ORIGINAL_ARTICLE
A study of the three R-type thinking in sustainable designs: assessing the energy efficiency through simulation in Australia
Energy crises and the continuous fluctuation cost of fossil fuels have moved researchers' attention towards new sustainable and renewable energy sources. The three R-type thinking (i.e., reduce, reuse, and recycle), utilizes three great ways to protect the environment by saving money, energy, and natural resources. The Australian Building Codes Board is considered as a project for energy efficiency. BCA has also identified eight different climate zones within Australia. This paper discusses the climate conditions of the state capital cities of Sydney, Adelaide, and Perth that belong to the same climate zone 5 of the BCA. On this basis, the present paper covered three major aims. At first, we are doing to identifying the similarities and the differences in climate conditions in case studies, as a result of bioclimatic features. Then, the thermal performance of the "green roof" was evaluated in all three cases. The simulation was carried out on a residential building block for one year (four seasons) using the Design Builder software. Finally, based on the findings of this paper, building orientation strategy was chosen to assess natural ventilation through BEopt V. 2.8 software on a residential building in Sydney. The results showed that the green roof in Sydney could be an optimal thermal performance, in comparison with other cases. Also, it can be stated that the findings of natural ventilation simulation show the most optimal building orientation in Sydney is 45 degrees to the southeast in which this among included 17% has better performance to improve wind flow.
https://www.jemat.org/article_95887_4c694f4b8b2b24d7f1e68f698dd539c9.pdf
2020-03-01
1
12
10.22109/jemt.2019.169212.1149
Three R-type
sustainable design
passive techniques
green roof
Energy efficiency
Building simulation
Natural Ventilation
Abdollah
Baghaei Daemei
baghaei.public@gmail.com
1
Young researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran
LEAD_AUTHOR
Mostafa
Kazemi
architectsafa@gmail.com
2
Department of Architecture and Urbanism, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
AUTHOR
Mahsa
Malekfarnoud
soltanxarmin@yahoo.com
3
Department of Architecture, Rasht Branch, Islamic Azad University, Rasht, Iran
AUTHOR
Seyed Mostafa
Tavakoli Mollasaraei
farnoodtavakoli@yahoo.com
4
Department of Architecture, Rasht Branch, Islamic Azad University, Rasht, Iran
AUTHOR
Ronak
Geravandi
geravandi40@gmail.com
5
Department of Architecture, University of Tehran, Tehran, Iran
AUTHOR
[1] Australian Bureau of Statistics. 4602.0 Environmental issues: people’s views and practices. Canberra: Australian Government; 2005.
1
[2] Australian Building Codes Board. Building code of Australia class 1 and 10 buildings. Canberra: Australian Government; 2006.
2
[3] Peterkin N (2009). Rewards for passive solar design in the Building Code of Australia, Renewable Energy, Volume 34, Issue 2: 440-443.
3
[4] McGee C (2013). www.yourhome.gov.au/passive-design.
4
[5] ASHRAE Standard 55 (2013) Thermal Environmental Conditions for Human Occupancy. Atlanta, ASHRAE Inc.
5
[6] ASHRAE Standard 62e (2013) Ventilation for Acceptable Indoor Air Quality, American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta, Ga, 2013.
6
[7] Baghaei Daemei A, Khalatbari Limaki A, Safari H (2016). Opening Performance Simulation in Natural Ventilation Using Design Builder (Case Study: A Residential Home in Rasht). Energy Procedia 100: 412-422.
7
[8] Zadfallah F, Baghaei Daemei A, Karimi A, Tavakoli Mollasaraei M (2018). Reflection vernacular architecture patterns of Rasht"s houses with an approach to form and climate (Case Studies), 5th National Conference on Applied Research in Civil Engineering, Architecture and Urban Management, Khajeh Nasir Toosi university of technology, Tehran, Iran.
8
[9] Baghaei Daemei A, Azmoodeh M, Zamani Z, Mehrinejad Khotbehsara E (2018). Experimental and simulation studies on the thermal behavior of vertical greenery system for temperature mitigation in urban spaces, Journal of Building Engineering, Volume 20: 277-284.
9
[10] https://energyplus.net/weather-region/southwest_pacific_wmo_region_5/AUS
10
[11] Milne M, Liggett R, Al-Shaali R (2007). Climate consultant 3.0: A tool for visualizing building energy implications of climates, Proceedings of the Solar Conference, American Solar Energy Society; American Institute of Architects, Volume 1: 466.
11
[12] Attia S, Lacombe T, Rakotondramiarana HT, Garde F, Roshan G (2019). Analysis tool for bioclimatic design strategies in hot humid climates, Sustainable Cities and Society, Volume 45: 8-24.
12
[13] Givoni B (1976) Man, Climate and Architecture. 2nd Ed. New York: Van Nostrand Reinhold.
13
[14] https://www.abcb.gov.au/Resources/Tools-Calculators/Climate-Zone-Map-NSW-and-ACT.
14
[15] Badescu V, Sicre B (2003). Renewable energy for passive house heating: Part I. Building description. Energy and Buildings 35(11): 1077-1084.
15
[16] Goudarzi H, Mostafaeipour A (2017). Energy saving evaluation of passive systems for residential buildings in hot and dry regions, In Renewable and Sustainable Energy Reviews, Volume 68, Part 1: 432-446.
16
[17] Gabriel P, Lídia R, Anna V, Josep MG, Luisa FC (2011). Green vertical systems for buildings as passive systems for energy savings, In Applied Energy, Volume 88, Issue 12: 4854-4859
17
[18] Gan G (1998). A parametric study of Trombe walls for passive cooling of buildings. Energy and Buildings 27(1): 37-43.
18
[19] Shaviv E, Yezioro A, Capeluto IG (2001). Thermal mass and night ventilation as passive cooling design strategy. Renewable Energy 24(3–4): 445-452.
19
[20] Baghaei Daemei A, Eghbali SR, Mehrinejad Khotbehsara E (2019). Bioclimatic design strategies: A guideline to enhance human thermal comfort in Cfa climate zones, Journal of Building Engineering, Volume 25: 100758.
20
[21] Baghaei Daemei A, Osmavandani PH, Nikpey MS (2018). Study on Vernacular Architecture Patterns to Improve Natural Ventilation Estimating in Humid Subtropical Climate, Civil Engineering Journal, Volume 4, No 9: 2097-2110.
21
[22] Mehrinejad Khotbehsara E, Purshaban F, Noormousavi Nasab S, Baghaei Daemei A, Eghbal Yakhdani P, Vali R (2018). Traditional Climate Responsible Solutions in Iranian Ancient Architecture in Humid Region, Civil Engineering Journal, Volume 4, No 10: 2502-2512.
22
[23] Shaviv E, Yezioro A, Capeluto IG (2001). Thermal mass and night ventilation as passive cooling design strategy. Renewable Energy 24(3–4): 445-452.
23
[24] Cole G (2002). Residential passive solar design. Environment design guide, GEN 12. Australian Institute of Architects, Melbourne. www.environmentdesignguide.com.au
24
[25] Mohammad S (2013). “Study of thermal behavior of common wall materials. Case Study: Tehran Residential Buildings”. journal of Fine Arts - Architecture and Urban Development, Vol. 18, No. 1: 69-78.
25
[26] Ouldboukhitine SE, Belarbi R, Jaffal I, Trabelsi A (2011). Assessment of green roof thermal behavior: A coupled heat and mass transfer model." Building and Environment 46(12): 2624-2631.
26
[27] Akbari H, Levinson R, Rainer L (2005). Monitoring the energy-use effects of cool roofs on California commercial buildings, Energy and Buildings, Volume 37, Issue 10: 1007-1016.
27
[28] Zinzi M, Agnoli S (2012). Cool and green roofs. An energy and comfort comparison between passive cooling and mitigation urban heat island techniques for residential buildings in the Mediterranean region, Energy and Buildings, Volume 55: 66-76.
28
[29] Almeida RMSF, Pinto M, Pinho PG, de Lemos LT (2017). Natural ventilation and indoor air quality in educational buildings: experimental assessment and improvement strategies, Energy Efficiency. 10: 839.
29
[30] Gan G, Riffat SB (1998). A numerical study of solar chimney for natural ventilation of buildings with heat recovery. Applied Thermal Engineering 18(12): 1171-1187.
30
[31] Baghaei Daemei A, Malekfarnoud M, Asgharzadeh Khorramdarehei M, Mardani H, Pilcheshm M (2018). Sustainability patterns in vernacular architecture In order to achieve passive design strategies (Case Study: Langroud and Lahijan Residential Houses), International Congress of the Sciences and Engineering of Hamburg, 21 March, Hamburg, Germany.
31
[32] Tavakoli M, Baghaei Daemei A, Safari H (2015), Simulation of the effect of Facade openings on building’s energy consumption with Design Builder Software Case Study: Anzali Village House, 2nd International Conference on Modern Research in Civil Engineering, Architecture and Urban Development, Karin Conference Institute, Turkey.
32
[33] CIBSE Guide A: Environmental Design (2015). Toronto Green Roof Construction Standard,” Supplementary Guidelines.
33
[34] https://mesonet.agron.iastate.edu/sites/windrose.phtml?station=YSSY&network=AU__ASOS
34
ORIGINAL_ARTICLE
Stochastic energy modeling with consideration of electrical vehicles and renewable energy resources-A review
Energy crisis and global warming due to fossil fuel implementation in the energy production sector and in the transportation sector have stimulated global trends to employ the electric vehicles (EVs) in the transportation sector and renewable energy sources (RESs) in the power generation. Coordinated charging of EVs can bring some benefits by itself such as voltage and frequency regulation, spinning reserve, load leveling, peak shaving, RESs support, GHG emission saving and so on. But implementation of this scenario with uncoordinated EV charging which can impose a huge amount of excess load on the grid. In this regard, EVs coordinated energy scheduling is inevitable. This paper comprehensively reviewed the pros and cons of integrating EVs to the grid and recent investigations in EV energy scheduling especially ones that focused on stochastic energy scheduling. Moreover, with knowing this fact that, microgrid with the presence of different distributed generation (DG) such as RESs and a diverse storage system such as EVs would have an important role in the future smart grid, thus, this paper aims to illustrate further research opportunities in this particular field. Also, different types of uncertain variables in recent studies and mathematical methods for optimizing the relevant objective functions of EVs charging are reviewed inclusively. Finally, future trends and investigation occasions in this field of study are discussed.
https://www.jemat.org/article_91186_e442fa3ba858a265a676c485a2b6c997.pdf
2020-03-01
13
26
10.22109/jemt.2019.174242.1162
stochastic optimization
electric vehicles
Renewable Energy Sources
microgrid
heuristic optimization
Younes
Noorollahi
noorollahi@ut.ac.ir
1
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Armin
Aligholian
y_noorollahi@yahoo.com
2
Department of Electrical and Computer Engineering, University of California Riverside
AUTHOR
Aminabbas
Golshanfard
golshanfard.amin@gmail.com
3
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
[1] "The way forward," www.IEA.org, 2014.
1
[2] Y. Noorollahi, R. Itoi, H. Yousefi, M. Mohammadi and a. A. Farhadi, "Modeling for Diversifying Electricity Supply by Maximizing Renewable Energy Use in Ebino City Southern Japan," no. 34, p. 371–84, 2017.
