基于多搜索器优化算法的含可再生能源协同优化调度

Collaborative optimization scheduling with renewable energy based on multi-searcher optimization algorithm

考虑到风光不确定性给能源系统运行带来的影响,本文建立了含有燃气发电厂、热电联产装置、供热厂、风电场和光伏电场的热电数学模型,并提出了多搜索器优化算法,用于解决热电能量管理优化问题.混沌搜索具有随机性、遍历性和规律性,混沌理论的引入能够不断缩小优化变量的搜索空间,并不断提高搜索精度,从而有较高的搜索效率.算法中的双层搜索器的设置既能保证快速收敛到近似最优解,也能及时避免陷入局部最优解.通过标准函数和27机组热电系统的仿真结果表明,本方法具有较高收敛稳定性且收敛速度快,能够有效解决热电能量管理系统中的高度非线性、非光滑、非凸问题.

Considering the influence of uncertainty of wind and solar on the operation of energy system, this paper establishes a combined heat and power mathematical model containing gas power plant, combined heat and power unit, heating plant, wind farm and photovoltaic farm, and proposes a multi-searcher optimization algorithm to solve the thermoelectric energy management optimization problem. Chaotic search has randomness, ergodicity and regularity, the introduction of chaos theory can continuously reduce the search space of optimized variables, and continuously improve the search accuracy, thus having higher search efficiency. The setting of the two-layer searcher in the algorithm can ensure fast convergence to the approximate optimal solution, and can avoid falling into the local optimal solution in time. The standard function and simulation results show that the proposed method has high convergence stability and fast convergence speed, and can effectively solve the highly nonlinear, non-smooth and non-convex problems in the thermoelectric energy management system.