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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2020 - 20222
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Fecha de inicio: 2020 ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Technical challenges for the optimum penetration of grid-connected photovoltaic systems: Spain as a case study
    (Elsevier, 2020-01) Linares Mena, Ana Rosa; Molina Ibáñez, Enrique Luis; Rosales Asensio, Enrique; Borge Díez, David; Colmenar Santos, Antonio
    This research reviews the technical requirements of grid-connected photovoltaic power plants to increase their competitiveness and efficiently integrate into the grid to satisfy future demand requirements and grid management challenges, focusing on Spain as a case study. The integration of distributed resources into the electric network, in particular photovoltaic energy, requires an accurate control of the system. The integration of photovoltaic energy has resulted in significant changes to the regulatory framework to ensure proper integration of distributed generation units in the grid. In this study, the requirements of the system operator for the management and smart control are first analysed and then the technical specifications established by the network operator in reference to the components of the facility are evaluated. This analysis identifies the shortcomings of the current legislation and concludes with a summary of the main technical recommendations and future regulatory challenges that need to be undertaken in the future. It is presented as a reference case that can be adapted worldwide.
  • 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.