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A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

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  • Àlex Alonso

    (Electric Engineering Department, Escola d’Enginyeria de Barcelona Est, Polytechnic University of Catalonia, 08019 Barcelona, Spain)

  • Jordi de la Hoz

    (Electric Engineering Department, Escola d’Enginyeria de Barcelona Est, Polytechnic University of Catalonia, 08019 Barcelona, Spain)

  • Helena Martín

    (Electric Engineering Department, Escola d’Enginyeria de Barcelona Est, Polytechnic University of Catalonia, 08019 Barcelona, Spain)

  • Sergio Coronas

    (Electric Engineering Department, Escola d’Enginyeria de Barcelona Est, Polytechnic University of Catalonia, 08019 Barcelona, Spain)

  • Pep Salas

    (km0.Energy, Carrer de Lepant, 43, 08223 Terrassa, Barcelona, Spain)

  • José Matas

    (Electric Engineering Department, Escola d’Enginyeria de Barcelona Est, Polytechnic University of Catalonia, 08019 Barcelona, Spain)

Abstract

As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.

Suggested Citation

  • Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5590-:d:434921
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    Cited by:

    1. Jordi de la Hoz & Helena Martín & José Matas, 2023. "Editorial on the Special Issue Entitled “Regulatory Frameworks Addressed to Promote Renewable Energy Sources and Microgrids. Regulatory Constraints and Implications on Conception, Design and Energy Ma," Energies, MDPI, vol. 16(13), pages 1-4, June.
    2. Helena Martín & Jordi de la Hoz & Arnau Aliana & Sergio Coronas & José Matas, 2021. "Analysis of the Net Metering Schemes for PV Self-Consumption in Denmark," Energies, MDPI, vol. 14(7), pages 1-22, April.
    3. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    4. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    5. Guodong Liu & Zhi Li & Yaosuo Xue & Kevin Tomsovic, 2022. "Microgrid Assisted Design for Remote Areas," Energies, MDPI, vol. 15(10), pages 1-23, May.

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