Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management

Abstract

Demand-side management (DSM) can transfer the dynamic fluctuations of load to match the renewable energy sources and realize the interaction of source-load. In most existing combined cooling heating and power (CCHP) system scheduling, user-side and system-side resources are independently optimized to achieve optimum economic performances without efficiently combining resources. In this paper, the devices with adjusted loads on the system-side and the user-side are integrated to construct generalized energy storage (GES) model, including traditional flexible resources, batteries, electric vehicles, cooling and heating load response, and electro-to-thermal conversion devices. Then, the GES model is integrated into the system for optimization including economy, load fluctuation, energy efficiency, and exergy efficiency. A case study demonstrates the application of the proposed method. The scheduling results of four DSM cases under different objectives are comprehensively obtained and compared. The optimized schemes are assessed in multi-dimension decision-making method and the effects of objective weights on decision results are analyzed. The simulation results illustrate that the load variation is decreased by 54% to obtain a smooth electrical load, resulting in the stable operation of equipment and improving the security and stability of the CCHP system. The exergy efficiency, energy efficiency, and economy performances through the DSM optimization are improved by up to 8.7%, 2.5%, and 2.4%, respectively. The system optimized with the objective of load fluctuation achieves the highest score in the TOPSIS decision-making scenarios and has the best comprehensive performance.