Research On Operation Mode And Decision-making Strategy For Demand Response Aggregator In Power Market

The development of smart grid and demand response technologies have enabled more and more users to participate in the demand response projects in electricity markets.As the emerging implementer of the power system,the Demand Response Aggregator(DRA)provides a load integration platform for small and medium-sized users to participate in wholesale electricity market.Currently,the research on portfolio design and load control strategies of DRA are unsystematic,which is necessary for further study.In this paper,based on the design of market-oriented model of DRA,decision-making strategy considering the uncertainty of the end-users load is studied.The paper mainly includes the following aspects:Firstly,the basic concepts and business functions of DRA are introduced,and the framework of energy market and ancillary service market with DRA is established,in which the lifecycle of a DRA is designed,and the specific mechanism of interaction between DRA,system operator,retailers and end-users is emphasized.Also,a high level process flow for DRA is presented.Secondly,a load dispatch decision model for DRA's self-scheduling considering the uncertainty of the demand response load is proposed.The uncertainty brought about by endusers is taken into account and analyzed using stochastic mathematical theory,and a normal distribution is used for simulation.Then,a resource allocation method to circumvent the penalty for breach of contract is set by ordering deterministic load.According to stochastic programming theory,a programming model for the expected value of profit is established with the aim of maximizing DRA's profit margin.In addition,an improved particle swarm algorithm is proposed to solve DRA's decision model and the solution flow is given.The standard particle swarm algorithm is improved by initializing particles with quasi-random sequences and adjusting inertia weight dynamically with particles' velocity.Also,a quasi-Monte Carlo method is introduced to calculate the particles' fitness and improve the reliability for global optimal solution.Finally,the practicability of DRA's load dispatch decision model and the effectiveness of the improved particle swarm optimization algorithm are verified through numerical examples.Through examples with different load and price data,it is proved that the model can increase DRA's profit and improve its service quality.Besides,the relationship between DRA's operation scale,uncertainty of demand response load,profit expectation of DRA and load optimal allocation is analyzed.