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A novel tool for the evaluation and assessment of demand response activities in the industrial sector

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  • Rodríguez-García, Javier
  • Álvarez-Bel, Carlos
  • Carbonell-Carretero, José-Francisco
  • Alcázar-Ortega, Manuel
  • Peñalvo-López, Elisa

Abstract

This paper introduces a novel tool for industrial customers to perform a cost-benefit analysis regarding the implementation of Demand Response (DR) strategies in their facilities with the final goal of softening the impact of RES intermittency in the grid. The dynamic simulation tool focuses on assessing the participation of industries in reserve energy markets in the same conditions as generators offering capacity reserve, energy reserve or both of them and taking into account all the technical restrictions of production processes as well as possible extra costs due to the implementation of DR (additional labour cost, productivity losses, etc.) Main innovations of the methodology are the DR assessment carried out per process and the introduction of the “margin of decision” as a decision making strategy for the energy consumer.

Suggested Citation

  • Rodríguez-García, Javier & Álvarez-Bel, Carlos & Carbonell-Carretero, José-Francisco & Alcázar-Ortega, Manuel & Peñalvo-López, Elisa, 2016. "A novel tool for the evaluation and assessment of demand response activities in the industrial sector," Energy, Elsevier, vol. 113(C), pages 1136-1146.
    • Handle: RePEc:eee:energy:v:113:y:2016:i:c:p:1136-1146
    DOI: 10.1016/j.energy.2016.07.146
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    References listed on IDEAS

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    1. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    2. Alcázar-Ortega, Manuel & Calpe, Carmen & Theisen, Thomas & Rodríguez-García, Javier, 2015. "Certification prerequisites for activities related to the trading of demand response resources," Energy, Elsevier, vol. 93(P1), pages 705-715.
    3. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    4. Alcázar-Ortega, Manuel & Álvarez-Bel, Carlos & Escrivá-Escrivá, Guillermo & Domijan, Alexander, 2012. "Evaluation and assessment of demand response potential applied to the meat industry," Applied Energy, Elsevier, vol. 92(C), pages 84-91.
    5. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    6. Lau, E.T. & Yang, Q. & Stokes, L. & Taylor, G.A. & Forbes, A.B. & Clarkson, P. & Wright, P.S. & Livina, V.N., 2015. "Carbon savings in the UK demand side response programmes," Applied Energy, Elsevier, vol. 159(C), pages 478-489.
    7. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
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    2. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    3. Herre, Lars & Tomasini, Federica & Paridari, Kaveh & Söder, Lennart & Nordström, Lars, 2020. "Simplified model of integrated paper mill for optimal bidding in energy and reserve markets," Applied Energy, Elsevier, vol. 279(C).
    4. Hyun, Minwoo & Kim, Yeong Jae & Eom, Jiyong, 2020. "Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment," Energy Policy, Elsevier, vol. 147(C).
    5. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    6. Schipfer, F. & Mäki, E. & Schmieder, U. & Lange, N. & Schildhauer, T. & Hennig, C. & Thrän, D., 2022. "Status of and expectations for flexible bioenergy to support resource efficiency and to accelerate the energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Jun Dong & Rong Li & Hui Huang, 2018. "Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method," Energies, MDPI, vol. 11(5), pages 1-27, April.
    8. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    9. Jun Dong & Huijuan Huo & Dongran Liu & Rong Li, 2017. "Evaluating the Comprehensive Performance of Demand Response for Commercial Customers by Applying Combination Weighting Techniques and Fuzzy VIKOR Approach," Sustainability, MDPI, vol. 9(8), pages 1-32, July.
    10. Javier Rodríguez-García & David Ribó-Pérez & Carlos Álvarez-Bel & Elisa Peñalvo-López, 2019. "Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy," Energies, MDPI, vol. 12(13), pages 1-23, July.
    11. Gómez, Javier & Chicaiza, William D. & Escaño, Juan M. & Bordons, Carlos, 2023. "A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms," Renewable Energy, Elsevier, vol. 215(C).
    12. Braeuer, Fritz & Rominger, Julian & McKenna, Russell & Fichtner, Wolf, 2019. "Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry," Applied Energy, Elsevier, vol. 239(C), pages 1424-1440.

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