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

Dengji ZHOU, Shixi MA, Dawen HUANG, Huisheng ZHANG, Shilie WENG

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Front. Energy ›› 2020, Vol. 14 ›› Issue (4) : 801-816. DOI: 10.1007/s11708-020-0687-y
RESEARCH ARTICLE
RESEARCH ARTICLE

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

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Abstract

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 sub-systems, the production, transmission, storage and consumption 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 paper. 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.

Keywords

integrated energy system / state estimation / electricity-gas coupling energy system / nonlinear coupling / distributed solution

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Dengji ZHOU, Shixi MA, Dawen HUANG, Huisheng ZHANG, Shilie WENG. An operating state estimation model for integrated energy systems based on distributed solution. Front. Energy, 2020, 14(4): 801‒816 https://doi.org/10.1007/s11708-020-0687-y

References

[1]
Lund H, Münster E. Integrated energy systems and local energy markets. Energy Policy, 2006, 34(10): 1152–1160
CrossRef Google scholar
[2]
Loh P C, Zhang L, Gao F. Compact integrated energy systems for distributed generation. IEEE Transactions on Industrial Electronics, 2013, 60(4): 1492–1502
CrossRef Google scholar
[3]
Shao C, Ding Y, Wang J, Song Y. Modeling and integration of flexible demand in heat and electricity integrated energy system. IEEE Transactions on Sustainable Energy, 2018, 9(1): 361–370
CrossRef Google scholar
[4]
Martinez-Mares A, Fuerte-Esquivel C R. A robust optimization approach for the interdependency analysis of integrated energy systems considering wind power uncertainty. IEEE Transactions on Power Systems, 2013, 28(4): 3964–3976
CrossRef Google scholar
[5]
Collins S, Deane J P, Poncelet K, Panos E, Pietzcker R C, Delarue E, Ó Gallachóir B P. Integrating short term variations of the power system into integrated energy system models: a methodological review. Renewable & Sustainable Energy Reviews, 2017, 76: 839–856
CrossRef Google scholar
[6]
Farfan J, Breyer C. Structural changes of global power generation capacity towards sustainability and the risk of stranded investments supported by a sustainability indicator. Journal of Cleaner Production, 2017, 141: 370–384
CrossRef Google scholar
[7]
Bilgili M, Ozbek A, Sahin B, Kahraman A. An overview of renewable electric power capacity and progress in new technologies in the world. Renewable & Sustainable Energy Reviews, 2015, 49: 323–334
CrossRef Google scholar
[8]
Qu Y. Gas generator assembly capacity in China has increased significantly since 2000. 2018–12–18, available at the website of mp.weixin.qq.com (in Chinese)
[9]
Zhao B, Conejo A J, Sioshansi R. Coordinated expansion planning of natural gas and electric power systems. IEEE Transactions on Power Systems, 2018, 33(3): 3064–3075
CrossRef Google scholar
[10]
Shao C, Shahidehpour M, Wang X, Wang X, Wang B. Integrated planning of electricity and natural gas transportation systems for enhancing the power grid resilience. IEEE Transactions on Power Systems, 2017, 32(6): 4418–4429
CrossRef Google scholar
[11]
Ling Z, Yang X, Li Z. Optimal dispatch of multi-energy system using power-to-gas technology considering flexible load on user side. Frontiers in Energy, 2018, 12(4): 569–581
CrossRef Google scholar
[12]
Li G, Zhang R, Jiang T, Chen H, Bai L, Li X. Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process. Applied Energy, 2017, 194: 696–704
CrossRef Google scholar
[13]
Clegg S, Mancarella P. Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems. IEEE Transactions on Sustainable Energy, 2016, 7(2): 718–731
CrossRef Google scholar
[14]
Fang J, Zeng Q, Ai X, Chen Z, Wen J. Dynamic optimal energy flow in the integrated natural gas and electrical power systems. IEEE Transactions on Sustainable Energy, 2018, 9(1): 188–198
CrossRef Google scholar
[15]
Cattivelli F S, Lopes C G, Sayed A H. Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Transactions on Signal Processing, 2008, 56(5): 1865–1877
CrossRef Google scholar
[16]
Li Z, Guo Q, Sun H, Wang J. Coordinated economic dispatch of coupled transmission and distribution systems using heterogeneous decomposition. IEEE Transactions on Power Systems, 2016, 31(6): 4817–4830
CrossRef Google scholar
[17]
Monticelli A, Wu F. Observability analysis for orthogonal transformation-based state estimation. IEEE Transactions on Power Systems, 1986, 1(1): 201–206
CrossRef Google scholar
[18]
