Abstract
Distributed trigeneration has been regarded as one of the leading solutions for the future energy production. Unlike centralized energy systems, trigeneration typically recovers otherwise wasted energy and supplies combined cooling, heating, and power products to end users simultaneously, which however causes difficulties in meeting weak temporal-correlated energy demands of end users. Inspired by the success in electric energy systems, energy storage may provide effective solutions to the challenges with respect to trigeneration by decoupling energy generation and consumption. However, multiple key questions are yet fully understood for planning storage-integrated trigeneration systems. The present study aims to answer the following questions: (i) what roles of energy storage are going to play in a trigeneration system? And (ii) how would energy storage affect the performance of the trigeneration system? A self-coded trigeneration system planning model is developed via Python programming to optimize capacities of different devices in the trigeneration system with the presence of energy storage to meet variable multi-energy demands. The effects of the energy storage on the performance of the trigeneration system are investigated. The underlying mechanisms of the energy storage affecting the system’s performance are also explored based on the feasibility region analysis and wasted energy analysis.
Corrigendum to ‘Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants’ Data in Brief, v. 36, p. 106986, 2021.
