Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources. (1st February 2023)

• Record Type: Journal Article
• Authors: Liu, Jizhen; Ma, Lifei; Wang, Qinghua
• Is Part Of: Energy. Volume 264(2023)
• Publication Date: 2023-02-01
• Subjects: Integrated energy system -- Energy management -- Collaborative optimization -- Two-stage scheduling -- Exergy efficiency; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125981
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Under the background of energy internet and low-carbon power, integrated energy system (IES) has become an important carrier of energy conservation and emission reduction. The IES utilizes innovative energy management mode to coordinate various energy sources such as natural gas, electric energy and heat energy. It is composed of energy production, conversion, storage and consumption subsystems, which emphasizes breaking the isolation of energy subsystems through reasonable scheduling, realizing energy cascade utilization and improving energy utilization efficiency. In this paper, an energy management model with two-stage scheduling before day and in real time is proposed aiming at the collaborative optimization of generator-load-storage of IES. Firstly, the first stage is the day-ahead economic dispatch, which aims to realize the power distribution of units in the system. The day-ahead economic dispatching model takes the maximization of economic benefits, the maximization of exergy efficiency and the minimization of carbon emission cost as the optimization objectives, so as to make the day-ahead global optimal dispatching decision. Secondly, the second stage is real-time optimal scheduling, which aims at real-time power adjustment of the previous scheduling plan. The real-time optimal scheduling model takes the minimum interactive power deviation punishment cost, wind abandonment punishment cost and user satisfaction loss cost as optimization objectives, so as to balance the energy supply and load demand deviation between planned output and actual output. Thirdly, according to the characteristics of the model, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the first-stage day-ahead economic scheduling, and YALMIP toolbox is used to solve the real-time optimal scheduling model. Finally, based on the established model, a typical IES is selected for case simulation, which verifies that the proposed method can effectively improve the economy of system operation. Highlights: A two-stage energy management framework for IES is designed. A multi-objective economic dispatching model is established. A collaborative optimization model in real-time scheduling is established.