基于机会约束规划的主动配电网能量优化调度研究

摘要

Active distribution network has gradually become the important direction of the future smart power grid. It plays an important role in increasing capability of renewable energy accommodation, improving the power utilization level and realizing the flexible intelligent distribution network management. The active distribution network energy management system (DMSs), which is the highest decision-making center of active distribution network, uses effective controlling and scheduling of the distributed power to guarantee global optimization operation of distribution network. This paper aims to improve the economy and reliability of the active distribution network. As the wind power and photovoltaic power generation have uncertainty, it uses random simulation technique and the penalty function method, based on the chance-constrained programming to establish a energy scheduling mathematical model, which has wind turbines, photovoltaic power generation unit and the active power energy storage device. Considering various constraint conditions, the model uses the improved particle swarm algorithm to solve. In order to verify the correctness and effectiveness of the provided model, an actual system in a certain area is used as an example and the standard particle swarm algorithm is compared.%主动配电网（Active Distribution Network）的产生对于加大可再生能源的消纳能力、提高用电互动化水平、实现配电网的灵活智能管理发挥着重要的作用，逐渐成为未来智能电网发展的重要方向。其中主动配电网能量管理系统（DMSs）作为主动配电网的最高决策中心，通过对各分布式电源的有效控制和调度，保障配电网的全局优化运行。为提高主动配电网运行的经济性和可靠性，通过对主动配电网能量优化调度技术进行分析，考虑到风力发电和光伏发电的不确定性，结合随机模拟技术和惩罚函数方法，基于机会约束规划建立了含有风力发电机、光伏发电单元以及储能装置的主动配电网能量调度随机数学模型。在满足各种约束条件的基础上，使用改进的粒子群算法求解该模型。并以某地区实际系统为算例，通过与标准粒子群算法进行比较，验证所提模型的正确性与有效性。