Optimal Sizing and Energy Management of a Microgrid Using Single and Multi-Objective Particle Swarm Optimization under Autonomous and Grid Connected Mode

Dayalan, Suchitra; Rathinam, Rajarajeswari; Valliappan, Subramaniyan

doi:10.4271/2019-28-0158

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…To mitigate these complexities, AI like DL, RL, and DRL are made effective adaptive solutions in decision‐making. Paper 30 proposed the PSO algorithm for the prediction of energy in a hybrid system, PSO made its contribution in optimal cost function with a lesser iterative process, whereas multi‐objective‐based PSO is implemented in Reference 31 under a different mode of operating conditions to archive minimum cost function. Paper 32 implements DRL in the microgrid to perform real‐time optimization in energy management for buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Section: Literature Reviewmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy Systems

Valliappan,

R.,

et al. 2024

Metaheuristic and Machine Learning Optimization Strategies for Complex Systems

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The demand for fossil fuels has created a need for renewable energy sources. In addition to fossil fuel shortages, many countries and companies are also concerned about global warming. These issues have led to the addition of many renewable energy sources to modern power distribution systems. However, the high penetration of renewable energy and the intermittent nature of some resources, such as photovoltaics and wind turbines, lead to fluctuations and unpredictability in power system production. In this situation, grouping these resources provides a solution to the problems arising from the different outcomes. A virtual power plant is a collection of control loads that are collectively managed by distributed generators, energy storage systems, and energy management systems. The purpose of VPP in this study is to reduce the total cost of ownership while considering the 24hour energy loss cost. As a solution to address this issue, a metaheuristic optimization algorithm, the Imperialist Competitive Algorithm, has been proposed to find the best possible Establish energy management.

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Optimal Sizing and Energy Management of a Microgrid Using Single and Multi-Objective Particle Swarm Optimization under Autonomous and Grid Connected Mode

Cited by 2 publications

References 23 publications

Deep learning and reinforcement learning approach on microgrid

Deep learning and reinforcement learning approach on microgrid

Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy Systems

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