基于场景分析的风电场与电转气厂站协同选址规划

电转气技术的出现为消纳风电等可再生能源的富余发电出力提供了新的途径。目前电转气厂站的投资和运行成本较高,因而单独投资建设电转气厂站的经济性就较差。在此背景下,以可再生能源(风力)发电公司为投资主体,探索风电场和电转气厂站的协同投资建设模式,建立风电场和电转气厂站的协同选址规划模型。首先,考虑电转气技术的特征,建立电转气厂站的数学模型。然后,以最大化投资净收益为优化目标,建立基于场景分析的协同选址规划数学模型,计及了电力系统和天然气系统的相关约束。在场景优化中,以历史风速时间序列为原始场景,引入一种密度聚类算法对众多历史数据进行聚类分析,以提高求解效率,从而缩减风电场出力场景。最后,采用AMPL/BONMIN求解器对所构建的风电场和电转气厂站的协同选址规划优化模型进行求解,并以修改的IEEE 39节点电力系统和比利时20节点天然气系统所构成的电力和天然气互联系统为例,说明了所提方法的可行性和有效性。

Scenario Analysis Based Collaborative Site Selection Planning of Wind Farms and Power-to-gas Plants

The emergence of power-to-gas(PtG)technology offers a potential option in absorbing the surplus generation of wind power and other renewable energy sources. However, it is of poor investment efficiency if only the construction investment of PtG plants is carried out owing to high investment and operation costs. Given this background, a collaborative site selection planning model is established, where renewable energy company will be in charge of the co-investment of wind farms and PtG plants. Firstly, the mathematical model of a PtG plant is developed based on its technical features. On this basis, a scenario analysis based collaborative site selection planning model is proposed which maximizes the net investment income considering the constraints of power system and natural gas system. Historical wind speed time series are considered as the original scenarios in scenario optimization, and a density clustering algorithm is introduced in scenario reduction in order to improve computational efficiency. Finally, the AMPL/BONMIN solver is adopted to solve the test case with interconnected electric power and natural gas systems, and the feasibility of the proposed model is verified by the interconnected electric power and gas systems consisted by modified IEEE 39-node power system and Belgium 20-node gas system.