2
[3] M. Mohammadi, Y. Noorollahi and B. Mohammadi-ivatloo, "Demand Response Participation in Renewable Energy Hubs,," in Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs,, Springer International Publishing,, 2018, p. pp. 129–16.
3
[4] B. Diouf and R. Pode, "Potential of lithium-ion batteries in renewable energy," Renewable Energy, vol. 76, pp. 375-380, 2015.
4
[5] W. Kempton and S. E. Letendre, "Electric vehicles as a new power source for electric utilities," Transportation Research Part D: Transport and Environment, vol. 2, no. 3, pp. 157-175, 1997.
5
[6] Y. Zhao, M. Noori and O. Tatari, "Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis," Applied Energy, vol. 170, pp. 161-175, 2016.
6
[7] P. K. S. M. M. Bhaskar Naik, "Smart public transportation network expansion and its interaction with the grid," Electrical Power and Energy Systems , vol. 105 , p. 365–380, 2019.
7
[8] E. L. Karfopoulos and N. Hatziargyriou, "Distributed Coordination of Electric Vehicles Providing V2G Services," IEEE TRANSACTIONS ON POWER SYSTEMS 1, 2015.
8
[9] S. Shafiee, M. Fotuhi-Firuzabad and M. Rastegar, "Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems," IEEE TRANSACTIONS ON SMART GRID, vol. 4, pp. 1351-1360, 2013.
9
[10] H. N. Nguyen, C. Zhang and a. M. A. Mahmud, "Optimal Coordination of G2V and V2G to Support Power Grids with High Penetration of Renewable Energy," IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2015.
10
[11] J. Soares, M. A. F. Ghazvini, Z. Valel and P. d. M. Oliveira, "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, vol. 162, pp. 1074-1088, 2016.
11
[12] H. Jadhav and R. Roy, "Stochastic optimal power flow incorporating offshore wind farm and electric vehicles," Electrical Power and Energy Systems, vol. 69, pp. 173-187, 2015.
12
[13] D. maringer, "heuristic optimization," in portfolio management with heuristic optimization, Springer, 2005, pp. 38-76.
13
[14] M. Mohammadi, Y. Noorollahi and a. B. Mohammadi-Ivatloo, "An Introduction to Smart Energy Systems and Definition of Smart Energy Hubs.," in In Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs, , Cham: Springer, 2018, pp. 1-21.
14
[15] C. Liu, K. T. Chau, D. Wu and S. Gao, "Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies," IEEE, vol. 101, no. 11, pp. 2409-2427, 2013.
15
[16] K. M. Tan, V. K. Ramachandaramurthy and J. Y. Yong, "Integration of electric vehicles in smart grid :A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, vol. 53, pp. 720-732, 2016.
16
[17] I. Rahman, P. M. Vasant, B. S. M. Singh, M. A. -A.-. Wadud and N. Adnan, "Review of recent trends inoptimization techniques for plug-in hybrid, and elecrtic vehicle for charging infrustructures," Renewable and Sustainable Energy Reviews, vol. 58, pp. 1039-1047, 2016.
17
[18] P. Grahn, J. Munkhammar, J. Widén, K. Alvehag and L. Söder, "PHEV Home-Charging Model Based on Residential Activity Patterns," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, pp. 2507-2515, 2013.
18
[19] V. T. Bina and D. Ahmadi, "Stochastic modeling for scheduling the charging demand of EV in distribution systems using copulas," Electrical Power and Energy Systems, vol. 71, pp. 15-25, 2015.
19
[20] "The impacts of demand response participation in capacity markets," Applied Energy , vol. 250, p. 444–451, 2019.
20
[21] J. M. M. S.-k. A. R. J. P. C. S. Muhammad Bagher Sadati, "Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach," International Journal of Electrical Power & Energy Systems, vol. 105, pp. 159-178, 2019.
21
[22] Z. Wang, R. Yang, L. Wang and J. Tan, "Reliability Assessment of Integrated Residential Distribution and PHEV Systems Using Monte Carlo Simulation," IEEE, 2013.
22
[23] M. Alipour, B. Mohammadi-Ivatloo, M. Moradi-Dalvand and K. Zare, "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, vol. 118, pp. 1168-1179, 2017.
23
[24] A. Nazarloo, M. R. Feyzi, m. sabahiorcid and M. B. B. Sharifian, "Energy Management of Electric Vehicles Aggregator Using a New Multi-Objective Algorithm," Journal of Energy Management and Technology (JEMT), vol. 2, no. 2, pp. 20-30, 2018.
24
[25] R. S. A. Luis Baringo, "A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator," Electric Power Systems Research , vol. 146, p. 362–370, 2017.
25
[26] R. J. Bessa and M. A. Matos, "The role of an Aggregator Agent for EV in the electricity market," in 7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion, Agia Napa, Cyprus, 2010.
26
[27] P. Aliasghari, B. Mohammadi-Ivatloo, M. Alipour, M. Abapour and K. Zare, "Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program," Journal of Cleaner Production, vol. 186, pp. 293-303, 2018.
27
[28] M. H. Amirioun and A. Kazemi, "A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex," Energy, vol. 69, pp. 186-198, 2014.
28
[29] M. Ghahramani, M. Nazari-Heris, K. Zare and B. Mohammadi-Ivatloo, " Energy Management of Electric Vehicles Parking in a Power Distribution Network Using Robust Optimization Method," Journal of Energy Management and Technology, vol. 2, no. 3, pp. 22-30, 2018.
29
[30] E. L. Karfopoulos and N. D. Hatziargyriou, "A Multi-Agent System for Controlled Charging of a Large Population of Electric Vehicles," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, 2013.
30
[31] C. Ma, J. Rautiainen, D. Dahlhaus, A. Lakshman, J. C. Toebermann and M. Braun, "Online optimal charging strategy for Electric Vehicles," Energy Procedia, vol. 73, no. 9th International Renewable Energy Storage Conference, p. 173 – 181, 2015.
31
[32] R. D.-L. J. Y.-L. J. A.-S. J. B.-A. J.A. Domínguez-Navarro⁎, "Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems," Electrical Power and Energy Systems, vol. 105 , p. 46–58, 2019.
32
[33] A. Khazali and M. Kalantar, "A stochastic–probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles," Energy conversion and management, vol. 105, pp. 1046-1058, 2015.
33
[34] M. Neaimeh, R. Wardle, A. M. Jenkins, J. Yi, G. Hill, P. F. Lyons, Y. Hübner, P. T. Blythe and P. C. Taylor, "A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts," applied energy, 2015.
34
[35] M. Moeini-Aghtaie, A. Abbaspour and M. Fotuhi-Firuzabad, "Online Multi-Criteria Framework for Charging Management of PHEVs," IEEE Transactions on Vehicular Technology, 2014.
35
[36] M. Mohammadi, Y. Noorollahi and a. B. Mohammadi-Ivatloo, "Impacts of Energy Storage Technologies and Renewable Energy Sources on Energy Hub Systems.," in In Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs,, Springer publishing International, 2018, pp. 22-53.
36
[37] A. Ahmadian, M. Sedghi, B. Mohammadi-ivatloo, A. Elkamel, M. A. Golkar and M. Fowler, "Cost-Benefit analysis of V2G implementation in distribution networks considering PEVs battery degradation," IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 961-970, 2018.
37
[38] R. Romo and O.Micheloud, "Power quality of actual grids with plug-in electric vehicles in presence of renewables and micr-grids," Renewable and Sustainable Energy Reviews, vol. 46, pp. 189-200, 2015.
38
[39] C. Guille and G. Gross, "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, vol. 37, no. 11, pp. 4379-4390, 2009.
39
[40] F. Fattori, N. Anglani and G. Muliere, "Combining photovoltaic energy with electric vehicles, smart charging and vehicle-to-grid," solar energy, vol. 110, pp. 438-451, 2014.
40
[41] H. Lund and W. Kempton, "Integration of renewable energy into the transport and electricity sectors through V2G," energy policy, vol. 36, pp. 3578-3587, 2008.
41
[42] Y. Ota, H. Taniguchi, T. Nakajima, K. M. Liyanage, J. Baba and A. Yokoyama, "Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging," IEEE TRANSACTIONS ON SMART GRID, vol. 3, 2012.
42
[43] A. Oshnoei, M. T. Hagh, R. Khezri and B. Mohammadi-Ivatloo, "Application of IPSO and fuzzy logic methods in electrical vehicles for efficient frequency control of multi-area power systems," in 2017 Iranian Conference on Electrical Engineering (ICEE) , Tehran, 2017.
43
[44] M. Panto, "Exploitation of Electric-Drive Vehicles in Electricity Markets," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 27, pp. 682-694, 2012.
44
[45] H. Hashemi-Dezaki, M. Hamzeh, H. Askarian-Abyaneh and H. Haeri-Khiavi, "Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation," Energy Conversion and Management, vol. 100, pp. 262-276, 2015.
45
[46] C. Rathore and R. Roy, "Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning," Electrical Power and Energy Systems, vol. 75, pp. 59-73, 2016.
46
[47] A. Rabiee, M. Sadeghi, J. Aghaeic and A. Heidari, "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, vol. 57, pp. 721-739, 2016.
47
[48] V. N. Coelho, I. M. Coelho, B. N. Coelho, M. W. Cohen, A. J. Reis, S. M. Silva, M. J. Souza, P. J. Fleming and F. G. G. aes, "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, vol. 89, pp. 730-742, 2016.
48
[49] E. Karan, S. Asadi and L. Ntaimo, "A stochastic optimization approach to reduce greenhouse gas emissions from buildings and transportation," Energy, vol. 106, pp. 367-377, 2016.
49
[50] A. Y. Saber and G. K. Venayagamoorthy, "Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 58, 2011.
50
[51] S. Khormali and F. Mottola, "single-objective approaches for microgrid scheduling in the presence of plug-in vehicle fleets and datacenters," IEEE, 2015.
51
[52] A. El-Zonkoly, "Intelligent energy management of optimally located renewable energy systems incorporating PHEV," Energy Conversion and Management, vol. 84, pp. 427-435, 2014.
52
[53] M. Mohammadi, Y. Noorollahi, B. Mohammadi-ivatloo and a. H. Yousefi’, "Energy Hub: From a Model to a Concept – A Review," Renewable and Sustainable Energy Reviews, vol. 80, p. 1512–27, 2017.
53
[54] B. Skugor and J. Deur, "Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model," energy, pp. 1-10, 2015.
54
[55] M. Honarmanda, A. Zakariazadeh and S. Jadid, "Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization," Journal of the Franklin Institute, vol. 352, pp. 449-467, 2015.
55
[56] J. Munkhammar, J. Widén and J. Rydén, "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, vol. 142, pp. 135-143, 2015.
56
[57] M. Wang, Y. Mu, H. e. Jia, P. Zeng, J. Wu and W. Sheng, "An Efficient Power Plant Model of Electric Vehicles for Unit Commitment of Large Scale Wind Farms," Energy Procedia, vol. 75, pp. 1059-1064, 2015.
57
[58] M. E. Khodayar, LeiWu and M. Shahidehpour, "Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC," IEEE TRANSACTIONS ON SMART GRID, vol. 3, pp. 1271-1279, 2012.
58
[59] A. Bilh, Naik, Kshirasagar, El-Shatshat and Ramadan, "An Adaptive Charging Algorithm for Electric Vehicles in Smart Grids," IEEE, vol. 81, no. Vehicular Technology Conference (VTC Spring), pp. 1-7, 2015.
