为应对气候变化和化石能源依赖带来的挑战，全球正在加速推进可再生能源的发展。虽然可再生能源在减少碳排放和提能增效方面显示出巨大的潜力，但其发展依然受到技术、成本以及电网管理复杂性的限制。在此背景下，含风光储的微电网作为一种有效的可再生能源整合策略而受到广泛关注。储能作为含风光储微电网的组成部分具有重要作用，而储能技术的应用更是为优化能源结构及增强电网弹性做出了巨大贡献。然而，微网中储能系统的成本相对较高，且使用寿命较其他设备较短，制约了微电网的总体经济性，因此，本文以含风光储的微电网为主要研究对象，在考虑微电网经济性和可靠性的同时，兼顾储能装置使用寿命对储能装置容量的影响，对微网储能容量进行优化。本文的主要工作包含以下几个方面：

       首先，本文强调了发展微电网的必要性，突出了储能在提升能源利用率和系统可靠性上的优势；接着，回顾了国内外储能系统容量配置及运营策略的研究现状；后进一步介绍了微电网系统的结构。在上述基础上，建立风光储系统的数学模型，分析光伏与风机的出力特性，为后文含风光储微电网的储能优化配置奠定了理论基础。

       其次，针对含风光储微电网的储能容量优化配置问题，建立了一个包含风光发电和电池储能系统（Battery Energy Storage System, BESS）的双层优化模型，采用改进的遗传算法（Improved Genetic Algorithm, IGA）和粒子群算法（Improved Particle Swarm Optimization, IPSO），结合MATPOWER工具，对BESS容量进行了优化。其中，上层模型主要使用遗传算法确定储能配置的最佳容量，下层模型则通过粒子群算法优化储能的运行策略，以提升系统的经济性和能源利用率。算例分析表明，在安装BESS的情况下，经济性及可靠性都有了显著提高，改进配置方案与传统配置方案相比，前者更能显著提高系统的经济效益。

       最后，针对某微电网的BESS进行优化配置及充放电策略研究。具体来说，在第三章提出的双层优化模型的基础上，在下层模型中新增了考虑BESS寿命衰减的影响，建立了BESS的寿命预估模型、充放电模型及全寿命周期内的经济效益模型，同时考虑了新能源接入后的负荷波动影响，将其作为可靠性指标进行分析；上层模型仍旧是针对储能容量进行求解，针对多目标粒子群算法（Multi-Objective Particle Swarm Optimization, MOPSO）的缺点，提出一种改进的多目标粒子群算法（Improved Multi-objective Particle Swarm Optimization Algorithm, IMPSO）用于求解该优化问题。通过算例分析验证了所提出方法的有效性和可行性，结果显示该方法在经济性和可靠性方面具有显著优势，特别是在降低负荷波动和提高系统稳定性方面表现优异。

       In response to the challenges posed by climate change and dependence on fossil fuels, the development of renewable energy is accelerating around the world. While renewables have shown great potential to reduce carbon emissions and improve efficiency, their development is still limited by technology, cost, and the complexity of grid management. In this context, microgrids with wind, solar and storage have attracted extensive attention as an effective renewable energy integration strategy. Energy storage plays an important role as a component of wind-solar-storage microgrids, and the application of energy storage technology has made great contributions to optimizing the energy structure and enhancing the resilience of the power grid. However, the cost of the energy storage system in the microgrid is relatively high, and the service life is shorter than that of other equipment, which restricts the overall economy of the microgrid. The main work of this paper includes the following aspects:

       Firstly, this paper emphasizes the necessity of developing microgrids, and highlights the advantages of energy storage in improving energy efficiency and system reliability. Then, the research status of capacity allocation and operation strategy of energy storage system at home and abroad was reviewed. The structure of the microgrid system is further introduced. On the basis of the above, the mathematical model of the wind-solar storage system is established, and the output characteristics of photovoltaic and wind turbines are analyzed, which lays a theoretical foundation for the optimal configuration of energy storage including wind-solar storage microgrid.

       Secondly, in order to solve the problem of optimal allocation of energy storage capacity of wind-solar-storage microgrid, a two-layer optimization model including wind-solar power generation and battery energy storage system (BESS) was established, and the improved Genetic Algorithm (IGA) and Improved Particle Swarm Optimization (Particle Swarm Optimization) were adopted. IPSO), combined with the MATPOWER tool, optimizes the BESS capacity. Among them, the upper-layer model mainly uses genetic algorithm to determine the optimal capacity of energy storage configuration, and the lower-level model optimizes the operation strategy of energy storage through particle swarm optimization to improve the economy and energy utilization rate of the system. The analysis of the case shows that the economy and reliability are significantly improved in the case of BESS installation, and the improved configuration scheme can significantly improve the economic benefits of the system compared with the traditional configuration scheme.

       Finally, the optimization configuration and charge-discharge strategy for a specific microgrid's BESS are studied, constructing a bi-level optimization model. Specifically, the lower model incorporates the impact of BESS lifespan degradation, establishing lifespan prediction, charge-discharge, and economic benefit models over the entire lifespan. The fluctuating load impact after the integration of new energy sources is also considered as a reliability metric for analysis, while the upper model still focuses on solving for storage capacity. To address the limitations of the multi-objective particle swarm optimization (MOPSO), an improved multi-objective particle swarm optimization algorithm (IMPSO) is proposed to solve this optimization problem. Case study analyses validate the effectiveness and feasibility of the proposed method, showing significant advantages in terms of economics and reliability, particularly in reducing load fluctuations and enhancing system stability.

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