Peak Shaving-Oriented Optimal Bidding Strategy Of Demand Response Aggregator In Day-ahead Electricity Market

Driven by the carbon peak and neutrality strategy,Chinese renewable energy installed capacity presents an exponentially growing trend.It is closely coupled with the continuously increasing load peak valley difference and results in a dilemma where the traditional supply-side active power balance mechanism fails to support the real-time system balance.Therefore,it has become an inevitable trend to exploit the flexibility on the demand side to help alleviate the peak shaving pressure.Demand response aggregators(DRA),as intermediaries between massive,decentralized,diverse,heterogeneous,and small-scale flexible demand-side resources and markets of different types,time and space scales,could realize the coordination and complementarity of massive heterogeneous flexible resources in time,space,and functional dimensions through professional technical means and directional incentives on the demand side,and participate in market transactions to provide various service required by the system as an independent entity on the market side.DRAs’ mastery of the customers’ response characteristics and its optimal market bidding strategy are directly related to the efficient utilization of flexible resources and the value improvement of all market participants,including the power system,DRAs,and customers.In view of this,this thesis targets the market bidding strategy optimization of DRAs under incentive-based demand response(DR),and corresponding works are carried out from three aspects: the construction of a theoretical supporting system,characterization of customers’ incentive-response characteristics,and the optimization of DRA’s market bidding strategy.The main contents are as follows:1.Construction of the DRA theoretical supporting system in market interactive operation.Because of the heterogeneity of demand-side flexible resources,the complexity of the market transaction process,and the immaturity of relevant research and demonstration projects,the systematic theoretical system has not been formed to support the formation and development of DRA’s business model.Therefore,the market transaction process of DRA in the Chinese peak shaving ancillary service market and the energy flow,information flow,and capital flow among different market participants are firstly combed,based on which the key technical supporting system of DRA’s market interactive operation is established,including 12 technical points in four parts: customer profile,information prediction,optimal decision-making,and compensation settlement.It provides a theoretical basis for the construction and improvement of the DRA’s business model.2.Construction of incentive response characteristics of resident customers based on the home energy management system(HEMS).Due to the complexity of physical constraints of different types of loads and the differences in psychological preferences among different customers,its actual response characteristics are difficult to accurately describe,resulting in the lack of accurate reference information in the bidding decision-making process of DRAs.Therefore,this thesis carries out classification modeling from the aspects of electrical characteristics and customers’ psychological preferences,so as to realize the accurate description of physical constraints and the dynamic simulation of response preferences,accordingly.The response behavior of customers’ flexible load is optimized and regulated through the HEMS,from which customers’ response capability under different incentives could be obtained.Then,the mapping relationship between incentive and response could be constructed by the polynomial fitting method based on the least square method.Taking the advantage of this relation,the optimal day-ahead market bidding strategy of DRA could be constructed.Case studies show that the incentive response characteristics play a beneficial role in the bidding strategy optimization of DRAs.3.Optimal bidding strategy of DRA in peak shaving ancillary service market based on information gap decision theory(IGDT).Due to the complex coupling of multiple uncertainties such as customers’ response uncertainty and distributed energy output uncertainty in the market bidding process,the DRA will face the risk of profit loss provided the adverse impact of uncertainties is not reasonably considered.Therefore,this thesis first reveals the limitations of existin g methods in dealing with uncertainty problems such as customers’ response behavior,which has the characteristic of Knightian uncertainty.Then,an IGDT-based framework for dealing with uncertainty problems is proposed for dealing with uncertainty problems.In accordance with the DRA’s risk preference,both risk-averse and opportunity-seeking optimal bidding models are established.The results show that the proposed method could ensure the robustness and opportunism of the decision without specific uncertain information and represents a strong generalization ability.