Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Smart Cities Pub Date : 2023-06-30 DOI:10.3390/smartcities6040077
Safaa Mimi, Yann Ben Maissa, A. Tamtaoui
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引用次数: 2

Abstract

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.
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智能电网需求侧管理优化方法:系统映射研究
智能电网的需求侧管理通常包括在表示电力消费习惯的约束条件下,针对变量优化与能源相关的目标函数。这些功能通常与用户的电费(成本)或峰值能耗(峰值与平均能量比)有关,这可能导致大规模的电网故障。然而,能源需求的增长,特别是在新兴国家,正在造成严重的能源危机。这就是为什么一些研究集中在这些优化方法上。据我们所知,没有一篇文章旨在用系统可重复的方法收集和分析峰均能耗比和成本优化的研究结果。我们的目标是通过对过去十年(2013-2022)的这一主题进行系统的地图研究来填补这一空白。首先使用的方法是根据特定的搜索字符串搜索数字图书馆(684项研究中有104项相关研究)。下一步是根据与算法趋势、能源、建筑类型、优化目标和定价方案相关的5个研究问题对作品进行分析(使用13个标准进行分类)。一些主要结果是遗传算法的优势,对可再生能源和存储系统的关注不足,对住宅建筑的偏好以及对实时定价方案的偏好。主要结论与遗传算法和群体优化方法之间有前途的杂交,以及在优化中更大程度地整合用户偏好有关。此外,还需要精确的可再生能源和储能模型,以及将优化范围扩大到其他目标,如二氧化碳排放或通信负荷。
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来源期刊
Smart Cities
Smart Cities Multiple-
CiteScore
11.20
自引率
6.20%
发文量
0
审稿时长
11 weeks
期刊介绍: Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.
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