RESEARCH ARTICLE

Modeling, evaluation, and optimization of gas-power and energy supply scenarios

  • Hossam A. GABBAR , 1 ,
  • Aboelsood ZIDAN 2
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  • 1. Faculty of Energy Systems and Nuclear Science; Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa LIH 7K4, Canada
  • 2. Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Oshawa LIH 7K4, Canada; Department of Electrical Engineering, Faculty of Engineering, Assiut University, Assiut 71515, Egypt

Received date: 26 Aug 2015

Accepted date: 28 Nov 2015

Published date: 17 Nov 2016

Copyright

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

Recently, renewable energy sources such as wind power and photovoltaic (PV) are receiving a wide acceptance because they are inexhaustible and nonpolluting. Renewable energy sources are intermittent ones because of climate changes in wind speed and solar irradiance. Due to the continuous demand growth and the necessity for efficient and reliable electricity supply, there is a real need to increase the penetration of gas technologies in power grids. The Canadian government and stakeholders are looking for ways to increase the reliability and sustainability of power grid, and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable generation technologies to provide a qualified, reliable, and environmentally friendly power system while satisfying regional electricity demands and reducing generation cost. Scenarios are evaluated using four key performance indicators (KPIs), economic, power quality, reliability, and environmental friendliness. Various scenarios outcomes are compared based on the defined performance indices. The proposed scenario analysis tool has three components, the geographic information system (GIS) for recording transmission and distribution lines and generation sites, the energy semantic network (ESN) knowledgebase to store information, and an algorithm created in Matlab/Simulink for evaluating scenarios. To interact with the scenario analysis tool, a graphical user interface (GUI) is used where users can define the desired geographic area, desired generation percentage via gas technology, and system parameters. To evaluate the effectiveness of the proposed method, the regional zone of the province of Ontario and Toronto are used as case studies.

Cite this article

Hossam A. GABBAR , Aboelsood ZIDAN . Modeling, evaluation, and optimization of gas-power and energy supply scenarios[J]. Frontiers in Energy, 0 , 10(4) : 393 -408 . DOI: 10.1007/s11708-016-0422-x

Acknowledgment

This project is funded by NSERC, Hydrogenics, Veridian, Intergraph, and MaRS as part of Ontario’s initiatives to promote gas-power grids.
1
Zidan A, Shaaban M F, El-Saadany E F. Long-term multi-objective distribution network planning by DG allocation and feeders’ reconfiguration. Electric Power Systems Research, 2013, 105: 95–104

DOI

2
Gabbar H A, Abdelsalam A A. Microgrid energy management in grid-connected and islanding modes based on SVC. Energy Conversion and Management, 2014, 86: 964–972

DOI

3
Majumder R, Dewadasa M, Ghosh A, Ledwich G, Zare F. Control and protection of a microgrid connected to utility through back-to-back converters. Electric Power Systems Research, 2011, 81(7): 1424–1435

DOI

4
Zhang L, Gari N, Hmurcik L V. Energy management in a microgrid with distributed energy resources. Energy Conversion and Management, 2014, 78: 297–305

DOI

5
Gabbar H A. Engineering design of green hybrid energy production and supply chains. Journal of Environmental Modelling & Software, 2009, 24(3): 423–435

DOI

6
Gabbar H A, Honarmand N, Abdelsalam A A. Resilient micro energy grids for continuous production in oil and gas facilities. Advances in Robotics and Automation, 2014, 3: 125

7
Gahleitner G. Hydrogen from renewable electricity: an international review of power-to-gas pilot plants for stationary applications. International Journal of Hydrogen Energy, 2013, 38(5): 2039–2061

DOI

8
Urbina M, Li L Z. A combined model for analyzing the interdependency of electrical and gas systems. In: IEEE 39th North American Power Symposium. 2007, 468–472

9
Jouneghani A, Parvizi R, Amidpour M, Chaibakhsh A. Gas based distributed generation systems, a key to Iran buildings growing energy demand. In: IEEE 2nd International Conference on Power and Energy. 2008, 1592–1596

10
Canadian Association of Petroleum Producers. Natural gas. 2015–05, http://www.capp.ca/canadian-oil-and-natural-gas/natural-gas

11
Hydrogenics. Power-to-gas: bridging the power grid and natural gas system. 2015–03, http://www.hydrogenics.com/hydrogen-products-solutions/energy-storage-fueling-solutions/power-to-gas

12
Gabbar H A, Bower L, Pandya D. Ontario energy supply network modeling evaluation and optimization of gas-power conversion and supply technologies.<Date> 2014–10–22</Date>, http://faculty.uoit.ca/gaber/GasPower.htm

13
Mehrizi-Sani A, Iravani R. Potential-function based control of a microgrid in islanded and grid-connected modes. IEEE Transactions on Power Systems, 2010, 25(4): 1883–1891

