带条件风险约束的发电商最优投标模型及计算

采用条件风险（CVaR）作为风险度量指标，建立了双层优化的发电商投标模型，上层解决社会效益最大和风险最小问题，下层解决发电商利润最大问题，设计了启发式粒子群算法（PSO）求解该复杂的双层优化模型.在4节点2机系统和9节点3机系统进行了实验，说明该模型和算法具有较好的计算效果和时效性，通过实验数据比较显示CVaR比VaR更准确地度量了发电商的风险.

Based on Risk Constraint of the Bidding Strategy Model and Computation for Generating Company

Take the Condition Value-at-Risk (CVaR) as a measure of risk indicators,a two-tier optimization of electricity market bidding model was built. The upper-level objective is to solve maximizing social profits and risk problem, the bi-level optimization is the largest power generation company profit optimization. In this paper, we designed heuristic particle swarm optimization (PSO) algorithm to solve the complex two-tier optimization. The IEEE 4-bus system and IEEE 9-bus system have been tested. Numerical examples of some standard tested IEEE systems show that the new model and algorithm have better effect of computation and practical. The contrast analysis show that CVaR can more accurately measure the risk of the power suppliers than VaR