余英,王海云,王维庆,武家辉,李笑竹.虚拟电厂参与下含高渗透可再生能源系统的运行策略[J].电测与仪表,2023,60(1):94-103. Yu Ying,Wang Hanyun,Wang Weiqing,Wu Jiahui,Li Xiaozhu.Operation Strategy of High permeability Renewable Energy with Virtual Power Plant Participation[J].Electrical Measurement & Instrumentation,2023,60(1):94-103.
虚拟电厂参与下含高渗透可再生能源系统的运行策略
Operation Strategy of High permeability Renewable Energy with Virtual Power Plant Participation
为电力系统能够规模化灵活控制分布式可再生能源与可控负荷参与系统市场交易和优化调度。本文提出一种虚拟电厂(Virtual Power Plant,VPP)参与下含高渗透可再生能源的双层优化调度模型,将虚拟电厂作为独立市场主体参与系统调度,两层之间交替求解,实现协同优化；同时利用威尔分布、贝塔分布模拟风电、光伏的随机性,计及可再生能源出力不确定性带来的高估与低估成本。针对双层优化模型,混合整数、非凸、非线性、多目标的特点提出一种新颖的多目标飞蛾扑火算法(Multi-objective Moth-flame optimization, MOMFO)。最后通过修改后的IEEE39节点为算例验证文中提出的模型可均衡各层收益主体,在维持安全稳定运行的前提下,系统运营利润最大化,有效实现了电力系统的可持续性发展；同时也验证了多目标飞蛾扑火算法的有效性与竞争性。
英文摘要:
In order to scale and flexibly control the distributed renewable energy and controllable load to participate in the system market transaction and optimal scheduling. This paper proposes a virtual power plant (VPP) to participate in a two-layer optimal scheduling model with high-permeability renewable energy. The VPP is involved in the system scheduling as an independent market entity, and the two layers are alternately solved to achieve collaborative optimization; At the same time, the Weibull distribution and Beta distribution are used to simulate the randomness of wind power and photovoltaics, taking into account the overestimation and underestimation of the uncertainty caused by the uncertainty of renewable energy output. A novel multi-objective moth flame optimization (MOMFO) algorithm is proposed for the two-level optimization model, which is mixed integer, nonconvex, nonlinear and multi-objective. Finally, the modified IEEE39 node is used as an example to verify that the proposed model can balance the income entities of each layer. Under the premise of maintaining safe and stable operation, the system operation profit is maximized, and the sustainable development of the power system is effectively realized. It also verifies the effectiveness and competitiveness of the multi-target moth fire-fighting algorithm.
