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An Incentive-Based Implementation of Demand Side Management in Power Systems

Author

Listed:
  • Vasileios M. Laitsos

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Dimitrios Bargiotas

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Aspassia Daskalopulu

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Athanasios Ioannis Arvanitidis

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Lefteri H. Tsoukalas

    (School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, USA)

Abstract

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES.

Suggested Citation

  • Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7994-:d:691648
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    Cited by:

    1. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    2. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    3. Vasileios Laitsos & Georgios Vontzos & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2023. "Enhanced Automated Deep Learning Application for Short-Term Load Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-21, June.

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