Energy management in microgrids considering the demand response in the presence of distributed generation resources on the IoT platform

IF 3.1 4区工程技术 Q3 ENERGY & FUELS Energy Sources Part B-Economics Planning and Policy Pub Date : 2022-02-19 DOI:10.1080/15567249.2022.2038729

Amirhossein Bolurian, H. Akbari, T. Daemi, S. A. Mirjalily, S. Mousavi

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引用次数: 3

Abstract

ABSTRACT Existing electricity networks do not have information about their endpoints due to their hierarchical structure. Internet of things technology allows two-way communication with customers. This work proposes an energy management system for optimal planning of a microgrid, considering demand response and uncertainties on the internet of things framework. The planning problem is solved using the first and the second-level Benders decomposition method. Then, the model third level is developed and optimized by genetic-fuzzy algorithm. For energy management in the internet of things platform, first the consumers are clustered based on their consumption by C-Means algorithm and then the network sensor energy consumption is optimized by genetic-fuzzy algorithm. To choose the optimal solution, a non-dominant fuzzy decision process beam is adopted. Based on the numerical results, the developed model outperforms the two-level model as well as the three-level model that uses particle swarm optimization.

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考虑物联网平台上分布式发电资源存在时需求响应的微电网能源管理

现有的电力网络由于其分层结构而没有关于其端点的信息。物联网技术可以实现与客户的双向沟通。本研究提出了一种用于微电网优化规划的能源管理系统，考虑了物联网框架下的需求响应和不确定性。采用一级和二级Benders分解法求解规划问题。然后，利用遗传模糊算法对模型第三层进行开发和优化。对于物联网平台的能源管理，首先采用C-Means算法对消费者的能耗进行聚类，然后采用遗传模糊算法对网络传感器的能耗进行优化。为了选择最优解，采用非优势模糊决策过程束。数值结果表明，所建立的模型优于采用粒子群优化的两层模型和三层模型。

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来源期刊

Energy Sources Part B-Economics Planning and Policy ENERGY & FUELS-

CiteScore

6.80

自引率

12.80%

发文量

审稿时长

6-12 weeks

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