Multi-Time Scale Optimization Strategy for New Distribution System Oriented to Photovoltaic Consumption and Low Carbon Demand Response of Ice Storage Air Conditioning Groups

ObjectivesIn view of the limitations of the traditional distribution network demand response model in regulating the flexible and controllable resources on the load side, especially ignoring the unique and efficient resources such as ice storage air conditioning systems. Therefore, this paper aims to deeply optimize the load-side control object such as ice storage air conditioning, so as to significantly improve the utilization rate of renewable energy in the new distribution system and deeply explore its low carbon operation potential.MethodsIce storage air conditioning with virtual energy storage characteristics is introduced as the control object. A new distribution system multi-time scale optimization strategy is proposed for photovoltaic consumption and low carbon demand response of ice storage air conditioning groups. Firstly, the operating characteristics and low carbon demand response model of ice storage air conditioning are established. Secondly, with the minimum incentive cost for low carbon demand response in power supply companies, the maximum profit for photovoltaic manufacturers, and the minimum electricity consumption cost for air conditioning users as optimization objectives, a multi-objective optimization model for the day ahead is constructed. The non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) is used to efficiently solve the model, and the optimal solution for the multi-objective optimization model is selected based on the linear programming method with multi-dimensional preference analysis. Subsequently, in order to eliminate the impact of prediction errors on the model results, a daily rolling correction optimization model is further constructed. Finally, the effectiveness of the proposed model is analyzed using test cases.ResultsThe optimal solution obtained after intra-day rolling correction results in a cost reduction of approximately 20% for both power supply companies and air conditioning users, as well as an increase in profit margins of about 3% for photovoltaic producers.ConclusionsThe multi-objective optimization and control method based on multiple time scales not only ensures the interests of all parties, but also eliminates the impact of prediction errors on model results, providing important technical support for low carbon operation of new distribution systems.