Data-driven Dynamic Programming: a Peak Shaving Application

Abstract

The rising number of electricity consumers poses a challenge to power generators and grid operators in maintaining a balanced grid. Peak shaving is a technique that consists of shifting electricity consumption from hours of high demand to times of low demand, and has been gaining popularity in recent years. It allows to reduce peak loads which must be met at all times, which in turn reduces the need to resort to less efficient and more expensive power plants to see that all peaks are met. This paper proposes two methods to operate a battery energy storage system (BESS) for peak shaving of daily load profiles, based on the dynamic programming (DP) algorithm. The first approach treats historical profiles as deterministic paths and solves the DP algorithm for each profile. Then, an operating plan is obtained in real-time by combining the policies calculated for each path. The second approach treats data as stochastic paths, from which it derives a probabilistic model for the target path. Given this model, the DP algorithm obtains an operating plan for the entire day ahead. We also propose three modifications that can be applied to the two methods which aim at the improving the reliability of the methods. The performance of the proposed methods is examined through numerical experiments on both artificial and real data.