Risk Measure Based Approach For CVPP Operation

Confronted with the growing energy crisis, development of renewable energy technologies, such as wind power and solar power, has become a broad national consensus. As the optimal utilization pattern of renewable energy, distributed generation is gradually replacing conventional generation. Meanwhile, it has won more integrated demand side resources integrated with the development of smart grid technologies and demand side management technologies which leads to more active support from end users. Virtual power plant, which can participate in power system operation as a whole by integrating sizes of widespread resources at both supply side and demand side, provides a promising solution to the system management troubles caused by the increasing uncertainty and complexity of power system.This paper focuses on the operational approach of virtual power plant containing intermittent generation sources in electricity markets. Firstly, differences between traditional controllable generators and intermittent generation sources are analyzed and roles that load and energy storage can play in VPP are presented. Then, a multi-VPP system model with various resources included is built in DIgSILENT/PowerFactory, which contains dynamic process simulation under varying source and load and operational process simulation of VPP as a participant in the market. In response to ever-increasing complexity of power system and electricity market, this paper draws risk measure theory into the issue of CVPP bidding strategy and comes up with CVPP conditional robust profit maximization model, which is effective and feasible demonstrated through practical examples. Based on it, a conclusion that multiple CVPP may improve overall profit by participating ahead market bidding jointly is drawn. To deal with the configuration issue of energy storage system in CVPP, a model that maximizes profit during planning period based on historical price is built and then solved more efficiently through an improved clustering method which deals with price data in advance. Finally, a detailed configuration method is presented with the conclusion that the optimal power-capacity factor on energy storage configuration exists.