Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model
DOI:
https://doi.org/10.13052/spee1048-5236.4349Keywords:
Integrated energy system, compressor working mode, convergence rate, energy flow calculation methodAbstract
This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.
Downloads
References
Brucker, J., Gasper, R., and Bessler, W. G. A grey-box model with neural ordinary differential equations for the slow voltage dynamics of lithium-ion batteries: Application to single-cell experiments. Journal of Power Sources, vol. 614, pp. 234918, 2024.
Chen, X., Wu, X.-N., Feng, J.-C., Wang, Y., Zhang, X.-C., Lin, Y.-L., Wang, B., and Zhang, S. Nonlinear differential equations and their application to evaluating the integrated impacts of multiple parameters on the biochemical safety of drinking water. Journal of Environmental Management, vol. 355, pp. 120493, 2024.
Deng, L., Fu, Y., Guo, Q., Li, Z., Xue, Y., and Zhang, Z. Energy and reserve procurement in integrated electricity and heating system: A high-dimensional covariance matrix approach based on stochastic differential equations. Energy, vol. 304, pp. 132042, 2024.
Jyotish, N. K., Singh, L. K., and Kumar, C. Availability analysis of safety-critical systems of nuclear power plant using ordinary differential equations and reachability graph. Progress in Nuclear Energy, vol. 159, pp. 104624, 2023.
Jyotish, N. K., Singh, L. K., and Kumar, C. Reliability Assessment of Safety-Critical Systems of Nuclear Power Plant using Ordinary Differential Equations and Reachability Graph. Nuclear Engineering and Design, vol. 412, pp. 112469, 2023.
Kim, G., and Heo, G. Solving partial differential equation for atmospheric dispersion of radioactive material using physics-informed neural network. Nuclear Engineering and Technology, vol. 55(6), pp. 2305–2314, 2023.
Kuzhiyil, J. A., Damoulas, T., and Widanage, W. D. Neural equivalent circuit models: Universal differential equations for battery modelling. Applied Energy, vol. 371, pp. 123692, 2024.
Liu, X., Yang, L., and Zhang, Z. The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions. Applied Energy, vol. 324, pp. 119794, 2022.
Owoyele, O., and Pal, P. ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers. Energy and AI, vol. 7, pp. 100118, 2022.
Saini, V., Bhattacharyya, D., Purdy, D., Parker, J., and Boohaker, C. Nonlinear state estimation of a power plant superheater by using the extended Kalman filter for differential algebraic equation systems. Applied Thermal Engineering, vol. 251, pp. 123471, 2024.
Székely, L., Kicsiny, R., Hermanucz, P., and Géczi, G. Explicit analytical solution of a differential equation model for solar heating systems. Solar Energy, vol. 222, pp. 219–229, 2021.
Taboga, V., Gehring, C., Cam, M. L., Dagdougui, H., and Bacon, P.-L. Neural differential equations for temperature control in buildings under demand response programs. Applied Energy, vol. 368, pp. 123433, 2024.
Wang, J., Pang, X., Yin, F., and Yao, J. A deep neural network method for solving partial differential equations with complex boundary in groundwater seepage. Journal of Petroleum Science and Engineering, vol. 209, pp. 109880, 2022.
Arsene, C., and Parisio, A. Deep convolutional neural networks for short-term multi-energy demand prediction of integrated energy systems. International Journal of Electrical Power & Energy Systems, vol. 160, pp. 110111, 2024.
Chen, L., Yang, D., Cai, J., and Yan, Y. Robust optimization based coordinated network and source planning of integrated energy systems. International Journal of Electrical Power & Energy Systems, vol. 157, pp. 109864, 2024.
Chong, Z., Yang, L., Jiang, Y., and Zhou, W. Hybrid-timescale optimal dispatch strategy for electricity and heat integrated energy system considering integrated demand response. Renewable Energy, vol. 232, pp. 121123, 2024.
Hu, C., Li, D., Zhao, W., and Xi, H. Deep reinforcement learning-based scheduling for integrated energy system utilizing retired electric vehicle battery energy storage. Journal of Energy Storage, vol. 97, pp. 112774, 2024.
Hu, R., Zhou, K., Yang, J., and Yin, H. Management of resilient urban integrated energy system: State-of-the-art and future directions. Journal of Environmental Management, vol. 363, pp. 121318, 2024.
Liang, T., Zhang, X., Tan, J., Jing, Y., and Liangnian, L. Deep reinforcement learning-based optimal scheduling of integrated energy systems for electricity, heat, and hydrogen storage. Electric Power Systems Research, vol. 233, pp. 110480, 2024.
Lin, S., Zhou, J., Tan, J., and Wu, Q. CVaR-based planning of park-level integrated energy system considering extreme scenarios of energy prices. International Journal of Electrical Power & Energy Systems, vol. 159, pp. 110001, 2024.
Liu, X., Zu, L., Wei, Z., Wang, Y., Pan, Z., Xiao, G., and Jenkins, N. Two-layer optimal scheduling of integrated electric-hydrogen energy system with seasonal energy storage. International Journal of Hydrogen Energy, vol. 82, pp. 1131–1145, 2024.
Mullanu, S., Chua, C., Molnar, A., and Yavari, A. Artificial intelligence for hydrogen-enabled integrated energy systems: A systematic review. International Journal of Hydrogen Energy, 2024.
Shao, Y., Wang, J., Ding, J., and Zhang, Y. Stochastic scheduling optimization of integrated energy system based on hybrid power to gas and hydrogen injection into gas grid. International Journal of Hydrogen Energy, vol. 80, pp. 381–393, 2024.
Wang, C., Chen, S., Zhao, J., Zhou, Y., Wei, Z., and Zheng, S. Coordinated scheduling of integrated electricity, heat, and hydrogen systems considering energy storage in heat and hydrogen pipelines. Journal of Energy Storage, vol. 85, pp. 111034, 2024.
Wang, J. L., Yan, T., and Pan, W. G. Design and evaluation of integrated energy system combining solar energy and compressed-air energy storage. Renewable Energy, vol. 232, pp. 121068, 2024.
Wang, L. L., Xian, R. C., Jiao, P. H., Chen, J. J., Chen, Y., and Liu, H. G. Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading. Renewable Energy, vol. 228, pp. 120597, 2024.
Yu, J., Chen, L., Wang, Q., Zhang, X., and Sun, Q. Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning. Energy, vol. 305, pp. 132278, 2024.
Zhao, Y., Yang, Z., Guo, Y., Song, H., and Sun, H. An optimal dispatch strategy of off-grid park integrated energy system considering source volatility. Energy Reports, vol. 12, pp. 1597–1607, 2024.