Frequency Regulation Market Research Articles

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

Bidding Strategy of Battery Energy Storage Power Station Participating in Frequency Regulation Market

Bidding Strategy of Battery Energy Storage Power Station Participating in Frequency Regulation Market

Model predictive control of smart districts participating in frequency regulation market: a case study of using heating network storage

Model predictive control of smart districts participating in frequency regulation market: a case study of using heating network storage

Data-Driven Fully Distributed Load Frequency Control for an IoT-Based Interconnected Grid Considering a Performance-Based Frequency Regulation Market

Data-Driven Fully Distributed Load Frequency Control for an IoT-Based Interconnected Grid Considering a Performance-Based Frequency Regulation Market

Grid frequency regulation through virtual power plant of integrated energy systems with energy storage

AbstractOwing to the widespread integration of renewable distributed energy resources (DERs), the system frequency stability has been jeopardized by the non‐inertial and stochastic units caused by power electronic devices. The integrated energy system (IES) that combines multi‐vector energy resources can provide energy compensation among sub‐systems in a coordinated fashion to further alleviate the volatility on the electric grid. Under the framework of IES, a virtual power plant (VPP) can aggregate multi‐entities and multi‐vector energy resources to participate in the frequency regulation service while pursuing profit maximization. A three‐stage optimal scheduling model of IES‐VPP that fully considers the cycle life of energy storage systems (ESSs), bidding strategies and revenue settlement has been proposed in this paper under the modified PJM frequency regulation market framework to motivate the aggregated resources to respond to the frequency regulation market actively. The simulation results under various scenarios have verified the feasibility and effectiveness of the proposed model through techno‐economic analysis, the advantages of IES‐VPP have been demonstrated compared with a single vector energy system.

Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master-Slave Game.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master-slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

This paper proposes an active coordinated fault tolerance load frequency control (AFCT-LFC) method, which effectively prevents sudden frequency changes caused by unit actuator failures or unplanned decommissioning in a multi-area interconnected grid subject to the performance-based frequency regulation market mechanism. It can also reduce regulation mileage payments and achieve multi-objective active fault-tolerant control. In addition, this paper proposes a brain-Inspired deep meta-deterministic policy gradient algorithm (BIMA-DMDPG), which adopts multi-agent centralized training, equates the controller of each area as an agent capable of independent decision making, and implements distributed training by dividing the environment into multiple environments. In addition, meta-reinforcement learning is employed to realize multi-task collaborative learning. The optimal policy is actively selected under different fault conditions to achieve active fault-tolerant control. The superior performance of the method is verified in a four-area LFC model of the China Southern Grid (CSG), in which it is tested alongside a selection of existing algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —AFCT-LFC is based on advanced artificial intelligence algorithm, which can effectively identify any fault in multi-area grids and make rapid response to achieve active fault-tolerant control. Compared with the existing model-based fault-tolerant control methods, the BIMA-DMDPG algorithm proposed in this paper does not need to rely on accurate mathematical models, and can be applied to practice through simple training, which is very suitable for practical applications. Therefore, AFCT-LFC is an advanced adaptive active fault-tolerant control method that can be truly applied in practice because of its fast-decision-making ability and performance.

Optimal bidding strategy for multi-energy virtual power plant participating in coupled energy, frequency regulation and carbon trading markets

Multi-energy virtual power plant (MEVPP) with diversified flexible resources can participate in energy market (EM), frequency regulation market (FRM) and carbon trading market (CM) to obtain extra benefits. However, the complicated market environment provokes grant challenges in the bidding strategy of MEVPP that coordinates heterogenous supply-side devices and demand-side users. To the end, this study proposes a multi-objective bidding strategy for MEVPP comprising various energy flows and multi-energy demand response (MDR) in EM, FRM and CM. Results are delivered that: 1) Compared to the only profit-oriented optimization, the profit of comprise solution under the multi-objective declines by 46% but the satisfaction level improves 8%. Relative to the single satisfaction-oriented optimization, a 5.2% satisfaction loss allows for an approximately 103% growth in profit, realizing a win-win result for MEVPP and users. 2) There were negative profits in the pure EM (Case 1) as well as EM and CM (Case3). Compared with positive profit in EM and FRM (Case 2), the profit in coupled EM, FRM and CM (Case 4) increases by 48.9%. Relative to the CO2 emission in EM and CM, the emission under the proposed strategy mitigates by 9.36%. 3) A carbon base price at 200 CNY/t CO2 and an emission interval length of 5–10 t are recommended to maintain the low-carbon economic operation for MEVPP.

