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Optimal Deployment of Energy Storage for Providing Peak Regulation Service in Smart Grid with Renewable Energy Sources

机译：可再生能源在智能电网中提供峰值调节服务的优化部署

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摘要

With the increasing penetration of renewable energy generation (such as wind power) in the future power systems, the requirement for peak regulation capacity is becoming an important issue for the utility operators. Energy storage is one of the most effective solutions to address this issue. Under this background, this paper proposes a novel multi-objective optimization model to determine the optimal allocation capacity of energy storage in a thermal power plant for provision of peak regulation service in smart grid. To achieve this, we limit our study to a context based on the compensation mechanism performed in the auxiliary peak regulation market of northeast China. On this basis, an optimal energy storage allocation model in a thermal power plant is proposed, which aims to maximize the total economic profits obtained from peak regulation and renewable energy utilization in the system simultaneously, while considering the operational constraints of energy storage and generation units. A classical weighted summation technique has been used to deal with the bi-objective structure of the problem and a compound solution algorithm based on Generic Algorithm is employed to resolve the developed model. The effectiveness of the proposed methodology is examined based on a real-world regional power system in northeast China and the obtained results verify the effectiveness of our approach.
机译：随着可再生能源发电的普及率越来越高（如风力发电），在未来的电力系统中，用于调峰容量的要求正成为公用事业运营商的一个重要问题。储能是目前最有效的解决方案来解决这个问题之一。在此背景下，本文提出了一种新颖的多目标优化模型确定为智能电网提供调峰服务的火电厂储能的优化配置能力。为了实现这一目标，我们限制我们的研究基于中国东北地区的辅助调峰市场进行的补偿机制的上下文。在此基础上，在一个热电厂的最优能量存储分配模型提出，其目的是最大限度地从调峰和可再生能源的利用在系统中同时获得的总经济利润，同时考虑能量存储和生成单元的操作限制。一个经典的加权求和技术已被用来解决这个问题的双目标结构和基于遗传算法的化合物溶液算法来解决开发的模型。检查设在中国东北一个真实世界的区域电力系统的建议方法的有效性和获得的结果验证了该方法的有效性。

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