Abstract Microgrid systems, such as solar photovoltaic (PV) power and wind energy, integrated with diesel generators are promising energy supplies and are economically feasible for current and future use in relation to increased demands for energy and depletion of conventional sources. It is thus important to optimize the size of hybrid microgrid system (HMS) components, including storage, to determine system cost and reliability. In this paper, optimal sizing of a PV/wind/diesel HMS with battery storage is conducted using the Multi-Objective Self-Adaptive Differential Evolution (MOSaDE) algorithm for the city of Yanbu, Saudi Arabia. Using the multi-objective optimization approach, the objectives are treated simultaneously and independently, thereby leading to a reduction in computational time. One of the main criteria to consider when designing and optimizing the HMS is the energy management strategy, which is required to coordinate the different units comprising the HMS. The multi-objective optimization approach is then used to analyze the Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE), and the Renewable Factor (RF) in relation to HMS cost and reliability and is tested using three case studies involving differing house numbers. Results verify its application in optimizing the HMS and in its practical implementation. In addition, optimization results using the proposed approach provided a set of design solutions for the HMS, which will assist researchers and practitioners in selecting the optimal HMS configuration. Moreover, it is important to select optimally sized HMS components to ensure that all load demands are met at the minimum energy cost and high reliability.

中文翻译：

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基于多目标自适应差分进化算法的光伏/风/柴油混合微电网系统优化选型

摘要 微电网系统，如太阳能光伏 (PV) 电力和风能，与柴油发电机集成是很有前途的能源供应，并且在当前和未来使用与能源需求增加和传统能源枯竭相关的经济上是可行的。因此，优化混合微电网系统 (HMS) 组件（包括存储）的大小以确定系统成本和可靠性非常重要。在本文中，使用多目标自适应差分进化 (MOSaDE) 算法对沙特阿拉伯延布市进行了具有电池存储的光伏/风能/柴油 HMS 的优化尺寸。使用多目标优化方法，目标被同时且独立地处理，从而导致计算时间的减少。设计和优化 HMS 时要考虑的主要标准之一是能源管理策略，需要协调组成 HMS 的不同单元。然后使用多目标优化方法来分析与 HMS 成本和可靠性相关的电源损耗概率 (LPSP)、电力成本 (COE) 和可再生因素 (RF)，并使用三个案例研究进行测试涉及不同的门牌号。结果验证了其在优化 HMS 及其实际实施中的应用。此外，使用所提出方法的优化结果为 HMS 提供了一套设计解决方案，这将有助于研究人员和从业人员选择最佳 HMS 配置。而且，