scholarly journals Hygro-thermal model for estimation of demand response flexibility of closed refrigerated display cabinets

2020 ◽  
pp. 116381
Author(s):  
Tommie Månsson ◽  
Angela Sasic Kalagasidis ◽  
York Ostermeyer
2016 ◽  
Vol 128 ◽  
pp. 56-67 ◽  
Author(s):  
Fabiano Pallonetto ◽  
Simeon Oxizidis ◽  
Federico Milano ◽  
Donal Finn

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1378
Author(s):  
Ildar Daminov ◽  
Rémy Rigo-Mariani ◽  
Raphael Caire ◽  
Anton Prokhorov ◽  
Marie-Cécile Alvarez-Hérault

(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.


2015 ◽  
Vol 155 ◽  
pp. 79-90 ◽  
Author(s):  
R. D’hulst ◽  
W. Labeeuw ◽  
B. Beusen ◽  
S. Claessens ◽  
G. Deconinck ◽  
...  

2018 ◽  
Vol 9 (4) ◽  
pp. 3616-3627 ◽  
Author(s):  
Ke Wang ◽  
Rongxin Yin ◽  
Liangzhong Yao ◽  
Jianguo Yao ◽  
Taiyou Yong ◽  
...  

2021 ◽  
Vol 13 (5) ◽  
pp. 2954
Author(s):  
Rostislav Krč ◽  
Martina Kratochvílová ◽  
Jan Podroužek ◽  
Tomáš Apeltauer ◽  
Václav Stupka ◽  
...  

As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.


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