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Academic Journal of Energy, 2022, 3(3); doi: 10.38007/RE.2022.030307.

Multi-Energy Based on Coupling System Cooperative Optimization Strategy


Xiang Li

Corresponding Author:
Xiang Li

School of Management, Northwest University of Political Science and Law, Xi’an 710122, China


Under the situation that non-renewable resources are depleting on a global scale, a multi-energy interconnection, interoperability and mutual aid energy interconnection system emerges as the times require. The realization of multi-energy interconnection system, multi-energy complementarity, source-network-load coordination, and safe and reliable energy supply are the core and key of the energy Internet project. The main purpose of this paper is to study multi-energy sources based on the cooperative optimization strategy of coupled systems. In this paper, combined with the coupled element model and the DC power flow model of the power grid, the bidirectional sensitivity matrix of the gas-electric coupling system is established, and the mutual influence relationship between the two energy subsystems is revealed. Experiments show that the IE system of the electricity-heat-cooling-air synergistic optimization mode covers more types of energy networks, more types of energy conversion, better effect of energy cascade utilization, higher energy utilization efficiency, and can meet the energy needs of users type is more comprehensive.


Coupling System, Collaborative Optimization, Multiple Energy Sources, Gas-Electric Coupling

Cite This Paper

Xiang Li. Multi-Energy Based on Coupling System Cooperative Optimization Strategy. Academic Journal of Energy (2022), Vol. 3, Issue 3: 58-67. https://doi.org/10.38007/RE.2022.030307.


[1] Kilcioglu E , Mirghasemi H , Stupia I , et al. An Energy-Efficient Fine-grained Deep Neural Network Partitioning Scheme for Wireless Collaborative Fog Computing. IEEE Access, 2021, PP(99):1-1. https://doi.org/10.1109/ACCESS.2021.3084689

[2] Leung M F , Wang J . Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization. IEEE Transactions on Neural Networks and Learning Systems, 2020, PP(99):1-12.

[3] Gupta V , De S . Collaborative Multi-Sensing in Energy Harvesting Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks, 2020, PP(99):1-1. https://doi.org/10.1109/TSIPN.2020.2995502

[4] Kishore R , Gurugopinath S , Muhaidat S , et al. Energy Efficiency Analysis of Collaborative Compressive Sensing Scheme in Cognitive Radio Networks. IEEE Transactions on Cognitive Communications and Networking, 2020, PP(99):1-1.

[5] Belhomme R , Corsetti E , Gutschi C , et al. Bottom-Up Flexibility in Multi-Energy Systems: Real-World Experiences From Europe. IEEE Power and Energy Magazine, 2021, 19(4):74-85.

[6] Osiadacz A J , Chaczykowski M . Modeling and Simulation of Gas Distribution Networks in a Multienergy System Environment. Proceedings of the IEEE, 2020, PP(99):1-16.

[7] Taguchi K . Assessment of Multi-Energy Inter-Pixel Coincidence Counters (MEICC) for Charge Sharing Correction or Compensation for Photon Counting Detectors with Boxcar Signals. IEEE Transactions on Radiation and Plasma Medical Sciences, 2020, PP(99):1-1. https://doi.org/10.1117/12.2545058

[8] Chicco G , Riaz S , Mazza A , et al. Flexibility From Distributed Multienergy Systems. Proceedings of the IEEE, 2020, PP(99):1-22.

[9] O'Malley M J , Anwar M B , Heinen S , et al. Multicarrier Energy Systems: Shaping Our Energy Future. Proceedings of the IEEE, 2020, 108(9):1437-1456.

[10] Cesena E , Loukarakis E , Good N , et al. Integrated Electricity-Heat-Gas Systems: Techno-Economic Modeling, Optimization, and Application to Multienergy Districts. Proceedings of the IEEE, 2020, PP(99):1-19.

[11] Chertkov M , Andersson G . Multienergy Systems. Proceedings of the IEEE, 2020, 108(9):1387-1391.

[12] Musigwa S , Morgan N , Swick R A , et al. Multi-carbohydrase enzymes improve feed energy in broiler diets containing standard or low crude protein. Animal Nutrition, 2021, 7(2):496-505.

[13] A M B , B A E , C F E , et al. Systematic analysis and multi-objective optimization of an integrated power and freshwater production cycle. International Journal of Hydrogen Energy, 2022, 47( 43):18831-18856.

[14] Lynd L R , Beckham G T , Guss A M , et al. Toward low-cost biological and hybrid biological/catalytic conversion of cellulosic biomass to fuels. Energy & Environmental Science, 2022, 15(3):938-990. https://doi.org/10.1039/D1EE02540F

[15] Parise F , Gentile B , Lygeros J . A distributed algorithm for average aggregative games with coupling constraints. IEEE Transactions on Control of Network Systems, 2020, 7(2):770-782.

[16] Persis C D , Grammatico S . Continuous-time integral dynamics for monotone aggregative games with coupling constraints. IEEE Transactions on Automatic Control, 2020, 65(5):2171-2176. https://doi.org/10.1109/TAC.2019.2939639

[17] A J W , B H B S , C S A S , et al. Palladium nanoparticles supported by three-dimensional freestanding electrodes for high-performance methanol electro-oxidation - ScienceDirect. International Journal of Hydrogen Energy, 2020, 45( 19):11089-11096. https://doi.org/10.1016/j.ijhydene.2020.02.046

[18] Zaharis Z D , Gravas I P , Lazaridis P I , et al. An Effective Modification of Conventional Beamforming Methods Suitable for Realistic Linear Antenna Arrays. IEEE Transactions on 

[19] Antennas and Propagation, 2020, PP(99):1-1.