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

    Universidad Politécnica de Cartagena

    Cartagena, España


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

    Universidad Politécnica de Valencia

    Valencia, España


  4. 4 Universidad de La Rioja

    Universidad de La Rioja

    Logroño, España


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

Éditorial: Publications Office of the European Union

ISBN: 978-92-76-99908-9

Année de publication: 2023

Pages: 478-495

Type: Communication dans un congrès


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

Références bibliographiques

  • [1] European Commission, “Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity and amending Directive 2012/27/EU.” [Online]. Available: [Accessed: 13-Jan-2020].
  • [2] N. Good, K. A. Ellis, and P. Mancarella, “Review and classification of barriers and enablers of demand response in the smart grid,” Renew. Sustain. Energy Rev., vol. 72, May 2017.
  • [3] NYISO, “Distributed Energy Resources Market Design Concept Proposal,” 2017. [Online]. Available: [Accessed: 28-Dec-2021].
  • [4] J. Mcanany, “2021 Demand Response Operations Markets Activity Report: December 2021,” 2021.
  • [5] RTE (France), “Règles pour la valorisation des effacements de consommation sur les marchés de l’énergie NEBEF 3.1,” 2018.
  • [6] C. Lake, “PJM Empirical Analysis of Demand Response Baseline Methods.,” 2011. [Online]. Available: [Accessed: 15-Jan-2020].
  • [7] California ISO, “Baseline Accuracy Work Group Proposal,” 2017. [Online]. Available: baseline. [Accessed: 13-Jan-2020].
  • [8] PJM, “Demand Response in PJM Markets and Operations.” [Online]. Available: [Accessed: 13-Jan-2020].
  • [9] K. Coughlin, M. A. Piette, C. Goldman, and S. Kiliccote, “Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings in California Environmental Energy Technologies Division,” Berkeley, 2008.
  • [10] P. Bertoldi, P. Zancanella, and B. Boza-Kiss, “Demand Response status in EU Member States,” 2016.
  • [11] N. Dawood, “Short-Term Prediction of Energy Consumption in Demand Response for Blocks of Buildings: DR-BoB Approach,” Build. 2019, Vol. 9, Page 221, vol. 9, no. 10, Oct. 2019.
  • [12] “DRIMPAC H2020 project – Unified DR interoperability framework enabling market participation of active energy consumers.” [Online]. Available: [Accessed: 28-Dec-2021].
  • [13] EnerNOC, “The Demand Response Baseline. White Paper,” 2009.
  • [14] L. Willoughby, J. Bode, M. St, and S. Francisco, “2012 San Diego Gas & Electric Peak Time Rebate Baseline Evaluation Prepared for : San Diego Gas & Electric Prepared by : The FSC Group Table of Contents,” 2013.
  • [15] conEdison, “Advanced Metering Infrastructure Business Plan,” 2015. [Online]. Available: [Accessed: 20-Jan-2020].
  • [16] Australia Renewable Energy Agency (ARENA), “Baselining the ARENA-AEMO Demand Response RERT Trial (September 2019),” 2019.
  • [17] N. Rossetto, “Measuring the Intangible: An Overview of the Methodologies for Calculating Customer Baseline Load in PJM,” Florence School of Regulation, no. 2018/5, pp. 1–10, 2018.
  • [18] FERC, “Demand Response Compensation in Organized Wholesale Energy Markets (Final Rule), Order 745, June 2011.” [Online]. Available: [Accessed: 13-Jan-2020].
  • [19] M. L. Goldberg and G. Kennedy Agnew, “Measurement and Verification for Demand Response Prepared for the National Forum on the National Action Plan on Demand Response: Measurement and Verification Working Group,” 2013.
  • [20] ISO/RTO Council, “North American Wholesale Electricity Demand Response Program Comparison,” 2018. [Online]. Available: [Accessed: 13-Jan-2020].
  • [21] F. Pallonetto, M. De Rosa, F. D’Ettorre, and D. P. Finn, “On the assessment and control optimisation of demand response programs in residential buildings,” Renew. Sustain. Energy Rev., vol. 127, p. 109861, Jul. 2020.
  • [22] S. Mohajeryami, M. Doostan, and P. Schwarz, “The impact of Customer Baseline Load (CBL) calculation methods on Peak Time Rebate program offered to residential customers,” Electr. Power Syst. Res., vol. 137, pp. 59–65, Aug. 2016.
  • [23] Y. Weng, J. Yu, and R. Rajagopal, “Probabilistic baseline estimation based on load patterns for better residential customer rewards,” Int. J. Electr. Power Energy Syst., vol. 100, pp. 508–516, Sep. 2018.
  • [24] C. Alvarez et al., “Methodologies and proposals to facilitate the integration of small and medium consumers in smart grids,” in CIRED - Open Access Proceedings Journal, 2017, vol. 2017, no. 1, pp. 1895–1898.
