Low-carbon economic dispatch of integrated energy system considering the uncertainty of energy efficiency

The integrated energy system (IES) is recognized as an effective measure to promote energy efficiency and environmental protection. However, the uncertain efficiency of energy devices under variant working conditions threatens the operation of the IES. In this paper, a low-carbon economic dispatch method for the IES considering the uncertainty of energy efficiency is developed. Specifically, a dynamic energy hub (DEH) is established by integrating an efficiency correction technique into the traditional energy hub (EH). The deep neural network (DNN) method with excellent accuracy in nonlinear mapping is utilized to correct energy efficiency affected by the load level, temperature and pressure. Based on the DEH, a low-carbon economic dispatch model is formulated to minimize operational costs. Case studies verify the effectiveness of the proposed method, which can enhance the accuracy of dispatch schemes and simultaneously, promote the low-carbon economic dispatch of the IES. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper develops a low-carbon economic dispatch method that considers the uncertainty of energy efficiency. By integrating a correction technique into the traditional energy hub, a dynamic energy hub is established. The deep neural network method is utilized to correct energy efficiency affected by load level, temperature, and pressure, and a low-carbon economic dispatch model is formulated to minimize operational costs.