Home Battery Dispatch under a Tiered Peak Power Tariff

TL;DR

This paper develops a model predictive control policy for home batteries to minimize electricity costs under tiered peak power tariffs, achieving near-optimal savings with simple forecasts.

Contribution

It introduces an MPC approach that uses simple load and price forecasts to effectively minimize costs under tiered peak tariffs, closely approaching the ideal prescient solution.

Findings

MPC policy achieves within 1.7% of the prescient cost bound.

The policy saves nearly three times more than rule-based policies.

Numerical experiments are based on one year of real data from Norway.

Abstract

We consider the problem of operating a battery in a home connected to the grid to minimize electricity cost, which combines an energy charge and a tiered peak power charge based on the average of the $N$ largest daily peak powers in each billing month. With perfect foresight of loads and prices, the minimum cost is the solution of a mixed-integer linear program (MILP), which provides a lower bound on the cost of any implementable policy. We propose a model predictive control (MPC) policy that uses simple forecasts of loads and prices and solves a small MILP at each time step. Numerical experiments on one year of data from a home in Trondheim, Norway, show that the MPC policy attains a cost within $1.7\%$ of the prescient bound, and saves close to three times as much as the best rule-based policy we consider.