Sustainable green energy management : Optimizing scheduling of multi-energy systems considered energy cost and emission using attractive repulsive shuffled frog-leaping
As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables en...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Multidisciplinary Digital Publishing Institute (MDPI)
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38371/1/Sustainable%20green%20energy%20management_Optimizing%20scheduling%20of%20multi-energy%20systems%20considered%20energy%20cost.pdf http://umpir.ump.edu.my/id/eprint/38371/ https://doi.org/10.3390/su151410775 https://doi.org/10.3390/su151410775 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA. |
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