59
[60] L. He, J. Yang, J. Yan, Y. Tang and H. He, "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, vol. 168, pp. 179-192, 2016.
60
[61] g. Romero-Ruiz, J. Pérez-Ruiz, S. Martin, J. Aguado and S. D. l. Torre, "Probabilistic congestion management using EVs in a smart grid with intermittent renewable generation," Electric Power Systems Research, vol. 137, pp. 155-162, 2016.
61
[62] A. Zakariazadeh, S. Jadid and P. Siano, "Integrated operation of electric vehicles and renewable generation in a smart distribution system," Energy Conversion and Management, vol. 89, pp. 99-110, 2015.
62
[63] M. Honarmand, A. Zakariazadeh and S. Jadid, "Integrated scheduling of renewable generation and electric vehiclesparking lot in a smart micro grid," Energy Conversion and Management, vol. 86, pp. 745-755, 2014.
63
[64] A. Banerji, D. Sen, A. K. Bera, D. Ray, D. Paul, A. Bhakat and S. K. Biswas, "Microgrid: A review," in Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), 2013 IEEE, Trivandrum , 2013.
64
[65] Y. Noorollahi, M. S. Shabbir, A. F. Siddiqi, L. K. Ilyashenko and a. E. Ahmadi, "Review of Two Decades Geothermal Energy Development in Iran, Benefits, Challenges, and Future Policy," Geothermics, vol. 77, p. 257–66., 2019.
65
[66] H. Liang and W. Zhuang, "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, vol. 7, pp. 2027-2050, 2014.
66
[67] W. Su, J. Wang and J. Roh, "Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources," IEEE TRANSACTIONS ON SMART GRID, 2013.
67
[68] M. Z. Oskouei and A. S. Yazdankhah, "Scenario-based stochastic optimal operation of wind, photovoltaic, pump-storage hybrid system in frequency- based pricing," Energy Conversion and Management, vol. 105, pp. 1105-1114, 2015.
68
[69] Y. Wang, B. Wang, C.-C. Chu, H. Pota and R. Gadh, "Energy management for a commercial building microgrid with stationary and mobile battery storage," Energy and Buildings, vol. 116, pp. 141-150, 2016.
69
[70] A. Kavousi-Fard, A. Abunasri, A. Zare and R. Hoseinzadeh, "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, vol. 78, pp. 904-915, 2014.
70
[71] A. Rabiee, M. Sadeghi, J. Aghaeic and A. Heidari, "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, vol. 57, pp. 721-739, 2016.
71
[72] A. J. Conejo, M. Carrión and J. M. Morales, "Stochastic Programming Fundamentals," in Decision Making Under Uncertainty in Electricity markets, New York, Springer, 2010, pp. 27-62.
72
[73] Y.-T. Liao and C.-N. Lu, "Dispatch of EV Charging Station Energy Resources for Sustainable Mobility," IEEE Transactions on Transportation Electrification, vol. 1, no. 1, pp. 86-93, 2015.
73
[74] M. Mohammadi, Y. noorollahi, B. Mohammadi-ivatloo, H. yousefi and S. jalilinasrabady, "Optimal Scheduling of Energy Hubs in the Presence of Uncertainty-A Review," Journal of Energy Management and Technology (JEMT), vol. 1, no. 1, pp. 1-17, 2017.
74
[75] A. Mirakyan and R. D. Guio, "Modelling and uncertainties in integrated energy planning," Renewable and Sustainable Energy Reviews, vol. 46, pp. 62-69, 2015.
75
[76] M. Alizadeh, A. Scaglione, J. Davies and K. S. Kurani, "A Scalable Stochastic Model for the Electricity Demand of Electric and Plug-In Hybrid Vehicles," IEEE TRANSACTIONS ON SMART GRID, vol. 2, pp. 848-560, 2014.
76
[77] L. Jian, Y. Zheng, X. Xiao and C. C. Chan, "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, vol. 146, pp. 150-161, 2015.
77
[78] A. Ghasemi, S. S. Mortazavi and E. Mashhour, "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, vol. 85, pp. 124-136, 2016.
78
[79] Z. YANG, K. LI, Q. NIU, Y. XUE and A. FOLEY, "A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads," J. Mod. Power Syst. Clean Energy, vol. 2, no. 4, pp. 298-307, 2014.
79
[80] M. Govardhan and R. Roy, "Economic analysis of unit commitment with distributed energy resources," Electrical Power and Energy Systems, vol. 71., pp. 1-14, 2015.
80
[81] L. Zhang, Q. Niu, Z. Yang and K. Li, "Integration of electric vehicles charging in unit commitment," International Journal of Computer Science and Electronics Engineering, vol. 3, no. 1, pp. 22-27, 2015.
81
[82] N. a. A. H. M. a. P. K. Taghizadegan Kalantari, "Bibliographic Review and Comparison of Optimal Sizing Methods for Hybrid Renewable Energy Systems," Journal of Energy Management and Technology (JEMT), vol. 2, no. 2, pp. 66-79, 2018.
82
[83] A. Zakariazadeh, S. Jadid and P. Siano, "Integrated operation of electric vehicles and renewable generation in a smart distribution system," Energy Conversion and Management, vol. 89, pp. 99-110, 2015.
83
[84] D. Madzharov, E. Delarue and W. D’haeseleer, "Integrating electric vehicles as flexible load in unit commitment modeling," Energy, vol. 65, pp. 285-294, 2014.
84
[85] B. M.-I. G. G. M. Nazari-Herisa, "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, vol. 81, p. 2128–2143, 2018.
85
[86] M. Ghofrani, A. Arabali and M. Etezadi-Amoli, "Electric drive vehicle to grid synergies with large scale wind resources," in IEEE Power and Energy Society General Meeting , San Diego, CA, 2012.
86
[87] M. F. Shaaban, Y. M. Atwa and E. F. El-Saadany, "PEVs Modeling and Impacts Mitigation in Distribution Networks," IEEE TRANSACTIONS ON POWER SYSTEMS 1, 2012.
87
[88] E. Talebizadeh, M. Rashidinejad and A. Abdollahi, "Evaluation of Plug-in Electric vehicles Impact on Cost-Based Unit Commitment," Journal of Power Sources, 2013.
88
[89] U. K. Debnath, I. Ahmad, D. Habibi and A. Y. Saber, "Energy storage model with gridable vehicles for economic load dispatch in the smart grid," Electrical Power and Energy Systems, vol. 64, pp. 1017-1024, 2015.
89
[90] N. Zhang, Z. Hu, D. Dai, S. Dang, M. Yao and Y. Zhou, "Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading," IEEE TRANSACTIONS ON SMART GRID 1, 2015.
90
[91] F. Jabari, H. Jabari, B. Mohammadi-ivatloo and J. Ghafouri, "Optimal short-term coordination of water-heat-power nexus incorporating plug-in electric vehicles and real-time demand response programs," Energy, 2019.
91
[92] A. El-Zonkoly and L. d. S. Coelho, "Optimal allocation, sizing of PHEV parking lots in distribution system," Electrical Power and Energy Systems, vol. 67, pp. 472-477, 2015.
92
[93] Y. Zheng, Z. Y. Dong, Y. Xu, K. Meng, J. H. Zhao and J. Qiu, "Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning," IEEE Transactions on Power Systems , vol. 29, no. 1, pp. 221 - 229, 2014.
93
[94] J. Tong, T. Zhao, X. Yang and J. Zhang, "Intelligent charging strategy for PHEVs in parking stations based on Multi-objective optimization in smart grid," in Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo , Beijing, 2014.
94
[95] F. Fazelpour, M. Vafaeipour, O. Rahbari and M. A. Rosen, "Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics," Energy Conversion and Management, vol. 77, pp. 250-261, 2014.
95
[96] W. Su and M.-Y. Chow, "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, vol. 96, no. smart grid, pp. 171-182, 2012.
96
[97] M. Ghofrani, A. Arabali, M. Etezadi-Amoli and M. S. Fadali, "Smart Scheduling and Cost-Benefit Analysis of Grid-Enabled Electric Vehicles for Wind Power Integration," IEEE TRANSACTIONS ON SMART GRID, 2014.
97
[98] Z. Luo, Z. Hu, Y. Song, Z. Xu and H. Lu, "Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part I: Enabling Techniques," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, no. 4, pp. 3546-3555, 2013.
98
[99] W. Su and M.-Y. Chow, "Performance evaluation of a PHEV parking station using Particle Swarm Optimization," in 2011 IEEE Power and Energy Society General Meeting , San Diego, CA, 2011.
99
[100] A. Y. Saber and G. K. Venayagamoorthy, "Optimization of vehicle-to-grid scheduling in constrained parking lots," in 2009 IEEE Power & Energy Society General Meeting , Calgary, AB , 2009.
100
[101] T. Ghanbarzadeh, S. Goleijani and M. P. Moghaddam, "Reliability constrained unit commitment with electric vehicle to grid using Hybrid Particle Swarm Optimization and Ant Colony Optimization," in 2011 IEEE Power and Energy Society General Meeting, San Diego, CA , 2011.
101
[102] S. Yang, M. Wu, X. Yao and J. Jiang, "Load Modeling and Identification Based on Ant Colony Algorithms for EV Charging Stations," IEEE Transactions on Power Systems , vol. 30, no. 4, pp. 1997-2003, 2015.
102
[103] C. Quinn, D. Zimmerle and T. H. Bradley, "An Evaluation of State-of-Charge Limitations and Actuation Signal Energy Content on Plug-in Hybrid Electric Vehicle, Vehicle-to-Grid Reliability, and Economics," IEEE TRANSACTIONS ON SMART GRID, vol. 3, 2012.
103
"The way forward," www.IEA.org, 2014.
104
Y. Noorollahi, R. Itoi, H. Yousefi, M. Mohammadi and a. A. Farhadi, "Modeling for Diversifying Electricity Supply by Maximizing Renewable Energy Use in Ebino City Southern Japan," no. 34, p. 371–84, 2017.
105
M. Mohammadi, Y. Noorollahi and B. Mohammadi-ivatloo, "Demand Response Participation in Renewable Energy Hubs,," in Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs,, Springer International Publishing,, 2018, p. pp. 129–16.
106
B. Diouf and R. Pode, "Potential of lithium-ion batteries in renewable energy," Renewable Energy, vol. 76, pp. 375-380, 2015.
107
W. Kempton and S. E. Letendre, "Electric vehicles as a new power source for electric utilities," Transportation Research Part D: Transport and Environment, vol. 2, no. 3, pp. 157-175, 1997.
108
Y. Zhao, M. Noori and O. Tatari, "Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis," Applied Energy, vol. 170, pp. 161-175, 2016.
109
P. K. S. M. M. Bhaskar Naik, "Smart public transportation network expansion and its interaction with the grid," Electrical Power and Energy Systems , vol. 105 , p. 365–380, 2019.
110
E. L. Karfopoulos and N. Hatziargyriou, "Distributed Coordination of Electric Vehicles Providing V2G Services," IEEE TRANSACTIONS ON POWER SYSTEMS 1, 2015.
111
S. Shafiee, M. Fotuhi-Firuzabad and M. Rastegar, "Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems," IEEE TRANSACTIONS ON SMART GRID, vol. 4, pp. 1351-1360, 2013.
