基于改进人工蜂鸟算法的微电网优化调度

Microgrid Optimization Scheduling Based on the Improved Artificial Hummingbird Algorithm

摘要:

微电网作为一种高效灵活的能源分配系统，能够集成可再生能源与传统发电资源，优化能源调度，提高系统的运行效率；针对微电网调度中的复杂性与动态性问题进行了研究，对微电网的结构进行了分析，建立了包括光伏电池、风力发电机、燃气轮机、柴油发电机及蓄电池等多种能源单元的微电网系统模型，通过改进人工蜂鸟算法进行优化调度，并结合目标函数（包括运行成本与环境治理成本）进行多目标优化，确保在满足负荷需求的同时，最小化运行成本与碳排放；算法方面采用了混合技术策略对基础人工蜂鸟算法进行了改进：引入拉丁超立方体采样（LHS）提升初始种群的多样性，有助于扩大搜索空间并提高全局搜索能力，引入模拟退火（SA）机制则增强了算法跳出局部最优的能力，从而提高了整体收敛性能；实验通过对不同调度策略的比较，结果表明改进人工蜂鸟算法在迭代初期表现出较快的收敛速度和较高的求解精度，显著优于传统的人工蜂鸟算法及粒子群算法。经微电网实际应用，基于改进人工蜂鸟算法的微电网优化调度实现了能源使用效率的提高、减少了弃风弃光现象，同时通过合理的分时电价策略降低了电力成本，优化了电力采购与销售过程，在应用于较为复杂的微电网环境中也具有良好的表现，具有较好的工程应用前景。

Abstract:

As an efficient and flexible energy distribution system, microgrid can integrate renewable energy and traditional power generation resources, optimize energy scheduling and improve the operation efficiency of the system. The complexity and dynamic problems in microgrid scheduling were studied, the structure of microgrid was analyzed, and a microgrid system model including photovoltaic cells, wind turbines, gas turbines, diesel generators and batteries was established. The scheduling was optimized by improving the artificial hummingbird algorithm, and multi-objective optimization was performed in combination with the objective function (including operating cost and environmental governance cost) to ensure that the operating cost and carbon emissions are minimized while meeting the load demand. In terms of algorithm, a hybrid technical strategy was used to improve the basic artificial hummingbird algorithm: the Latin hypercube sampling (LHS) was introduced to improve the diversity of the initial population, which helped to expand the search space and improve the global search ability. The simulated annealing (SA) mechanism was introduced to enhance the ability of the algorithm to jump out of the local optimum, thereby improving the overall convergence performance. The experiment compared different scheduling strategies. The results showed that the improved artificial hummingbird algorithm showed faster convergence speed and higher solution accuracy in the early stage of iteration, which was significantly better than the traditional artificial hummingbird algorithm and particle swarm algorithm. Through practical application in microgrids, the optimized scheduling of microgrids based on the improved artificial hummingbird algorithm has achieved the improvement of energy utilization efficiency and reduced the phenomenon of wind and solar power abandonment. At the same time, it has reduced electricity costs through reasonable time-of-use electricity price strategies and optimized the electricity procurement and sales process. It also has good performance when applied to more complex microgrid environments and has good engineering application prospects.