Design of the distribution of electricity in a renewable energy community including storage
The last decade has marked a significant transformation for the power systems globally. With the goal of decarbonization, a large portion of the traditional fossil-fuel generators have already been replaced, highlighting RES as a key solution for green electricity generation. However, this process b...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/431408 |
| Acceso en línea: | https://hdl.handle.net/2117/431408 |
| Access Level: | acceso abierto |
| Palabra clave: | Renewable energy sources Energy storage Energy conservation Energies renovables Energia -- Emmagatzematge Energia -- Estalvi Àrees temàtiques de la UPC::Energies |
| Sumario: | The last decade has marked a significant transformation for the power systems globally. With the goal of decarbonization, a large portion of the traditional fossil-fuel generators have already been replaced, highlighting RES as a key solution for green electricity generation. However, this process brings significant challenges with it but also creates an open field for research and innovation towards a more sustainable way of living. The EU has introduced a series of policies empowering Energy Communities (ECs) and providing a legal framework for their activities, thus promoting the democratization of the power system and incentivizing EU citizens to cooperate and actively take part in the efforts for the desired transition towards sustainability. In this direction, the thesis presented hereafter consists of an analysis and a proposal of energy management for Renewable Energy Communities (RECs) that collectively own a PV installation opting for integration of Battery Systems (BS). This project intends to analyze the potential integration of BS in Collective SelfConsumption (CSC) RECs and also explore the possible strategies that could be deployed for the charging and discharging of the battery in order to minimize the electricity cost or the energy imports from the grid. More specifically, two different approaches are examined. The first is based on a MILP algorithm considering forecasted values whereas the second is based on Q-learning, a specific family of Reinforcement Learning algorithms. The results indicate that, for the specific setup of the EC considered based on the assumptions of the battery specs, only 2 days-ahead of input data is sufficient to implement an effective charging strategy of the battery. In addition, the sensitivity analysis conducted outlined the positive effect of the incorporation of battery systems. In all cases of considered scenarios of PV+BS setups, the self-consumption, self-sufficiency and cost savings showed better results than the No-Battery (NB) or Dummy scenario. Finally, the Q-learning algorithm was also successful, approaching the optimization results. However, future research is encouraged considering the high processing time along with the difficulties of combining Machine Learning (ML) algorithms with various engineering problems as the one researched in this project. |
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