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Sobre

Sobre

José P. Iria nasceu em Chaves, Portugal em 1988. Ele recebeu o Mestrado Integrado em Engenharia Eletrotécnica e de Computadores, da Faculdade de Engenharia da Universidade do Porto em 2011. Atualmente, ele é estudante de doutoramento do programa Sistemas Sustentáveis de Energia do MIT|Portugal, da Faculdade de Engenharia da Universidade do Porto. Ele é também investigador no centro de potência e sistemas de energia (CPES) do INESC TEC. As suas atividades no INESC TEC incluem projetos europeus e nacionais com parceiros industriais. 

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Pedro Iria
  • Cargo

    Investigador Colaborador Externo
  • Desde

    10 fevereiro 2011
001
Publicações

2024

Multi-objective planning of community energy storage systems under uncertainty

Autores
Anuradha, K; Iria, J; Mediwaththe, CP;

Publicação
Electric Power Systems Research

Abstract

2023

Real-time management of distributed multi-energy resources in multi-energy networks

Autores
Coelho, A; Iria, J; Soares, F; Lopes, JP;

Publicação
SUSTAINABLE ENERGY GRIDS & NETWORKS

Abstract
The replacement of fossil fuel power plants by variable renewable energy sources is reducing the flexibility of the energy system, which puts at risk its security. Exploiting the flexibility of distributed multi-energy resources through aggregators presents a solution for this problem. In this context, this paper presents a new hierarchical model predictive control framework to assist multi-energy aggregators in the network-secure delivery of multi-energy services traded in electricity, natural gas, green hydrogen, and carbon markets. This work builds upon and complements a previous work from the same authors related to bidding strategies for day-ahead markets - it closes the cycle of aggregators' participation in multi-energy markets, i.e., day-ahead bidding and real-time activation of flexibility services. This new model predictive control framework uses the alternating direction method of multipliers on a rolling horizon to negotiate the network-secure delivery of multi-energy services between aggregators and distribution system operators of electricity, gas, and heat networks. We used the new model predictive control framework to conduct two studies. In the first study, we found that considering multi-energy network constraints at both day-ahead and real-time optimization stages produces the most cost-effective and reliable solution to aggregators, outperforming state-of-the-art approaches in terms of cost and network security. In the second study, we found that the adoption of a green hydrogen policy by multi-energy aggregators can reduce their consumption of natural gas and respective CO2 emissions significantly if carbon and green hydrogen prices are competitive.& COPY; 2023 Elsevier Ltd. All rights reserved.

2023

Evaluation of the Economic Benefits of Community Energy Storage Systems for Prosumers

Autores
Anuradha, K; Iria, J; Mediwaththe, CP;

Publicação
2023 IEEE Region 10 Symposium (TENSYMP)

Abstract

2023

Adjustable Price-Sensitive DER Bidding within Network Envelopes

Autores
Attarha, A; Mahmoodi, M; R.A., SMN; Scott, P; Iria, J; Thiébaux, S;

Publicação
IEEE Transactions on Energy Markets, Policy and Regulation

Abstract

2023

Evaluation of the economic, technical, and environmental impacts of multi-energy system frameworks in distribution networks

Autores
Coelho, A; Soares, F; Iria, J; Lopes, JP;

Publicação
2023 IEEE BELGRADE POWERTECH

Abstract
This paper presents a general comparison between network-secure and network-free optimization frameworks to manage flexible multi-energy resources. Both frameworks were implemented in a test case that includes electricity, gas, and heat distribution networks. Several potential scenarios for the decarbonization of the multi-energy system were simulated. The economic, technical, and environmental impacts were compiled. The network-secure framework is highly recommended to avoid service disruptions due to network violations, but its implementation comes with a price - overall operational costs increase, sometimes substantially.