Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities
The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach t...
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6358 |
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| author | Gianluca Carraro Enrico Dal Cin Sergio Rech |
| author_facet | Gianluca Carraro Enrico Dal Cin Sergio Rech |
| author_sort | Gianluca Carraro |
| collection | DOAJ |
| description | The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO<sub>2</sub> emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO<sub>2</sub> emissions by up to 30%. |
| format | Article |
| id | doaj-art-4cd5f0c7f9bd44dbb85f2125ca4db4e1 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-4cd5f0c7f9bd44dbb85f2125ca4db4e12024-12-27T14:23:34ZengMDPI AGEnergies1996-10732024-12-011724635810.3390/en17246358Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy CommunitiesGianluca Carraro0Enrico Dal Cin1Sergio Rech2Industrial Engineering Department, University of Padova, Via Venezia 1, 35131 Padova, ItalyIndustrial Engineering Department, University of Padova, Via Venezia 1, 35131 Padova, ItalyIndustrial Engineering Department, University of Padova, Via Venezia 1, 35131 Padova, ItalyThe optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO<sub>2</sub> emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO<sub>2</sub> emissions by up to 30%.https://www.mdpi.com/1996-1073/17/24/6358energy communitydecarbonizationMILPmulti-objective optimizationdemand responseusers aggregation |
| spellingShingle | Gianluca Carraro Enrico Dal Cin Sergio Rech Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities Energies energy community decarbonization MILP multi-objective optimization demand response users aggregation |
| title | Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities |
| title_full | Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities |
| title_fullStr | Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities |
| title_full_unstemmed | Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities |
| title_short | Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities |
| title_sort | integrating energy generation and demand in the design and operation optimization of energy communities |
| topic | energy community decarbonization MILP multi-objective optimization demand response users aggregation |
| url | https://www.mdpi.com/1996-1073/17/24/6358 |
| work_keys_str_mv | AT gianlucacarraro integratingenergygenerationanddemandinthedesignandoperationoptimizationofenergycommunities AT enricodalcin integratingenergygenerationanddemandinthedesignandoperationoptimizationofenergycommunities AT sergiorech integratingenergygenerationanddemandinthedesignandoperationoptimizationofenergycommunities |