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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

Front. Energy    2018, Vol. 12 Issue (4) : 540-549    https://doi.org/10.1007/s11708-018-0597-4
RESEARCH ARTICLE
Decoupling optimization of integrated energy system based on energy quality character
Shixi MA, Shengnan SUN, Hang WU, Dengji ZHOU, Huisheng ZHANG(), Shilie WENG
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

Connections among multi-energy systems become increasingly closer with the extensive application of various energy equipment such as gas-fired power plants and electricity-driven gas compressor. Therefore, the integrated energy system has attracted much attention. This paper establishes a gas-electricity joint operation model, proposes a system evaluation index based on the energy quality character after considering the grade difference of the energy loss of the subsystem, and finds an optimal scheduling method for integrated energy systems. Besides, according to the typical load characteristics of commercial and residential users, the optimal scheduling analysis is applied to the integrated energy system composed of an IEEE 39 nodes power system and a 10 nodes natural gas system. The results prove the feasibility and effectiveness of the proposed method.

Keywords integrated energy system      energy quality character      optimization      electric power system      natural gas system     
Corresponding Author(s): Huisheng ZHANG   
Online First Date: 03 December 2018    Issue Date: 21 December 2018
 Cite this article:   
Shixi MA,Shengnan SUN,Hang WU, et al. Decoupling optimization of integrated energy system based on energy quality character[J]. Front. Energy, 2018, 12(4): 540-549.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0597-4
https://academic.hep.com.cn/fie/EN/Y2018/V12/I4/540
Fig.1  Coupling process of subsystem
Fig.2  Independent optimal scheduling process for subsystems
Fig.3  Unified optimization process
Fig.4  Architecture of integrated energy system
Categories Nodes
Commercial load 1, 3, 4, 7, 8, 9, 18, 25, 31, 39
Resident load 12, 15, 16, 20, 21, 23, 24, 26, 27, 28, 29
Tab.1  Categories of load nodes
No. Rated power Efficiency
Rated active power/MW Rated reactive power/Mvar
Gas-fired power plant 1 500 200 0.4
2 600 300 0.4
3 650 300 0.4
4 600 250 0.4
5 450 150 0.4
6 650 300 0.4
7 550 240 0.4
8 500 250 0.4
9 450 –150 0.4
10 350 –100 0.4
Compressor A 15 0.85
B 5 0.85
C 5 0.85
Wind power plant 100
Solar power plant 100
Tab.2  Parameters of gas power plant and compressor
Fig.5  Load curve of commercial area and residential area
Fig.6  Power curve of wind power plant and solar power plant
Fig.7  Comparison of network loss using different methods
Fig.8  Loss reduction by using Method 2
Method 1 Method 2
Power output of gas power plants/MWh 86032.5 86381.3
Total energy consumption of compressor/MW 255.3 336.2
Loss of electric network WE/MWh 341.1 289.93
Loss of gas network WG/MWh 295.33 386.4
System loss IIES/MWh 636.43 676.33
Loss reduction/MWh 39.9
Tab.3  Comparison before and after optimization
Fig.9  Comparison of power output of each generator in condition 1
Fig.10  Comparison of power output of each generator in condition 2
Fig.11  Comparison of power consumption of compressor and loss in electric network in condition 1
Fig.12  Comparison of power consumption of compressor and loss in electric network in condition 2
1 L QBai, F X Li, H T Cui, T Jiang, H BSun, J XZhu. Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty. Applied Energy, 2016, 167: 270–279
https://doi.org/10.1016/j.apenergy.2015.10.119
2 QZeng, J K Fang, J H Li, Z Chen. Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion. Applied Energy, 2016, 184: 1483–1492
https://doi.org/10.1016/j.apenergy.2016.05.060
3 J HZheng, Q H Wu, Z X Jing. Coordinated scheduling strategy to optimize conflicting benefits for daily operation of integrated electricity and gas networks. Applied Energy, 2017, 192: 370–381
https://doi.org/10.1016/j.apenergy.2016.08.146
4 AGhasemi, M Banejad, MRahimiyan. Integrated energy scheduling under uncertainty in a micro energy grid. IET Generation, Transmission & Distribution, 2018, 12(12): 2887–2896
https://doi.org/10.1049/iet-gtd.2017.1631
5 WGu, J Wang, SLu, ZLuo, C Y Wu. Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings. Applied Energy, 2017, 199: 234–246
https://doi.org/10.1016/j.apenergy.2017.05.004
6 Z GPan, Q L Guo, H B Sun. Interactions of district electricity and heating systems considering time-scale characteristics based on quasi-steady multi-energy flow. Applied Energy, 2016, 167: 230–243
https://doi.org/10.1016/j.apenergy.2015.10.095
7 SCollins, J P Deane, K Poncelet, EPanos, R CPietzcker, EDelarue, B PÓ Gallachóir. Integrating short term variations of the power system into integrated energy system models: a methodological review. Renewable & Sustainable Energy Reviews, 2017, 76: 839–856
https://doi.org/10.1016/j.rser.2017.03.090
8 HRashidi, J Khorshidi. Exergoeconomic analysis and optimization of a solar based multigeneration system using multiobjective differential evolution algorithm. Journal of Cleaner Production, 2018, 170: 978–990
https://doi.org/10.1016/j.jclepro.2017.09.201
