Persona:
Colmenar Santos, Antonio

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ORCID
0000-0001-8543-4550
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Colmenar Santos
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Antonio
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2019 - 201912020 - 20221
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Autor: Muñoz Gómez, Antonio Miguel × search.filters.applied.f.itemFacultad: E.T.S. de Ingenieros Industriales ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario
    (Elsevier, 2019-09-15) Muñoz Gómez, Antonio Miguel; Rosales Asensio, Enrique; Colmenar Santos, Antonio; López-Rey García-Rojas, África
    The EU has undertaken a thorough reform of its energy model. Current EU 2050 climate commitment sets an 80–95% GHG reduction goal. To reach this goal, the EU must make continued progress towards a low-carbon society. Renewable energy sources and electric vehicle play an important role for a gradual transition. The power grid faces a challenging future due to intermittency and the non-dispatchable nature of wind and solar energy production, but flexibility needs can migrate from generation to load, with the expansion of demand-side resources and storage technologies. A novel grid technique is presented and evaluated in this paper for the optimal integrated operation of renewable resources and electric vehicle to increase penetration of renewable energy. It is proposed a distribute control system to manage a charge and discharge strategy to support mismatching between load and renewable generation thru V2G technology. Demand response, peak saving and ancillary services are introduced to keep a reliable power quality, stable frequency and flatten load profile.
  • Miniatura
    Publicación
    Adaptive model predictive control for electricity management in the household sector
    (Elsevier, 2022-05) Muñoz Gómez, Antonio Miguel; Rosales Asensio, Enrique; Fernández Aznar, Gregorio; Galán Hernández, Noemi; Colmenar Santos, Antonio
    This paper focuses on the optimisation of electricity consumption in residential buildings. To deal with the increase in electricity consumption, the intermittency of renewable energy generation and grid contingencies, a greater effort is required towards residential management optimisation. A novel adaptive model predictive control algorithm is proposed to achieve this objective. The challenges for this research included recognising and modelling the economic and technical constraints of the sources and appliances and addressing the uncertainties concerning the weather and user behaviour. Data-driven models are developed and trained to predict the user behaviour and buildings. Artificial neural networks and statistical models based on the weighted moving average are proposed to capture the patterns of deferrable and non-deferrable appliances, battery storage, electric vehicles, photovoltaic modules, buildings and grid connections. A dual optimisation method is devised to minimise the electricity bill and achieve thermal comfort. The proposed optimisation solver is a two-step optimisation method based on genetic algorithm and mixed integer linear programming. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method through a set of simulation scenarios. The results of the quantitative analysis undertaken as part of this study show the effectiveness of the proposed algorithm towards reducing electricity charges and improving grid elasticity.