改进人工蜂群算法的孤岛混合可再生能源发电系统容量优化

容量优化对提高风电-光伏-电池混合发电系统的经济性和可靠性具有重要意义。为进一步提高容量优化的精度，本研究提出了一种基于改进蜂群算法的容量优化方法。首先，在建立组件模型、设计能源管理规则库的基础上，以最小化单位度电成本为目标，以系统缺电率为约束，建立了混合发电系统容量优化模型；其次，通过在蜂群算法雇佣蜂阶段中引入差分进化算子，提出了一种改进蜂群算法的模型求解方法，并通过与蜂群、差分进化算法对比，验证了改进蜂群算法的有效性；最后，分别在不同缺电率要求下优化混合系统容量，得出了单位度电发电成本与缺电率的关系，并通过灵敏度分析，研究了设备价格，气象等因素对单位度电成本的影响。结果表明，在缺电率为3%时，混合系统总投资成本为779 564.26 美元（$）, 其中，光伏、风电、电池及变换器成本分别占总成本的33%、29%、34%和3%，单位度电成本为0.349 447 $/kWh；单位度电成本随缺电率增加而下降且下降速率逐渐降低；单位度电成本在组件价格方面受光伏组件价格影响更明显，在气象方面，受风速均值影响更明显。该研究成果可为科学设计混合系统容量，促进风、光资源互补利用提供科学依据。

Optimizing the capacity of standalone hybrid renewable energy power generation system by improved artificial bee colony algorithm

To obtain the optimal capacity and analyze the relationship between the optimal Levelized Cost of energy and the loss of power supply probability of a wind/photovoltaic/battery hybrid power generation system, a capacity optimization method based on an improved artificial bee colony algorithm was proposed. Firstly, the component models of the hybrid power generation system (wind turbine, photovoltaic, battery, and grid loads) were established and the rule-based energy management strategy was carefully designed to coordinate the different components of the hybrid power generation system. According to whether the electric power generated and converter capacity was sufficient, the energy management strategy was designed as four scenarios and the corresponding rules were designed; Secondly, the model for the capacity optimization problem was built with the objective function of minimizing the Levelized Cost of energy, where the requirement of satisfying the loss of power supply probability was handled as a constraint; Lastly, the penalty function method was applied to handle the loss of power supply probability constraint and an improved artificial bee colony was applied to improve the accuracy of the solution to the capacity optimization problem. In the algorithm, differential evolution operators were introduced to balance the bees'' ability of exploration and exploitation in different stages of the optimization process and a new food source could be generated by differential evolution operators with a probability increasing adaptively with the iterations in the employed bee stage. To verify the effectiveness of the algorithm, contrast tests with artificial bee colony and differential evolution were performed. The comparison results showed the proposed algorithm had better accuracy, robustness, and convergence rate. Then, to obtain the relationship between the optimal Levelized Cost of energy and the loss of power supply probability, capacity optimizations of the hybrid system was performed under the different loss of power supply probability requirements, which was changed from 0 to 5% with an equal step of 0.1%. Results showed that the Levelized Cost of energy decreases with the increase of loss of power supply probability and the descent rate was gradually decreased. This implied that it could obtain more obvious economic benefits by reducing the reliability requirement appropriately when the loss of power supply probability was at a smaller value. Furthermore, the influence of main parameters, such as the initial capital of the main component (wind turbine, photovoltaic, and battery) and meteorological resources (annual average of solar radiation and wind speed) on the Levelized Cost of energy was carried out by sensitivity analyses. The sensitivity analyses of the initial capital of the components were carried out by capacity optimizations of the hybrid system with different initial capital values of (wind turbine, photovoltaic, and battery system), which were changed from 60% to 140% of their best estimate value with a step of 20%, the results showed that the influence of initial capital on the Levelized Cost of energy presented a linear relationship, the line of the photovoltaic was with the highest slope, and the line of the battery had the lowest slope. This implied that it could generate more obvious economic benefits by reducing the photovoltaics'' initial capital. The sensitivity analyses of the meteorological resources were carried out by capacity optimizations of the hybrid system with different annual average values of solar radiation and wind speed, which were changed from 80% to 120% of their best estimate value with a step of 10%, the results showed that the influence of meteorological resources on the Levelized Cost of energy presented a linear relationship, and the slope of wind speed was higher than that of the solar radiation. This implied that it should pay more attention to the wind resource evaluation.