Investment method of cooling electric load demand response equipment embedded with operation strategy optimization
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Abstract:
With the further advancement of electricity market reform, the introduction of time-of-use price and other policies has provided a favorable market environment for the application and development of demand response technology. The cooling electric load demand response technology represented by the cold storage air conditioning system can effectively alleviate the problem of power supply and demand balance. The demand response capability and potential benefits of cooling electric load are closely related to the investment planning and operation methods of demand response equipment. The-existing-cooling-technology-based demand response equipment investment planning methods mostly consider a single equipment type, and based on fixed or simplified system operation strategy for operation simulation, it is difficult to achieve the optimal matching of different types of equipment and user cold demand characteristics, resulting in the limited optimization space of the system demand response capacity and economic value. Therefore, this paper investigates the unified investment planning modeling method for cooling electric load demand response equipment considering the optimization of operation strategy. Firstly, this paper proposes a demand response model considering multiple types of chillers and operation strategy optimization in the electricity market. It realizes flexible response of cooling electric load to price signals and fully saves electricity cost. Then, this paper constructs a demand response equipment investment planning model with embedded unified operation strategy optimization to minimize the total cost of investment and operation. This model considers the price policy, cold demand characteristics, investment cost and operating characteristics of related equipment, and adopts linearization techniques to build a mixed-integer linear programming model. It can be efficiently solved by a commercial solver. The analysis shows that the proposed method can fully adapt to the current electricity market environment, effectively improve the investment efficiency of customer, optimize the electricity load curve, and leverage the value of demand response technology.
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Supported by the Science and Technology Project of State Grid Chongqing Electric Power Company (SGCQ0000DKJS2100217), and National Natural Science Foundation of China (U2066209).
