[논문 리뷰] Optimization Algorithms in Smart Grids: A Systematic Literature Review

Sidra Aslam, Ala Altaweel외 1명|arXiv (Cornell University)|2023. 01. 16.

Smart Grid Energy Management인용 수 9

한 줄 요약

본 논문은 스마트 그리드에서 에너지 관리 및 비용 효율성을 위한 GA, PSO, 및 GWO의 활용을 조사하고, 2011–2022년 문헌을 분석하며 PSO를 가장 인기 있는 알고리즘으로 강조한다.

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.

연구 동기 및 목표

• Smart Grid의 응용 및 최적화 필요성 파악과 특징 파악.
• GA, PSO, 및 GWO가 에너지 관리 및 비용 절감을 위해 어떻게 사용되었는지 평가.
• SG 최적화 문헌에서의 패턴, 지역적 경향, 주요 목적 함수 식별.
• SG 최적화의 향후 연구 과제와 방향성 제시 및 개방형 연구 도전 과제 강조

제안 방법

• Kitchenham과 Charters 지침에 따라 체계적 문헌 고찰 수행.
• IEEE, Google Scholar, Springer's, Elsevier 등 데이터베이스에서 145편의 논문(2011–2022) 수집.
• SG에서 GA/PSO/GWO를 다루는 질적 논문으로 좁히기 위한 포함/배제 기준 적용.
• 합성을 위한 6문항 품질 평가를 사용.
• 목표, 매개변수, 가정 및 제안된 에너지 관리 솔루션에 관한 데이터 추출

실험 결과

연구 질문

• RQ1 RQ1: Smart Grids의 응용 분야는 무엇인가?
• RQ2 RQ2: Smart Grid가 왜 중요하고 유망한 연구 영역인가?
• RQ3 RQ3: SG의 최적 에너지 관리/비용 효율화를 위해 사용되는 목적 함수와 매개변수는 무엇인가?
• RQ4 RQ4: GA, GWO, 및 PSO가 Smart Grids의 에너지 및 비용 처리 강화를 위해 얼마나 광범위하게 사용되는가?
• RQ5 (암시) RQ5: SG 최적화의 개방형 도전 과제와 향후 연구 방향은 무엇인가?

주요 결과

• 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.