9 J LLiu, A N Wang, Y H Qu, W H Wang. Coordinated operation of multi-integrated energy system based on linear weighted sum and grasshopper optimization algorithm. IEEE Access: Practical Innovations, Open Solutions, 2018, 6: 42186–42195
https://doi.org/10.1109/ACCESS.2018.2859816
10 J NLiu, H Z Zhong, K W Zeng, H X Fan, Q X Chen. Optimal scheduling of multiple energy system considering power to gas unit. In: 2017 IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, 2017: 58–63
11 Y LWang, H Y Yu, M Y Yong, Y J Huang, F L Zhang, X H Wang. Optimal scheduling of integrated energy systems with combined heat and power generation, photovoltaic and energy storage considering battery lifetime loss. Energies, 2018, 11(7): 1676
https://doi.org/10.3390/en11071676
12 JYe, R X Yuan. Integrated natural gas, heat, and power dispatch considering wind power and power-to-gas. Sustainability, 2017, 9(4): 602
https://doi.org/10.3390/su9040602
13 SChen, Z N Wei, G Q Sun, K W Cheung, D Wang. Identifying optimal energy flow solvability in electricity-gas integrated energy systems. IEEE Transactions on Sustainable Energy, 2017, 8(2): 846–854
https://doi.org/10.1109/TSTE.2016.2623631
14 Y BJiang, J Xu, Y ZSun, C YWei, JWang, S Y Liao, D P Ke, X Li, JYang, X TPeng. Coordinated operation of gas-electricity integrated distribution system with multi-CCHP and distributed renewable energy sources. Applied Energy, 2018, 211: 237–248
https://doi.org/10.1016/j.apenergy.2017.10.128
15 X JZhang, G G Karady, S T Ariaratnam. Optimal allocation of CHP-based distributed generation on urban energy distribution networks. IEEE Transactions on Sustainable Energy, 2014, 5(1): 246–253
https://doi.org/10.1109/TSTE.2013.2278693
16 AMartinez-Mares, C R Fuerte-Esquivel. A unified gas and power flow analysis in natural gas and electricity coupled networks. IEEE Transactions on Power Systems, 2012, 27(4): 2156–2166
https://doi.org/10.1109/TPWRS.2012.2191984
17 Z HJiang, X W Yu. Modeling and control of an integrated wind power generation and energy storage system. In: 2009 IEEE Power & Energy Society General Meeting, Calgary, Canada, 2009, 1–8: 2612–2619
18 S XMa, D J Zhou, H S Zhang, Z H Lu. Modeling and optimal operation of a network of energy hubs system with distributed energy resources. In: Proceedings of the ASME Turbo Expo: Turbine Technical Conference and Exposition, 2017
19 EGholamian, P Hanafizadeh, AHabibollahzade, PAhmadi. Evolutionary based multi-criteria optimization of an integrated energy system with SOFC, gas turbine, and hydrogen production via electrolysis. International Journal of Hydrogen Energy, 2018, 43(33): 16201–16214
https://doi.org/10.1016/j.ijhydene.2018.06.130
20 Z SChen, W H Xie, P Hu, LJia, MShi. An effective thermodynamic transformation analysis method for actual irreversible cycle. Science China. Technological Sciences, 2013, 56(9): 2188–2193
https://doi.org/10.1007/s11431-013-5298-y
21 SChen, Z N Wei , G G Sun , Y LSun, H XZang , YZhu. Optimal power and gas how with a limited number of control actions IEEE Transactions on Smart Grid , 2018, 9(5): 5371–5380
https://doi.org/10.1109/TSG.2017.2687621
22 YHu, H Lian, ZBie, BZhou. Unified probabilistic gas and power flow. Journal of Modern Power Systems and Clean Energy, 2017, 5(3): 400–411
https://doi.org/10.1007/s40565-017-0284-1
23 C AKang, A R Brandt, L J Durlofsky. Optimal operation of an integrated energy system including fossil fuel power generation, CO2 capture and wind. Energy, 2011, 36(12): 6806–6820
https://doi.org/10.1016/j.energy.2011.10.015
24 MGranovskii, I Dincer, M ARosen. Exergy and industrial ecology: an application to an integrated energy system. International Journal of Exergy, 2008, 5(1): 52–63
https://doi.org/10.1504/IJEX.2008.016012
25 S XMa, D J Zhou, H S Zhang, Z H Lu. Micro gas turbine/renewable hybrid power system for distributed generation: effects of ambient conditions on control strategy. In: Proceedings of the ASME Turbo Expo: Turbine Technical Conference and Exposition, 2016
26 I GDamousis, A G Bakirtzis, P S Dokopoulos. A solution to the unit-commitment problem using integer-coded genetic algorithm. IEEE Transactions on Power Systems, 2004, 19(2): 1165–1172
https://doi.org/10.1109/TPWRS.2003.821625
27 SAn, Q Li, T WGedra. Natural gas and electricity optimal power flow. In: Proceedings of 2003 IEEE PES Transmission and Distribution Conference & Exposition, Dallas, TX, USA, 2003, 1–3: 138–143
28 S XQi, X L Wang, Y F Wang, S J Tian, R G Wang. Integrated probabilistic energy flow analysis in natural gas and electricity coupled systems considering the randomness of wind power. In: 2017 IEEE Conference on Energy Internet and Energy System Integration, 2017
29 YYuan, J Kubokawa, HSasaki. A solution of optimal power flow with multicontingency transient stability constraints. IEEE Transactions on Power Systems, 2003, 18(3): 1094–1102
https://doi.org/10.1109/TPWRS.2003.814856
30 GChicco, R Napoli, PPostolache, MScutariu, C MToader. Customer characterization options for improving the tariff offer. IEEE Transactions on Power Systems, 2003, 18(1): 381–387
https://doi.org/10.1109/TPWRS.2002.807085
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