Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups
Shantanu Chakraborty, Toshiya Okabe

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…
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