Dynamic Matching in Power Systems using Model Predictive Control

Abstract

Integration of distributed renewable energy sources (D- RES) has been introduced as a viable solution to offer cheap and clean energy to customers in decentralized power system. D- RES can offer local generation to flexible customers based on their servicing deadline and constraints, benefiting both D- RES owners and customers in terms of providing economic revenue and reducing the cost of supplied energy. In this context, this paper proposes a dynamic matching framework using model predictive control (MPC) to enable local energy sharing in power system operation. The proposed matching framework matches flexible customers with D- RES to maximize social welfare in the matching market, while meeting the customers' servicing constraints prior to their deadline. Simulations are conducted on a test power system using multiple matching algorithms across different load and generation scenarios and the results highlighted the efficiency of proposed framework in matching flexible customers with the appropriate supply sources to maximize social welfare in the matching market.