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首页> 外文期刊>Transactions on Electrical and Electronic Materials >An Optimal Installation Strategy for Allocating Energy Storage Systems and Probabilistic-Based Distributed Generation in Active Distribution Networks
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An Optimal Installation Strategy for Allocating Energy Storage Systems and Probabilistic-Based Distributed Generation in Active Distribution Networks

机译:主动配电网中储能系统和基于概率的分布式发电的最优安装策略

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Recently, owing to increased interest in low-carbon energy supplies, renewable energy sources such as photovoltaics and wind turbines in distribution networks have received considerable attention for generating clean and unlimited energy. The presence of energy storage systems (ESSs) in the promising field of active distribution networks (ADNs) would have direct impact on power system problems such as encountered in probabilistic distributed generation (DG) model studies. Hence, the optimal procedure is offered herein, in which the simultaneous placement of an ESS, photovoltaic-based DG, and wind turbine-based DG in an ADN is taken into account. The main goal of this paper is to maximize the net present value of the loss reduction benefit by considering the price of electricity for each load state. The proposed framework consists of a scenario tree method for covering the existing uncertainties in the distribution network`s load demand as well as DG. The collected results verify the considerable effect of concurrent installation of probabilistic DG models and an ESS in defining the optimum site of DG and the ESS and they demonstrate that the optimum operation of an ESS in the ADN is consequently related to the highest value of the loss reduction benefit in long-term planning as well. The results obtained are encouraging.
机译:近来,由于对低碳能源供应的兴趣日益增加,配电网络中的光伏和风力涡轮机等可再生能源在产生清洁无限能源方面受到了广泛关注。主动配电网(ADN)的前景广阔的领域中,储能系统(ESS)的存在将直接影响电力系统问题,例如概率分布式发电(DG)模型研究中遇到的问题。因此,本文提供了最佳过程,其中考虑了在ADN中同时放置ESS,基于光伏的DG和基于风力涡轮机的DG。本文的主要目标是通过考虑每种负载状态的电价来最大程度地降低损失减少收益的净现值。所提出的框架由情景树方法组成,用于覆盖配电网负荷需求和DG中的现有不确定性。收集的结果证明了同时安装概率DG模型和ESS在定义DG和ESS的最佳位置方面的显著作用,并且它们表明ESS在ADN中的最佳操作因此与损耗的最大值有关长期计划中的减排收益也是如此。获得的结果令人鼓舞。

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