Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and Privacy-Preserving Strategy

TL;DR

This paper develops a decentralized, privacy-preserving dynamic pricing strategy for demand-side management in smart grids, effectively balancing demand control, fairness, and computational efficiency using a Markov chain model.

Contribution

It introduces a novel Markov chain-based approach for optimal pricing that ensures fairness, privacy, and linear computational complexity in demand management.

Findings

Strategy maintains demand close to target levels.

Computational complexity scales linearly with users.

Numerical example demonstrates effectiveness for 100 users.

Abstract

Motivated by demand-side management in smart grids, a decentralized controlled Markov chain formulation is proposed to model a homogeneous population of users with binary demands (i.e., off or on). The binary demands often arise in scheduling applications such as plug-in hybrid vehicles. Normally, an independent service operator (ISO) has a finite number of options when it comes to providing the users with electricity. The options represent various incentive means, generation resources, and price profiles. The objective of the ISO is to find optimal options in order to keep the distribution of demands close to a desired level (which varies with time, in general) by imposing the minimum price on the users. A Bellman equation is developed here to identify the globally team-optimal strategy. The proposed strategy is fair for all users and also protects the privacy of users. Moreover, its computational complexity increases linearly (rather than exponentially) with the number of users. A numerical example with 100 users is presented for peak-load management.