112
H. N. Nguyen, C. Zhang and a. M. A. Mahmud, "Optimal Coordination of G2V and V2G to Support Power Grids with High Penetration of Renewable Energy," IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2015.
113
J. Soares, M. A. F. Ghazvini, Z. Valel and P. d. M. Oliveira, "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, vol. 162, pp. 1074-1088, 2016.
114
H. Jadhav and R. Roy, "Stochastic optimal power flow incorporating offshore wind farm and electric vehicles," Electrical Power and Energy Systems, vol. 69, pp. 173-187, 2015.
115
D. maringer, "heuristic optimization," in portfolio management with heuristic optimization, Springer, 2005, pp. 38-76.
116
M. Mohammadi, Y. Noorollahi and a. B. Mohammadi-Ivatloo, "An Introduction to Smart Energy Systems and Definition of Smart Energy Hubs.," in In Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs, , Cham: Springer, 2018, pp. 1-21.
117
C. Liu, K. T. Chau, D. Wu and S. Gao, "Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies," IEEE, vol. 101, no. 11, pp. 2409-2427, 2013.
118
K. M. Tan, V. K. Ramachandaramurthy and J. Y. Yong, "Integration of electric vehicles in smart grid :A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, vol. 53, pp. 720-732, 2016.
119
I. Rahman, P. M. Vasant, B. S. M. Singh, M. A. -A.-. Wadud and N. Adnan, "Review of recent trends inoptimization techniques for plug-in hybrid, and elecrtic vehicle for charging infrustructures," Renewable and Sustainable Energy Reviews, vol. 58, pp. 1039-1047, 2016.
120
P. Grahn, J. Munkhammar, J. Widén, K. Alvehag and L. Söder, "PHEV Home-Charging Model Based on Residential Activity Patterns," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, pp. 2507-2515, 2013.
121
V. T. Bina and D. Ahmadi, "Stochastic modeling for scheduling the charging demand of EV in distribution systems using copulas," Electrical Power and Energy Systems, vol. 71, pp. 15-25, 2015.
122
"The impacts of demand response participation in capacity markets," Applied Energy , vol. 250, p. 444–451, 2019.
123
J. M. M. S.-k. A. R. J. P. C. S. Muhammad Bagher Sadati, "Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach," International Journal of Electrical Power & Energy Systems, vol. 105, pp. 159-178, 2019.
124
Z. Wang, R. Yang, L. Wang and J. Tan, "Reliability Assessment of Integrated Residential Distribution and PHEV Systems Using Monte Carlo Simulation," IEEE, 2013.
125
M. Alipour, B. Mohammadi-Ivatloo, M. Moradi-Dalvand and K. Zare, "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, vol. 118, pp. 1168-1179, 2017.
126
A. Nazarloo, M. R. Feyzi, m. sabahiorcid and M. B. B. Sharifian, "Energy Management of Electric Vehicles Aggregator Using a New Multi-Objective Algorithm," Journal of Energy Management and Technology (JEMT), vol. 2, no. 2, pp. 20-30, 2018.
127
R. S. A. Luis Baringo, "A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator," Electric Power Systems Research , vol. 146, p. 362–370, 2017.
128
R. J. Bessa and M. A. Matos, "The role of an Aggregator Agent for EV in the electricity market," in 7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion, Agia Napa, Cyprus, 2010.
129
P. Aliasghari, B. Mohammadi-Ivatloo, M. Alipour, M. Abapour and K. Zare, "Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program," Journal of Cleaner Production, vol. 186, pp. 293-303, 2018.
130
M. H. Amirioun and A. Kazemi, "A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex," Energy, vol. 69, pp. 186-198, 2014.
131
M. Ghahramani, M. Nazari-Heris, K. Zare and B. Mohammadi-Ivatloo, " Energy Management of Electric Vehicles Parking in a Power Distribution Network Using Robust Optimization Method," Journal of Energy Management and Technology, vol. 2, no. 3, pp. 22-30, 2018.
132
E. L. Karfopoulos and N. D. Hatziargyriou, "A Multi-Agent System for Controlled Charging of a Large Population of Electric Vehicles," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, 2013.
133
C. Ma, J. Rautiainen, D. Dahlhaus, A. Lakshman, J. C. Toebermann and M. Braun, "Online optimal charging strategy for Electric Vehicles," Energy Procedia, vol. 73, no. 9th International Renewable Energy Storage Conference, p. 173 – 181, 2015.
134
R. D.-L. J. Y.-L. J. A.-S. J. B.-A. J.A. Domínguez-Navarro⁎, "Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems," Electrical Power and Energy Systems, vol. 105 , p. 46–58, 2019.
135
A. Khazali and M. Kalantar, "A stochastic–probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles," Energy conversion and management, vol. 105, pp. 1046-1058, 2015.
136
M. Neaimeh, R. Wardle, A. M. Jenkins, J. Yi, G. Hill, P. F. Lyons, Y. Hübner, P. T. Blythe and P. C. Taylor, "A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts," applied energy, 2015.
137
M. Moeini-Aghtaie, A. Abbaspour and M. Fotuhi-Firuzabad, "Online Multi-Criteria Framework for Charging Management of PHEVs," IEEE Transactions on Vehicular Technology, 2014.
138
M. Mohammadi, Y. Noorollahi and a. B. Mohammadi-Ivatloo, "Impacts of Energy Storage Technologies and Renewable Energy Sources on Energy Hub Systems.," in In Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs,, Springer publishing International, 2018, pp. 22-53.
139
A. Ahmadian, M. Sedghi, B. Mohammadi-ivatloo, A. Elkamel, M. A. Golkar and M. Fowler, "Cost-Benefit analysis of V2G implementation in distribution networks considering PEVs battery degradation," IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 961-970, 2018.
140
R. Romo and O.Micheloud, "Power quality of actual grids with plug-in electric vehicles in presence of renewables and micr-grids," Renewable and Sustainable Energy Reviews, vol. 46, pp. 189-200, 2015.
141
C. Guille and G. Gross, "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, vol. 37, no. 11, pp. 4379-4390, 2009.
142
F. Fattori, N. Anglani and G. Muliere, "Combining photovoltaic energy with electric vehicles, smart charging and vehicle-to-grid," solar energy, vol. 110, pp. 438-451, 2014.
143
H. Lund and W. Kempton, "Integration of renewable energy into the transport and electricity sectors through V2G," energy policy, vol. 36, pp. 3578-3587, 2008.
144
Y. Ota, H. Taniguchi, T. Nakajima, K. M. Liyanage, J. Baba and A. Yokoyama, "Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging," IEEE TRANSACTIONS ON SMART GRID, vol. 3, 2012.
145
A. Oshnoei, M. T. Hagh, R. Khezri and B. Mohammadi-Ivatloo, "Application of IPSO and fuzzy logic methods in electrical vehicles for efficient frequency control of multi-area power systems," in 2017 Iranian Conference on Electrical Engineering (ICEE) , Tehran, 2017.
146
M. Panto, "Exploitation of Electric-Drive Vehicles in Electricity Markets," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 27, pp. 682-694, 2012.
147
H. Hashemi-Dezaki, M. Hamzeh, H. Askarian-Abyaneh and H. Haeri-Khiavi, "Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation," Energy Conversion and Management, vol. 100, pp. 262-276, 2015.
148
C. Rathore and R. Roy, "Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning," Electrical Power and Energy Systems, vol. 75, pp. 59-73, 2016.
149
A. Rabiee, M. Sadeghi, J. Aghaeic and A. Heidari, "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, vol. 57, pp. 721-739, 2016.
150
V. N. Coelho, I. M. Coelho, B. N. Coelho, M. W. Cohen, A. J. Reis, S. M. Silva, M. J. Souza, P. J. Fleming and F. G. G. aes, "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, vol. 89, pp. 730-742, 2016.
151
E. Karan, S. Asadi and L. Ntaimo, "A stochastic optimization approach to reduce greenhouse gas emissions from buildings and transportation," Energy, vol. 106, pp. 367-377, 2016.
152
A. Y. Saber and G. K. Venayagamoorthy, "Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 58, 2011.
153
S. Khormali and F. Mottola, "single-objective approaches for microgrid scheduling in the presence of plug-in vehicle fleets and datacenters," IEEE, 2015.
154
A. El-Zonkoly, "Intelligent energy management of optimally located renewable energy systems incorporating PHEV," Energy Conversion and Management, vol. 84, pp. 427-435, 2014.
155
M. Mohammadi, Y. Noorollahi, B. Mohammadi-ivatloo and a. H. Yousefi’, "Energy Hub: From a Model to a Concept – A Review," Renewable and Sustainable Energy Reviews, vol. 80, p. 1512–27, 2017.
156
B. Skugor and J. Deur, "Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model," energy, pp. 1-10, 2015.
157
M. Honarmanda, A. Zakariazadeh and S. Jadid, "Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization," Journal of the Franklin Institute, vol. 352, pp. 449-467, 2015.
158
J. Munkhammar, J. Widén and J. Rydén, "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, vol. 142, pp. 135-143, 2015.
159
M. Wang, Y. Mu, H. e. Jia, P. Zeng, J. Wu and W. Sheng, "An Efficient Power Plant Model of Electric Vehicles for Unit Commitment of Large Scale Wind Farms," Energy Procedia, vol. 75, pp. 1059-1064, 2015.
160
M. E. Khodayar, LeiWu and M. Shahidehpour, "Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC," IEEE TRANSACTIONS ON SMART GRID, vol. 3, pp. 1271-1279, 2012.
161
A. Bilh, Naik, Kshirasagar, El-Shatshat and Ramadan, "An Adaptive Charging Algorithm for Electric Vehicles in Smart Grids," IEEE, vol. 81, no. Vehicular Technology Conference (VTC Spring), pp. 1-7, 2015.
162
L. He, J. Yang, J. Yan, Y. Tang and H. He, "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, vol. 168, pp. 179-192, 2016.
163
g. Romero-Ruiz, J. Pérez-Ruiz, S. Martin, J. Aguado and S. D. l. Torre, "Probabilistic congestion management using EVs in a smart grid with intermittent renewable generation," Electric Power Systems Research, vol. 137, pp. 155-162, 2016.
164
A. Zakariazadeh, S. Jadid and P. Siano, "Integrated operation of electric vehicles and renewable generation in a smart distribution system," Energy Conversion and Management, vol. 89, pp. 99-110, 2015.
165
M. Honarmand, A. Zakariazadeh and S. Jadid, "Integrated scheduling of renewable generation and electric vehiclesparking lot in a smart micro grid," Energy Conversion and Management, vol. 86, pp. 745-755, 2014.
166
A. Banerji, D. Sen, A. K. Bera, D. Ray, D. Paul, A. Bhakat and S. K. Biswas, "Microgrid: A review," in Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), 2013 IEEE, Trivandrum , 2013.
167
Y. Noorollahi, M. S. Shabbir, A. F. Siddiqi, L. K. Ilyashenko and a. E. Ahmadi, "Review of Two Decades Geothermal Energy Development in Iran, Benefits, Challenges, and Future Policy," Geothermics, vol. 77, p. 257–66., 2019.
168
H. Liang and W. Zhuang, "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, vol. 7, pp. 2027-2050, 2014.
169
W. Su, J. Wang and J. Roh, "Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources," IEEE TRANSACTIONS ON SMART GRID, 2013.
