Risk-based Stochastic Continuous-time Scheduling of Flexibility Reserve for Energy Storage Systems

Abstract

This paper develops a novel risk-based stochastic continuous-time model for optimizing the role of energy storage (ES) systems in managing the financial risk imposed to power system operation by large-scale integration of uncertain renewable energy sources (RES). The proposed model is formulated as a two-stage continuous-time stochastic optimization problem, where the generation of generating units, charging and discharging power of ES, as well as flexibility reserve capacity from both resources are scheduled in the first stage, while the flexibility reserve is deployed in the second stage to offset the uncertainty of RES generation in each scenario. The Conditional Value at Risk (CVaR) is integrated as the risk metric measuring the average of the higher tail of the system operation costs. The proposed model is implemented on the IEEE Reliability Test System using load and solar power data of CAISO. Numerical results demonstrate that the proposed model enables the system operators to effectively utilize the flexibility of ES and generating units to minimize the system operation cost and renewable energy curtailment at a given risk tolerance level.