Multi‐objective fuzzy rank based scheduling of utility connected microgrid with high renewable energy using differential evolution with dynamic mutation,International Transactions on Electrical Energy Systems

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Multi‐objective fuzzy rank based scheduling of utility connected microgrid with high renewable energy using differential evolution with dynamic mutation
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-01-11 , DOI: 10.1002/2050-7038.12788
Sunita Shukla ₁ , Manjaree Pandit ₁

Affiliation

With the rapid depletion of coal reserves and rising concern to reduce the harmful fossil fuel emissions, the inclusion of renewable energy sources like wind and solar is gaining acceptance worldwide. In this paper, a multi‐objective optimization (MOO) model of a wind‐solar integrated microgrid is solved using a mixed integer fuzzy membership based differential evolution algorithm with dynamically varying mutation rates. The idea is to determine the best compromise solution for minimizing total cost, emission, and power losses, together. Wind uncertainty is modeled by an additional probabilistic cost term to include the cost of storage/reserve and penalty imposed for curtailment of renewable power. The proposed model reduces the impact of the control parameters and maximizes the attainment level of all three objectives. The results obtained using the proposed approach are compared and validated with the traditional weighted sum price penalty method and some recently published algorithms. The solutions attained by all these methods are found to be non‐dominating. However, the best compromise solution obtained for the MOO problem by the proposed method is found to have a higher fuzzy rank than the solutions obtained by the other methods. Moreover, there are no tuning parameters in the proposed approach.

中文翻译：

基于多目标模糊秩的动态变异差分进化基于电网的可再生能源微电网调度

随着煤炭储量的迅速枯竭以及人们对减少有害化石燃料排放的关注日益浓厚，风能和太阳能等可再生能源的使用已在全球范围内得到认可。本文采用基于混合整数模糊隶属度的具有动态变化突变率的差分进化算法，求解了风光集成微电网的多目标优化模型。这个想法是确定最佳的折衷解决方案，以使总成本，排放和功率损耗最小化。风的不确定性由一个附加的概率成本术语建模，包括存储/储备成本和削减可再生能源的罚款。提出的模型减少了控制参数的影响，并最大化了所有三个目标的达到水平。使用建议的方法获得的结果与传统的加权和价格惩罚方法以及一些最近发布的算法进行了比较和验证。所有这些方法所获得的解决方案都是非主导性的。然而，发现通过提出的方法针对MOO问题获得的最佳折衷解决方案具有比通过其他方法获得的解决方案更高的模糊等级。而且，在所提出的方法中没有调整参数。发现通过所提出的方法获得的关于MOO问题的最佳折衷解决方案具有比通过其他方法获得的解决方案更高的模糊等级。而且，在所提出的方法中没有调整参数。发现通过所提出的方法获得的关于MOO问题的最佳折衷解决方案具有比通过其他方法获得的解决方案更高的模糊等级。而且，在所提出的方法中没有调整参数。

更新日期：2021-03-02

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