采用分布式算法的多区域电网风-光-储容量优化配置

Optimal Allocation of Wind-Photovoltaic-Storage Capacity in Multi-Area Power Grid Based on Distributed Algorithm

针对新能源发电在整个电网中渗透率不断提高而造成的电能质量下降、电力系统无法可靠运行等一系列问题,基于随机规划理论,综合考虑风电、光伏等电源以及联络线、储能设备运行约束,以典型新能源出力时序特征序列下平均新能源消纳水平最大为目标,提出了一种计及电网新能源消纳承载能力的多区域风-光-储容量优化配置方法。该方法主要考虑将风电、光伏发电满足负荷需求后的无法消纳量在不超过储能设备容量的前提下进行储电,而储能设备将在新能源发电低谷期提供电能,充分发挥风、光的时空互补性和储能的调峰优势。首先构造了考虑时-空相关性的典型新能源出力时序特征序列不确定集,然后按典型序列将模型进行分解,采用分布式惩罚原始-对偶次梯度算法实现目标函数的分布式优化求解,最后基于IEEE-24、31节点系统进行算例分析,得到风-光-储容量配置结果,并建议多区域电网中储能设备应考虑建设在含有风光装机的区域。仿真结果表明:相比传统集中式优化方法和拉格朗日松弛法,所提分布式优化方法随着电网区域增多,计算效率优势会越来越明显,10个典型序列时所提分布式方法相比拉格朗日松弛法收敛速度提升了57.1%。所提方法可以为考虑新能源随机性的风-光-储多区域电网容量优化配置问题提供系统性解决方案。

Aiming at a series of problems such as power quality degradation and unreliable operation of the power system caused by the continuous increase of the penetration rate of renewable energy power generation in the entire power grid, this paper proposes a multi-area wind-photovoltaic-storage capacity optimization method for maximizing the average renewable energy accommodation level under the typical renewable energy generation time series based on the stochastic programming theory by comprehensively considering the wind power, photovoltaics and other power sources, as well as the operation constraints of tie lines and energy storage equipment. This method mainly considers that the amount of the generated wind power and photovoltaic power that cannot be accommodated by the power grid after meeting the load demand will be stored without exceeding the capacity of the energy storage equipment and will then be provided by the energy storage equipment during the off-peak period of renewable energy generation, giving full play to the space-time complementarity of the wind power and photovoltaic power and the peak load regulation advantage of energy storage. First, an uncertain set of typical renewable energy power time series characteristic sequences is constructed with the time-space correlation considered;then, the model is decomposed according to the typical sequences, and the distributed optimization solution of the objective function is realized by the distributed penalty primal-dual subgradient algorithm;finally, the case is analyzed based on the IEEE-24, 31-node system to obtain the wind-photovoltaic-storage capacity allocation results, and it is suggested that the energy storage equipment in the multi-area power grid should be considered to be built in the area with installed wind and photovoltaic power capacity. The simulation results show that compared with the traditional centralized optimization method and the Lagrangian relaxation method, the proposed distributed optimization method has more and more obvious advantages in computing efficiency as the number of power grid areas increases. The proposed distributed method, compared with the Lagrangian relaxation method, improves the convergence speed by 57.1% in 10 typical sequences. The proposed method can provide a systematic solution for the optimal allocation of the capacity of the wind-photovoltaic-storage multi-area power grid considering the uncertainty of renewable energy sources.