170
M. Z. Oskouei and A. S. Yazdankhah, "Scenario-based stochastic optimal operation of wind, photovoltaic, pump-storage hybrid system in frequency- based pricing," Energy Conversion and Management, vol. 105, pp. 1105-1114, 2015.
171
Y. Wang, B. Wang, C.-C. Chu, H. Pota and R. Gadh, "Energy management for a commercial building microgrid with stationary and mobile battery storage," Energy and Buildings, vol. 116, pp. 141-150, 2016.
172
A. Kavousi-Fard, A. Abunasri, A. Zare and R. Hoseinzadeh, "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, vol. 78, pp. 904-915, 2014.
173
A. Rabiee, M. Sadeghi, J. Aghaeic and A. Heidari, "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, vol. 57, pp. 721-739, 2016.
174
A. J. Conejo, M. Carrión and J. M. Morales, "Stochastic Programming Fundamentals," in Decision Making Under Uncertainty in Electricity markets, New York, Springer, 2010, pp. 27-62.
175
Y.-T. Liao and C.-N. Lu, "Dispatch of EV Charging Station Energy Resources for Sustainable Mobility," IEEE Transactions on Transportation Electrification, vol. 1, no. 1, pp. 86-93, 2015.
176
M. Mohammadi, Y. noorollahi, B. Mohammadi-ivatloo, H. yousefi and S. jalilinasrabady, "Optimal Scheduling of Energy Hubs in the Presence of Uncertainty-A Review," Journal of Energy Management and Technology (JEMT), vol. 1, no. 1, pp. 1-17, 2017.
177
A. Mirakyan and R. D. Guio, "Modelling and uncertainties in integrated energy planning," Renewable and Sustainable Energy Reviews, vol. 46, pp. 62-69, 2015.
178
M. Alizadeh, A. Scaglione, J. Davies and K. S. Kurani, "A Scalable Stochastic Model for the Electricity Demand of Electric and Plug-In Hybrid Vehicles," IEEE TRANSACTIONS ON SMART GRID, vol. 2, pp. 848-560, 2014.
179
L. Jian, Y. Zheng, X. Xiao and C. C. Chan, "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, vol. 146, pp. 150-161, 2015.
180
A. Ghasemi, S. S. Mortazavi and E. Mashhour, "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, vol. 85, pp. 124-136, 2016.
181
Z. YANG, K. LI, Q. NIU, Y. XUE and A. FOLEY, "A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads," J. Mod. Power Syst. Clean Energy, vol. 2, no. 4, pp. 298-307, 2014.
182
M. Govardhan and R. Roy, "Economic analysis of unit commitment with distributed energy resources," Electrical Power and Energy Systems, vol. 71., pp. 1-14, 2015.
183
L. Zhang, Q. Niu, Z. Yang and K. Li, "Integration of electric vehicles charging in unit commitment," International Journal of Computer Science and Electronics Engineering, vol. 3, no. 1, pp. 22-27, 2015.
184
N. a. A. H. M. a. P. K. Taghizadegan Kalantari, "Bibliographic Review and Comparison of Optimal Sizing Methods for Hybrid Renewable Energy Systems," Journal of Energy Management and Technology (JEMT), vol. 2, no. 2, pp. 66-79, 2018.
185
A. Zakariazadeh, S. Jadid and P. Siano, "Integrated operation of electric vehicles and renewable generation in a smart distribution system," Energy Conversion and Management, vol. 89, pp. 99-110, 2015.
186
D. Madzharov, E. Delarue and W. D’haeseleer, "Integrating electric vehicles as flexible load in unit commitment modeling," Energy, vol. 65, pp. 285-294, 2014.
187
B. M.-I. G. G. M. Nazari-Herisa, "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, vol. 81, p. 2128–2143, 2018.
188
M. Ghofrani, A. Arabali and M. Etezadi-Amoli, "Electric drive vehicle to grid synergies with large scale wind resources," in IEEE Power and Energy Society General Meeting , San Diego, CA, 2012.
189
M. F. Shaaban, Y. M. Atwa and E. F. El-Saadany, "PEVs Modeling and Impacts Mitigation in Distribution Networks," IEEE TRANSACTIONS ON POWER SYSTEMS 1, 2012.
190
E. Talebizadeh, M. Rashidinejad and A. Abdollahi, "Evaluation of Plug-in Electric vehicles Impact on Cost-Based Unit Commitment," Journal of Power Sources, 2013.
191
U. K. Debnath, I. Ahmad, D. Habibi and A. Y. Saber, "Energy storage model with gridable vehicles for economic load dispatch in the smart grid," Electrical Power and Energy Systems, vol. 64, pp. 1017-1024, 2015.
192
N. Zhang, Z. Hu, D. Dai, S. Dang, M. Yao and Y. Zhou, "Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading," IEEE TRANSACTIONS ON SMART GRID 1, 2015.
193
F. Jabari, H. Jabari, B. Mohammadi-ivatloo and J. Ghafouri, "Optimal short-term coordination of water-heat-power nexus incorporating plug-in electric vehicles and real-time demand response programs," Energy, 2019.
194
A. El-Zonkoly and L. d. S. Coelho, "Optimal allocation, sizing of PHEV parking lots in distribution system," Electrical Power and Energy Systems, vol. 67, pp. 472-477, 2015.
195
Y. Zheng, Z. Y. Dong, Y. Xu, K. Meng, J. H. Zhao and J. Qiu, "Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning," IEEE Transactions on Power Systems , vol. 29, no. 1, pp. 221 - 229, 2014.
196
J. Tong, T. Zhao, X. Yang and J. Zhang, "Intelligent charging strategy for PHEVs in parking stations based on Multi-objective optimization in smart grid," in Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo , Beijing, 2014.
197
F. Fazelpour, M. Vafaeipour, O. Rahbari and M. A. Rosen, "Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics," Energy Conversion and Management, vol. 77, pp. 250-261, 2014.
198
W. Su and M.-Y. Chow, "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, vol. 96, no. smart grid, pp. 171-182, 2012.
199
M. Ghofrani, A. Arabali, M. Etezadi-Amoli and M. S. Fadali, "Smart Scheduling and Cost-Benefit Analysis of Grid-Enabled Electric Vehicles for Wind Power Integration," IEEE TRANSACTIONS ON SMART GRID, 2014.
200
Z. Luo, Z. Hu, Y. Song, Z. Xu and H. Lu, "Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part I: Enabling Techniques," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, no. 4, pp. 3546-3555, 2013.
201
W. Su and M.-Y. Chow, "Performance evaluation of a PHEV parking station using Particle Swarm Optimization," in 2011 IEEE Power and Energy Society General Meeting , San Diego, CA, 2011.
202
A. Y. Saber and G. K. Venayagamoorthy, "Optimization of vehicle-to-grid scheduling in constrained parking lots," in 2009 IEEE Power & Energy Society General Meeting , Calgary, AB , 2009.
203
T. Ghanbarzadeh, S. Goleijani and M. P. Moghaddam, "Reliability constrained unit commitment with electric vehicle to grid using Hybrid Particle Swarm Optimization and Ant Colony Optimization," in 2011 IEEE Power and Energy Society General Meeting, San Diego, CA , 2011.
204
S. Yang, M. Wu, X. Yao and J. Jiang, "Load Modeling and Identification Based on Ant Colony Algorithms for EV Charging Stations," IEEE Transactions on Power Systems , vol. 30, no. 4, pp. 1997-2003, 2015.
205
C. Quinn, D. Zimmerle and T. H. Bradley, "An Evaluation of State-of-Charge Limitations and Actuation Signal Energy Content on Plug-in Hybrid Electric Vehicle, Vehicle-to-Grid Reliability, and Economics," IEEE TRANSACTIONS ON SMART GRID, vol. 3, 2012.
206
ORIGINAL_ARTICLE
Contingency ranking for timely power system security assessment using a new voltage-angle index and based on the PMU data
Given the importance of the power system security and the role of the operator in enhancing this feature, improving the operator’s actions and information in the power system management is critical. The proper tools and available information for the operator can continuously improve the power system security. During power system operation, the operator needs to identify probable hazardous contingencies to assess power network security online. Thus, contingency ranking based on their importance has always been of interest to researchers. In present study, a new method is proposed for appropriate contingency ranking and online power network security assessment based on the Phasor Measurement unit (PMU) data. In the proposed method, unlike the previous methods, two voltage and angle indices were used. Since the variables of load-flow studies are used to calculate the proposed index, this index can provide a comprehensive assessment of the network security. The proposed index is implemented on three IEEE 14-, 30- and 57-bus test systems to evaluate its performance. First, using this index, contingency analysis is carried out in 2000 operational points and the obtained results are compared with a randomly selected operating point. The results indicated the performance and response time of the proposed index.
https://www.jemat.org/article_96003_cfdac11a8168cd6b84ca3d4386abfc00.pdf
2019-11-06
27
33
10.22109/jemt.2019.169696.1151
power systems
Security assessment
Contingency rating
Phasor measurement units
Voltage-angle index
Sassan
Azad
azad_hmf@yahoo.com
1
faculty of power electrical,Shahid beheshti University,Tehran,Iran
LEAD_AUTHOR
Mohammad Mehdi
Amiri
mehdi_hmf@yahoo.com
2
faculty of power electrical,Shahid beheshti University,Tehran,Iran
AUTHOR
Mohammad Taghi
Ameli
mtameli@yahoo.com
3
faculty of power electrical engineering ,shahid beheshti university,Tehran,Iran
AUTHOR
[1] Daniel S. Kirschen, Goran Strbac, " Fundamentals of Power System Economics ", translated and published by Office of Electricity Market Regulation, Second Edition, November 2007.
1
[2] Ming Ni and B. James D. McCalley and Vijay Vittal and Scott Greene and Tayyib Tayyib, “Software Implementation of Online Risk-Based Security Assessment” IEEE Trans. On Power Systems, Vol. 18, No. 3, pp. 1165-1172, 2003.
2
[3] K. Morison, L. Wang, and P. Kundur. (2004) Power System Security Assessment. IEEE power & energy magazine. 30-39.
3
[4] J. Nahman and I. S. kokljev, "Probabilistic steady-state power system security indices," International Journal of Electrical Power & Energy Systems, vol. 21, pp. 515–522, 1999.
4
[5] S. Kalyani and K. S. Swarup, "Classification and Assessment of Power System Security Using Multiclass SVM," IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, vol. 41, no. 5, pp. 753-758, 2011.
5
[6] N. D. Hatziargyriou, G. C. Contaxis, and N. C. Sideris, "A Decision Tree Method for On-line Steady State Security Assessment," IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 9, No. 2, 1994.
6
[7] I. Musirin and T. Kh. A. Rahnian,“ Fast Automatic Contingency Analysis and Ranking Technique for Power System Security Assessment,” Student Conrerence on Research and Development (SCOReD) IEEE Proceedings, Putrajaya, Malaysia, 2003.
7
[8] S. Greene, I. Dobson, F. L. Alvarado,“Contingency Ranking for Voltage Collapse from a Single Nose Curve,” IEEE Transactions on Power Systems, Vol. 14, No. 1, February 1999.
8
[9] Flueck, A. J., and Q. Wei. “ A New Technique for Evaluating the Severity of Branch Outage Contingencies Based on Two-Parameter Continuation,” Proceedings of IEEE PES General Meeting, June 2003, pp. 1-5.
9
[10] Saini K, Saxena A. Online Power System Contingency Screening and Ranking Methods Using Radial Basis Neural Networks. International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN (P). 2016 Jun 30.
