2023/07/14 by David Pérez-Piñeiro, Pérez-Piñeiro, David, Sigurd Skogestad +3
Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #FOS: Mathematics #Integrated Energy Systems Optimization #Optimization and Control (math.OC) #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2307.07580
openalex publication_date 2023/07/14 · openalex created_date 2023/07/19 · openalex updated_date 2026/07/28
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.