Achieving an optimal trade-off between revenue and energy peak within a smart grid environment

TL;DR

This paper proposes a bilevel optimization approach for energy providers to balance revenue, user costs, and peak demand in smart grids, considering monopolistic and competitive scenarios.

Contribution

It introduces a novel bilevel model that optimally balances revenue and peak load constraints in smart grid environments.

Findings

The approach effectively balances revenue, user disutilities, and peak demand.

Numerical results validate the model's applicability in different market scenarios.

Abstract

We consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. In our bilevel setting, the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its decisions on the hourly prices set by the leader, as well as the schedule preferences set by the users for each task. Considering both the monopolistic and competitive situations, we illustrate numerically the validity of the approach, which achieves an 'optimal' trade-off between three objectives: revenue, user cost, and peak demand.