Energy Storage Economic Analysis of Multi-Application Scenarios in an Electricity Market: A Case Study of China

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

New Energy Aggregators Optimal Dispatching Strategies Based on Price Incentive Agreement

The ubiquitous power Internet of things (UPIoT) can realize the wide interconnection of all links of the power system. Based on this background, this paper proposes the price incentive agreement of distributed new energy aggregators and establishes distributed dispatching architecture. Firstly, the information transmission matrix of distributed new energy aggregators with the property of double random matrix is established, and the distributed sub-gradient algorithm based on information transmission matrix is used to solve the distributed dispatching model of new energy aggregators. Finally, the feasibility and effectiveness of the optimization model and its solution method are analyzed through simulation examples, and the information interrupt, information error that may be encountered are also discussed

Adaptive robust unit commitment with renewable integration: An extreme scenarios driven model

As the penetration rate of renewable energy continues to increase, the uncertainty problem brought by it is becoming more and more serious. Robust optimization is widely used in the process of unit combination as a method of dealing with uncertainty. However, traditional uncertainty coping method, two-stage robust optimization unit commitment, has problems of nonanticipativity and all-scenario feasibility. For this reason, this paper improves the traditional two-stage robust optimization model. The extreme scenarios are first generated from the vertex scenarios of the polyhedron uncertainty set. According to the generated extreme scenarios set, the two-stage robust optimization is transformed into a stochastic programming simultaneously. Finally, this paper incorporates all-scenario-feasibility and nonanticipativity constraints into the model and an example is designed to verify the validity of the model. The results show that the designed model can meet the requirements of all-scenario-feasibility and nonanticipativity