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

Aiming at the problem of insufficient consideration of the interaction between electric vehicles and wind power bidding in energy and frequency regulation (FR) market, a day-ahead bidding model for electric vehicle aggregator (EVA) and wind power supplier (WPS) participating in energy-FR market is established in this paper. Firstly, the bidding framework for the energy-FR market is designed. Then, combined with the bidding model of electric vehicle aggregator considering battery loss, a day-ahead bidding strategy for EVA and WPS participating in the energy-FR market is proposed and a Nash-Stackelberg game model that takes into account the three-party of EVA, WPS and electricity trading center is established. Finally, the simulation results verify the rationality of the proposed strategy. The market clearing results secure the benefits of EVA and WPS.

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

Energy storage has attracted more and more attention for its advantages in ensuring system safety and improving renewable generation integration. In the context of China’s electricity market restructuring, the economic analysis, including the cost and benefit analysis, of the energy storage with multi-applications is urgent for the market policy design in China. This paper uses an income statement based on the energy storage cost–benefit model to analyze the economic benefits of energy storage under multi-application scenarios (capacity, energy, and frequency regulation markets) in China’s future electricity market. The results show that the economic benefits of energy storage can be improved by joining in the capacity market (if it exists in the future) and increasing participation in the frequency regulation market. Nevertheless, the benefits under multi-application scenarios can hardly guarantee the cost recovery of energy storage under the current market mechanism or at the current price levels. Moreover, the economic benefits under different subsidy policies are studied, and the results show that energy storage can recover the cost with appropriate subsidy policies (the subsidy of 0.071 USD/kWh for pumped storage power stations is sufficient while the subsidy of 0.142 USD/kWh is required for electrochemical power stations). Finally, the sensitivity analysis of an energy storage power station to different price levels is carried out considering the difference in electricity price between China and the United States.

With the gradual establishment of market-based trading mode of power frequency regulation auxiliary service, new power supply formats such as integrated fire-storage system are developing rapidly. At the same time, the bidding strategy of integrated fire-storage system in frequency regulation market has become the focus of research in this field. In order to meet the demand of high frequency and high precision market transaction of the integrated fire storage system, a frequency regulation market declaration strategy based on Q-reinforcement learning is proposed. Q-reinforcement learning is a model-free reinforcement learning algorithm. Considering the bidding characteristics of the current frequency regulation market, the environment variables, action library and return function of the frequency regulation market declaration agent of the integrated fire storage system are constructed, and the implementation process of the algorithm is proposed. The effectiveness of the proposed method is verified by constructing an example based on the actual grid data of a province. Example results show that the results of this algorithm are basically consistent with the current target declaration strategy, and its computational efficiency is significantly higher than the existing algorithm, which is more in line with the trading needs of operators of integrated fire storage system in frequency regulation market.

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

With the increasing proportion of installed capacity of renewable energy such as wind power and photovoltaic in power system, the frequency regulation pressure of power system becomes larger. Renewable energy generators should undertake part of the frequency regulation task. Taking advantage of the complementarity between wind power and battery storage and making them participate in the energy-frequency regulation market has become a hot topic. In the existing studies, when solving the robust optimization model, the frequency regulation performance index is regarded as a constant. However, the index will change with the variation of bidding capacity, real-time reserved capacity and battery storage frequency regulation capacity. In this paper, the performance index is modeled and the prediction function of frequency regulation performance index is adopted in the robust optimization model. By establishing a more accurate model and changing the bidding strategy, the wind power and battery storage can obtain more income. And at the same time, the improvement of frequency regulation performance will encourage wind power and battery storage participating more in frequency regulation market. Finally, an example is given to prove that this method can encourage wind power and battery storage participating in frequency regulation market and obtain more profits.

Renewable energy generation, with the ability of participating in power systems frequency regulation, should be admitted into frequency regulation market to enrich regulatory resources. But the fluctuation and uncertainty of renewable energy generation should be seriously considered in the mechanism of frequency regulation market. This paper presents a new index of renewable energy generation participating in frequency regulation - a performance preciseness Conditional Value at Risk pCVaR as a quantitative expression of renewable energy generation units performance deviation with directive value. Considering this performance preciseness pCVaR, frequency regulation market clearing strategy model is established, which would be excepted to limit the risks of renewable energy generation participating into frequency regulation market. The contrasts of frequency regulation market clearing strategy with pCVaR between without pCVaR are illuminated in simulation cases to show the clearing strategy model presented in this paper effective.