10
[11] Tan, Wen-Shan, and Mohamed Shaaban. "Ranking of power system contingencies based on a risk quantification criterion." In 2015 IEEE Student Conference on Research and Development (SCOReD), pp. 356-361. IEEE, 2015.
11
[12] Tao Ding; Cheng Li; Chao Yan; Fangxing Li; Zhaohong Bie,“A Bilevel Optimization Model for Risk Assessment and Contingency Ranking in Transmission System Reliability evaluation, IEEE transaction on power systems, Vol. 32, Issue 5, pp, 3803-3813, 2017.
12
[13] Arenas-Crespo, O. S. W. A. L. D. O., and John E. Candelo. "A power constraint index to rank and group critical contingencies based on sensitivity factors." Archives of Electrical Engineering 67, no. 2 (2018): 247-261.
13
[14] Jain, T., L. Srivastava, and S. N. Singh. “Fast Voltage Contingency Screening Using Radial Basis Function Neural Network.” IEEE Transactions on Power Systems. 18.4 (Nov. 2003): 1359-1366.
14
[15] Lo, K. L., and A. K. I. Abdelaal. “Fuzzy Logic Based Contingency Analysis.” Proceedings of Electric Utility Deregulation, Restructuring, and Power Technologies, DRPT. London, April, 2000.
15
[16] Pyman Yadegari, "Power System Security Assessment Using the Data of Phasor Measurement Units". Shahid Beheshti University .2013.
16
[17] Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2015). Optimal placement of phasor measurement units to attain power system observability utilizing an upgraded binary harmony search algorithm. Energy Systems, 6(2), 201-220.
17
[18] Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2015). Application of heuristic algorithms to optimal PMU placement in electric power systems: an updated review. Renewable and Sustainable Energy Reviews, 50, 214-228.
18
ORIGINAL_ARTICLE
Energy storage systems integrated transmission expansion planning
The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.
https://www.jemat.org/article_92906_e8458a9364cb330e1759e3ffab7d4567.pdf
2020-03-01
34
45
10.22109/jemt.2019.186056.1178
Energy storage systems
Locational marginal price
Multi-Objective Optimization
power market
Transmission expansion planning
Amir
Amini
amini.amir@birjand.ac.ir
1
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
AUTHOR
Hamid
Falaghi
falaghi@birjand.ac.ir
2
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
LEAD_AUTHOR
Majid
Oloomi Buygi
m.oloomi@um.ac.ir
3
Faculty of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
AUTHOR
[1] B. Dunn, H. Kamath and J. Tarascon, "Electrical energy storage for the grid: A battery of choices," Science, vol. 334, no. 6058, pp. 928-935, 2011.
1
[2] C. Munoz, E. Sauma, J. Aguado, J. Contreras and S. Torre, "Impact of high wind power penetration on transmission network expansion planning," IET Generation, Transmission & Distribution, vol. 6, no. 12, pp. 1281-1291, 2012.
2
[3] H. Pandzic, Y. Wang, T. Qiu, Y. Dvorkin, and D. Kirschen, "Near-optimal method for siting and sizing of distributed storage in a transmission network," IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2288-2300, 2015.
3
[4] D. Gayme, and U. Topcu, "Optimal power flow with large-Scale storage integration," IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 709-717, 2013.
4
[5] A. Akbari, S. Nojavan, and K. Zare, "Optimal sitting and sizing of energy storage systems in a smart distribution network considering network constraints and demand response program," Journal of Energy Management and Technology (JEMT), vol. 3, no. 2, pp. 14-25, 2019.
5
[6] Y. Makarov, P. Du, M. Kintner-Meyer, C. Jin, and H. Illian, "Sizing energy storage to accommodate high penetration of variable energy resources," IEEE Transactions on Sustainable Energy, vol. 3, no. 1, pp. 34-40, 2012.
6
[7] B. Grainger, G. Reed, A. Sparacino, and P. Lewis, "Power electronics for grid-scale energy storage," Proceedings of the IEEE, vol. 102, no. 6, pp. 1000-1013, 2014.
7
[8] S. Babrowski, P. Jochem and W. Fichtner, "Electricity storage systems in the future German energy sector," Computers & Operations Research, vol. 66, pp. 228-240, 2016.
8
[9] Y. Dvorkin, R. Fern´andez-Blanco, D. S. Kirschen, H. Pandˇzi´c, J.-P. Watson, and C. A. Silva-Monroy, "Ensuring profitability of energy storage," IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 611-623, 2016.
9
[10] M. Khodayar, L. Abreu and M. Shahidehpour, "Transmission-constrained intrahour coordination of wind and pumped-storage hydro units," IET Generation, Transmission & Distribution, vol. 7, no. 7, pp. 755-765, 2013.
10
[11] R. Sioshansi, P. Denholm and T. Jenkin, "A comparative analysis of the value of pure and hybrid electricity storage," Energy Economics, vol. 33, no. 1, pp. 56-66, 2011.
11
[12] R. Sioshansi, "Welfare Impacts of Electricity Storage and the Implications of Ownership Structure," The Energy Journal, vol. 31, no. 2, 2010.
12
[13] R. Jabr, I. Dzafic, and B. Pal, "Robust optimization of storage investment on transmission networks," IEEE Transactions on Power Systems, vol. 30, no. 1, pp. 531-539, 2015.
13
[14] Z. Hu, F. Zhang and B. Li, "Transmission expansion planning considering the deployment of energy storage systems," IEEE Power and Energy Society General Meeting, pp. 1-6,2012.
14
[15] M. M. Eusuff, and K. E. Lansey, "Optimization of water distribution network design using the shuffled frog leaping algorithm," J. Water Res. Pl. ASCE, vol. 129, no. 3, pp. 210–225, 2003.
15
[16] F. Zhang, Y. Song, and Z. Hu, "Mixed-integer linear model for transmission expansion planning with line losses and energy storage systems," IET Generation, Transmission & Distribution, vol. 7, no. 8, pp. 919-928, 2013.
16
[17] M. Hedayati, J. Zhang, and K. W. Hedman, "Joint transmission expansion planning and energy storage placement in smart grid towards efficient integration of renewable energy," IEEE PES T&D Conference and Exposition, 2014.
17
[18] J. Aguado, S. de la Torre, and A. Triviño, "Battery energy storage systems in transmission network expansion planning," Electric Power Systems Research, vol. 145, pp. 63-72, 2017.
18
[19] V. Virasjoki, P. Rocha, A. Siddiqui, and A. Salo, "Market impacts of energy storage in a transmission-constrained power system," IEEE Transactions on Power Systems, vol. 35, no. 5, pp. 4108-4117, 2016.
19
[20] C. Bustos, E. Sauma, S. de la Torre, J. Aguado, J. Contreras and D. Pozo, "Energy storage and transmission expansion planning: substitutes or complements?," IET Generation, Transmission & Distribution, vol. 12, no. 8, pp. 1738-1746, 2018.
20
[21] S. Dehghan, and N. Amjady, "Robust transmission and energy storage expansion planning in wind farm-integrated power systems considering transmission switching," IEEE Transactions on Sustainable Energy, vol. 7, no. 2, pp. 765-774, 2016.
21
[22] EPRI-DOE, U.S. Department of Energy, "Handbook of energy storage for transmission & distribution application," 1001834 Final Report, 2003.
22
[23] S. Santander-Jimenez, M. A. Vega-Rodriguez, and L. Sousa, "Multiobjective frog-leaping optimization for the study of ancestral relationships in protein data," IEEE Transactions on Evolutionary Computation, vol. 22, no. 6, pp. 879–893, 2018.
23
[24] K. Deb, "A fast and elitist multiobjective genetic algorithm: NSGA II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
24
[25] P. Maghouli, S. H. Hosseini, M. Oloomi Buygi, and M. Shahidehpour, "A Scenario-based multi-objective model for multi-stage transmission expansion planning," IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 470–478, 2011.
25
[26] Reliability test system task force of the application of probability methods subcommittee, "IEEE reliability test system," IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047–2054, Nov./Dec. 1979.
26
[27] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, "MATPOWER: Steady-state operations, planning, analysis tools for power systems research and education," IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12–19, 2011.
27
[28] P. Maghouli, S. Hosseini, M. Oloomi Buygi, and M. Shahidehpour, "A multi-objective framework for transmission expansion planning in deregulated environments," IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 1051-1061, 2009.
28
[29] "Electricity storage and renewables: cost and markets to 2030," International Reneable Energy Agency-IRENA, 2016.
29
[30] "World energy resources E-Storage| 2016," World Energy Council, 2016.
30
[31] R. Fang, and D. Hill, "A new strategy for transmission expansion in competitive electricity markets," IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 374-380, 2003.
31
[32] E. Hirst, and B. Kirby, “Key transmission planning issues,” The Electricity Journal, vol. 14, no. 8, pp. 59–70, 2001.
32
[33] K. Hughes, and D. Brown, "Transmission line capital costs," Technical Report, Pacific Northwest Lab, May 1995. Available at: http://www.osti.gov/bridge/product.biblio.jsp?osti_id=67758.
33
[34] PJM, "A survey of transmission cost allocation issues, methods and practices," Technical Report, PJM, March 2010. Available at :http://www.pjm.com/~/media/documents/reports/20100310-transmissionallocation-cost-web.ashx
34
ORIGINAL_ARTICLE
Power enhancement of photovoltaic arrays under partial shading conditions by a new dynamic reconfiguration method
Dynamic reconfiguration of photovoltaic arrays is one of the effective ways to decrease partial shading effects. In this paper, by using auxiliary modules and after a suitable fixed reconfiguration, an optimizer and economic method based on dynamic reconfiguration is presented. In this method, Auxiliary modules are arranged next to the photovoltaic array and replaced with shaded modules to maximize the energy delivery. The best connection between the auxiliary modules and the array is determined by an optimal decision process. The objective function for this decision process is energy delivery of the solar array in shadow conditions which is maximized by the genetic algorithm. Significant improvement in the output power of the photovoltaic array and smaller number of switches than the other dynamic reconfiguration methods are the main advantages of the proposed method. Benefits and effectiveness of this method are compared with other recently dynamic configuration approaches, and the results confirm power enhancement of the photovoltaic arrays in various shadow patterns.
https://www.jemat.org/article_96718_b86c9da6295117b8508a3d6eb3034039.pdf
2020-03-01
46
51
10.22109/jemt.2019.150205.1126
Photovoltaic array
partial shading
dynamic reconfiguration
Power Enhancement
Ghaleb
Mostafaee
gh.mostafaee@srttu.edu
1
Shahid Rajaee University
AUTHOR
Reza
Ghandehari
r_ghandehari@sru.ac.ir
2
Shahid Rajaee University
LEAD_AUTHOR
1. H. Patel and A. Vivek, “Maximum power point tracking scheme for PV systems operating under partially shaded conditions,” IEEE transactions on industrial electronics, vol. 55, no. 4, pp. 1689–1698, 2008.
1
2. N. Kaushika and A. K. Rai, “An investigation of mismatch losses in solar photovoltaic cell networks,” Energy, vol. 32, no. 5, pp. 755–759, 2007.
2
3. E. Karatepe, M. Boztepe, and M. Colak, “Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells,” Solar Energy, vol. 81, no. 8, pp. 977–992, 2007.
