Micro-energy network multi-time scale optimization control method based on double-layer model predictive control

Abstract

The invention relates to a micro-energy network multi-time scale optimization control method based on double-layer model predictive control. A micro-energy network is coupled with three energy sources of electricity, heat and gas, and electricity-to-gas and battery-supercapacitor hybrid energy storage equipment is integrated into the micro-energy net. The invention provides a multi-time scale optimization control method based on double-layer model predictive control aiming at the problem of low reliability of an optimization control result of a micro-energy network system caused by uncertain factors such as wind and light output and load demand, and the multi-time scale optimization control method comprises an upper layer of long-time scale rolling optimization and a lower layer of short-time scale real-time rolling adjustment. The upper layer makes a long-time scale scheduling plan through multi-step rolling solution by taking system operation economy optimization as a target and combining time-of-use electricity price and natural gas price; and the lower layer aims at tracking the upper layer scheduling plan, and a super capacitor is introduced to cope with short-time scale power fluctuation of wind, light and load. According to the method, economical and safe operation of the micro-energy network can be realized on the premise of ensuring the wind and light absorption capability of the micro-energy network.

Description

technical field [0001] The invention relates to a multi-time scale optimal control method for a micro-energy network based on a two-layer model predictive control. Background technique [0002] As an important part of the Energy Internet, Micro Energy Grid (MEG) is one of the effective technical means to realize multi-energy complementarity and improve energy utilization and environmental benefits. However, the renewable energy output in the micro-energy grid is greatly affected by natural factors, showing the characteristics of intermittent, fluctuating and random, and the existing prediction methods have limitations, making the prediction of renewable energy output and user load demand The accuracy is limited, which has a certain impact on the accuracy of the micro-energy grid optimization control results. Therefore, how to deal with the influence of uncertain factors such as wind power output and load demand on the optimal control results of the micro-energy grid, and to...

AI Technical Summary

Problems solved by technology

[0004] In the micro-energy grid system with uncertain factors, the above two optimization control methods have the disadvantages of over-reliance on prediction accuracy and low reliability of scheduling plan

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