Optimal scheduling of the energy storage system in a hybrid micro-grid considering uncertainties, using the stochastic quasi-gradient method

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2023-06-01 DOI:10.1049/stg2.12115
Masoud Ghazipour Shirvan, Mohamad Hosseini Abardeh, Mehrdad Hojjat
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引用次数: 0

Abstract

Energy storage and renewable sources play a unique role in the future advances of smart grids. In this article, the optimal scheduling of the energy storage system in a hybrid microgrid is presented considering the uncertainties of the renewable generations and the load. The optimisation problem in this article is non-linear and non-convex, therefore conventional optimisation methods such as linear programming (LP) are unable to solve this problem. On the other hand, because of parameters uncertainty, special considerations are required to simulate these parameters. In this regard, a new optimisation algorithm that can solve the non-linearity and non-convexity of the objective function is proposed based on the Stochastic Quasi-Gradient optimisation Method (SQGM). Moreover, the uncertainties of the wind, PV generation, and the load are modelled. Different optimisation algorithms: the conventional Stochastic Dynamic Programming (SDP), the Stochastic Dual Dynamic Programming (SDDP) and the proposed SQGM are compared. A 9-bus benchmark system with distributed generation units is used to evaluate the optimisation strategies.

Abstract Image

考虑不确定性的混合微电网储能系统优化调度研究
能源储存和可再生能源在智能电网的未来发展中发挥着独特的作用。本文研究了考虑可再生能源发电机组和负荷不确定性的混合微电网储能系统优化调度问题。本文的优化问题是非线性和非凸的,因此传统的优化方法,如线性规划(LP)无法解决这个问题。另一方面,由于参数的不确定性,在模拟这些参数时需要特别考虑。为此,在随机拟梯度优化方法(SQGM)的基础上,提出了一种求解目标函数非线性和非凸性的优化算法。此外,还对风力、光伏发电和负荷的不确定性进行了建模。不同的优化算法:传统的随机动态规划(SDP),随机对偶动态规划(SDDP)和提出的SQGM进行了比较。采用带有分布式发电机组的9总线基准系统对优化策略进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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