Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

Abstract

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load tracking ability of CCHP-MES. For this reason, this paper proposes a novel tracking-economy extreme scenario tradeoff model by comparing the differences between tracking ability and economic benefit. The corresponding extreme scenarios based on these two objectives are characterized and the concept of uncertainty adjustment parameter is introduced to weigh the preference of extreme scenarios. On this basis, a multi-objective two-stage stochastic-robust optimization approach is developed, where the first stage optimizes the investment cost and the second stage optimizes the operating cost. Case study using typical load demand and renewable energy data in Nanjing verify the efficacy of the optimization model. The results indicate that the proposed tracking-economy extreme scenario can better characterize the frequent fluctuations of uncertainties compared to the traditional definition of extreme scenario. The equipment capacity configuration obtained under tracking-economy extreme scenario tradeoff model can better cope with the uncertainty fluctuations under extreme weather. Depending on different optimization preference, the economic robustness and tracking robustness can be improved by 13.80% and 36.06%, respectively.