Co-optimizing Consumption and EV Charging under Net Energy Metering

TL;DR

This paper develops a stochastic dynamic programming approach to optimize household energy consumption, EV charging, and distributed energy resources under net metering, revealing a threshold policy that minimizes electricity costs.

Contribution

It introduces a novel co-optimization framework with a procrastination threshold policy, simplifying complex dynamic decisions into closed-form solutions.

Findings

Threshold policy effectively delays EV charging to reduce costs

Co-optimization yields significant energy cost savings

Decoupled decision rules simplify implementation

Abstract

We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind-the-meter distributed energy resources under the net energy metering tariff. Using a stochastic dynamic programming formulation, we show that the solution to the dynamic programming co-optimization is a procrastination threshold policy that delays and minimizes electricity purchasing for EV charging in each time interval. The policy thresholds can be computed off-line, simplifying the continuous action space dynamic optimization to decoupled closed-form charging and consumption decisions. Empirical studies using renewable, consumption, and EV data demonstrate the benefits of co-optimization.