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Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm

Author

Listed:
  • Julian Garcia-Guarin

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia)

  • Diego Rodriguez

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
    GERS USA, Weston, FL 33326, USA)

  • David Alvarez

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia)

  • Sergio Rivera

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
    Electrical Engineering Department, University of Florida, Gainesville, FL 32601, USA)

  • Camilo Cortes

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia)

  • Alejandra Guzman

    (Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia)

  • Arturo Bretas

    (Electrical Engineering Department, University of Florida, Gainesville, FL 32601, USA)

  • Julio Romero Aguero

    (Quanta Technology, Houston, TX 77056, USA)

  • Newton Bretas

    (Department of Electrical and Computer Engineering, University of Sao Paulo, São Paulo 12652, Brazil)

Abstract

Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Suggested Citation

  • Julian Garcia-Guarin & Diego Rodriguez & David Alvarez & Sergio Rivera & Camilo Cortes & Alejandra Guzman & Arturo Bretas & Julio Romero Aguero & Newton Bretas, 2019. "Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 12(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3149-:d:258134
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    References listed on IDEAS

    as
    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Arsalan Najafi & Mousa Marzband & Behnam Mohamadi-Ivatloo & Javier Contreras & Mahdi Pourakbari-Kasmaei & Matti Lehtonen & Radu Godina, 2019. "Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response," Energies, MDPI, vol. 12(8), pages 1-20, April.
    3. Marzband, Mousa & Azarinejadian, Fatemeh & Savaghebi, Mehdi & Pouresmaeil, Edris & Guerrero, Josep M. & Lightbody, Gordon, 2018. "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, Elsevier, vol. 126(C), pages 95-106.
    4. Masoumeh Javadi & Mousa Marzband & Mudathir Funsho Akorede & Radu Godina & Ameena Saad Al-Sumaiti & Edris Pouresmaeil, 2018. "A Centralized Smart Decision-Making Hierarchical Interactive Architecture for Multiple Home Microgrids in Retail Electricity Market," Energies, MDPI, vol. 11(11), pages 1-22, November.
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    Citations

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    Cited by:

    1. Muhammad Shahzad Nazir & Zhang Chu & Ahmad N. Abdalla & Hong Ki An & Sayed M. Eldin & Ahmed Sayed M. Metwally & Patrizia Bocchetta & Muhammad Sufyan Javed, 2022. "Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm," Sustainability, MDPI, vol. 14(23), pages 1-18, December.
    2. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    3. Javier Solano & Diego Jimenez & Adrian Ilinca, 2020. "A Modular Simulation Testbed for Energy Management in AC/DC Microgrids," Energies, MDPI, vol. 13(16), pages 1-23, August.
    4. Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
    5. Gianfranco Chicco & Andrea Mazza, 2020. "Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’," Energies, MDPI, vol. 13(19), pages 1-38, September.

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