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Joint optimal allocation methodology for renewable distributed generation and energy storage for economic benefits

机译：联合优化分配方法，用于可再生分布式发电和能量存储，以实现经济效益

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摘要

Nowadays renewable distributed generation (RDG) is observed as an option to the conventional distributed generation due to increased demand for electric energy and green energy concerns. Despite sustainable energy choice, RDGs are also becoming more cost effective. This study presents a joint optimal allocation methodology of RDG and energy storage (ES) to achieve economic benefits. The proposed method minimises costs of distribution company (DISCOM) while assuring the benefits of RDG owner (RDGO). The main novelty of this study is that the contract price of renewable energy between RDGO and DISCOM is obtained along with the allocation of RDG and ES to achieve cost-benefits, and the ES contributes the peak shaving. This study also includes the formulation of generation models for solar power and wind power from the seasonal probabilistic generation models and integration of this renewable generation and load model with an ES model to achieve economic benefits. Moreover, the generation model, storage model, and load model are combined into an optimal power flow model to obtain energy loss minimisation. This constrained non-linear problem is solved using a highly competitive algorithm called grey wolf optimiser in MATLAB®. Three case studies are presented on the 34-bus test system.
机译：如今，由于对电能的需求增加和对绿色能源的关注，可再生能源分布式发电（RDG）被认为是传统分布式发电的一种选择。尽管选择了可持续的能源，但RDG的成本效益也越来越高。这项研究提出了RDG和能量存储（ES）的联合最优分配方法，以实现经济利益。所提出的方法使分销公司（DISCOM）的成本最小化，同时又确保了RDG所有者（RDGO）的利益。这项研究的主要新颖之处在于，RDGO和DISCOM之间的可再生能源合同价格是与RDG和ES的分配一起获得的，以实现成本收益，而ES则为削峰做出了贡献。这项研究还包括根据季节性概率发电模型制定太阳能和风能的发电模型，并将此可再生发电和负荷模型与ES模型相集成，以实现经济效益。此外，将发电模型，存储模型和负载模型组合为最佳潮流模型，以实现能量损失最小化。使用MATLAB®中称为灰狼优化器的竞争激烈的算法可以解决此受约束的非线性问题。在34总线测试系统上提出了三个案例研究。

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