A robust biobjective optimization approach for operating a shared energy storage under price uncertainty

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.

Newsvendor model with voucher sales: a distributionally robust approach

“…Moreover, in the context of the agricultural supply chain, Fu et al (2018) establish a distributionally robust Stackelberg game model to study the profit-sharing contract under limited information on demand and price. Some scholars have also applied robust optimization methods to other lenses, such as risk management (Ribas et al, 2010), oil supply chain (Sun et al, 2022), shared energy storage (Dai et al, 2022), and emission reduction (Bai et al, 2022a). Different from the above studies, we consider the intersection of marketing and supply chain inventory and use a distributionally robust newsvendor model to study the retailer's voucher promotion strategy with voucher buyback policy.…”

Online Dispatch of Shared Energy Storage with Remote Renewable Generations

“…[5] pays attention to the electricity losses during the sharing process and develops a hierarchical capacity planning approach for community-level shared batteries. Taking into account the price uncertainty, [6] proposes a sharing strategy of energy storage with two users based on a robust optimization model. Based on the equilibrium approach, [7] presents a pricing and dispatch strategy of a residential system with shared energy storage and multiple consumers.…”

“…In the real-time dispatch stage, after observing the real accessible total output of renewables r t p  , the scheduling of shared energy storage is determined based on the following model.  are the pre-specified allowable discharging range, and they are the optimal solution to problem (6). It can be proved that if the non-anticipative day-ahead scheduling problem (6) has feasible solutions, then there must be at least one feasible scheduling strategy for the real-time dispatch under all the uncertainty realizations that reside in the pre-specified sets (1) [10][11].…”