基于遗传算法并考虑稳定约束的最优潮流开发

功角稳定(暂态或振荡)是电力系统运行的一个重要约束.本文描述了一种基于遗传算法(GA)并考虑功角稳定性约束的最优潮流求解方法.在该GA算法中,染色体的控制参量为发电机组的出力.本文应用实例选择英国电力市场中的平衡机制发电机组(BMU).平衡机制机组的选择和优化由GA算法依据BMU的出价(竞价)高低或其出力变化对系统稳定性影响的程度来确定.本文使用灵敏度因子来代表BMU机组对系统稳定性的影响程度,而这些灵敏度因子则是通过开展系统扰动仿真得出的.为确保发电出力始终与用电负荷相匹配,本文采用一种新的映射方法来保持系统有功平衡.文中使用因停电而导致的罚金成本或费用来处理系统稳定性约束,且这些稳定约束对系统目标函数适切性的贡献也被单独评估,以使算法对最优潮流的搜索集中于切实可行的解.对一个化简的英国电力系统开展的仿真测试表明,本文提出的GA算法能够应付此类高度非线性的最优化问题,并给出了对测试系统的数值仿真结果.

Development of a Genetic Algorithm Based Optimal Power Flow Considering Stability Constraints

Angle stability(both transient and oscillatory)is an important constraint in power system operation.This paper describes a genetic algorithm (GA) based approach for solving the problem of angle stability constrained optimal power flow,The control parameter modeled in the chromosome of the GA is generation power of the units.The application presented here is in the UK balancing market using Balancing Mechanism Units(BMUs).The selection of BMUs to be optimized by the GA depends on their bid/offer price or their impact of generation change on system stability.Sensitivity factors,obtained by doing perturbations.are used to represent a BMU's impact on system stability.To ensure that the generation is always matching the demand,a novel mapping method is employed to maintain the power balance.Stability constraints are dealt with as penalty cost.and their contribution to the fitness of the objective function is evaluated independently, so that the search for the optimal solution concentrates on feasible solutions.Tests on a reduced UK system show that the proposed GA is able to cope with the highly non-linear optimization Droblem.Numerical simulation results of the test system are presented.