基于学习理论的含光储联合系统的输电网双层规划

针对传统输电网规划中对光伏出力不确定性处理中存在的问题,提出一种基于学习理论的含光储联合系统的输电网双层规划模型。下层基于学习理论对光储联合系统进行优化,目标为光伏电站长期运行收益最大与计划功率不确定性最小。将下层优化求解得到的光储联合系统计划功率代入上层的输电网规划模型,以线路投资成本、运行成本和弃光成本最小为目标进行规划。最后用改进的IEEE118节点算例验证了光储联合系统可以减小计划功率的不确定性,提高规划结果的可信度。本研究建立的Q学习控制器具有良好的在线学习能力,通过大量数据的学习后能对光储联合系统的计划出力进行有效的指导。

Bi-level planning of transmission network with solar-storage combination system based on learning theory

In order to address the solar power output uncertainty in transmission network planning, a bi-level planning model of transmission network was proposed in which the solar-storage combination system was modeled by learning theory. In the lower level, the scheduled power of solar-storage combination system submitted to the large power system was optimized by maximizing the long-term profit of the solar-storage combination system and minimizing the uncertainty of the planned power. Substituting the planned power of the solar-storage combination system obtained by the lower layer optimization into the upper transmission network planning model, then we minimized the transmission line investment cost, power system operating cost, and solar-shedding cost. The modified IEEE-118 bus system experimental results verified that the solar-storage combination system could reduce the uncertainty of the planned power and enhanced the credibility of planning result. The Q-learning controller established in this paper had good online learning ability and could effectively guide the planned output of the solar-storage combination system after learning a large amount of data.