Day-Ahead Optimal Reserve Capacity Planning Based on Stochastic RE and DR Models

Abstract

Renewable energy (RE) is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affect the capacity planning of operating reserve and thus reduce the reliability and security of power system. Appropriate planning of reserve capacity is, therefore, needed to solve these problems while maintaining cost minimization and power system stability. The proposed planning is performed based on a day-ahead market with the reserve providers including external grid, automatic generation control, demand response (DR) program and RE curtailment. Stochastic models including independent uncertainty-related factors of RE generation and load are constructed in Monte Carlo simulations. To keep the dynamic reserve adequate and solve the aforementioned risk and cost balance problem, a chance-constrained optimal power flow is employed as a probabilistic constraint to enforce operating reserve to offer a certain extent backup capacity and risk tolerance. Moreover, the effectiveness of DR is also imitated with cross-elasticity and self-elasticity for the amount and price a consumer will bid in DR market. To verify the proposed approach for reserve capacity planning, the proposed method is tested in a modified IEEE 30-bus system with high RE penetration. The result shows a day-ahead arrangement of operating reserve with good efficiency and economy.