Multi-time-scale optimal control method for micro-energy grid based on double-layer model predictive control
A model predictive control and multi-time scale technology, which is applied in design optimization/simulation, communication network with energy trade/energy transfer authority, energy storage, etc., can solve the problems of low reliability of forecasting accuracy and dispatching plan, and achieve a solution Micro-energy network optimization control method has a large deviation from the ideal and the effect of strong robustness
Active Publication Date: 2022-06-07
FUZHOU UNIV
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[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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[0097] The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.
[0098] The present invention provides a multi-time-scale optimal control method for a micro-energy grid based on a two-layer model predictive control, comprising the following steps:
[0099] Establish a combined electricity, heat and gas micro-energy grid model with electricity-to-gas and battery-supercapacitor hybrid energy storage;
[0100] A multi-time-scale optimal control method based on two-layer model predictive control is established;
[0101] Through the multi-time-scale optimal control method based on the two-layer model predictive control, on the premise of ensuring the wind and light absorption capacity of the micro-energy network, the influence of uncertainty on the optimal control of the micro-energy network is dealt with, and the economic and safe operation of the micro-energy network is realized.
[0102] The following is a ...
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The invention relates to a multi-time scale optimal control method for a micro-energy network based on a two-layer model predictive control. The micro-energy grid couples the three energy sources of electricity, heat and gas, and integrates power-to-gas and battery-supercapacitor hybrid energy storage devices. Aiming at the problem of low reliability of micro-energy grid system optimization control results caused by uncertain factors such as wind power output and load demand, a multi-time-scale optimal control method based on two-layer model predictive control is proposed, including long-term scale rolling optimization The upper layer and the lower layer for real-time rolling adjustments on short timescales. Among them, the upper layer aims at the optimal system operation economy, combines the time-of-use electricity price and natural gas price, and formulates a long-term scale dispatch plan through multi-step rolling solution; Short-timescale power fluctuations of loads. This method can realize the economical and safe operation of the micro-energy grid under the premise of ensuring the wind-solar consumption capacity of the micro-energy grid.
Description
technical field [0001] The invention relates to a multi-time-scale optimal control method for a micro-energy network based on double-layer model prediction control. Background technique [0002] Micro Energy Grid (MEG), as an important part of the Energy Internet, is one of the effective technical means to achieve multi-energy complementarity and improve energy utilization and environmental benefits. However, the output of renewable energy 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, which makes the prediction of renewable energy output and user load demand. The accuracy is limited, which has a certain impact on the accuracy of the optimal control results of the micro-energy grid. Therefore, how to deal with the influence of uncertain factors such as wind and solar output and load demand on the optimal control results of the micro-en...
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IPC IPC(8): G06F30/20H02J3/38H02J3/32H02J3/00H02J7/34
CPCH02J3/38H02J3/32H02J3/008H02J7/345G06F30/20H02J2203/20Y02E70/30
Inventor 陈飞雄林炜晖邵振国邓宏杰
Owner FUZHOU UNIV