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Efficient Buyer Groups With Prediction-of-Use Electricity Tariffs

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Sep 2018 United Kingdom, France Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Smart Grid, volume 9, pages 4,468-4,479 (issn: 1949-3053, eissn: 1949-3061,

Authors: Valentin Robu; Meritxell Vinyals; Alex Rogers; Nicholas R. Jennings;

doi: 10.1109/tsg.2017.2660580

handle: 10044/1/53401

Efficient Buyer Groups With Prediction-of-Use Electricity Tariffs

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Abstract

Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use (POU) tariff that better reflects the predictability cost of a customer. Our tariff asks customers to pre-commit to a baseline consumption, and charges them based on both their actual consumption and the deviation from the anticipated baseline. First, we study, from a cooperative game theory perspective, the cost game induced by a single such tariff, and show customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. Second, we study the efficient (i.e., cost-minimizing) structure of buying groups for the more realistic setting when multiple , competing POU tariffs are available. We propose a polynomial time algorithm to compute the efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic consumers in the U.K.

Countries

United Kingdom, France

Related Organizations

University of Oxford
United Kingdom
Heriot-Watt University
United Kingdom
CEA LETI
France
Heriot-Watt University
United Kingdom
Imperial College London
United Kingdom

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Keywords

[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], Technology, 330, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, [QFIN.PM]Quantitative Finance [q-fin]/Portfolio Management [q-fin.PM], DEMAND, CONSUMERS, GAME, 0915 Interdisciplinary Engineering, [QFIN.PM] Quantitative Finance [q-fin]/Portfolio Management [q-fin.PM], MARKETS, [SHS.INFO] Humanities and Social Sciences/Library and information sciences, CONGESTION, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, Engineering, RETAILERS, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], MANAGEMENT, Heuristic algorithms, [QFIN.TR]Quantitative Finance [q-fin]/Trading and Market Microstructure [q-fin.TR], Forward contracts, Game theory, Electricity tariffs, collective switching, Science & Technology, [SPI.NRJ]Engineering Sciences [physics]/Electric power, 0906 Electrical And Electronic Engineering, [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing, [QFIN.TR] Quantitative Finance [q-fin]/Trading and Market Microstructure [q-fin.TR], Smart grids, Engineering, Electrical & Electronic, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, demand forecasting, coalition formation, Electrical & Electronic, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Games, Switches, cooperative game theory, Pricing, [SPI.NRJ] Engineering Sciences [physics]/Electric power

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	25
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%