3
4. M. A. Ghasemi, H. Mohammadian, and M. Parniani, “Partial shading detection and smooth maximum power point tracking of PV arrays under PSC,” IEEE Transactions on Power Electronics, vol. 31, no. 9, pp. 6281–6292, 2016.
4
5. A. Woyte, J. Nijs, and R. Belmans, “Partial shadowing of photovoltaic arrays with different system configurations: literature review and field test results,” Solar energy, vol. 74, no. 3, pp. 217–233, 2003.
5
6. G. Velasco-Quesada, F. Guinjoan-Gispert, R. Piqué-López, M. RománLumbreras, and A. Conesa-Roca, “Electrical PV array reconfiguration strategy for energy extraction improvement in grid-connected PV systems,” Solar energy, vol. 56, no. 11, pp. 4319–4331, 2009.
6
7. L. Gao, R. Dougal, Sh. Liu, and A. P. Iotova, “Parallel-connected solar PV system to address partial and rapidly fluctuating shadow conditions,” IEEE Transactions on industrial Electronics, vol. 56, no. 5, pp. 1548–1556, 2009.
7
8. P. S. Rao, G. S. Ilango, and Ch. Nagamani, “Maximum power from PV arrays using a fixed configuration under different shading conditions,” IEEE journal of Photovoltaics, vol. 4, no. 2, pp. 679–686, 2014.
8
9. M. Horoufiany, and R. Ghandehari, “Optimal fixed reconfiguration scheme for PV arrays power enhancement under mutual shading conditions,” IET Renewable Power Generation, vol. 11, no. 11, pp. 1456–1463, 2017.
9
10. M. Horoufiany, and R. Ghandehari, “Optimization of the Sudoku based reconfiguration technique for PV arrays power enhancement under mutual shading conditions,” Solar Energy, vol. 156, pp. 1037–1046, 2018.
10
11. M. Horoufiany, and R. Ghandehari, “A new photovoltaic arrays fixed reconfiguration method for reducing effects of one- and two-sided mutual shading,” Journal of Solar Energy Engineering, Transactions of the ASME, vol. 141, pp. 031013-1–031013-7, 2019.
11
12. G. Acciari, D. Graci, and A. La Scala, “Higher PV module efficiency by a novel CBS bypass,” IEEE Transactions on Power Electronics, vol. 26, no. 5, pp. 1333–1336, 2011.
12
13. F. Rong, X. Gong, and Sh. Huang, “A novel grid-connected PV system based on MMC to get the maximum power under partial shading conditions,” IEEE Transactions on Power Electronics, vol. 32, no. 6, pp. 4320–4333, 2017.
13
14. A. Ramyar, H. Iman-Eini, and Sh. Farhangi, “Global maximum power point tracking method for photovoltaic arrays under partial shading conditions,” IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 2855–2864, 2017.
14
15. L. F. L. Villa, T. Ph. Ho, J. Ch. Crebier, and B. Raison, “A power electronics equalizer application for partially shaded photovoltaic modules,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp. 1179–1190, 2013.
15
16. D. Nguyen, and B. Lehman, “An adaptive solar photovoltaic array using model-based reconfiguration algorithm,” IEEE Transactions on Industrial Electronics, vol. 55, no. 7, pp. 2644–2654, 2008.
16
17. J. P. Storey, P. R. Wilson, and D. Bagnall, “Improved optimization strategy for irradiance equalization in dynamic photovoltaic arrays,” IEEE Transactions on power Electronics, vol. 28, no. 6, pp. 2946–2956, 2013.
17
18. MZ Sh. El-Dein, M. Kazerani, and MMA Salama, “Optimal photovoltaic array reconfiguration to reduce partial shading losses,” IEEE Trans. Sustain. Energy, vol. 4, no. 1, pp. 145–153, 2013.
18
ORIGINAL_ARTICLE
Expansion planning of the Iranian gas and electricity energy systems: An integrated approach
Following increases in interdependencies of gas and electricity energy systems (G\&ES) in parallel with the incremental growth of demands for the relevant energy carriers, the need for a more optimal capacity expansion planning approach, in particular in developing countries, is felt more than before. By considering the most important factors that can affect expansion strategies of the Iranian G\&ES, the present paper proposes a comprehensive planning model for the expansion of the G\&ES using an integrated approach. The interactions between the energy systems (ESs), environmental issues, renewables penetration rate under the implementation of supportive energy policies in a semi-deregulated environment, and the possibility of employing the salt caverns and/or depleted fields for storing natural gas, are included the aforementioned factors. Formulated as a mixed-integer linear programming problem in the GAMS software environment, the model aims to identify the least-cost planning schedule of candidate infrastructures, while applied techno-economic constraints are satisfied. Two different scenarios are conducted to investigate the superiority of employed planning methodology. The simulation results demonstrate that in order to cope with the challenges, co-expansion planning of the G\&ES in a coordinated framework can reach more optimal and realistic strategies compared with the traditional separate expansion planning models. In addition, analysis shows that the integrated expansion planning of the ESs gives the opportunity of exploring the impact of different aspects on each other and better perception of the interactions with planners.
https://www.jemat.org/article_97075_6487a163ab44efd481a28f1ddd9f50d6.pdf
2020-03-01
52
66
10.22109/jemt.2019.190753.1182
Integrated expansion planning
Iranian gas and electricity systems
Gas storage systems
Hadi
Sadeghi
h.sadeghi@eng.uk.ac.ir
1
Electrical Engineering Department, Shahid Bahonar University of Kerman
AUTHOR
Masoud
Rashidinejad
mrashidi@uk.ac.ir
2
Department of Electrical Engineering, Shahid Bahonar University of Kerman
LEAD_AUTHOR
Moein
Moeini-Aghtaie
m.moeini@ieee.org
3
Energy Technology Laboratory, Sharif Energy Research Institute
AUTHOR
Amir
Abdollahi
a.abdollahi@uk.ac.ir
4
Department of Electrical Engineering, Shahid Bahonar University
AUTHOR
[1] International Energy Agency, The World Energy Outlook 2018, Available Online: https://webstore.iea.org/download/summary/ 190?fileName=English-WEO-2018-ES.pdf.
1
[2] Y. Wang et al., "Planning and operation method of the Regional Integrated Energy System considering economy and environment," Energy, 2019.
2
[3] Q. Zeng, J. Fang, Z. Chen, J. Li, and B. Zhang, "A multistage coordinative optimization for sitting and sizing P2G plants in an integrated electricity and natural gas system," in 2016 IEEE International Energy Conference (ENERGYCON), 2016, pp. 1-6: IEEE.
3
[4] C. Unsihuay-Vila, J. Marangon-Lima, A. Z. De Souza, I. J. Perez-Arriaga, and P. P. Balestrassi, "A model to long-term, multiarea, multistage, and integrated expansion planning of electricity and natural gas systems," IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1154-1168, 2010.
4
[5] C. A. Saldarriaga, R. A. Hincapié, and H. Salazar, "A holistic approach for planning natural gas and electricity distribution networks," IEEE transactions on power systems, vol. 28, no. 4, pp. 4052-4063, 2013.
5
[6] J. Qiu, Z. Y. Dong, J. H. Zhao, K. Meng, Y. Zheng, and D. J. Hill, "Low carbon oriented expansion planning of integrated gas and power systems," IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 1035-1046, 2014.
6
[7] F. Barati, H. Seifi, M. S. Sepasian, A. Nateghi, M. Shafie-khah, and J. P. Catalão, "Multi-period integrated framework of generation, transmission, and natural gas grid expansion planning for large-scale systems," IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2527-2537, 2014.
7
[8] C. Unsihuay, J. Marangon-Lima, and A. Z. de Souza, "Integrated power generation and natural gas expansion planning," in 2007 IEEE Lausanne Power Tech, 2007, pp. 1404-1409: IEEE.
8
[9] Y. Hu, Z. Bie, T. Ding, and Y. Lin, "An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning," Applied energy, vol. 167, pp. 280-293, 2016.
9
[10] J. Qiu et al., "A linear programming approach to expansion co-planning in gas and electricity markets," IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3594-3606, 2015.
10
[11] J. Qiu, Z. Y. Dong, J. H. Zhao, K. Meng, H. Tian, and K. P. Wong, "Expansion co-planning with uncertainties in a coupled energy market," in 2014 IEEE PES General Meeting| Conference & Exposition, 2014, pp. 1-5: IEEE.
11
[12] T. Ding, Y. Hu, and Z. Bie, "Multi-stage stochastic programming with nonanticipativity constraints for expansion of combined power and natural gas systems," IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 317-328, 2017.
12
[13] C. Saldarriaga-Cortés, H. Salazar, R. Moreno, and G. Jiménez-Estévez, "Stochastic planning of electricity and gas networks: An asynchronous column generation approach," Applied energy, vol. 233, pp. 1065-1077, 2019.
13
[14] Y. Yang, L. Wang, Y. Fang, and C. Mou, "Integrated value of shale gas development: A comparative analysis in the United States and China," Renewable and Sustainable Energy Reviews, vol. 76, pp. 1465-1478, 2017.
14
[15] B. Odetayo, J. MacCormack, W. Rosehart, and H. Zareipour, "A real option assessment of flexibilities in the integrated planning of natural gas distribution network and distributed natural gas-fired power generations," Energy, vol. 143, pp. 257-272, 2018.
15
[16] Z. Zhen, L. Lou, L. Tian, and Q. Gao, "Investment optimization path of NG power generation in China based on carbon value realization and market linkage," Applied energy, vol. 210, pp. 241-255, 2018.
16
[17] B. Yang, J. Gu, T. Zhang, and K. M. Zhang, "Near-source air quality impact of a distributed natural gas combined heat and power facility," Environmental pollution, vol. 246, pp. 650-657, 2019.
17
[18] X. Zhang, M. Shahidehpour, A. S. Alabdulwahab, and A. Abusorrah, "Security-constrained co-optimization planning of electricity and natural gas transportation infrastructures," IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 2984-2993, 2014.
18
[19] J. Qiu et al., "Multi-stage flexible expansion co-planning under uncertainties in a combined electricity and gas market," IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 2119-2129, 2014.
19
[20] H. Zhou, J. Zheng, Z. Li, Q. Wu, and X. Zhou, "Multi-stage contingency-constrained co-planning for electricity-gas systems interconnected with gas-fired units and power-to-gas plants using iterative Benders decomposition," Energy, vol. 180, pp. 689-701, 2019.
20
[21] J. B. Nunes, N. Mahmoudi, T. K. Saha, and D. Chattopadhyay, "Multi-stage co-planning framework for electricity and natural gas under high renewable energy penetration," IET Generation, Transmission & Distribution, vol. 12, no. 19, pp. 4284-4291, 2018.
21
[22] Y. Zhang, J. Le, F. Zheng, Y. Zhang, and K. Liu, "Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration," Renewable energy, vol. 135, pp. 122-135, 2019.
22
[23] M. Chaudry, N. Jenkins, M. Qadrdan, and J. Wu, "Combined gas and electricity network expansion planning," Applied Energy, vol. 113, pp. 1171-1187, 2014.
23
[24] M. Hamedi, R. Z. Farahani, and G. Esmaeilian, "Optimization in natural gas network planning," Logistics operations and management, pp. 393-420, 2011.
24
[25] F. Holz, C. v. Hirschhausen, and C. Kemfert, "A strategic model of European gas supply," 2005.
