KEYWORDS: Particle swarm optimization, Mathematical optimization, Solar energy, Power supplies, Power grids, Detection and tracking algorithms, Computer simulations, Solar cells, Optimal decision systems, Batteries
Optimizing the energy use of adjustable load and improving the reliability and economy of building power supply are the key issues of building power supply system scheduling. Aiming at the problem of load energy optimization of public buildings, this paper proposes a building load energy optimization method based on the improved Grey Wolf Optimizer. Firstly, an optimal scheduling model was established based on the energy supply system of public buildings. Secondly, the teaching factor is introduced into Grey Wolf Optimizer (GWO) to improve its iterative updating mode, and to improve the searching ability of the algorithm. Finally, the improved Grey Wolf Optimizer is used to solve the economic optimization model of public building energy supply system, and the optimal results of day-ahead operation and cost of the public building energy supply system are obtained. Simulation examples show that compared with the particle swarm optimization algorithm and Grey Wolf Optimizer, the improved Grey Wolf Optimizer (IGWO) proposed in this paper has obvious advantages in terms of fast convergence and global optimization, and the proposed method can more effectively reduce the operating cost of the energy supply system of public buildings.
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