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A robust bi-objective optimization approach for operating a shared energy storage under price uncertainty

Rui Dai (ruidai***at***mail.usf.edu)
Hadi Charkhgard (hcharkhgard***at***usf.edu)
Fabian Rigterink (f.rigterink***at***aurubis.com)

Abstract: Energy storage 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 energy storage 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 if allowing the energy storage 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 energy storage 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 ambinary 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 energy storage 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.

Keywords: energy storage sharing, robust optimization, piecewise McCormick relaxation, Nash bargaining solution, biobjective mixed integer linear programming

Category 1: Applications -- Science and Engineering (Smart Grids )

Category 2: Other Topics (Multi-Criteria Optimization )

Category 3: Robust Optimization


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Entry Submitted: 07/18/2018
Entry Accepted: 07/19/2018
Entry Last Modified: 11/23/2018

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