New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island

Yeh, Wei-Chang; He, Ming; Huang, Chia‐Ling; Tan, Shi-Yi; Zhang, Xianyong; Huang, Yaohong; Li, Li

doi:10.1016/j.apenergy.2020.114508

Cited by 59 publications

(18 citation statements)

References 35 publications

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“…solutions may be stuck in local optima. The complexity of formulating and solving deterministic nonlinear programming methods have also resulted in a surge of metaheuristic and AI-based techniques such as particle swarm optimisation (PSO) [27,28], genetic algorithms (GA) [29,30], artificial bee colony [31], grey wolf optimisation [32], etc. (see [33] and [34] for a summary and recent works, respectively).…”

Section: Context: Optimisation and Simulation Of Desmentioning

confidence: 99%

Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Mel

Klymenko

Short

2022

Sustainable Energy Technologies and Assessments

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Section: Context: Optimisation and Simulation Of Desmentioning

confidence: 99%

Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Mel

Klymenko

Short

2022

Sustainable Energy Technologies and Assessments

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“…Constraint (10) is the state of Blayer and required for MPC. The proposed MPC-based dispatch strategy of the GER is constraint and nonlinear model, and thus is solved by the genetic algorithm (GA) in this paper [33]. res E ∆ Fig.…”

Section: Energy Management Of Energy Buffermentioning

confidence: 99%

Hierarchical structure design and primary energy dispatching strategy of net energy router

Chen

Xia

Chen

2021

Electric Power Systems Research

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“…Metaheuristic algorithms are often used to solve nonlinear optimization problems. In the article [4], an improved genetic algorithm is proposed to optimize the economic dispatch of stand-alone modular microgrids in DongAo Island. Hossain et al [5] proposed a particle swarm optimization (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid.…”

Section: Introductionmentioning

confidence: 99%

Energy management in microgrid based on deep reinforcement learning with expert knowledge

Zhang

Qiao²,

xu³

et al. 2022

International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022)

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Microgrid is an effective way to improve the utilization rate of renewable energy and is an indispensable part of recent power networks. In microgrids, the deployment of energy management systems (EMS) ensures stable operation and maximizes energy efficiency. Due to the uncertainty of non-steerable generation and non-flexible consumption in the microgrid, it is challenging to design an energy management algorithm to schedule the steerable generators and storage. To address this problem, energy management system is modeled as a Markov Decision Process (MDP) with continuous action space in this paper. Then an off-line reinforcement learning algorithm is leveraged to help EMS make scheduling decisions. Compared with other EMS schemes based on deep reinforcement learning, our method can effectively utilize the optimal decision data generated by mathematical programming, i.e., expert knowledge, to improve learning efficiency and decision-making ability. Simulation based on real world data verifies that the proposed algorithm has better performance than other reinforcement learning algorithms.

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New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in DongAo Island

Cited by 59 publications

References 35 publications

Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Hierarchical structure design and primary energy dispatching strategy of net energy router

Energy management in microgrid based on deep reinforcement learning with expert knowledge

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