  • [25] A. Gabaldon, “REDYD 2050 Research Network on Distributed Energy Resources web page.” [Online]. Available: [Accessed: 25-Jan-2020].
  • [26] E. Lee, D. Jang, and J. Kim, “A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response,” Energies, vol. 11, no. 12, p. 3417, Dec. 2018.
  • [27] S. Pati, S. J. Ranade, and O. Lavrova, “Methodologies for customer baseline load estimation and their implications,” 2020 IEEE Texas Power Energy Conf. TPEC 2020, Feb. 2020.
  • [28] H. Jiang, Y. Zhang, E. Muljadi, J. J. Zhang, and D. W. Gao, “A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization,” IEEE Trans. Smart Grid, vol. 9, no. 4, Jul. 2018
  • [29] D. Niu, Y. Wang, and D. D. Wu, “Power load forecasting using support vector machine and ant colony optimization,” Expert Syst. Appl., vol. 37, no. 3, pp. 2531–2539, Mar. 2010.
  • [30] P. F. Pai and W. C. Hong, “Support vector machines with simulated annealing algorithms in electricity load forecasting,” Energy Convers. Manag., vol. 46, no. 17, pp. 2669–88, Oct. 2005.
  • [31] M. del C. Ruiz-Abellón, A. Gabaldón, and A. Guillamón, “Load forecasting for a campus university using ensemble methods based on regression trees,” Energies, vol. 11, no. 8, p. 2038, Aug. 2018.
  • [32] H. Nie, G. Liu, X. Liu, and Y. Wang, “Hybrid of ARIMA and SVMs for short-term load forecasting,” in Energy Procedia, 2012, vol. 16, no. PART C, pp. 1455–1460.
  • [33] S. Karthika, V. Margaret, and K. Balaraman, “Hybrid short term load forecasting using ARIMA-SVM,” in 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017, 2017, vol. 2017-January, pp. 1–7.
  • [34] P. Ray, S. Sen, and A. K. Barisal, “Hybrid methodology for short-Term load forecasting,” in 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2014, 2014.
  • [35] K. Li, F. Wang, Z. Mi, M. Fotuhi-Firuzabad, N. Duić, and T. Wang, “Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation,” Appl. Energy, vol. 253, p. 113595, Nov. 2019.
  • [36] A. Gabaldón et al., “Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response,” Sustainability, vol. 10, no. 2, p. 483, Feb. 2018.
  • [37] M. H. Shoreh, P. Siano, M. Shafie-khah, V. Loia, and J. P. S. Catalão, “A survey of industrial applications of Demand Response,” Electr. Power Syst. Res., vol. 141, pp. 31–49, Dec. 2016.
  • [38] C. Dinesh, S. Welikala, Y. Liyanage, M. P. B. Ekanayake, R. I. Godaliyadda, and J. Ekanayake, “Non-intrusive load monitoring under residential solar power influx,” Appl. Energy, vol. 205, pp. 1068–1080, Nov. 2017.
  • [39] E. G. Cazalet, M. Kohanim, and O. Hasidim, “Complete and Low-Cost Retail Automated Transactive Energy System (RATES) , California Energy Commission,” 2020.
  • [40] A. Gabaldón, A. García-Garre, M. C. Ruiz-Abellón, A. Guillamón, C. Álvarez-Bel, and L. A. Fernandez-Jimenez, “Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models,” Util. Policy, vol. 70, p. 101213, Jun. 2021.
  • [41] NAESB, “Business Practices for Measurement and Verification of Wholesale Electricity Demand Response,” 2009.
  • [42] S. Lei, J. Mathieu, and R. Jain, “Performance of existing Baseline Models in quantifying the effects of Short-Term Load Shifting of campus buildings,” SLAC-R-11, 2019.
  • [43] S. Lee, “Comparing Methods for Customer Baseline Load Estimation for Residential Demand Response in South Korea and France: Predictive Power and Policy Implications Chaire European Electricity Markets,” Work. Pap. 39.
  • [44] F. Wang, K. Li, C. Liu, Z. Mi, M. Shafie-Khah, and J. P. S. Catalao, “Synchronous pattern matching principle-based residential demand response baseline estimation: Mechanism analysis and approach description,” IEEE Trans. Smart Grid, vol. 9, no. 6, Nov. 2018.
  • [45] C. Yang, Q. Xu, and X. Wang, “Strategy of constructing virtual peaking unit by public buildings’ central air conditioning loads for day-ahead power dispatching,” J. Mod. Power Syst. Clean Energy, vol. 5, no. 2, pp. 187–201, Mar. 2017.
  • [46] L. M. Saini and M. K. Soni, “Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods,” IEE Proc. Gener. Transm. Distrib., vol. 149, no. 5, pp. 578–584, Sep. 2002.
  • [47] L. M. Saini, “Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks,” Electr. Power Syst. Res., vol. 78, no. 7, pp. 1302–1310, Jul. 2008.
  • [48] L. M. Saini and M. K. Soni, “Artificial neural network-based peak load forecasting using conjugate gradient methods,” IEEE Trans. Power Syst., vol. 17, no. 3, Aug. 2002.
  • [49] NYISO, “Emergency Demand Response Program Manual.,” 2019. [Online]. Available: [Accessed: 23-Jan-2020].