Abstract
In the new paradigm of smart grids, load flexibility remuneration strategies play a key role in defining innovative business models and market frameworks using distributed energy resources and demand response. This article establishes a set of use cases used to support the application of remuneration strategies considering contextual load flexibility. In other words, the various new tariffs and remuneration plans consider the context provided by solar and wind power, different times of the day and week, temperature, and electricity market prices. Also, depending on the circumstances, dynamically specified consumer clusters are envisaged for a realistic validation of results while also analysing higher smart grid efficiency, decreased pollutant emissions, and cheaper energy prices. The proposed use cases are proposed in the context of COLORS (COntextual LOad flexibility Remuneration Strategies), a project investigating business models and market frameworks with a particular emphasis on demand response.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antonopoulos I et al (2020) Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review. Renew Sustain Energy Rev 130:109899
Elma O, Selamogullari US (2017) An overview of demand response applications under smart grid concept. In: 2017 4th international conference on electrical and electronic engineering (ICEEE), pp 104–107
Faria P, Vale Z (2022) Application of distinct demand response program during the ramping and sustained response period. Energy Rep 8:411–416
Faria P, Lezama F, Vale Z, Khorram M (2021) A methodology for energy key performance indicators analysis. Energy Inform 4(1):6
Ghadi MJ, Rajabi A, Ghavidel S, Azizivahed A, Li L, Zhang J (2019) From active distribution systems to decentralized microgrids: a review on regulations and planning approaches based on operational factors. Appl Energy 253:113543
Groppi D, Pfeifer A, Garcia DA, Krajačić G, Duić N (2021) A review on energy storage and demand side management solutions in smart energy islands. Renew Sustain Energy Rev 135:110183
Lezama F, Pinto T, Vale Z, Santos G, Widergren S (2021) From the smart grid to the local electricity market. Local electricity markets. Elsevier, pp 63–76
Limmer S, Lezama F, Soares J, Vale Z (2021) Coordination of home appliances for demand response: an improved optimization model and approach. IEEE Access 9:146183–146194
Mengelkamp E, Bose S, Kremers E, Eberbach J, Hoffmann B, Weinhardt C (2018) Increasing the efficiency of local energy markets through residential demand response. Energy Inform 1(1):11
O'Connell N, Pinson P, Madsen H, O’Malley M (2014) Benefits and challenges of electrical demand response: a critical review. Renew Sustain Energy Rev 39:686–699
Siano P, Sarno D (2016) Assessing the benefits of residential demand response in a real time distribution energy market. Appl Energy 161:533–551
Acknowledgements
The present work has received funding from European Regional Development Fund through COMPETE 2020—Operational Programme for Competitiveness and Internationalisation through the P2020 Project TIoCPS (ANI|P2020 POCI-01-0247-FEDER-046182) and has been developed under the EUREKA—ITEA3 Project TIoCPS (ITEA-18008), we also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
de São José, D., Lezama, F., Faria, P., Vale, Z. (2023). Use Cases for Contextual Load Flexibility Remuneration Strategies. In: Caetano, N.S., Felgueiras, M.C. (eds) The 9th International Conference on Energy and Environment Research. ICEER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-43559-1_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-43559-1_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43558-4
Online ISBN: 978-3-031-43559-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)