A Study On Stochastic Optimal Operation Of Regional Energy Internet Considering Backup Service Of Electric Vehicles

Due to fossil fuel depletion and environmental pollution,renewable energy resources and energy internet system will play essential roles in the future energy structure.However,renewable generation is naturally uncertain,and the renewable generation forecasting error is a random variable.Forecasting errors may lead to operation risks,such as loss of load and wind spillage.For regional energy internet systems with high penetration of renewable energy,low forecasting ability and weak electric network,forecasting errors would significantly reduce system operation reliability.This thesis investigates the reliable optimization of the regional(park-level)energy internet system containing wind power.The main research works and achievements are summarized as follows:First,modeling methods for regional energy internet systems are proposed,such as equipment modeling and energy hub modeling.The chance constrained theory is presented,including the deterministic equivalent of chance constraints and risk cost modeling.These works provide a theoretical basis for the research on stochastic optimization for regional energy internet systems.Second,the gas turbine's reserve capacity is considered to improve system operation reliability,and a stochastic optimization method for regional energy internet systems is proposed.In the proposed model,a chanced constraint for reserve capacity is established,and the risk cost of reserve setting mismatch is modeled in the objective function.Simulation results indicate that the proposed method can effectively tackle operation risks to improve system operation reliability.Moreover,it implies that more reserve resources are expected to liberate gas turbine's operation interval,reduce reserve cost,wind spillage cost and risk cost.Third,electrical vehicle demand resources are aggregated to supplement reserve capacity and liberate the gas turbine's operation interval for the regional energy internet system.A multi-armed bandit-based electrical vehicle demand aggregation method and the associated user selecting algorithm are proposed.Simulation results indicate that the proposed method can learn each electrical vehicle user's actual probabilities of responding to demand aggregation request signals and deal with the uncertain user behavior.As a result,the proposed method can provide more reliable upward/downward reserve services.Additionally,the proposed aggregation method for upward reserve service can reduce the aggregation cost and trade-off demand aggregation mismatch and cost.Forth,demand aggregation cost significantly impacts the operation economy of regional energy internet systems.The relationship between aggregation unit price and aggregation amount is investigated to determine the optimal economic range of demand aggregation amount.Then,a stochastic optimization method considering electric vehicle reserve capacity is proposed.Simulation results show that the proposed method can liberate the gas turbine's extruded operation interval,improve system operation flexibility,and reduce wind spillage cost and reserve cost.Moreover,it can supplement reserve capacity for regional energy internet to eliminate the wind spillage risk.