Energy storage (ES) can help the renewable energy sources to smooth their output and enhance their profits, which promotes the installation of ES. However, it is inappropriate for small-scale renewable energy communities (REC) to invest costly ES, which requests a new business model to explore the possibility to rent ES for more returns. This paper proposes an ES rental strategy for REC to participate in the frequency regulation market (FRM). Firstly, the FRM is modelled considering the regulation capacity and mileage price. Then, the rental model for REC is built considering capacity rental costs and ES using costs. The correlation of rental capacity with reserved capacity and mileage is built through rental costs and revenue in FRM. The operation method for ES is carried out to maximise the total profits of REC. Finally, the whole model is demonstrated with a REC which has 35 MW photovoltaic and 113 MW wind turbine. The results show that under different rental and market prices, the REC can effectively choose the optimal rental strategy and its profits can mostly be raised by 19.63%.

With increasing penetration of renewable source in power system, higher requirements for power quality are put forward. Energy storage system represented by chemical battery and flywheel energy storage system is fast-ramping and responses quickly in frequency regulation market. It shows outstanding performance in frequency regulation comparing with the traditional frequency regulation resource. This paper reports a review of the energy storage system participating in frequency regulation, including frequency regulation market and energy storage technology. Also, it contrasts the frequency regulation characteristics and total costs between battery energy storage system (BESS) and flywheel energy storage system (FESS) both applied widely in the projects. The operation mode and Simulink modelling of energy storage system, along with the control strategy and capacity configuration, are also discussed through relative literature.

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.

With the large-scale integration of renewable energy and the deepening reform of electricity marketization, the role of energy storage technology in power systems has become increasingly prominent. Energy storage not only addresses the mismatch between renewable energy output and peak load demand but also provides ancillary services such as frequency regulation and reserve capacity, enhancing grid stability and reliability. This paper establishes a joint clearing model for energy storage participation in electricity and frequency regulation markets, optimizing power resource allocation through market-oriented mechanisms, leveraging the performance advantages of energy storage systems, and improving electricity market efficiency. A case study based on a 39-node system integrated with energy storage devices verifies the feasibility and effectiveness of the proposed model. The results demonstrate that the joint clearing mechanism can reduce system marginal costs, increase the utilization frequency of energy storage, and simultaneously meet the dual requirements of electricity supply and frequency regulation services.

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”.

Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach

To implement the carbon peaking and carbon neutrality goals, improving market mechanism to maximize the utilization of energy storage is attracting more and more attention. This paper addresses the trading strategy of independent energy storage station participating in both energy market and frequency regulation market. A restrictive coefficient of available capacity of energy storage participating in frequency regulation market is introduced, which characterizes the constraints of the energy storage between the two markets. Consequently, the market optimization model with the restrictive coefficient is constructed. Then, different strategies are set based on the capacity allocation of energy storage in the two markets, and the energy storage station’s role and revenue are analyzed. Simulations under different participation strategies are done, the results show that with the increase of renewable energy connected to the grid, taking the energy storage more as an excellent frequency regulation resource is beneficial to renewable energy consumption and system operation.

Battery Energy Storage Systems typically procure their primary revenues from regulated energy and ancillary services markets; nonetheless, they have great potential in supporting distribution network operators and their users. This paper evaluates the potential business case of battery storage systems integrating market application and services to a photovoltaic assisted electric vehicle fast-charging station. A mathematical deterministic optimization problem is formulated using mixed-integer linear programming to combine battery storage system operation in the day-ahead and frequency regulation market and the remunerated services offered to the charging station. The technical and economic feasibility of the solution and the applicability of the proposed framework is verified through a case study reflecting an existing photovoltaic assisted charging station in the Netherlands and considering the Dutch energy market framework and prices. The study shows that such battery storage system implementation is economically and technically advantageous for the players involved. The battery storage system can stack additional revenues on top of the market revenues. The charging station benefits from a reduced peak power and a 30% tariff reduction, and the system operator would indirectly benefit from the shaved charging station profile. Furthermore, the analysis shows that providing services to the charging station from the battery storage system does not significantly impact its market-related revenues.