在风电场出口配置储能，以风储联合系统的方式接入电网，可从源端降低风电波动性和不确定性对电网造成的负面影响，实现风电从被动接纳到主动接入的角色转变。风储联合系统是一种有限可控的随机电源，行为特征复杂，其大量分散的无序控制将显著降低储能效率、增加电网运行难度。本文以风储联合系统为研究对象，从建模、运行和规划三个层面，深入研究了风储联合系统的分析建模和优化决策方法。 在建模分析层面，提出了考虑时空相关性的风电不确定性建模方法，可适应储能多时段耦合特性及储能应用场景的需求，准确描述风电的概率分布、时空相关特性以及周期特性，具有灵活性高、适应性强的优点，突破了传统正态相关模型难以刻画风电非线性和非对称相依结构的局限，显著提高了风电不确定性建模水平，为风储联合系统随机特征的模拟与分析、风储联合运行和规划的研究奠定了基础。 在协调运行层面，针对单个风储联合系统，提出了不同场景下风储协调优化运行模型。基于联合运行的高维随机规划模型提出了两层优化迭代的实用化算法，可处理大规模风电随机场景，实现储能调节容量在不同应用模式的合理分配。进一步针对多个风储联合系统，提出了集群系统广域优化调度模式，分别建立了集群内部多风储系统的广域协调模型和集群与电网的协调交互模式，可充分利用风电空间互补特性和储能互济能力，提高储能利用效率，最大化集群及电网收益。为建立公平合理的广域协调补偿机制，提出了基于合作博弈理论的集群联盟利益分配策略，实现了个体利益与整体利益的协调一致。 在规划配置层面，提出了常规机组、电网和风储联合系统共同承担风电消纳责任的模式，建立了储能随机规划模型，通过序贯蒙特卡罗仿真法揭示了电网弃风电量的季节性、日特性及厚尾分布特点，模拟了风储系统的长期运行特性，进而提出风电消纳约束条件下成本最优的储能功率和容量配比方法，实现了风储系统与常规机组和电网的统筹规划发展。 本文的研究工作拓展了风电不确定性建模理论，提高了风储联合系统的分析和决策能力，完善了风储联合运行和规划的关键技术体系，为有效提高风电和储能利用效率、促进风储联合系统的推广应用提供了理论指导。

Integrating the energy storage system (ESS) with wind farm has become one of the most promising methods that can diminish the negative influences of the intermittent wind power and accommodate the wind power initiatively and efficiently. Wind-Storage Integrated System (WSIS) is a stochastic source with limited controllability, its complicated behaviors and decentralized control mode would significantly reduce the efficiency of ESS and increase the operation difficulties of power grids. This dissertation focuses on the WSIS and carries out thorough studies on the analysis modeling, operation strategies and planning methodology of the WSIS. The Pair-Copula modeling method for wind power uncertainty is proposed in this thesis to precisely describe the probability distribution of wind power uncertainty as well as its spatial and temporal dependences, which are required by the WSIS due to the multi-stage coupling characteristic and various applied patterns of ESS. The proposed method can model the nonlinear and asymmetric properties of wind power, which the traditional methods cannot depict, thus it is more flexible in practice. The Pair-Copula approach significantly advances the modeling of wind power uncertainties and provides a powerful tool for the studies of stochastic operation and planning of WSIS. For the operation strategy of WSIS, the coordinated optimization models for single WSIS are established for different applied patterns. The double-layer algorithm is proposed to handle the large-scale wind power stochastic scenarios in the optimization model and assign the control capacity of ESS in different applied patterns. Furthermore, the wide-area coordinated control frame is presented for cluster WSISs, and the coordinated optimization models of the multiple WSISs within the cluster and between the cluster and the power grid are studied respectively, in which the spatial smooth characteristics of cluster wind farms and mutual-aid of multiple ESSs are utilized to improve the operation efficiency and maximize the benefits of the cluster and the power grid. To build a fair market environment in the cluster, a strategy based on cooperative game theory is proposed to allocate the cluster benefits to each WSIS, which attracts the participation of WSISs and solidifies the cluster control pattern. With respect to the energy storage planning problem, a future mode, in which the traditional generators, power grids and WSISs share the wind power integration responsibility together, is presented and used for ESS planning. The stochastic planning model of WSIS is proposed to simulate the long term operation of WSIS. The seasonal and daily characteristics of wind curtailments are revealed, as well as the flat-tail distribution of curtailed quantity, which figures out the boundary of ESS requirements. The power and capacity optimization method is subsequently proposed to satisfy the wind power integration demands and minimize the investment at the same time, thus achieving the coordinated planning of different resources. The research in this dissertation extends the modeling methodology of wind power uncertainty, advances the analysis tool and improves the operation and planning capabilities of WSIS. The research results can significantly increase the utilization efficiency of wind power and ESS, and provide theoretical instructions for the practical applications and future promotions of WSIS.