distribution networks; active network management; dynamic access limits; renewable energy
[en] Electrical distribution systems need to integrate more and more renewable energy generation in their network. Since networks cannot be quickly upgraded at a low cost, new generators are connected to the network under non-firm access contracts. These contracts allow distribution system operators to specify dynamic access limits according to a given regulatory policy, e.g. “last-in, first-out” or a similar policy. Due to operational delays, access limits must be communicated before realtime, e.g. ten minutes ahead. This paper presents an operational
method to compute these dynamic access limits using correlated probabilistic forecasts of power consumption and production processes. The method is illustrated on a test-case based on real data where no additional production would be allowed under firm access. Results show that the method allows to safely integrate additional production capacity while limiting congestion events, provided that efficient probabilistic forecasts able to anticipate sudden and important changes are available.
Electrical & electronics engineering Computer science Energy
Author, co-author :
Mathieu, Sébastien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
Ernst, Damien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
Short-term active distribution network operation under uncertainty
Publication date :
Event name :
16th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2020)
Event place :
Event date :
from 18-08-2020 to 21-08-2020
Main work title :
Proceeedings of the16th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2020)
Q. Gemine, D. Ernst, and B. Cornelusse, "Active network management for electrical distribution systems: Problem formulation, benchmark, and approximate solution, " Optimization and Engineering, vol. 18, no. 3, pp. 587-629, 2017.
B. Cornelusse, D. Vangulick, M. Glavic, and D. Ernst, "Global capacity announcement of electrical distribution systems: A pragmatic approach, " Sustainable Energy, Grids and Networks, vol. 4, pp. 43-53, 2015.
S. Mathieu, D. Ernst, and B. Cornelusse, "Agent-based analysis of dynamic access ranges to the distribution network, " in 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), IEEE, 2016, pp. 1-6.
L. Kane and G. W. Ault, "Evaluation of wind power curtailment in active network management schemes, " IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 672-679, 2014.
R. Bessa, C. Möhrlen, V. Fundel, M. Siefert, J. Browell, S. Haglund El Gaidi, B.-M. Hodge, U. Cali, and G. Kariniotakis, "Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry, " Energies, vol. 10, no. 9, p. 1402, 2017.
T. Boehme, G. P. Harrison, and A. R. Wallace, "Assessment of distribution network limits for non-firm connection of renewable generation, " IET renewable power generation, vol. 4, no. 1, pp. 64-74, 2010.
M. Džamarija, M. Bakhtvar, and A. Keane, "Operational characteristics of non-firm wind generation in distribution networks, " in 2012 IEEE Power and Energy Society General Meeting, IEEE, 2012, pp. 1-8.
I. Bilibin and F. Capitanescu, "Contributions to thermal constraints management in radial active distribution systems, " Electric Power Systems Research, vol. 111, pp. 169-176, 2014.
S.-Y. Lin and A.-C. Lin, "RLOPF (risk-limiting optimal power flow) for systems with high penetration of wind power, " Energy, vol. 71, pp. 49-61, 2014.
J. Buire, F. Colas, J.-Y. Dieulot, and X. Guillaud, "Stochastic optimization of PQ powers at the interface between distribution and transmission grids, " Energies, vol. 12, no. 21, p. 4057, 2019.
Service public de Wallonie, Arrête du gouvernement wallon relatif à l?analyse co?t-benefice et aux modalites de calcul et de mise en oeuvre de la compensation financière
Scikit-learn: Machine learning in Python, https://scikitlearn.org/, 2020.