An operating state estimation model for integrated energy systems based on distributed solution

In view of the disadvantages of the traditional energy supply systems,such as separate planning,separate design,independent operating mode,and the increasingly prominent nonlinear coupling between various subsystems,the production,transmission,storage and corn sumption of multiple energy sources are coordinated and optimized by the integrated energy system,which improves energy and infrastructure utilization,promotes renewable energy consumption,and ensures reliability of energy supply.In this paper,the mathematical model of the electricity-gas interconnected integrated energy system and its state estimation method are studied.First,considering the nonlinearity between measurement equations and state variables,a performance simulation model is proposed.Then,the state consistency equations and constraints of the coupling nodes for multiple energy sub-systems are established,and constraints are relaxed into the objective function to decouple the integrated energy system.Finally,a distributed state estimation framework is formed by combining the synchronous alternating direction multiplier method to achieve an efficient estimation of the state of the integrated energy system.A simulation model of an electricity-gas interconnected integrated energy system verifies the efficiency and accuracy of the state estimation method proposed in this pape.The results show that the average relative errors of voltage amplitude and node pressure estimated by the proposed distributed state estimation method are only 0.0132%and 0.0864%,much lower than the estimation error by using the Lagrangian relaxation method.Besides,compared with the centralized estimation method,the proposed distributed method saves 5.42 s of computation time.The proposed method is more accurate and efficient in energy allocation and utilization.

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.