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Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

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  • Antonio Gabaldón

    (Department of Electrical Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

  • Carlos Álvarez

    (Institute for Energy Engineering, Universidad Politécnica de Valencia, 46022 Valencia, Spain)

  • María Del Carmen Ruiz-Abellón

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

  • Antonio Guillamón

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

  • Sergio Valero-Verdú

    (Department of Mechanics and Energy Engineering, Universidad Miguel Hernández de Elche, 03202 Elche, Spain)

  • Roque Molina

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

  • Ana García-Garre

    (Department of Electrical Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

Abstract

The objectives of improving the efficiency, and integration, of renewable sources by 2030–2050 are complex in practice and should be linked to an increase of demand-side flexibility. The main challenges to achieving this flexibility are the lack of incentives and an adequate framework. For instance, customers’ revenue is usually low, the volatility of prices is high and there is not any practical feedback to customers from smart meters. The possibility of increasing customer revenue could reduce the uncertainty with respect to economic concerns, improving investments in efficiency, enabling technology and thus, engaging more customers in these policies. This objective could be achieved by the participation of customers in several markets. Moreover, Demand Response and Energy Efficiency can share ICT technologies but this participation needs to perform an aggregation of demand. The idea of this paper is to present some methodologies for facilitating the definition and evaluation of energy versus cost curves; and subsequently to estimate potential revenues due to Demand Response. This can be accomplished by models that estimate: demand and energy aggregation; economic opportunities and benefits; impacts on customer convenience; customer feedback and price analysis. By doing so, we would have comprehensive information that can help customers and aggregators to define energy packages and their monetary value with the objective of fostering their market participation.

Suggested Citation

  • Antonio Gabaldón & Carlos Álvarez & María Del Carmen Ruiz-Abellón & Antonio Guillamón & Sergio Valero-Verdú & Roque Molina & Ana García-Garre, 2018. "Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response," Sustainability, MDPI, vol. 10(2), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:483-:d:131442
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    1. Agustin J. Ros, 2017. "An Econometric Assessment of Electricity Demand in the United States Using Utility-specific Panel Data and the Impact of Retail Competition on Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    2. Eva Heiskanen & Kaarina Hyvönen & Senja Laakso & Päivi Laitila & Kaisa Matschoss & Irmeli Mikkonen, 2017. "Adoption and Use of Low-Carbon Technologies: Lessons from 100 Finnish Pilot Studies, Field Experiments and Demonstrations," Sustainability, MDPI, vol. 9(5), pages 1-20, May.
    3. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    4. Ellegård, Kajsa & Palm, Jenny, 2011. "Visualizing energy consumption activities as a tool for making everyday life more sustainable," Applied Energy, Elsevier, vol. 88(5), pages 1920-1926, May.
    5. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2012. "Estimation of elasticity price of electricity with incomplete information," Energy Economics, Elsevier, vol. 34(3), pages 627-633.
    6. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    7. Gomes, A. & Antunes, C. Henggeler & Martinho, J., 2013. "A physically-based model for simulating inverter type air conditioners/heat pumps," Energy, Elsevier, vol. 50(C), pages 110-119.
    8. Ana Medina & Ángeles Cámara & José-Ramón Monrobel, 2016. "Measuring the Socioeconomic and Environmental Effects of Energy Efficiency Investments for a More Sustainable Spanish Economy," Sustainability, MDPI, vol. 8(10), pages 1-21, October.
    9. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    10. Kris Kessels & Carolien Kraan & Ludwig Karg & Simone Maggiore & Pieter Valkering & Erik Laes, 2016. "Fostering Residential Demand Response through Dynamic Pricing Schemes: A Behavioural Review of Smart Grid Pilots in Europe," Sustainability, MDPI, vol. 8(9), pages 1-21, September.
    11. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    12. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    Cited by:

    1. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    2. Zhuang Zheng & Hainan Chen & Xiaowei Luo, 2018. "A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    3. Lim, Keumju & Lee, Jongsu & Lee, Hyunjoo, 2021. "Implementing automated residential demand response in South Korea: Consumer preferences and market potential," Utilities Policy, Elsevier, vol. 70(C).
    4. Ana García-Garre & Antonio Gabaldón & Carlos Álvarez-Bel & María Del Carmen Ruiz-Abellón & Antonio Guillamón, 2018. "Integration of Demand Response and Photovoltaic Resources in Residential Segments," Sustainability, MDPI, vol. 10(9), pages 1-31, August.

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