Percorrer por autor "Ribeiro, Bruno"
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- Demand Response Contextual Remuneration of Prosumers with Distributed StoragePublication . Silva, Cátia; Faria, Pedro; Ribeiro, Bruno; Gomes, Luis; Vale, ZitaProsumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events triggered by a community manager. If balance is achieved, the participants should be compensated for the discomfort caused. The authors in this paper propose a methodology to optimally manage a community, with a focus on the remuneration of community members for the provided flexibility. Four approaches were compared and evaluated, considering contextual tariffs. The obtained results show that it was possible to improve the fairness of the remuneration, which is an incentive and compensation for the loss of comfort. The single fair remuneration approach was more beneficial to the community manager, since the total remuneration was lower than the remaining approaches (163.81 m.u. in case study 3). From the prosumers’ side, considering a clustering method was more advantageous, since higher remuneration was distributed for the flexibility provided (196.27 m.u. in case study 3).
- Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent CommunitiesPublication . Pereira, Helder; Ribeiro, Bruno; Gomes, Luis; Vale, ZitaThe modeling of smart grids using multi-agent systems is a common approach due to the ability to model complex and distributed systems using an agent-based solution. However, the use of a multi-agent system framework can limit the integration of new operation and management models, especially artificial intelligence algorithms. Therefore, this paper presents a study of available open-source multi-agent systems frameworks developed in Python, as it is a growing programming language and is largely used for data analytics and artificial intelligence models. As a consequence of the presented study, the authors proposed a novel open-source multi-agent system framework built for smart grid modeling, entitled Python-based framework for heterogeneous agent communities (PEAK). This framework enables the use of simulation environments but also allows real integration at pilot sites using a real-time clock. To demonstrate the capabilities of the PEAK framework, a novel agent ecosystem based on agent communities is shown and tested. This novel ecosystem, entitled Agent-based ecosystem for Smart Grid modeling (A4SG), takes full advantage of the PEAK framework and enables agent mobility, agent branching, and dynamic agent communities. An energy community of 20 prosumers, of which six have energy storage systems, that can share energy among them, using a peer-to-peer market, is used to test and validate the PEAK and A4SG solutions.