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

Àlex Alonso, Jordi de la Hoz, Helena Martín, Sergio Coronas, Pep Salas and José Matas
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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

Energies, 2020, vol. 13, issue 21, 1-26

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.

Keywords: microgrid; stochastic programming; sizing; energy management; uncertainty; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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