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


We consider an energy provider who minimizes peak through day-ahead pricing.

We propose a bilevel framework to combine customer choice with provider’s problem.

Monopolist and competitive scenarios are discussed.

A smoother load curve is obtained without causing inconvenience to customers.

An optimal trade-off between peak cost, revenue and user cost is achieved.


In this paper, we consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. The problem is cast within a bilevel setting where 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 actions on the hourly prices set by the leader, as well as the preference schedules set by the users for each task. We consider both the monopolistic and competitive situations, and validate numerically the potential of this approach to achieve an ‘optimal’ trade-off between three conflicting objectives: revenue, user cost and peak demand.


  • Demand response;
  • Smart grid;
  • Day-ahead pricing;
  • Demand side management;
  • Bilevel programming;
  • Peak minimization

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