Mixed-integer linear programming based optimization strategies for renewable energy communities

Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Energy (Oxford) Ročník 237; s. 121559
Hlavní autoři: Cosic, Armin, Stadler, Michael, Mansoor, Muhammad, Zellinger, Michael
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 15.12.2021
Elsevier BV
Témata:
Austria
Carbon dioxide
Carbon dioxide emissions
Case studies
Clean energy
Community
Decarbonization
Distributed generation
Electric power rates
electricity
Electricity pricing
Emissions
Energy
Energy Communities
Energy costs
Energy storage
Energy technology
European Union
Integer programming
Linear programming
markets
Microgrids
MILP
Mixed integer
Optimal Planning
Optimization
Photovoltaics
Renewable Energy
renewable energy sources
Renewable resources
Storage systems
tariffs
Austria
Optimal Planning
Renewable Energy
MILP
Energy Communities
Microgrids
Decarbonization
ISSN:0360-5442, 1873-6785
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality because of several limitation factors. Challenges are already encountered during the planning phase since a large number of decision variables have to be considered depending on the number and type of community participants and distributed technologies. This paper overcomes these challenges by establishing a mixed-integer linear programming based optimal planning approach for renewable energy communities. A real case study is analyzed by creating an energy community testbed with a leading energy service provider in Austria. The case study considers nine energy community members of a municipality in Austria, distributed photovoltaic systems, energy storage systems, different electricity tariff scenarios and market signals including feed-in tariffs. The key results indicate that renewable energy communities can significantly reduce the total energy costs by 15% and total carbon dioxide emissions by 34% through an optimal selection and operation of the energy technologies. In all the optimization scenarios considered, each community participant can benefit both economically and ecologically. •Novel optimization strategies for renewable energy communities in microgrids.•Advanced mixed-integer linear programming model with 8760-hour time horizon.•A real Austrian case study with nine community participants and six scenarios.•Renewable energy transfer with different tariff scenarios and market signals.•Reduction of community costs by 15% and community carbon emissions by 34%.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.121559