Matrix modeling and optimization calculation method for large scale integrated energy system

Document Type : Research Paper

Authors

1 Department of Electrical Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran

2 AAU ENERGY, Aalborg University, Esbjerg, Denmark

Abstract

We-Energy (WE), as an important energy unit with full duplex and multi-energy carriers in the integrated energy system (IES), uses the coupling matrix to connect the network-side and demand-side energy. However, the coupling matrix of WE is very hard to be formulated directly due to the complicated internal structure and the flexibility of operation mode. This paper proposes a multi-step method for the modeling of WE. According to the method, the conversion process in the WE can be separated into several steps and the WE model can be built by coupling matrix in each step. Then, the WE model is extended by considering the renewable energy, location of storage and different types of demand response. Because of the non-linearity caused by the dispatch factors, the computational complexity increases greatly for solving the optimal scheduling issue of the WE. In order to reduce the computational burden, the variable substitution is added in the proposed modeling method. The results of simulation cases are presented to demonstrate the performance of proposed modeling and calculation method.

Keywords

[1] M. Alipour, K. Zare and M. Abapour, MINLP probabilistic scheduling model for demand response programs integrated energy hubs, IEEE Trans Ind. Inf. 14 (2018), no. 1, 79–88.
[2] M.H. Barmayoon, M. Fotuhi-Firuzabad, A. Rajabi-Ghahnavieh and M. Moeini Aghtaie, Energy storage in renewable-based residential energy hubs, IET Gener. Transm. Distrib. 10 (2016), no. 13, 3127–3134.
[3] F. Brahman, M. Honarmand and S. Jadid, Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system, Energy Build. 90 (2015), 65–75.
[4] G. Chicco and P. Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy 34 (2009), no. 3, 261–273.
[5] S. Clegg and P. Mancarella, Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems, IEEE Trans. Sustain. Energy. 7 (2016), no. 2, 718–731.
[6] M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson and K. Frohlich, Energy hubs for the future, IEEE Power Energy Mag. 5 (2007), no. 1, 24–30.
[7] J. Hinker, H. Knappe and J.M.A. Myrzik, Precise assessment of technically feasible power vector interactions for arbitrary controllable multi-energy systems, IEEE Trans. Smart Grid 10 (2019), no. 1, 1146–1155.
[8] Y. Li, H. Zhang and X. Liang, Event-triggered based distributed cooperative energy management for multienergy systems, IEEE Trans Ind. Inf. 15 (2019), no. 14, 2008–2022.
[9] K. Mahmud, B. Khan, J. Ravishankar, A. Ahmadi and P. Siano, An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview, Renew. Sustain. Energy Rev. 127 (2020), 1–19.
[10] M. Moeini-Aghtaie, P. Dehghanian, M. Fotuhi-Firuzabad and A. Abbaspour, Multiagent genetic algorithm: An online probabilistic view on economic dispatch of energy hubs constrained by wind availability, IEEE Trans. Sustain. Energy 5 (2014), no. 2, 699–708.
[11] K. Orehounig, R. Evins and V. Dorer, Integration of decentralized energy systems in neighbourhoods using the energy hub approach, Appl. Energy 154 (2015), 277–289.
[12] A. Rahmani, J. Haddadnia and A. Sanai, Intelligent detection of electrical equipment faults in the overhead substations-based machine vision, 2nd Int. Conf. Mech. Electron. Engin., IEEE, 2 (2010), V2–141.
[13] O. Rahmani Seryasat, J. Haddadnia and H. Ghayoumi Zadeh, Assessment of a novel computer aided mass diagnosis system in mammograms, Iran. J. Breast Disease 9 (2016), no. 3, 31–41.
[14] H. Razavi, H. Sarabadani, A. Karimisefat and J.F. Lebraty, Profitability prediction for ATM transactions using artificial neural networks: a data-driven analysis, 5th Conf. Knowledge Based Engin. Innov. (KBEI), IEEE, 2019, pp. 661–665.
[15] Q. Sun, Energy internet and we-energy, Berlin, Germany: Springer, 2018.
[16] Q. Sun, R. Fan, Y. Li, B. Huang and D. Ma, A distributed double-consensus algorithm for residential we-energy, IEEE Trans. Ind. Inf. 15 (2019), no. 8, 4830–4842.
[17] Q. Sun and L. Yang, From independence to interconnection–A review of AI technology applied in energy systems, CSEE J. Power Energy Syst. 5 (2019), no. 1, 21–34.
[18] Y. Wang, J. Cheng, N. Zhang, and C. Kang, Automatic and linearized modeling of energy hub and its flexibility analysis, Appl. Energy 211 (2018), 705–714.
[19] D. Wang, L. Liu, H. Jia, W. Wang, Y. Zhi, Z. Meng and B. Zhou, Review of key problems related to integrated energy distribution systems, CSEE J. Power Energy Syst. 4 (2018), no. 2, 130–145.
[20] Y. Wang, N. Zhang, C. Kang, D.S. Kirschen, J. Yang and Q. Xia, Standardized matrix modeling of multiple energy systems, IEEE Trans. Smart Grid 10 (2019), no. 1, 257–270.
[21] L. Yang, Q. Sun, D. Ma and Q. Wei, Nash Q-learning based equilibrium transfer for integrated energy management game with we-energy, Neurocomput. 396 (2019), 216–223.
[22] M. Zarif, S. Khaleghi and M.H. Javidi, Assessment of electricity price uncertainty impact on the operation of multi-carrier energy systems, IET Gener. Transm. Distrib. 9 (2015), no. 16, 2586–2592.
[23] D. Zhang and T. Liu, A multi-step modeling and optimal operation calculation method for large-scale energy hub model considering two types demand responses, IEEE Trans. Smart Grid 10 (2019), no. 6, 6735–6746.
[24] N. Zhang, Q. Sun and L. Yang, A two-stage multi-objective optimal scheduling in the integrated energy system with we-energy modeling, Energy 205 (2020).
Volume 14, Issue 1
January 2023
Pages 2455-2468
  • Receive Date: 19 April 2022
  • Revise Date: 16 May 2022
  • Accept Date: 07 June 2022