A stochastic approach to integrating electrical thermal storage in distributed demand response for nordic communities with wind power generation

Domínguez-Jiménez, J., Henao, N., Agbossou, K., Parrado, A., Campillo, J. et Nagarsheth, S. H. (2023). A stochastic approach to integrating electrical thermal storage in distributed demand response for nordic communities with wind power generation. IEEE Open Journal of Industry Applications, 4 . pp. 121-138. ISSN 2644-1241 DOI 10.1109/OJIA.2023.3264651

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Résumé

Abstract

Demand response and distributed energy storage play a crucial role in improving the efficiency and reliability of electric grids. This article describes a strategy for optimally integrating distributed energy storage units within a forward market to address space heating demand under a Stackelberg game in isolated microgrids. The proposed strategy performs distributed management in an offline fashion through proximal decomposition methods. It leverages stochastic programming to consider user flexibility degree and wind power generation uncertainties. Also, flexibility for demand response is realized through electric thermal storage (ETS). The performance of the proposed strategy is evaluated via simulation studies carried out through a case study in Kuujjuaq, Quebec. Ten residential agents compose the demand side, each with flexibility levels and economic preferences. The simulation results show that adapting ETS results in economic savings for the customers. Those benefits increased in the presence of wind power, from 25% to 40% on average. Likewise, coordinated strategies led the coordinator to obtain reduced operational costs and peak-to-average ratio by over 35% and 56%, respectively. The proposed approach reveals that optimal coordination of ETS in the presence of dynamic tariffs can reduce diesel consumption, maximize renewable production and reduce grid stress.

Type de document:	Article
Mots-clés libres:	Electric thermal storage (ETS) Distributed demand response (DR) Stochastic programming Microgrids Co-simulation
Date de dépôt:	22 janv. 2026 14:48
Dernière modification:	22 janv. 2026 14:48
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12546

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