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Development of optimization algorithms for the Leaf Community microgrid

Author

Listed:
  • Provata, Elena
  • Kolokotsa, Dionysia
  • Papantoniou, Sotiris
  • Pietrini, Maila
  • Giovannelli, Antonio
  • Romiti, Gino

Abstract

The aim of this work is the development of an optimization model in order to minimize the cost of Leaf Community microgrid. This cost is a sum of energy cost and the maintenance cost of the energy storage system (ESS). The developed objective function is constrained and the problem here is solved by using the method of genetic algorithms at Matlab. The genetic algorithm decides about the transportation of the energy from or to the ESS and it calculates an optimum cost. The optimization time horizon is 24 h ahead, thus the prediction of energy production and consumption was necessary. This was achieved by using neural networks. In order to verify the performance of the developed model, some scenarios were tested. This study concludes that a management of a microgrid can achieve energy and money savings.

Suggested Citation

  • Provata, Elena & Kolokotsa, Dionysia & Papantoniou, Sotiris & Pietrini, Maila & Giovannelli, Antonio & Romiti, Gino, 2015. "Development of optimization algorithms for the Leaf Community microgrid," Renewable Energy, Elsevier, vol. 74(C), pages 782-795.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:782-795
    DOI: 10.1016/j.renene.2014.08.080
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    References listed on IDEAS

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    1. Llaria, Alvaro & Curea, Octavian & Jiménez, Jaime & Camblong, Haritza, 2011. "Survey on microgrids: Unplanned islanding and related inverter control techniques," Renewable Energy, Elsevier, vol. 36(8), pages 2052-2061.
    2. Kyriakarakos, George & Dounis, Anastasios I. & Arvanitis, Konstantinos G. & Papadakis, George, 2012. "A fuzzy logic energy management system for polygeneration microgrids," Renewable Energy, Elsevier, vol. 41(C), pages 315-327.
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    1. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    2. Nikolaos Kampelis & Georgios I. Papayiannis & Dionysia Kolokotsa & Georgios N. Galanis & Daniela Isidori & Cristina Cristalli & Athanasios N. Yannacopoulos, 2020. "An Integrated Energy Simulation Model for Buildings," Energies, MDPI, vol. 13(5), pages 1-23, March.
    3. Kovács, András & Bátai, Roland & Csáji, Balázs Csanád & Dudás, Péter & Háy, Borbála & Pedone, Gianfranco & Révész, Tibor & Váncza, József, 2016. "Intelligent control for energy-positive street lighting," Energy, Elsevier, vol. 114(C), pages 40-51.
    4. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    5. Wenhao Zhuo & Andrey V. Savkin, 2019. "Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting," Energies, MDPI, vol. 12(15), pages 1-17, July.
    6. Warneryd, Martin & Håkansson, Maria & Karltorp, Kersti, 2020. "Unpacking the complexity of community microgrids: A review of institutions’ roles for development of microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    7. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    8. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Oscar Núñez-Mata, 2020. "Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures," Energies, MDPI, vol. 13(3), pages 1-32, January.

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