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

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.

Key words： integrated energy system; state estimation; electricity-gas coupling energy system; nonlinear coupling; distributed solution

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).

References

1 Lund H, Münster E. Integrated energy systems and local energy markets. Energy Policy, 2006, 34(10): 1152–1160

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

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

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

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

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

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

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

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

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

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

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

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

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

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

17 Monticelli A, Wu F. Observability analysis for orthogonal transformation-based state estimation. IEEE Transactions on Power Systems, 1986, 1(1): 201–206

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

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

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

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

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

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

27 Battistelli G, Chisci L. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 2016, 68: 169–178

28 Primadianto A, Lu C N. A review on distribution system state estimation. IEEE Transactions on Power Systems, 2017, 32(5): 3875–3883

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

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

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

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

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

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

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

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

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

38 Marelli D E, Fu M. Distributed weighted least-squares estimation with fast convergence for large-scale systems. Automatica, 2015, 51: 27–39

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

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

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