Optimal Settlement Model of Energy and Reactive Power Market Using Stochastic Programming

Background: The global community focusing on improving energy efficiency and reducing environmental pollutants through distributed energy resources (DER) such as distributed generation (DG) and energy storage system (ESS) initiatives has highlighted Demand Response (DR) as a key initiative to achieve its aims. Methods: This study, addresses a novel optimization approach of energy and reactive power market settlement through Stochastic Programming (SP) and Artificial Fish Swarm Algorithm (AFSA). The research introduces a virtual power plant concept to enable market participation and optimize network utilization. It combines stochastic programming, CAES, and the AFSA to address uncertainties, optimize financial gain, and enhance market efficiency. The two-settlement energy market model considers uncertainties in day-ahead energy prices, imbalance prices, and storage capacity. The study utilizes a two-stage stochastic planning strategy, presenting a mathematical model to maximize financial gain while adhering to constraints. The study evaluates the proposed approach on the IEEE 30 bus network, considering nine various scenarios with different weighting coefficients. It discusses the impact of weighting on indices such as the unit participation rate, market profitability rate, profitable player number rate, net surplus distribution rate, and generation sharing rate. Results: Relative to the other scenarios, the CVRPCC value in scenario 5 is lower than that of separate life. This demonstrates the impact of direct optimization of the GSR index on the equitable distribution of the units' income. Conclusion: The effectiveness of the proposed settlement approach in decision-making amidst uncertainties, ultimately contributing to the advancement of energy market optimization strategies.