A robust biobjective optimization approach for operating a shared energy storage under price uncertainty. (8th June 2020)

• Record Type: Journal Article
• Authors: Dai, Rui; Charkhgard, Hadi; Rigterink, Fabian
• Is Part Of: International transactions in operational research. Volume 29:Number 3(2022)
• Page Start: 1627
• Page End: 1658
• Publication Date: 2020-06-08
• Subjects: energy storage sharing -- robust optimization -- piecewise McCormick relaxation -- Nash bargaining solution -- biobjective mixed integer linear programming; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12834
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: Energy storage (ES) is acknowledged to play an important role in modern energy technologies due to its potential to reduce operational costs, enhance the resilience, and level energy load for energy systems. Efficient ES management can achieve cost savings, also known as energy arbitrage, by charging at off‐peak prices and discharging at peak prices. This arbitrage can be further boosted by allowing the ES to be shared by multiple users/buildings. However, since energy arbitrage relies on the variation of energy prices, it is hard to achieve this arbitrage if the prices are uncertain. To address this challenge, we present a robust optimization approach to fairly and efficiently operate an ES shared between two users under price uncertainty. This sharing strategy is formulated as a biobjective mixed integer bilinear programming model. To facilitate solution efficiency, we propose a binary formulation for piecewise McCormick relaxations to approximate the bilinear model by a tractable linear model. A computational study demonstrates the effectiveness of our robust sharing strategy for managing ES sharing under price uncertainty. Also, it shows that the proposed binary formulation for piecewise McCormick relaxations reduces the runtime by around 80% compared to the traditional unary formulation.