Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime
| Autor: | Anton V. Prokhorov, Remy Rigo-Mariani, Ildar Daminov, Marie-Cecile Alvarez-Herault, Raphael Caire |
|---|---|
| Přispěvatelé: | School of Energy and Power Engineering Tomsk Polytechnic University, Laboratoire de Génie Electrique de Grenoble (G2ELab ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Optimization problem hosting capacity Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology 7. Clean energy law.invention динамическая оценка Demand response Linearization law Control theory 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Transformer Engineering (miscellaneous) ComputingMilieux_MISCELLANEOUS dynamic thermal rating Transformer (machine learning model) lcsh:T Renewable Energy Sustainability and the Environment [SPI.NRJ]Engineering Sciences [physics]/Electric power 020208 electrical & electronic engineering Mode (statistics) Demand Response Grid flexibility Variable (computer science) трансформаторы transformer Energy (miscellaneous) |
| Zdroj: | Energies Volume 14 Issue 5 Pages: 1378 Energies, Vol 14, Iss 1378, p 1378 (2021) Energies, MDPI, 2021, 14 (5), pp.1378. ⟨10.3390/en14051378⟩ |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en14051378 |
| Popis: | (1) Background: This paper proposes a strategy coupling Demand Response Program with Dynamic Thermal Rating to ensure a transformer reserve for the load connection. This solution is an alternative to expensive grid reinforcements. (2) Methods: The proposed methodology firstly considers the N-1 mode under strict assumptions on load and ambient temperature and then identifies critical periods of the year when transformer constraints are violated. For each critical period, the integrated management/sizing problem is solved in YALMIP to find the minimal Demand Response needed to ensure a load connection. However, due to the nonlinear thermal model of transformers, the optimization problem becomes intractable at long periods. To overcome this problem, a validated piece-wise linearization is applied here. (3) Results: It is possible to increase reserve margins significantly compared to conventional approaches. These high reserve margins could be achieved for relatively small Demand Response volumes. For instance, a reserve margin of 75% (of transformer nominal rating) can be ensured if only 1% of the annual energy is curtailed. Moreover, the maximal amplitude of Demand Response (in kW) should be activated only 2–3 h during a year. (4) Conclusions: Improvements for combining Demand Response with Dynamic Thermal Rating are suggested. Results could be used to develop consumer connection agreements with variable network access. |
| Databáze: | OpenAIRE |
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