Verification of Demand Response: the customer baseline load in small/medium customers

  1. Antonio Gabaldon 1
  2. Ana Garcia-Garre 1
  3. Ramon Ruiz-Molina 2
  4. Carlos Alvarez 3
  5. Maria Carmen Ruiz-Abellón 1
  6. Luis Alfredo Fernandez-Jimenez 4
  7. Antonio Guillamon 1
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

  2. 2 MIWenergia, Murcia, Spain
  3. 3 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  4. 4 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

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Actas:
Proceeding of the 11th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL’22)
  1. Bertoldi, Paolo (coord.)

Editorial: Publications Office of the European Union

ISBN: 978-92-76-99908-9

Año de publicación: 2023

Páginas: 478-495

Tipo: Aportación congreso

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Resumen

The development of Demand Response (DR) is a basic step to achieve an increase of the flexibility in PowerSystems, in the short and medium, term to balance the volatility of the new generation mix foreseen in thehorizon 2020. At the same time, it is necessary to deploy tools to evaluate the performance of DR policies toobtain precise economic feedback for all the actors. This should increase the engagement of new resourcesfrom the demand-side. The verification of DR involves a right estimation of the customers’ steady-state loadwithout control: the customer baseline load (CBL). The aim of this paper is to compare the accuracy of thetraditional and simple methods based on historical data to calculate CBLs with a specific Neural Networkbased method and, with both methods test the significance of adjustment coefficients in the increase of theaccuracy or results. To develop this proposal, a demand database from a SME customer in the south east ofSpain is analysed. Results show that it is possible to improve the performance of CBLs without increasingtheir complexity, which enables the removal of some technical barriers of more complex baseline approaches

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