A two-stage robust configuration optimization framework for integrated energy system considering multiple uncertainties

Abstract

The optimal configuration of integrated energy system (IES) is the cornerstone of efficient investment and low-carbon operation, which is of high importance for realizing energy system sustainable transformation. Yet, the intermittency of renewable energy and uncertainty of multiple energy demands pose severe challenges to IES configuration and operation. To address the issue, this research presents a two-stage robust configuration optimization model incorporating the multiple uncertainties of supply and demand. The model takes economic benefits, environmental benefits and energy utilization efficiency as objective functions, and is executed by column and constraint generation algorithm. A case study justifies the proposed model. The findings indicate the uncertainty from renewable energy and demand raises the equipment capacity and results in an increase of investment cost. Contrasted with the deterministic scenario, the total costs in the uncertainty scenario increase from 7043795.8CNY to 7785296CNY, but the performance shows more robust and the loss of load penalty is saved up to 296888CNY. Sensitivity analysis reveals energy price fluctuations primarily impair the operating costs, whereas they cause moderate effects on the configuration results. Load fluctuations cause greater consequences on both configuration and operating costs. The research provides references for IES configuration under multiple uncertainties with renewable energy penetration.