Multiperiod Stochastic Peak Shaving Using Storage

TL;DR

This paper develops an online stochastic model predictive control framework for demand charge management in grid-connected consumers with energy storage, accounting for stochastic demand and inflow over multiple periods.

Contribution

It introduces a novel weighting scheme for multi-period optimization and proposes a policy based on causal affine decision rules for demand charge management.

Findings

The proposed policy effectively manages demand charges in a real building setting.

The weighting scheme improves optimization over multiple demand charge periods.

Simulation results demonstrate the approach's practical viability.

Abstract

We present an online stochastic model predictive control framework for demand charge management for a grid-connected consumer with attached electrical energy storage. The consumer we consider must satisfy an inflexible but stochastic electricity demand, and also receives a stochastic electricity inflow. The optimization problem formulated solves a stochastic cost minimization problem, with given weather forecast scenarios converted into forecast demand and inflow. We introduce a novel weighting scheme to account for cases where the optimization horizon spans multiple demand charge periods. The optimization scheme is tested in a setting with building demand and photovoltaic array inflow data from a real office building. The simulation study allows us to compare various design and modeling alternatives, ultimately proposing a policy based on causal affine decision rules.