Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system

• We proposed metaheuristic optimization methods for energy systems.
• The proposed method, m-PSO can calculate the optimal solution quickly and accurately.
• The proposed method can find a solution 62,068 times as fast as previous method.
• The proposed methods can solve nonlinear and non-differentiable problems quickly.

Storage equipment, such as batteries and thermal energy storage (TES), has become increasingly important recently for peak-load shifting in energy systems. Mathematical programming methods, used frequently in previous studies to optimize operating schedules, can always be used to derive a theoretically optimal solution, but are computationally time consuming. Consequently, we use metaheuristics, such as genetic algorithms (GAs), particle swarm optimization (PSO), and cuckoo search (CS), to optimize operating schedules of energy systems that include a battery, TES, and an air-source heat pump. In this paper, we used a GA, differential evolution (DE), our own proposed mutation-PSO (m-PSO), CS, and the self-adaptive learning bat algorithm (SLBA), of which m-PSO was the fastest, and CS was the most accurate. CS obtained the semi-optimal solution 135 times as fast as dynamic programming (DP), a mathematical programming method with 0.22% tolerance. Thus, we showed that metaheuristics, especially m-PSO and CS, have advantages over DP for optimization of the operating schedules of energy systems that include a battery and TES.