[论文解读] Optimization Algorithms in Smart Grids: A Systematic Literature Review

Sidra Aslam, Ala Altaweel+1 more|arXiv (Cornell University)|Jan 16, 2023

Smart Grid Energy Management被引用 9

一句话总结

本论文综述了在智能电网中使用 GA、PSO 和 Grey Wolf Optimization 的能量管理和成本效益，分析 2011–2022 的文献，并突出 PSO 为最受欢迎的算法。

ABSTRACT

Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. SGs are the outcome of different modern applications that are used for managing data and security, i.e., modeling, monitoring, optimization, and/or Artificial Intelligence. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Therefore, we collected 145 research works (2011 to 2022) in this systematic literature review. This research work aims to figure out different features and applications of SGs proposed in the last decade and investigate the trends in popularity of SGs for different regions of world. Our finding is that the most popular optimization algorithm being used by researchers to bring forward new solutions for energy management and cost effectiveness in SGs is Particle Swarm Optimization. We also provide a brief overview of objective functions and parameters used in the solutions for energy and cost effectiveness as well as discuss different open research challenges for future research works.

研究动机与目标

• 调查智能电网及其优化需求的应用与特征。
• 评估 GA、PSO 和 GWO 在能源管理和成本降低方面的应用。
• 识别 SG 优化文献中的模式、区域趋势和关键目标函数。
• 强调 SG 优化领域的未解决研究挑战及未来工作方向。

提出的方法

• 按照 Kitchenham 和 Charters 的指南进行了系统文献综述。
• 从 IEEE、Google Scholar、Springer、Elsevier 等数据库收集了 145 篇论文（2011–2022）。
• 应用纳入/排除标准，将范围缩小到在 SGs 中涉及 GA/PSO/GWO 的高质量文章。
• 使用六个问题的质量评估来选择用于综合的核心论文。
• 提取关于目标、参数、假设以及提出的能量管理解决方案的数据。

实验结果

研究问题

• RQ1 RQ1：智能电网的应用有哪些？
• RQ2 RQ2：为什么智能电网是一个关键且有前景的研究领域？
• RQ3 RQ3：在 SG 中用于实现最优能源管理/成本效率的目标函数和参数有哪些？
• RQ4 RQ4：在提升智能电网的能源与成本处理方面，GA、GWO 和 PSO 的使用有多广泛？
• RQ5 （隐含）RQ5：在 SG 优化中存在哪些开放挑战和未来研究方向？

主要发现

• Particle Swarm Optimization (PSO) is the most popular optimization algorithm in SG studies.
• GA, PSO, and GWO are used to address energy management and cost efficiency in SGs.
• Studies cover applications such as DSM, HEM, energy forecasting, and fault/attack detection.
• Probabilistic, robust, and distributionally robust optimization approaches are discussed for SG energy management.
• The review aggregates common objective functions, parameters, and constraints used in SG optimization.
• Open challenges include security, cyber-physical threats, and the need for scalable, real-world implementations.