Noncooperative Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization With Real-Time Adjustments: A GNEP Approach

• Published in: IEEE transactions on signal processing 2014-05, Vol.62 (9), p.2397-2412
• Main Authors: Atzeni, Italo, Ordonez, Luis G., Scutari, Gesualdo, Palomar, Daniel P., Fonollosa, Javier R.
• Format: Article
• Language: English
• Subjects: Aggregates; Applied sciences; Construction; Day-ahead/real-time demand-side management; Demand-side management (Electric utilities); Detection, estimation, filtering, equalization, prediction; Distributed memory; Electric utilities; Energy consumption; Energy management; Energy storage; Energy use; Enginyeria electrònica; Exact sciences and technology; Game theory; Generalized Nash equilibrium problem; Information, signal and communications theory; Jocs, Teoria de; Mathematical models; Optimization; Production; Proximal decomposition algorithm; Real-time systems; Signal and communications theory; Signal, noise; Smart grid; Smart grids; Smart power grids; Strategy; Telecommunications and information theory; Variational inequality; Vectors; Xarxes eléctriques; Àrees temàtiques de la UPC
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2014.2307835

The envisioned smart grid aims at improving the interaction between the supply- and the demand-side of the electricity network, creating unprecedented possibilities for optimizing the energy usage at different levels of the grid. In this paper, we propose a distributed demand-side management (DSM) method intended for smart grid users with load prediction capabilities, who possibly employ dispatchable energy generation and storage devices. These users participate in the day-ahead market and are interested in deriving the bidding, production, and storage strategies that jointly minimize their expected monetary expense. The resulting day-ahead grid optimization is formulated as a generalized Nash equilibrium problem (GNEP), which includes global constraints that couple the users' strategies. Building on the theory of variational inequalities, we study the main properties of the GNEP and devise a distributed, iterative algorithm converging to the variational solutions of the GNEP. Additionally, users can exploit the reduced uncertainty about their energy consumption and renewable generation at the time of dispatch. We thus present a complementary DSM procedure that allows them to perform some unilateral adjustments on their generation and storage strategies so as to reduce the impact of their real-time deviations with respect to the amount of energy negotiated in the day-ahead. Finally, numerical results in realistic scenarios are reported to corroborate the proposed DSM technique.