面向间歇性新能源与电动汽车大规模渗透下的电力系统智能优化调度研究

Under the background of high penetration of large scale renewable energy, optimal scheduling of unit commitment in power systems integrated with electric vehicles is investigated in this project. First, an adaptive robust optimization model for unit commitment is established based on the analysis of uncertainties in wind and solar power generations as well as for the charging and discharging time, and load and capacity of electric vehicles, with the aim to prolong the time and level of penetration of renewable energy, promote large-scale adoption of electric vehicles, increase the flexibility of power systems and for the intelligence of all participants in the smart grid. Large scale robust optimization unit commitment problems are mixed integer optimization problems with the characteristics of nonlinearity, multiple constraints, and time varying. Two novel evolutionary algorithms, namely Negatively Correlated Search (NCS) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are investigated in this project. By designing effective updating strategies for the integer variables, these two algorithms are extended from continuous optimization problems to the mixed integer unit commitment problems with high penetration of renewable energy and electric vehicles. Further, the two algorithms will be improved to suit large scale problems and constraint-handling technique will be improved. Finally, many-objective evolutionary algorithms will be proposed to solve optimization problems with four or more objectives. The outcomes of this project will lay a solid theoretic and application foundation for the development of new technologies for unit commitment of power systems penetrated with renewable energy power and integration of electric vehicles.

在大规模间歇性新能源并网背景下，对电动汽车接入的电力系统机组组合调度与优化展开研究。首先，针对风光电功率不确定性、电动汽车充放电时间及负荷与容量的不确定性，提出一种面向间歇性新能源与电动汽车渗透下的自适应鲁棒优化机组组合调度模型，以有效提高新能源的并网时间和渗透率，有力保障电动汽车的大规模推广和灵活并网应用，并增强智能电网中各参与者的智能性。针对大规模自适应鲁棒优化机组组合这一类具有非线性、多约束、时变特性的混合整数规划问题，对两种新型进化算法--负相关搜索算法(NCS)及协方差矩阵自适应进化策略(CMA-ES)进行深入研究，通过设计有效的整数变量更新机制，将这两种算法从解决连续优化问题成功拓展至面向间歇性新能源与电动汽车大规模渗透下的机组组合问题。此外，对两种算法在大规模问题及约束处理机制上进行改进，并提出可解决众目标问题的进化算法，为新能源与智能汽车并网技术的理论和应用打下坚实基础。

该项目针对可再生能源及电动汽车接入的电力系统机组组合调度问题展开研究。研究工作按照项目计划进行，取得了预期的研究结果。主要工作体现在以下四个方面：（1）深入研究电动汽车入网对电网的影响，针对风光电功率不确定性、电动汽车的不确定性以及负荷的不确定性，建立了面向间歇性新能源与电动汽车渗透下的不确定机组组合调度数学模型。（2）针对上述复杂的机组组合调度问题，提出了适应于解决机组调度问题的负相关搜索算法和协方差矩阵自适应进化策略算法。并对两种算法进行了改进，提升了算法处理机组调度问题的性能。（3）针对多目标机组组合问题进行研究，提出适应于求解大规模多目标问题的协方差矩阵自适应进化策略算法以及多目标交叉熵算法。（4）通过大量的电力系统仿真实例验证了所提出调度模型的有效性。可再生能源并入电网后和电动汽车合作通过互相补偿来增加系统的稳定性从而使整个电力系统受益，对缓解能源危机、提高企业经济效益都有重要的现实意义。