Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

微电网 灵活性(工程) 计算机科学 智能电网 数学优化 需求响应 能源管理 可再生能源 分布式发电 网格 整数规划 分布式计算 线性规划 操作员(生物学) 储能 功率(物理) 能量(信号处理) 工程类 电气工程 算法 数学 生物化学 统计 化学 几何学 抑制因子 转录因子 基因 物理 量子力学
作者
Mohammad Hosein Sabzalian,Sasan Pirouzi,Maurício Aredes,Bruno W. França,Ana Carolina Cunha
出处
期刊:International Transactions on Electrical Energy Systems [Wiley]
卷期号:2022: 1-19 被引量:53
标识
DOI:10.1155/2022/3378538
摘要

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

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