A stochastic generation scheduling model for large-scale wind power penetration considering demand-side response and energy storage

The presence of high levels of wind power production brings technical and economic challenges to system operators. To better accommodate wind power, this paper proposes a coordinated scheduling optimization model considering demand-side response (DR) and energy storage (ES), incorporating conditional value-at-risk (CVaR) assessment. In this paper we use CVaR as the risk measure to evaluate the risk of loss of load and wind curtailment and indicate decision attitudes. Price-based DR program and ES are incorporated into generation scheduling and uncertainty from wind power prediction and DR is considered. The proposed model is tested in a 5-bus system. The results show that scheduling costs are different with different security risk weight and the coordination of DR and ES efficiently increases the level of wind power integration.