Scenario-based Economic Dispatch with Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers

On an Information and Control Architecture for Future Electric Energy Systems

This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification. This paper describes the new requirements and challenges of an extended information and control architecture that need to be addressed for continued reliable delivery of electricity. It identifies several new actionable information and control loops, along with their spatial and temporal scales of operation, which can together meet the needs of future grids and enable deep decarbonization of the electricity sector. The present architecture of electric power grids designed in a different era is thereby extensible to allow the incorporation of increased renewables and other emerging electric loads.Comment: This paper is accepted, to appear in the Proceedings of the IEE

An Overview of Demand Response : From its Origins to the Smart Energy Community

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework

A Scenario Approach for Operational Planning with Deep Renewables in Power Systems

This work is both enabled by and motivated by the development of new resources and technologies into the power system market operation practice. On one hand, penetration level of uncertain generation resources is constantly increasing and on the other hand, retirement of some of the conventional energy resources like coal power plants makes market operations an attractive topic for both theoretical and state-of-the-art research. In addition, as generation uncertainty increases, it impacts the true cost of energy and causes it to be volatile and on average higher. This work targets flexibility enhancement to the grid to potentially eliminate the impact of uncertainty. Two different viewpoints in two different markets for electricity is targeted. This dissertation looks at the real-time market generation adequacy from the Independent System Operator’s point of view, and the day-ahead scheduling of energy and reserve procurement from the market participant’s point of view. At the real time scale, the emphasis is on developing fast and reliable optimization techniques in solving look-ahead security constrained economic dispatch. The idea is when forecast accuracy gets sharper closer to the real-time and slower power plants retiring in recent years, market participants will spend more and more attention to the real-time market in comparison to the day ahead operation in terms of the energy market. To address it, a data-driven model with rigorous bounds on the risk is proposed. In particular, we formulate the Look-Ahead Security Constrained Economic Dispatch (LAED) problem using the scenario approach techniques. This approach takes historical sample data as input and guarantees a tunable probability of violating the constraints according to the input data size. Scalability of the approach to real power systems was tested on a 2000 bus synthetic grid. The performance of the solution was compared against state-of-the-art deterministic approach as well as a robust approach. Although the real-time market is primarily for energy trading, the day-ahead market is the market for ancillary service trading. In this dissertation, at the day-ahead scale, the focus is on providing ancillary service to the grid by controlling the consumption of millions of privately owned ii pool pumps in the US, while benefiting from energy arbitrage. A conceptual framework, a capacity assessment method, and an operational planning formulation to aggregate flexible loads such as inground swimming pool pumps for a reliable provision of spinning reserve is introduced. Enabled by the Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This work offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, optimal scheduling of pool pumps is formulated and solved using the deterministic approach and the scenario approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework

Power System Operations with Probabilistic Guarantees

This study is motivated by the fact that uncertainties from deepening penetration of renewable energy resources have posed critical challenges to the secure and reliable operations of future electrical grids. Among various tools for decision making in uncertain environments, this study focuses on chance-constrained optimization, which provides explicit probabilistic guarantees on the feasibility of optimal solutions. Although quite a few methods have been proposed to solve chance-constrained optimization problems, there is a lack of comprehensive review and comparative analysis of the proposed methods. In this work, we provide a detailed tutorial on existing algorithms and a survey of major theoretical results of chance-constrained optimization theory. Data-driven methods, which are not constrained by any specific distributions of the underlying uncertainties, are of particular interest. Built upon chance-constrained optimization, we propose a three-stage power system operation framework with probabilistic guarantees: (1) the optimal unit commitment in the operational planning stage; (2) the optimal reactive power dispatch to address the voltage security issue in the hours-ahead adjustment period; and (3) the secure and reliable power system operation under uncertainties in real time. In the day-ahead operational planning stage, we propose a chance-constrained SCUC (c-SCUC) framework, which ensures that the risk of violating constraints is within an acceptable threshold. Using the scenario approach, c-SCUC is reformulated to the scenario-based SCUC (s-SCUC) problem. By choosing an appropriate number of scenarios, we provide theoretical guarantees on the posterior risk level of the solution to s-SCUC. Inspired by the latest progress of the scenario approach on non-convex problems, we demonstrate the structural properties of general scenario problems and analyze the specific characteristics of s-SCUC. Those characteristics were exploited to benefit the scalability and computational performance of s-SCUC. In the adjustment period, this work first investigates the benefits of look-ahead coordination of both continuous-state and discrete-state reactive power support devices across multiple control areas. The conventional static optimal reactive power dispatch is extended to a “moving-horizon” type formulation for the consideration of spatial and temporal variations. The optimal reactive power dispatch problem is further enhanced with chance constraints by considering the uncertainties from both renewables and contingencies. This chance-constrained optimal reactive power dispatch (c-ORPD) formulation offers system operators an effective tool to schedule voltage support devices such that the system voltage security can be ensured with quantifiable level of risk. Security-constrained Economic Dispatch (SCED) lies at the center of real-time operation of power systems and modern electricity markets. It determines the most cost-efficient output levels of generators while keeping the real-time balance between supply and demand. In this study, we formulate and solve chance-constrained SCED (c-SCED), which ensures system security under uncertainties from renewables. The c-SCED problem also serves as a benchmark problem for a critical comparison of existing algorithms to solve chance-constrained optimization problems