An Algorithmic View on Optimal Storage Sizing

TL;DR

This paper develops an optimal control and investment strategy for energy storage under complex time-of-use pricing, enabling users to minimize costs through strategic charging and discharging, with solutions applicable to multi-peaked schemes.

Contribution

It introduces a dynamic programming approach for optimal storage control and investment under general ToU schemes, including multi-peaked pricing, which was not addressed before.

Findings

Optimal control policy effectively reduces user costs.

Aggregation of users enhances overall savings.

Performance varies with demand randomness.

Abstract

Users can arbitrage against Time-of-Use (ToU) pricing with storage by charging in off-peak period and discharge in peak periods. In this paper we design the optimal control policy and the solve optimal investment for general ToU scheme. We formulate the problem as dynamic programming for efficient solution. Our result is feasible facing multi-peaked ToU scheme. Simulation studies examine how the user's cost varies with respect to the user's demand randomness; we also demonstrate the performance of our scheme when aggregating users for extra savings.