MFG model with a long-lived penalty at random jump times: application to demand side management for electricity contracts

TL;DR

This paper models a demand side management system as a stochastic game with random jump penalties, deriving a mean-field game solution with explicit formulas, and demonstrating its effectiveness through numerical experiments.

Contribution

It introduces a novel MFG framework with jump penalties for DSM, providing a semi-explicit solution and an approximation for large player systems.

Findings

Derived a stochastic maximum principle for MFG with jumps.

Obtained a semi-explicit solution via Riccati BDSDEs.

Numerical experiments validate the approach.

Abstract

We consider an energy system with $n$ consumers who are linked by a Demand Side Management (DSM) contract, i.e. they agreed to diminish, at random times, their aggregated power consumption by a predefined volume during a predefined duration. Their failure to deliver the service is penalised via the difference between the sum of the $n$ power consumptions and the contracted target. We are led to analyse a non-zero sum stochastic game with $n$ players, where the interaction takes place through a cost which involves a delay induced by the duration included in the DSM contract. When $n \to \infty$, we obtain a Mean-Field Game (MFG) with random jump time penalty and interaction on the control. We prove a stochastic maximum principle in this context, which allows to compare the MFG solution to the optimal strategy of a central planner. In a linear quadratic setting we obtain an semi-explicit solution through a system of decoupled forward-backward stochastic differential equations with jumps, involving a Riccati Backward SDE with jumps. We show that it provides an approximate Nash equilibrium for the original $n$-player game for $n$ large. Finally, we propose a numerical algorithm to compute the MFG equilibrium and present several numerical experiments.