Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy
As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5261 |
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| author | Xiang Liao Runjie Lei Shuo Ouyang Wei Huang |
| author_facet | Xiang Liao Runjie Lei Shuo Ouyang Wei Huang |
| author_sort | Xiang Liao |
| collection | DOAJ |
| description | As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system. |
| format | Article |
| id | doaj-art-724e9af4fa2f43b3ae8c271b83e5d308 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-724e9af4fa2f43b3ae8c271b83e5d3082024-11-08T14:35:06ZengMDPI AGEnergies1996-10732024-10-011721526110.3390/en17215261Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority StrategyXiang Liao0Runjie Lei1Shuo Ouyang2Wei Huang3Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan 430068, ChinaChangjiang Water Resources Committee, Wuhan 430010, ChinaHubei Energy Group New Energy Development Co., Wuhan 430077, ChinaAs the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.https://www.mdpi.com/1996-1073/17/21/5261multi-energy couplingintegrated energy systemmulti-objective algorithmenergy allocation strategy |
| spellingShingle | Xiang Liao Runjie Lei Shuo Ouyang Wei Huang Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy Energies multi-energy coupling integrated energy system multi-objective algorithm energy allocation strategy |
| title | Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy |
| title_full | Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy |
| title_fullStr | Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy |
| title_full_unstemmed | Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy |
| title_short | Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy |
| title_sort | capacity optimization allocation of multi energy coupled integrated energy system based on energy storage priority strategy |
| topic | multi-energy coupling integrated energy system multi-objective algorithm energy allocation strategy |
| url | https://www.mdpi.com/1996-1073/17/21/5261 |
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