Research On Dynamic Optimal Scheduling Method Of Integrated Energy System Based On Micro-gas Turbine

How to improve energy utilization and renewable energy penetration is a key issue for the energy system to gradually diversify and decarbonize.Through the coordination and optimization of various forms of energy,the integrated energy system has realized the cascade utilization of energy and the substantial consumption of renewable energy,and has gradually become a key solution to the energy problems.However,with the continuous expansion of the integrated energy system,there are many types of equipment involved,complex working conditions,various forms of energy and serious coupling,which make its optimal dispatching face many difficulties and severe challenges.Based on the above background,this paper takes the integrated energy system based on micro-gas turbine(MGT-IES)as the research object,and studies the optimal scheduling method considering the dynamic characteristics of the equipment.The main research contents include:(1)Taking the minimum annual comprehensive cost as the optimization goal,combined with the constraints of energy supply and demand balance and equipment operating characteristics,the capacity configuration optimization model of the MGT-IES is established,and the optimal capacity configuration of each equipment was obtained by solving this model.The idea of constructing an equipment model for dynamic optimal scheduling is proposed.According to the dynamic characteristics of the equipment,it is divided into fast equipment and slow equipment.A steady-state model is established for fast equipment.For slow equipment,a closed-loop dynamic characteristic model of main equipment suitable for optimal scheduling calculation is established,while ensuring the accuracy of the model and reducing its complexity.(2)A research on optimal scheduling considering the dynamic characteristics of equipment is carried out,and a centralized multi-time-scale dynamic optimal scheduling strategy combining day-ahead steady-state scheduling and intraday dynamic scheduling is proposed.In the day-ahead stage,a day-ahead steady-state scheduling model is constructed with the goal of economical optimization,and the day-ahead scheduling plan is obtained by solving this model.In the intraday stage,a closed-loop dynamic characteristic model with a smaller time scale is used to describe the slow equipment,and a steady-state characteristic model with a larger time scale is used to describe the fast equipment.Besides a multi-time-scale objective that considers the optimal economy,the optimal environmental protection and the optimal dynamic process tracking characteristics is constructed and an intraday dynamic optimal scheduling model based on model predictive control is established to adjust the day-ahead scheduling plan.The simulation results show that,compared with the traditional steady-state scheduling method,the proposed method effectively improves the rationality of the system’s scheduling decision and the stability of energy supply.(3)Considering the differences in different types of equipments’ dynamic characteristics,the complete intraday centralized dynamic optimal scheduling sub-problem is decomposed into three relatively independent dynamic scheduling problems of cold energy,thermal energy and electric energy,and the sampling time scale of the equipment model can be automatically adjusted.Two MGT-IES distributed dynamic optimization scheduling models that meet the requirements of the optimization models based on the alternating direction method of multiplier and the block coordinate descent method are constructed,and two intraday distributed dynamic optimization scheduling methods based on the alternating direction method of multipliers and the block coordinate descent method are proposed.The simulation study analyzes the impact of sampling time scale on the results of the distributed dynamic optimal scheduling method and makes a detailed comparison and analysis with the intraday centralized dynamic scheduling method.The results show that the distributed dynamic scheduling method using the same sampling time scale for the three subsystems can effectively improve the computational efficiency,while the distributed dynamic scheduling method using multiple sampling time scales significantly reduces the power imbalance deviation of dynamic process effectively and improves the system energy supply stability.