Cooperated Operation for Renewable Energy Community With Energy Storage Capacity Rental in the Frequency Regulation Market

Cooperated Operation for Renewable Energy Community With Energy Storage Capacity Rental in the Frequency Regulation Market

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Comprehensive-Contribution-Based Primary Frequency Regulation Market Design for the Converter-Integrated Power System

Comprehensive-Contribution-Based Primary Frequency Regulation Market Design for the Converter-Integrated Power System

Probabilistic Multi-Product Trading of Hydro Power Plants in Sequential Intraday and Frequency-Regulation Markets

Probabilistic Multi-Product Trading of Hydro Power Plants in Sequential Intraday and Frequency-Regulation Markets

Techno-Economic Investment Risk Modeling of Battery Energy Storage System Participating in Day-Ahead Frequency Regulation Market

Techno-Economic Investment Risk Modeling of Battery Energy Storage System Participating in Day-Ahead Frequency Regulation Market

A tri-level programming-based frequency regulation market equilibrium under cyber attacks

Owing to their flexibility and rapid response, grid-connected distributed energy resources (DERs) are wielding growing influence in frequency regulation markets (FRMs). Nevertheless, compared with conventional large-scale generators, small-scale DERs are usually weakly shielded by cyber security measures. This offers attackers the opportunity of disrupting the clearing and settlement of FRMs by manipulating the bid information of DERs. In this paper, the frequency regulation market equilibrium is studied considering the dynamic gaming between attackers and defenders, both of which need the knowledge of FRMs for their interventions. Specifically, a tri-level programming model characterizing the attacker–defender–operator (ADO) interdiction problem in FRMs is developed and then analyzed using a column and constraint generation algorithm, thereby achieving market equilibrium representing the defender's best response to the attacker. The defense strategy in the market equilibrium can attenuate the negative influence of cyber attacks upon the FRMs to the maximum extent. Finally, based on the operating rules of the California Independent System Operator, the FRM process considering the ADO interdiction is simulated and the numerical equilibrium results are presented.

Strategic behavior modeling and energy management for electric-thermal-carbon-natural gas integrated energy system considering ancillary service

Carbon capture system (CCS) and power to gas (P2G), as key low-carbon technologies, have obtained the widespread attention. Thus, it is necessary to explore the operation pattern and benefits of CCS and P2G. This paper investigates their roles in the electric-thermal-carbon-natural gas integrated energy system (IES) and focuses on the multi-market participation and multi-energy management. IES participates in the electricity, carbon trading, and natural gas markets. In the electricity market, IES can provide two types of ancillary services: deep peak regulation and frequency regulation services. Then, owing to the small scale of the ancillary service market, the strategic behaviors of IES are discussed. A bi-level model is established to deal with the complex couplings in the peak regulation market. IES's strategic behavior in the frequency regulation market is depicted by the price quota curve. Next, a solution method based on the particle swarm optimization algorithm is proposed to deal with the nonconvexity of the lower-level model. Finally, case studies show that the strategic behavior modeling in this paper can increase the profits of the deep peak regulation and the frequency regulation services. When IES is equipped with CCS and P2G, the operation cost decreases by 16.6%.

Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market

Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.

A Comparative Analysis of Two Pricing Mechanisms, MCP and PAB, in the Chinese Frequency Regulation Market

In the face of the new reform of China’s “unified electricity market”, the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the “unified electricity market”. Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the “price signal”, making it suitable for late adoption in the “unified electricity market”.

Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with Batteries

Falling costs of lithium-ion (Li-ion) batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants' load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.

Benefits of integrating power-to-heat assets in CHPs

Integrating power-to-heat (P2H) assets in combined heat and power plants (CHPs) is an attractive option, which can improve the flexibility in CHPs. This paper compares the potential benefits of integrating an electrical boiler (EB) and a heat pump (HP) in a CHP from providing flexibility services in both the day-ahead market and the frequency regulation market. An optimization model is developed for the operation of P2H assets and the CHP to maximize the profit. A case study is carried out using the data of a real CHP and electricity prices of Nord Pool. It is found that when an EB or a HP is integrated, the annual profit of the studied CHP from providing frequency regulation can be increased by 3.1 % (EB) or 27.7 % (HP) respectively compared to the CHP without P2H. Despite the high capital cost, a HP can increase the net present value up to 21.8 %, and achieve a payback period of 3 year, which are better than an EB (0.8 % and 5 year). Sensitivity analysis shows that prices of fuel and electricity have significant impacts on the net present value and payback period for the integration of P2H assets. Even though the increase of the fuel price decreases the NPV, it can lead to a decline in the payback period. Meanwhile, the increase of the electricity price results in a large growth in the profit and NPV, but a big reduction in payback period.

An Incentive-Compatible Frequency Regulation Market for Flexible Resources in Microgrid

Frequency regulation services can be provided by emerging flexible resources such as electric vehicles and energy storage which can rapidly adjust the output in response to regulation signals, as long as there is an effective market to provide proper incentives. However, current regulation markets were designed for conventional thermal generating units, and it is difficult for the mileage-based bidding structure to express the cost characteristics of flexible resources. Therefore, the current mechanisms are not conducive to encouraging the participation of flexible resources in providing frequency regulation services. This paper proposes an incentive-compatible frequency regulation market for flexible resources, whose revisions to the existing mechanism include the bidding structure, clearing rules, and settlement rules that adapt to the characteristics of new resources. Compared with the current mechanism, the proposed mechanism can reduce the costs of deploying regulation and more reasonably allocate the revenues for regulation services. The case studies verify the effectiveness of the proposed mechanism.