Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups

TL;DR

This paper introduces a robust energy storage scheduling method combined with demand aggregation using Bayesian techniques to reduce imbalance costs in energy markets, validated on real demand data from Tokyo.

Contribution

It presents a novel demand aggregation strategy using Bayesian MCMC and a robust online storage scheduling method considering demand uncertainty, improving imbalance reduction.

Findings

Effective demand aggregation increases demand predictability.

The proposed scheduling reduces imbalance energy and costs.

Validated on real-world data from Tokyo apartment buildings.

Abstract

Imbalance (on-line energy gap between contracted supply and actual demand, and associated cost) reduction is going to be a crucial service for a Power Producer and Supplier (PPS) in the deregulated energy market. PPS requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy. This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise aggregated demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance for a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a Probabilistic Programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical demand statistics. Apart from the group formation, the aggregation strategy (with the help of Bayesian Inference) also clears out the upper-limit of the required storage capacity for a formed group, fraction of which is to be utilized in on-line operation. For on-line operation, a robust energy storage scheduling method is proposed that minimizes expected imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo, Japan. Simulation results are presented to verify the effectiveness of the proposed methods.