RETRACTED: Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

Wang, Yanliang; Wang, Bo; Farjam, Hashem

doi:10.1016/j.jestch.2024.101649

Cited by 5 publications

(1 citation statement)

References 31 publications

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“… References Year Components of test system used Objective functions Methodology Remarks 54 2024 WT, PV, battery, MT, diesel generator, FC Operation cost Intelligent golden jackal optimization Integration of electric vehicle is not considered 55 2023 PV, battery, MT, thermal generator, CHP Operation cost, emission Epsilon constraint algorithm Integration of WT and FC not considered 56 2024 WT, PV, battery, MT, diesel generator, FC Operation cost, emission Manta ray foraging optimization Analysis of environmental pollution is ignored. Multi-objective optimization not implemented 57 2023 CES, EES, CAES, EHP, AC, heat pump Operation cost, emission Blue whale optimization algorithm Integration of WT, PV, and MT not considered. Different charging modes of EV not analyzed 58 2023 PV, WT, battery MG and EV cost Enhanced variant multi-objective particle swarm optimization algorithm Analysis of environmental pollution is ignored 59 2023 CHP, gas boiler, WT, PV, HS, BS Operating cost of multi-microgrid, profit of the distribution company Mixed-integer linear programming, ε-constraint approach, mixed-integer nonlinear programming Analysis of environmental pollution is ignored 60 2023 WT, PV, battery, MT, diesel generator, FC and grid ...…”

Section: Introductionmentioning

confidence: 99%

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

Rajagopalan,

Nagarajan,

Bajaj

et al. 2024

Sci Rep

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The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

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