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首页> 外文期刊>Applied Soft Computing >Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems
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Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems

机译：求解混合整数二进制小种群进化粒子群算法以解决电力系统中的最优潮流问题

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摘要

This research discusses the application of a mixed-integer-binary small-population-based evolutionary particle swarm optimization to the problem of optimal power flow, where the optimization problem has been formulated taking into account four decision variables simultaneously: active power (continuous), voltage generator (continuous), tap position on transformers (integer) and shunt devices (binary). The constraint handling technique used in the algorithm is based on a strategy to generate and keep the decision variables in feasible space through the heuristic operators. The heuristic operators are applied in the active power stage and the reactive power stage sequentially. Firstly, the heuristic operator for the power balance is computed in order to maintain the power balance constraint through a re-dispatch of the thermal units. Secondly, the heuristic operators for the limit of active power flows and the bus voltage constraint at each generator bus are executed through the sensitivity factors. The advantage of our approach is that the algorithm focuses the search of the decision variables on the feasible solution space, obtaining a better cost in the objective function. Such operators not only improve the quality of the final solutions but also significantly improve the convergence of the search process. The methodology is verified in several electric power systems.
机译：本研究讨论了基于混合整数二进制小种群的进化粒子群算法在最优潮流问题上的应用，该最优问题的制定同时考虑了四个决策变量：有功功率（连续），电压发生器（连续），变压器的分接位置（整数）和分流装置（二进制）。该算法中使用的约束处理技术基于一种策略，该策略通过启发式运算符将决策变量生成并保留在可行的空间中。启发式运算符依次应用于有功功率级和无功功率级。首先，计算功率平衡的启发式运算符，以便通过重新分配热量单位来保持功率平衡约束。其次，通过敏感度因子执行启发式运算符来限制有功功率流和每个发电机母线的母线电压约束。我们方法的优点是该算法将决策变量的搜索集中在可行解空间上，从而在目标函数中获得了更好的成本。这样的运算符不仅可以提高最终解决方案的质量，而且可以显着提高搜索过程的收敛性。该方法已在多个电力系统中得到验证。

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