An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid

Authors

  • Sambaran Bandyopadhyay IBM Research
  • Ramasuri Narayanam IBM Research
  • Pratyush Kumar IBM Research
  • Sarvapali Ramchurn University of Southampton
  • Vijay Arya IBM Research
  • Iskandarbin Petra Universiti Brunei Darussalam

DOI:

https://doi.org/10.1609/aaai.v30i1.9900

Keywords:

Smart Grid, Ex-ante Dynamic Pricing, Axiomatic Framework, Impossibility Theorem, Demand Response

Abstract

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.

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Published

2016-03-05

How to Cite

Bandyopadhyay, S., Narayanam, R., Kumar, P., Ramchurn, S., Arya, V., & Petra, I. (2016). An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9900

Issue

Section

Special Track: Computational Sustainability