An important factor in reviewing the performance of generation units and calculating their profit is the calculation of their locational marginal price (LMP), and this depends on our knowledge on the capacity of transmission lines and optimal power flow (OPF) based on reality by which we aim to minimize the total cost of the generators, solve the congestion of transmission lines, and hence, reduce the price of electricity in the market. Since power flow equations are nonlinear, they should be solved using numerical and repetition-based methods. In this paper, genetic algorithm (GA) has been employed to solve these equations, and in order to improve the performance of GA in its structure, generating scaling factor (GSF) has also been used for simultaneous calculations of power passing through in transmission lines so that by gaining some knowledge on the capacity of transmission lines, in addition to optimal power flow becoming real, we could determine the electricity price by uniform market pricing or LMP methods depending on using the full capacity of lines and generating power of the units, and thus, we can calculate the profit of generators. Finally, the output of the proposed GA-GSF algorithm would include values of buses voltages, lines losses, injected power to buses, power passing through lines, total generation cost, and generators’ profits. Also, the proposed algorithm in this paper has been tested on IEEE 14-BUS, IEEE 30-BUS, IEEE 57-BUS network, and the results show improvements on the OPF problem.

中文翻译：

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基于GA-GSF算法的拥塞管理计算区位边际价格及求解最优潮流问题

审查发电机组性能和计算其利润的一个重要因素是其位置边际价格 (LMP) 的计算，这取决于我们对输电线路容量和基于现实的最佳潮流 (OPF) 的了解。我们的目标是最大限度地降低发电机的总成本，解决输电线路的拥堵问题，从而降低市场电价。由于潮流方程是非线性的，它们应该使用数值和基于重复的方法求解。本文采用遗传算法（GA）来求解这些方程，为了提高遗传算法在其结构上的性能，发电比例因子 (GSF) 也被用于同时计算输电线路中通过的电力，这样通过获得有关输电线路容量的一些知识，除了真实的最佳潮流之外，我们还可以通过统一的方式确定电价市场定价或LMP方法取决于使用线路的全部容量和机组的发电量，因此我们可以计算出发电机的利润。最后，所提出的 GA-GSF 算法的输出将包括总线电压、线路损耗、总线注入功率、通过线路的功率、总发电成本和发电机利润的值。此外，本文提出的算法已经在 IEEE 14-BUS、IEEE 30-BUS、IEEE 57-BUS 网络上进行了测试，结果表明对 OPF 问题的改进。