Ni X, Zhang B. A state estimation method for bad data detection and identification based on equivalent current measurement transformation. Power System Technology, 2002, 26(8): 12–15
[19]
Jalving J, Zavala V M. An optimization-based state estimation framework for large-scale natural gas networks. Industrial & Engineering Chemistry Research, 2018, 57(17): 5966–5979
CrossRef Google scholar
[20]
Ahmadian Behrooz H, Boozarjomehry R B. Modeling and state estimation for gas transmission networks. Journal of Natural Gas Science and Engineering, 2015, 22: 551–570
CrossRef Google scholar
[21]
Ma S, Sun S, Wu H, Zhou D, Zhang H, Weng S. Decoupling optimization of integrated energy system based on energy quality character. Frontiers in Energy, 2018, 12(4): 540–549
CrossRef Google scholar
[22]
Ge S, Liu X, Ge L, Liu H, Li J. State estimation of regional interconnected electricity and gas networks. Energy Procedia, 2017, 142: 1920–1932
CrossRef Google scholar
[23]
Dong J, Sun H, Guo Q. State estimation for combined electricity and heat networks. Power System Technology, 2016, 40(6): 1635–1641
[24]
Zhang H, Zhang C, Wen F, Xu Y. A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system. Frontiers in Energy, 2018, 12(4): 582–590
CrossRef Google scholar
[25]
Zhong J, Li Y, Cao Y, Zhong J, Li Y, Cao Y, Sidorov D, Panasetsky D. A uniform fault identification and positioning method of integrated energy system. Energy Systems Research, 2018, 1(3): 14–24
[26]
Xie L, Choi D H, Kar S, Poor H V. Fully distributed state estimation for wide-area monitoring systems. IEEE Transactions on Smart Grid, 2012, 3(3): 1154–1169
CrossRef Google scholar
[27]
Battistelli G, Chisci L. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 2016, 68: 169–178
CrossRef Google scholar
[28]
Primadianto A, Lu C N. A review on distribution system state estimation. IEEE Transactions on Power Systems, 2017, 32(5): 3875–3883
CrossRef Google scholar
[29]
Wang D, Guan X, Liu T, Gu Y, Shen C, Xu Z. Extended distributed state estimation: a detection method against tolerable false data injection attacks in smart grids. Energies, 2014, 7(3): 1517–1538
CrossRef Google scholar
[30]
Zhang T, Li Z, Wu Q H, Zhou X. Decentralized state estimation of combined heat and power systems using the asynchronous alternating direction method of multipliers. Applied Energy, 2019, 248: 600–613
CrossRef Google scholar
[31]
Ahmadian I, Abedinia O, Ghadimi N. Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Frontiers in Energy, 2014, 8(4): 412–425
CrossRef Google scholar
[32]
Jiang X S, Jing Z X, Li Y Z, Wu Q H, Tang W H. Modelling and operation optimization of an integrated energy based direct district water-heating system. Energy, 2014, 64: 375–388
CrossRef Google scholar
[33]
Schweppe F C, Wildes J. Power system static-state estimation, part I: exact model. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89(1): 120–125
CrossRef Google scholar
[34]
Schweppe F C, Rom D B. Power system static-state estimation, part II: approximate model. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89(1): 125–130
CrossRef Google scholar
[35]
Schweppe F C. Power system static-state estimation, part III: implementation. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89(1): 130–135
CrossRef Google scholar
[36]
Basetti V, Chandel A K, Subramanyam K. Power system static state estimation using JADE-adaptive differential evolution technique. Soft Computing, 2018, 22(21): 7157–7176
CrossRef Google scholar
[37]
Qing X, Karimi H R, Niu Y, Wang X. Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems. International Journal of Electrical Power & Energy Systems, 2015, 65: 26–33
CrossRef Google scholar
[38]
Marelli D E, Fu M. Distributed weighted least-squares estimation with fast convergence for large-scale systems. Automatica, 2015, 51: 27–39
CrossRef Google scholar
[39]
Woldeyohannes A D, Majid M A A. Simulation model for natural gas transmission pipeline network system. Simulation Modelling Practice and Theory, 2011, 19(1): 196–212
CrossRef Google scholar
[40]
Deng W, Yin W. On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing, 2016, 66(3): 889–916
CrossRef Google scholar
[41]
Martinez-Mares A, Fuerte-Esquivel C R. A unified gas and power flow analysis in natural gas and electricity coupled networks. IEEE Transactions on Power Systems, 2012, 27(4): 2156–2166
CrossRef Google scholar

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51706132 and 51876116) and National Science and Technology Major Project (Nos. 2017-I-0002-0002, and 2017-I-0011-0012).

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2020 Higher Education Press
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