Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Mauro Jurado (), Eduardo Salazar, Mauricio Samper, Rodolfo Rosés and Diego Ojeda Esteybar
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Mauro Jurado: Institute of Electrical Energy (IEE), National University of San Juan—National Scientific and Technical Research Council (CONICET), San Juan 5400, Argentina
Eduardo Salazar: Institute of Electrical Energy (IEE), National University of San Juan—National Scientific and Technical Research Council (CONICET), San Juan 5400, Argentina
Mauricio Samper: Institute of Electrical Energy (IEE), National University of San Juan—National Scientific and Technical Research Council (CONICET), San Juan 5400, Argentina
Rodolfo Rosés: Institute of Electrical Energy (IEE), National University of San Juan—National Scientific and Technical Research Council (CONICET), San Juan 5400, Argentina
Diego Ojeda Esteybar: Institute of Electrical Energy (IEE), National University of San Juan—National Scientific and Technical Research Council (CONICET), San Juan 5400, Argentina

Energies, 2023, vol. 16, issue 20, 1-20

Abstract: Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

Keywords: active distribution systems; demand response; Volt/Var control; genetic algorithm; dynamic programming; forecasting; behind-the-meter; deep learning (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: 2023
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