Dynamic scheduling optimization model for virtual power plant connecting with wind-photovoltaic-energy storage system
In order to promote wind power and solar power as the representative of the development of the distributed energy grid, this paper based on the wind power, photovoltaic power generation, gas turbine, the energy storage system and incentive model to build a virtual power plant (vpp) demand response, introducing the theory of conditional value at risk (cvar) and confidence method to describe the vpp operating uncertainty, and operating income maximization as the objective function. Vpp routine scheduling optimization model is established. Then this paper further to determine the threshold value of vpp operating earnings. Using cvar theory to describe the vpp operating uncertainty factors in the objective function, confidence method is applied to convert the constraint conditions including uncertainty variables, which run the risk of vpp stochastic scheduling optimization model is established. Improved ieee30 node system is chosen as the finally, the simulation system, the simulation results show that the price type of demand response can gently electricity load curve, energy storage system and incentive mode demand response can increase the vpp operating income. This means that the cvar theory and confidence method can be used to describe confidence vpp operating risks, thus by setting the threshold value and the confidence to reflect the decision makers for risk attitude, thus helping the risk control.