25
[26] W. Lise, B. F. Hobbs, and F. Van Oostvoorn, "Natural gas corridors between the EU and its main suppliers: Simulation results with the dynamic GASTALE model," Energy Policy, vol. 36, no. 6, pp. 1890-1906, 2008.
26
[27] R. Egging, S. A. Gabriel, F. Holz, and J. Zhuang, "A complementarity model for the European natural gas market," Energy policy, vol. 36, no. 7, pp. 2385-2414, 2008.
27
[28] Y. Smeers, "Gas models and three difficult objectives," 2008.
28
[29] A. Afful-Dadzie, E. Afful-Dadzie, I. Awudu, and J. K. Banuro, "Power generation capacity planning under budget constraint in developing countries," Applied energy, vol. 188, pp. 71-82, 2017.
29
[30] M. Sheikhhoseini, M. Rashidinejad, M. Ameri, and A. Abdollahi, "Economic analysis of support policies for residential photovoltaic systems in Iran," Energy, vol. 165, pp. 853-866, 2018.
30
[31] J. Silvente and L. G. Papageorgiou, "An MILP formulation for the optimal management of microgrids with task interruptions," Applied energy, vol. 206, pp. 1131-1146, 2017.
31
[32] B. Wang, M. Yuan, H. Zhang, W. Zhao, and Y. Liang, "An MILP model for optimal design of multi-period natural gas transmission network," Chemical Engineering Research and Design, vol. 129, pp. 122-131, 2018.
32
[33] M. T. Kelley, R. C. Pattison, R. Baldick, and M. Baldea, "An MILP framework for optimizing demand response operation of air separation units," Applied energy, vol. 222, pp. 951-966, 2018.
33
[34] L. Yang, X. Zhao, X. Li, X. Feng, and W. Yan, "An MILP-Based Optimal Power and Gas Flow in Electricity-gas Coupled Networks," Energy Procedia, vol. 158, pp. 6399-6404, 2019.
34
[35] H. Sadeghi, M. Rashidinejad, and A. Abdollahi, "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, vol. 67, pp. 1369-1394, 2017.
35
[36] X. Zhang, L. Che, M. Shahidehpour, A. S. Alabdulwahab, and A. Abusorrah, "Reliability-based optimal planning of electricity and natural gas interconnections for multiple energy hubs," IEEE Transactions on Smart Grid, vol. 8, pp. 1658-1667, 2015.
36
[37] X. Zhang, M. Shahidehpour, A. Alabdulwahab, and A. Abusorrah, "Optimal expansion planning of energy hub with multiple energy infrastructures," IEEE Transactions on Smart Grid, vol. 6, pp. 2302-2311, 2015.
37
[38] F. Careri, C. Genesi, P. Marannino, M. Montagna, S. Rossi, and I. Siviero, "Generation expansion planning in the age of green economy," IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2214-2223, 2011.
38
ORIGINAL_ARTICLE
Assessment of trend and determinant factors for household energy utilization choice in urban areas of Ethiopia: Case of Eastern Amhara
The major energy consuming sector in Ethiopia is the domestic usage. Cooking takes the major share from household energy consumption. Although urban areas of Ethiopia are mainly accessible to electricity, most households still mainly depend on biomass-based energy sources, which are very traditional and associated with inefficient technologies. The primary objective of this study is to assess the general trend of household energy utilization and the factors that affect the choice of energy sources and the associated energy technologies in urban areas of Eastern Amhara. The study analyzes the primary and secondary data collected from the selected sample of households and experts in the study area. The study covers the determinant factors for household energy choice, especially for cooking application; the community awareness level, the energy appliance types in use, the energy experts’ contribution, and the future energy/energy technology demand. The analysis is conducted mainly based on demographic variables such as residence type, educational status, and availability of technology. The result shows that the energy sources type and the energy technology preference at the household level are largely depend on the education level of house heads and type of residence they live in. For instance; 76.3%, 34.1%, and 22.5% of the households are who are living in the condominium, own apartment, and rented houses respectively are using electricity for injera baking. Model to validate the findings of the descriptive statistics, to estimate the trend and relations among different factors for the whole population of the study region has been developed.
https://www.jemat.org/article_91185_0dc2e189695a9608fb64ed9a458a00aa.pdf
2020-03-01
67
74
10.22109/jemt.2019.176291.1166
Household energy
Injera baking
Stew cooking
Utilization trend, Eastern Amhara
Muluken
GETIE
mulez1997@gmail.com
1
Mechanical engineering, Bahir Dar Energy center, Bahir Dar Institute of technology, Bahir Dar, Ethiopia
LEAD_AUTHOR
Mehare
DEGEFA
mewedeja@gmail.com
2
Bahirdar Energy Center, Ethiopian Institute for Textile and Fashion Technology, BahirDar University, BahirDar Ethiopia
AUTHOR
International Energy Agency, “Energy access outlook report,” tech. rep., 2014.
1
International Energy Agency, “Energy access outlook,” tech. rep., International Energy Agenecy, 2017.
2
O. Davidson and Y. Sokona, “Energy and sustainable development: key issues for africa,” in Proceedings of the High-level Regional Meeting on Energy and Sustainable Development for the Ninth Sessions, Roskilde, Denmark: UNEP Collaborating Centre on Energy & Environment, 2001.
3
F. Johnson, E. Mayaka, M. Ogeya, and I. Wanjiru, H.and Ngare, “Energy access and climate change in sub-saharan africa: linkages, synergies and conflicts,” tech. rep., TRANSrisk, 2017.
4
Energypedia, “Ethiopia energy situation-energypedia.info,” 2018.
5
D. D. Guta, “Effect of fuelwood scarcity and socio-economic factors on household bio-based energy use and energy substitution in rural ethiopia,” Energy policy, vol. 75, pp. 217–227, 2014.
6
M. Mengistu, B. Simane, G. Eshete, and T. Workneh, “A review on biogas technology and its contributions to sustainable rural livelihood in ethiopia,” Renewable and Sustainable Energy Reviews, vol. 48, pp. 306–316, 2015.
7
K. Kaygusuz, “Energy for sustainable development: A case of developing countries,” Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp. 1116–1126, 2012.
8
J. Turkson and N. Wohlgemuth, “Power sector reform and distributed generation in sub-saharan africa,” Energy Policy, vol. 29, no. 2, pp. 135– 145, 2001.
9
M. A. H. Mondal, E. Bryan, C. Ringler, D. Mekonnen, and M. Rosegrant, “Ethiopian energy status and demand scenarios: Prospects to improve energy efficiency and mitigate ghg emissions,” Energy, vol. 149, pp. 161–172, 2018.
10
GIZ, “The energy development intervention in ethiopia. report, giz energy coordination office, ethiopia (giz eco ethiopia), report available at world bank, www.worldbank.org,” tech. rep., GIZ, 2011.
11
Energypedia, “Ethiopia energy situation-energypedia.info,” 2017.
12
DANAS Electrical Engineering, “Project document on locally manufactured electric stoves energy efficiency standards and labeling,” tech. rep., EEA, 2017.
13
International Energy Agency, “world energy outlook report,” tech. rep., 2016.
14
N. A. Ejigu, “Energy modeling in residential houses: A case study of single family houses in bahir dar city, ethiopia,” tech. rep., KTH, 2016.
15
R. Jones, J. C. Diehl, L. Simons, and M. Verwaal, “The development of an energy efficient electric mitad for baking injeras in ethiopia,” in Domestic Use of Energy (DUE), 2017 International Conference on, pp. 75–82, IEEE, 2017.
16
DANAS Electrical Engineering, “Energy efficiency standard and labeling project document for injera mitad,” tech. rep., Ethiopian Electric agency, 2015.
17
K. D. Adem and D. A. Ambie, “A review of injera baking technologies in ethiopia: Challenges and gaps,” Energy for Sustainable Development, vol. 41, pp. 69–80, 2017.
18
M. S. B. Aïssa, M. B. Jebli, and S. B. Youssef, “Output, renewable energy consumption and trade in africa,” Energy Policy, vol. 66, pp. 11– 18, 2014.
19
K. Louw, B. Conradie, M. Howells, and M. Dekenah, “Determinants of electricity demand for newly electrified low-income african households,” Energy policy, vol. 36, no. 8, pp. 2812–2818, 2008.
20
A. Mekonnen and G. Köhlin, “Determinants of household fuel choice in major cities in ethiopia,” 2009.
21
T. Ekholm, V. Krey, S. Pachauri, and K. Riahi, “Determinants of household energy consumption in india,” Energy Policy, vol. 38, no. 10, pp. 5696–5707, 2010.
22
A. A. Woldeamanuel, “Determinants of household energy consumption in urban areas of ethiopia,” in Poster session: Population, consumption and the environment, no. XXVIII, (Cape Town, South Africa,), IUSSP International Population Conference, Oct. 2017.
23
M. G. Mengistu, B. Simane, G. Eshete, and T. S. Workneh, “Factors affecting households’ decisions in biogas technology adoption, the case of ofla and mecha districts, northern ethiopia,” Renewable Energy, vol. 93, pp. 215–227, 2016.
24
S. Malla and G. R. Timilsina, Household cooking fuel choice and adoption of improved cookstoves in developing countries: a review. The World Bank, 2014.
25
M. Bazilian, P. Nussbaumer, H.-H. Rogner, A. Brew-Hammond, V. Foster, S. Pachauri, E. Williams, M. Howells, P. Niyongabo, L. Musaba, et al., “Energy access scenarios to 2030 for the power sector in subsaharan africa,” Utilities Policy, vol. 20, no. 1, pp. 1–16, 2012.
26
D. Barnes, R. Golumbeanu, and I. Diaw, “Beyond electricity access: Output-based aid and rural electrification in ethiopia,” 2016.
27
Z. Gebreegziabher, Household fuel consumption and resource use in rural-urban Ethiopia. Wageningen University, 2007.
28
E. Kebede, J. Kagochi, and C. M. Jolly, “Energy consumption and economic development in sub-sahara africa,” Energy economics, vol. 32, no. 3, pp. 532–537, 2010.
29
K. C. van Blommestein and T. U. Daim, “Residential energy efficient device adoption in south africa,” Sustainable Energy Technologies and Assessments, vol. 1, pp. 13–27, 2013.
30
M. Kankal, A. Akpınar, M. ˙I. Kömürcü, and T. S¸ . Özs¸ahin, “Modeling and forecasting of turkey’s energy consumption using socio-economic and demographic variables,” Applied Energy, vol. 88, no. 5, pp. 1927–1939, 2011.
31
B. Numbi and S. Malinga, “Optimal energy cost and economic analysis of a residential grid-interactive solar pv system-case of ethekwini municipality in south africa,” Applied Energy, vol. 186, pp. 28–45, 2017.
32
S.-J. Lin, M. Beidari, and C. Lewis, “Energy consumption trends and decoupling effects between carbon dioxide and gross domestic product in south africa,” Aerosol Air Qual. Res, vol. 15, pp. 2676–2687, 2015.
33
P. Alstone, D. Gershenson, and D. M. Kammen, “Decentralized energy systems for clean electricity access,” Nature Climate Change, vol. 5, no. 4, p. 305, 2015.
34
H. H. Mesele, B. K. Mulu, H. T. Asfafaw, and I. D. Oumer, “Energy consumption performance analysis of electrical mitad at mekelle city,” Momona Ethiopian Journal of Science, 2017.
35