Dynamic simulation of GEH-IES with distributed parameter characteristics for hydrogen-blending transportation

For the purpose of environment protecting and energy saving,renewable energy has been distributed into the power grid in a considerable scale.However,the consuming capacity of the power grid for renewable energy is relatively limited.As an effective way to absorb the excessive renewable energy,the power to gas(P2G)technology is able to convert excessive renewable energy into hydrogen.Hydrogen-blending natural gas pipeline is an efficient approach for hydrogen transportation.However,hydrogen-blending natural gas complicates the whole integrated energy system(IES),making it more problematic to cope with the equipment failure,demand response and dynamic optimization.Nevertheless,dynamic simulation of distribution parameters of gas-electricity-hydrogen(GEH)energy system,especially for hydrogen concentration,still remains a challenge.The dynamics of hydrogen-blending IES is undiscovered.To tackle the issue,an iterative solving framework of the GEH-IES and a cell segment-based method for hydrogen mixing ratio distribution are proposed in this paper.Two typical numerical cases studying the conditions under which renewables fluctuate and generators fail are conducted on a real-word system.The results show that hydrogen blending timely and spatially influences the flow parameters,of which the hydrogen mixing ratio and gas pressure loss along the gas pipeline are negatively correlated and the response to hydrogen mixing ratio is time-delayed.Moreover,the hydrogen-blending amount and position also have a significant impact on the performance of the compressor.

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Since gas turbine plays a key role in electricity power generating,the requirements on the safety and reliability of this classical thermal system are becoming gradually strict.With a large amount of renewable energy being integrated into the power grid,the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines.The startup,shutdown,and load fluctuation are dominating the operating condition of gas turbines.Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design,operation,and maintenance.However,conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations.Although data-driven simulating methods,to some extent,can mitigate the problem,it is impossible to perform simulations with insufficient data.To tackle the issue,a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data.A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring.The simulation accuracy is significantly increased by 24.6%and the predicting error reduced by 63.6%compared with the baseline model.Moreover,compared with the other classical transfer learning modes,the method proposed has the best simulating performance on field testing data set.Furthermore,the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.