-stage sequential game. In this game, the SO allocates the available resources (Stage 0) and also responds to the adversary’s action by optimally dispatching them (Stage 2). The adversary, on the other hand, compromises a subset of vulnerable components with the objective of inducing operating bound violations (Stage 1). We consider qualitatively different allocation strategies in Stage 0 and develop a scalable greedy heuristic to solve Stages 1–2 (i.e., bilevel optimization problem). We utilize this greedy heuristic to obtain structural insights about optimal adversarial compromises and desirable allocation strategies of the SO.

Recent research has explored the potential for distributed, consumer-based equipment to participate in control action seeking to improve grid dynamic performance. Renewable resources are displacing synchronous generators, reducing the electrically coupled rotating inertia supplied to the system as a percentage of generation. However, this loss may be mitigated by feedback control emulating the dynamics of rotating inertia and so-called “emulated inertia” control may be implemented in distributed, consumer-based resources. The case study presented illustrates that emulated inertia feedback is also extremely well-suited to subversion by a cyberattacker. In particular, local inertia-emulating feedback can create wide-area instabilities with only slight modification of feedback parameters. The amount of affected load can be relatively modest and the attacker can “target” particular generators, producing oscillations that would likely trip rate-of-change-of-frequency protective relays within one minute. The authors believe this scenario is particularly troubling, because it is likely that distributed consumer-based control systems will lack the strong cybersecurity protection afforded large generation resources.