A novel optimization framework for integrated local energy system multi-objective dispatch problem based on dynamic knowledge base. (June 2021) - Li, Xin; An, Qing; Zhang, Jun; Mao, Xiaobing; Tang, Ruoli; Xu, Fan; Dong, Zhengcheng; Feng, Yulong; Li, Xiao
Highlights: An establishment method for iLES-MDP knowledge base is proposed. A dynamic updating approach is introduced to expand reference solutions in knowledge base. A multi-objective optimization framework is designed to find feasible trade-off dispatch schemes. Feasible Pareto solutions can be obtained efficiently for iLES-MDPs. Abstract: Integrated local energy system (iLES) can take full advantage of local energy resources and diversify energy supplies. However, the complexity and flexibility of multiple-input multiple-output system structure makes it difficult for iLES to guarantee energy supply quality, which needs be solved by making reliable dispatching schemes. In this paper, the optimization model of typical iLES multi-objective dispatch problem (MDP) is built and the common problems are analyzed. Aiming at adapting to the changes of the iLES-MDPs model during the practical operation process, a dynamic knowledge base-based multi-objective optimization method is proposed, by which a knowledge base is established for iLES-MDPs taking account of the characteristic of system before optimization process. Besides, considering the frequent variations of system parameters, iLES structure, energy input and load demands, an updating approach is introduced to dynamically adjust the knowledge base. Thereafter, a multi-objective optimization framework is designed to find the feasible Pareto solutions for the iLES-MDPs, by utilizing the reference solutions in knowledge base during the evolutionary procedure. Finally, the proposed method is applied to a series of iLES-MDPs, which involve renewable energy sources, combined cooling heating and power system, energy storage devices and different operation modes. The simulation results verify that the method can well adapt to the challenges of all types of iLES-MDPs. Moreover, it can obtain feasible Pareto solutions efficiently as well as reduce the performance requirement of optimization algorithms.