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Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

机译：多目标入侵杂草优化：在电力市场中帕累托改进模型的分析中的应用

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摘要

This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed non-dominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.
机译：本文提出了基于解决方案的非支配排序的多目标入侵杂草优化（IWO）的建议。 IWO是一种受生态启发的随机优化算法，已为全局优化显示了成功的结果。在当前的工作中，通过多个多目标优化的知名基准评估了所提出的非支配排序IWO（NSIWO）算法的性能。测试问题的仿真结果表明，该算法与其他多目标进化算法具有可比性，并且在某些情况下也能够找到更好的解分布。接下来，该算法被用于研究两个复杂电力市场中的帕累托改进模型。首先，针对具有非线性需求函数的三人寡头垄断电力市场，获得帕累托改进解决方案集。然后，考虑了具有传输约束的IEEE 30总线电力系统，并为具有确定性成本函数的模型找到了Pareto改进解决方案。另外，在风险管理问题中，使用NSIWO算法来分析具有随机成本数据的系统，该问题使预期的总利润最大化，但使市场中的利润风险最小。

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