DOI

14
Wang Z, Wang J. Self-healing resilient distribution systems based on sectionalization into microgrids. IEEE Transactions on Power Systems, 2015, 30(6): 3139–3149

DOI

15
Gabbar H A, Abdelsalam A. Microgrid energy managements in grid-connected and islanded modes based SVC. Energy Conversion and Management, 2014, 86: 964–972

DOI

16
Abdelsalam A A, Gabbar H A, Sharaf A M. Performance enhance-ment of hybrid AC/DC microgrid based D-FACTS. International Journal of Electrical Power & Energy Systems, 2014, 63: 382– 393

DOI

17
Abdelsalam A A, Gabbar H A, Sharaf A M. Control for hybrid AC/DC microgrid with D-FACTS using genetic algorithm. International Journal of Power and Energy Systems, 2014, 34

18
Islam Md R, Gabbar H A. Study of small modular reactors in modern microgrids. International Transactions on Electrical Energy Systems, 2015, 25(9): 1943–1951

DOI

19
Gabbar H A, Ming X, Abdelsalam A, Honarmand N. Key performance indicator modeling for micro grid design and operation evaluation. International Journal of Distributed Energy Resources and Smart Grids, 2014, 10: 219–242

20
Zangeneh A, Jadid S, Rahimi-Kian A. A hierarchical decision making model for the prioritization of distributed generation technologies: a case study for Iran. Energy Policy, 2009, 37(12): 5752–5763

DOI

21
Intergraph’s GeoMedia software. <Date>2015–03</Date>, http://www.hexagongeospatial.com/products/gis/geomedia/overview2015

22
Sayed H E, Gabbar H A, Miyazaki S. A hybrid statistical genetic-based demand forecasting expert system. Expert Systems with Applications, 2009, 36(9): 11662–11670

DOI

23
Sayed H E, Gabbar H A, Miyazaki S. Dynamic supply chain management information system simulator. ISCM2009, China, 2009

24
Union Gas. Full system map.<Date> 2014–09–20</Date>, http://uniongasairemissionplan.ca

25
Independent Electricity System Operator. System Status Report 2014. <Date>2014–09–20</Date>, http://reports.ieso.ca/public/SSR/

26
Independent Electricity System Operator. Real-time Constrained Totals Report 2014. <Date>2014–09</Date>, http://reports.ieso.ca/public/RealtimeConstTotals/

27
Lee S H, Kang H G. Integrated societal risk assessment framework for nuclear power and renewable energy sources. Nuclear Engineering and Technology, 2015, 47(4): 461–471

DOI

28
Open E I. Transparent Cost Database.<Date> 2014–09</Date>, http://en.openei.org/apps/TCDB/

29
Karady G, Sirisooriya P, Farmer R G. Investigation of fuel cell system performance and operation: a fuel cell as a practical distributed generator. Project Report, Arizona State University, PSERC Publication, 2002

30
Thomas S, Lee S C, Sahu A K, Park S. Online health monitoring of a fuel cell using total harmonic distortion analysis. International Journal of Hydrogen Energy, 2014, 39(9): 4558–4565

DOI

31
Bruno J C, Massagues L, Coronas A. Harmonic distortion analysis of a micro gas turbine interconnected to the electricity grid. <Date>2015–05</Date>, http://www.icrepq.com/PONENCIAS/4.289.BRUNO.pdf

32
White S A, Barkhausen A. Noise testing system for CANDU feeder pipes. Dissertation for the Doctoral Degree. Queen’s University, <Date>2014–09</Date>, http://qspace.library.queensu.ca/handle/1974/1994?mode=full

33
Ribeiro L C, Oliveira L, Bonaldi E, Silva L, Salomon C, Silva J, Lambert-Torres G. Automatic system for failure detection in hydro-power generators. Journal of Power and Energy Engineering, 2014, 2(4): 36–46

DOI

34
Chen Z, Spooner E. Grid power quality with variable-speed wind turbines. IEEE Power Engineering Review, 2001, 21(6): 148–154

DOI

35
Urbanetz J, Braun P, Rüther R. Power quality analysis of grid-connected solar photovoltaic generators in Brazil. Energy Conversion and Management, 2012, 64: 8–14

DOI

36
Kandil M S, El-Saadawi M M, Hassan E A, Abo-Al-Ez K M. Performance analysis of a biomass micro-turbine DG for rural electrification. In: The 3rd International Conference on Power Engineering and Optimization. Selangor, Malaysia, 2009, 1–7

37
Power Generation. Dry low NOx 2.6+ combustion solution. <Date>2015–04</Date>, http://site.ge-energy.com/prod_serv/products/tech_docs/en/downloads/ger4172.pdf

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