Abstract
AbstractSmart metering infrastructure allows for two-way communication and power transfer. Based on this promising technology, we propose a demand-side management (DSM) scheme for a residential neighbourhood of prosumers. Its core is a discrete time dynamic game to schedule individually owned home energy storage. The system model includes an advanced battery model, local generation of renewable energy, and forecasting errors for demand and generation. We derive a closed-form solution for the best response problem of a player and construct an iterative algorithm to solve the game. Empirical analysis shows exponential convergence towards the Nash equilibrium. A comparison of a DSM scheme with a static game reveals the advantages of the dynamic game approach. We provide an extensive analysis on the influence of the forecasting error on the outcome of the game. A key result demonstrates that our approach is robust even in the worst-case scenario. This grants considerable gains for the utility company organising the DSM scheme and its participants.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Statistics and Probability,Economics and Econometrics
Reference29 articles.
1. Aghassi M, Bertsimas D (2006) Robust game theory. Math Program Ser B 107:231–273. https://doi.org/10.1007/s10107-005-0686-0
2. Bahn O, Haurie A, Malhamé R (2009) A stochastic control/game approach to the optimal timing of climate policies. In: Filar J, Haurie A (eds) Uncertainty and environmental decision making. International series in operations research & management science, vol 138. Springer, Boston, MA
3. Bichpuriya YK, Soman SA, Subramanyam A (2016) Combining forecasts in short term load forecasting: empirical analysis and identification of robust forecaster. Sadhana 41(10):1123–1133. https://doi.org/10.1007/s12046-016-0542-3
4. Celik B, Roche R, Bouquain D, Miraoui A (2017) Coordinated neighborhood energy sharing using game theory and multi-agent systems. In: 2017 IEEE Manchester PowerTech. Manchester, pp 1–6
5. Dolara A, Leva S, Manzolini G (2015) Comparison of different physical models for PV power output prediction. Solar Energy 119:83–99. https://doi.org/10.1016/j.solener.2015.06.017
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