TY - JOUR
T1 - Optimizing planning and operation of renewable energy communities with genetic algorithms
AU - Lazzari, Florencia
AU - Mor, Gerard
AU - Cipriano, Jordi
AU - Solsona, Francesc
AU - Chemisana, Daniel
AU - Guericke, Daniela
N1 - Funding Information:
This work was developed during the PhD thesis of F. Lazzari. D. Chemisana thanks ICREA for the ICREA Acad‘emia. This work emanated from research conducted with the financial support of the European Commission through the POCTEFA project EKATE, grant agreement EFA312/19 and the H2020 project ePLANET, grant agreement 101032450 .
Funding Information:
This work was developed during the PhD thesis of F. Lazzari. D. Chemisana thanks ICREA for the ICREA Acad‘emia. This work emanated from research conducted with the financial support of the European Commission through the POCTEFA project EKATE, grant agreement EFA312/19 and the H2020 project ePLANET, grant agreement 101032450.
Publisher Copyright:
© 2023 The Authors
PY - 2023/5/15
Y1 - 2023/5/15
N2 - Renewable Energy Communities (REC) have the potential to become a key agent for the energy transition. Since consumers have different consumption patterns depending on their habits, their grouping allows for a better use of the resource. REC provide both economic and environmental benefits. However, its potential drastically diminishes when grouping of prosumers and energy al- location is performed improperly, as the energy generated ends up not being consumed. Given the importance of extracting the maximum potential of REC, this study presents a tool to assist in both the planning and the operation phases. We present a combinatorial optimization method for participant selection and a multi-objective (MO) optimization of solar energy allocation. Specific Ge- netic Algorithms (GA) were developed including problem-specific approaches for reducing the search space, encoding, techniques for space ordering, fitness functions, special operators to replace duplicate individuals and decoding for equality constraints. The performance of the novel solution approach was exper- imentally proved with an electrical solar installation and electricity consumers from Northern east Spain. The results show that the developed tool achieves energy sharing in REC with low solar energy excess, high self-consumption and high avoided CO2 emissions while assuring low payback periods for all partic- ipants. This tool will be essential to increase revenues of REC schemes and boost their beneficial environmental impact.
AB - Renewable Energy Communities (REC) have the potential to become a key agent for the energy transition. Since consumers have different consumption patterns depending on their habits, their grouping allows for a better use of the resource. REC provide both economic and environmental benefits. However, its potential drastically diminishes when grouping of prosumers and energy al- location is performed improperly, as the energy generated ends up not being consumed. Given the importance of extracting the maximum potential of REC, this study presents a tool to assist in both the planning and the operation phases. We present a combinatorial optimization method for participant selection and a multi-objective (MO) optimization of solar energy allocation. Specific Ge- netic Algorithms (GA) were developed including problem-specific approaches for reducing the search space, encoding, techniques for space ordering, fitness functions, special operators to replace duplicate individuals and decoding for equality constraints. The performance of the novel solution approach was exper- imentally proved with an electrical solar installation and electricity consumers from Northern east Spain. The results show that the developed tool achieves energy sharing in REC with low solar energy excess, high self-consumption and high avoided CO2 emissions while assuring low payback periods for all partic- ipants. This tool will be essential to increase revenues of REC schemes and boost their beneficial environmental impact.
U2 - 10.1016/j.apenergy.2023.120906
DO - 10.1016/j.apenergy.2023.120906
M3 - Article
SN - 0306-2619
VL - 338
JO - Applied energy
JF - Applied energy
M1 - 120906
ER -
