Dynamic Pricing in Smart Grids under Thresholding Policies: Algorithms and Heuristics

TL;DR

This paper develops algorithms and heuristics for dynamic pricing in smart grids to reduce peak demand and better match supply and demand, addressing computational challenges with theoretical analysis and real-data experiments.

Contribution

It introduces new heuristics and optimal algorithms for dynamic pricing under threshold policies, with theoretical insights and practical evaluations.

Findings

Heuristics effectively approximate solutions for peak demand minimization.

Optimal algorithms perform well in scenarios with short task execution windows.

Experimental results validate the proposed methods on real smart grid data.

Abstract

Minimizing the peak power consumption and matching demand to supply, under fixed threshold polices, are two key requirements for the success of the future electricity market. In this work, we consider dynamic pricing methods to minimize the peak load and match demand to supply in the smart grid. As these optimization problems are computationally hard to solve in general, we propose generic heuristics for approximating their solutions. Further, we provide theoretical analysis of uniform pricing in peak-demand minimization. Moreover, we propose optimal-pricing algorithms for scenarios in which the time-period in which tasks must be executed is relatively small. Finally, we conduct several experiments to evaluate the various algorithms on real data.