基于区间建模的新能源电网无功优化策略

  目的  新能源发电具有间歇性和随机性，其功率为不确定性数据，会造成电网电压和频率的变化，对电力系统安全运行构成威胁。为保证大规模新能源并网后电网电压的安全，考虑新能源发电波动不确定性，提出一种基于区间建模的新能源电网无功优化策略。  方法  该策略采用区间数描述无功优化模型中的不确定参数，进而建立区间无功优化模型，采用基于优化场景的区间潮流算法求解区间潮流方程，获取状态变量区间，确定控制变量的可行性，在此基础上采用改进的粒子群优化算法求解区间无功优化模型，在粒子群算法中加入局部搜索环节和离散变量交叉处理操作以提高算法寻优能力。为了验证所提方法的有效性和优越性，分别采用IEEE 14节点和IEEE 30节点算例进行仿真计算，与自适应遗传算法和普通粒子群算法进行对比分析。  结果  仿真结果表明:与自适应遗传算法和普通粒子群算法相比，采用改进粒子群的区间无功优化策略具有更快的收敛速度，更强的寻优能力，并且可有效处理模型中离散变量。  结论  所提策略可有效解决区间无功优化问题，能保障大规模新能源并网后电网电压的运行安全。

Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

  Introduction  New energy power generation is intermittent and random, and its power is uncertain data, which will cause changes in grid voltage and frequency, thus pose a threat to the safe operation of the power system. In order to ensure the safety of grid voltage after large-scale new energy grid connection, considering the uncertainty of new energy generation, a reactive power optimization strategy of power grid incorporating renewable energy based on interval modeling is proposed.  Method  This strategy used interval to describe uncertain parameters in reactive power optimization model, and then established interval reactive power optimization model. The interval power flow algorithm based on optimization scenario was used to solve the interval power flow equation, thus obtaining the interval of state variables and determining the feasibility of control variables. On this basis, the improved particle swarm optimization algorithm was used to solve the interval reactive power optimization model, and the local search method and discrete variable cross-processing operation were added to the particle swarm optimization algorithm to improve optimization ability. In order to verify the effectiveness and superiority of the proposed method, IEEE 14 - bus and IEEE 30 - bus examples were used for simulation, and the proposed algorithm was compared with the adaptive genetic algorithm and the ordinary particle swarm optimization algorithm.  Result  The simulation results show that compared with adaptive genetic algorithm and ordinary particle swarm optimization algorithm, the improved particle swarm interval reactive power optimization strategy has a faster convergence speed, stronger optimization capabilities, and can effectively solve the discrete variables in the model.  Conclusion  Our data suggest that the proposed strategy can effectively solve the interval reactive power optimization problem and ensure the operation safety of grid voltage after large-scale new energy grid connection.