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首页> 外文期刊>Journal of Industrial Engineering International >Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators
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Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

机译：使用和比较元启发式算法优化发电商利润最大化的竞价策略观点

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With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators’ strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran’s wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.
机译：随着世界竞争性电力市场的形成，投标策略的优化已成为与市场设计有关的研究中的主要讨论之一。市场设计受到需要满足的多个目标的挑战。这些多目标问题的解决方案经常在组合策略空间中进行搜索，因此需要同时优化多个参数。该问题是使用纳什均衡概念对包含大量具有离散和较大策略空间的玩家组成的游戏进行分析得出的。解决方案方法基于函数最小值的纳什均衡特征，并依靠元启发式优化方法找到这些最小值。本文提出了一些元启发式算法，根据遗传算法（GA），模拟退火（SA）和混合模拟退火遗传算法（HSAGA），根据其他发电商的策略，模拟发电商在现货电力市场中如何最大化利润的出价。 ）并比较其结果。由于GA和SA都是通用搜索方法，因此HSAGA也是通用搜索方法。基于实际数据的模型是在2012年德黑兰批发现货市场的高峰时段实施的。仿真结果表明，在计算时间，功能评估和计算稳定性以及结果方面，GA优于SA和HSAGA。由GA计算得出的Nash平衡的差异比其他算法少，且彼此不同。

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