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Contract design of direct-load control programs and their optimal management by genetic algorithm

Author

Listed:
  • Lujano-Rojas, Juan M.
  • Zubi, Ghassan
  • Dufo-López, Rodolfo
  • Bernal-Agustín, José L.
  • García-Paricio, Eduardo
  • Catalão, João P.S.

Abstract

A computational model for designing direct-load control (DLC) demand response (DR) contracts is presented in this paper. The critical and controllable loads are identified in each node of the distribution system (DS). Critical loads have to be supplied as demanded by users, while the controllable loads can be connected during a determined time interval. The time interval at which each controllable load can be supplied is determined by means of a contract or compromise established between the utility operator and the corresponding consumers of each node of the DS. This approach allows us to reduce the negative impact of the DLC program on consumers’ lifestyles. Using daily forecasting of wind speed and power, solar radiation and temperature, the optimal allocation of DR resources is determined by solving an optimization problem through a genetic algorithm where the energy content of conventional power generation and battery discharging energy are minimized. The proposed approach was illustrated by analyzing a system located in the Virgin Islands. Capabilities and characteristics of the proposed method in daily and annual terms are fully discussed, as well as the influence of forecasting errors.

Suggested Citation

  • Lujano-Rojas, Juan M. & Zubi, Ghassan & Dufo-López, Rodolfo & Bernal-Agustín, José L. & García-Paricio, Eduardo & Catalão, João P.S., 2019. "Contract design of direct-load control programs and their optimal management by genetic algorithm," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314793
    DOI: 10.1016/j.energy.2019.07.137
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    Citations

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    Cited by:

    1. Groppi, Daniele & Pfeifer, Antun & Garcia, Davide Astiaso & Krajačić, Goran & Duić, Neven, 2021. "A review on energy storage and demand side management solutions in smart energy islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    3. El-Sayed, Wael T. & El-Saadany, Ehab F. & Zeineldin, Hatem H. & Al-Sumaiti, Ameena S., 2020. "Fast initialization methods for the nonconvex economic dispatch problem," Energy, Elsevier, vol. 201(C).
    4. Ruben Barreto & Calvin Gonçalves & Luis Gomes & Pedro Faria & Zita Vale, 2022. "Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response," Energies, MDPI, vol. 15(7), pages 1-18, March.
    5. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    6. Zayed, Mohamed E. & Zhao, Jun & Li, Wenjia & Elsheikh, Ammar H. & Elaziz, Mohamed Abd, 2021. "A hybrid adaptive neuro-fuzzy inference system integrated with equilibrium optimizer algorithm for predicting the energetic performance of solar dish collector," Energy, Elsevier, vol. 235(C).

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