Generation expansion planning considering renewable energy integration and optimal unit commitment: A case study of Afghanistan

Abdul Matin Ibrahimi; Mohammad Masih Sediqi; Harun Or Rashid Howlader; Mir Sayed Shah Danish; Shantanu Chakraborty; Tomonobu Senjyu; Abdul Matin Ibrahimi; Mohammad Masih Sediqi; Harun Or Rashid Howlader; Mir Sayed Shah Danish; Shantanu Chakraborty; Tomonobu Senjyu

doi:10.3934/energy.2019.4.441

AIMS Energy

2019, Volume 7, Issue 4: 441-464. doi: 10.3934/energy.2019.4.441

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Research article Special Issues

Generation expansion planning considering renewable energy integration and optimal unit commitment: A case study of Afghanistan

1.
Electrical and Electronics Engineering Department, University of the Ryukyus, Okinawa Japan
2.
University of Melbourne, Australia

Received: 09 March 2019 Accepted: 05 July 2019 Published: 30 July 2019

The main focus of the proposed framework is to examine the importance of electricity interconnections with a high share of intermittent Renewable Energy (RE) sources and attempts to link the gap between planning model and operation with considering realistic operating details. Therefore optimal Unit Commitment (UC) is considered to analyze how operational aspects are appropriately done over the planning period. More specifically, a Mixed Integer Linear Programming (MILP) model is developed to address the specific challenges of the underlying UC problem. For modelling purposes, demand forecast, applicable RE potentials and the cost of RE technologies are estimated. To reduce the expenses and improve system stability, energy storage systems (pump storage hydro and thermal energy storage) are considered as well. For optimal UC, a typical day (24 h) is employed to determine the capacity expansion and daily operational planning. Each selected day expresses a part of the year (e.g., a season). Incorporation of short-term decisions into the long-term planning framework can strengthen the accuracy of the decisions and guaranty the stability of power networks. The proposed approach can provide valuable insights into the appropriate energy strategies followed by the investors and policymakers at a national and regional level.
- energy expansion plan,
- demand forecast,
- optimal unit commitment,
- renewable energy technologies,
- energy storage system
Citation: Abdul Matin Ibrahimi, Mohammad Masih Sediqi, Harun Or Rashid Howlader, Mir Sayed Shah Danish, Shantanu Chakraborty, Tomonobu Senjyu. Generation expansion planning considering renewable energy integration and optimal unit commitment: A case study of Afghanistan[J]. AIMS Energy, 2019, 7(4): 441-464. doi: 10.3934/energy.2019.4.441

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Abstract

The main focus of the proposed framework is to examine the importance of electricity interconnections with a high share of intermittent Renewable Energy (RE) sources and attempts to link the gap between planning model and operation with considering realistic operating details. Therefore optimal Unit Commitment (UC) is considered to analyze how operational aspects are appropriately done over the planning period. More specifically, a Mixed Integer Linear Programming (MILP) model is developed to address the specific challenges of the underlying UC problem. For modelling purposes, demand forecast, applicable RE potentials and the cost of RE technologies are estimated. To reduce the expenses and improve system stability, energy storage systems (pump storage hydro and thermal energy storage) are considered as well. For optimal UC, a typical day (24 h) is employed to determine the capacity expansion and daily operational planning. Each selected day expresses a part of the year (e.g., a season). Incorporation of short-term decisions into the long-term planning framework can strengthen the accuracy of the decisions and guaranty the stability of power networks. The proposed approach can provide valuable insights into the appropriate energy strategies followed by the investors and policymakers at a national and regional level.

References

[1]	Timmons D, Harris JM, Roach B (2014) The economics of renewable energy. Global Development And Environment Institute, Tufts University 52: 1–52.
[2]	Sen S, Ganguly S (2017) Opportunities, barriers and issues with renewable energy development–A discussion. Renewable Sustainable Energy Rev 69: 1170–1181. doi: 10.1016/j.rser.2016.09.137
[3]	Moncecchi M, Falabretti D, Merlo M (2019) Regional energy planning based on distribution grid hosting capacity. AIMS Energy 7: 264–284.
[4]	Poncelet K, Delarue E, Six D, et al. (2016) Impact of the level of temporal and operational detail in energy-system planning models. Appl Energy 162: 631–643. doi: 10.1016/j.apenergy.2015.10.100
[5]	Wang X, Chang J, Meng X, et al. (2018) Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems. Appl Energy 229: 945–962. doi: 10.1016/j.apenergy.2018.08.034
[6]	Lai CS, Jia Y, Lai LL, et al. (2017) A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage. Renewable Sustainable Energy Rev 78: 439–451. doi: 10.1016/j.rser.2017.04.078
[7]	Thang VV, Thanhtung Ha (2019) Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties. AIMS Energy 7: 211–226. doi: 10.3934/energy.2019.2.211
[8]	Dagoumas AS, Koltsaklis NE (2019) Review of models for integrating renewable energy in the generation expansion planning. Appl energy 242: 1573–1587. doi: 10.1016/j.apenergy.2019.03.194
[9]	Koltsaklis NE, Georgiadis MC (2015) A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints. Appl energy 158: 310–331. doi: 10.1016/j.apenergy.2015.08.054
[10]	Palmintier BS, Webster MD (2013) Heterogeneous unit clustering for efficient operational flexibility modeling. IEEE Trans Power Syst 29: 1089–1098.
[11]	Li Z, Jin T, Zhao S, et al. (2018) Power system day-ahead unit commitment based on chance-constrained dependent chance goal programming. Energies 11: 1718. doi: 10.3390/en11071718
[12]	Koltsaklis NE, Gioulekas I, Georgiadis MC (2018) Optimal scheduling of interconnected power systems. Comput Chem Eng 111: 164–182. doi: 10.1016/j.compchemeng.2018.01.004
[13]	Palmintier B, Webster M (2011) Impact of unit commitment constraints on generation expansion planning with renewables. In 2011 IEEE Power and Energy Society General Meeting 1–7.
[14]	Zheng H, Jian J, Yang L, et al. (2016) A deterministic method for the unit commitment problem in power systems. Comput Oper Res 66: 241–247. doi: 10.1016/j.cor.2015.01.012
[15]	Melamed M, Ben-Tal A, Golany B (2018) A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source. Renewable energy 118: 909–917. doi: 10.1016/j.renene.2016.05.095
[16]	Alemany J, Kasprzyk L, Magnago F (2018) Effects of binary variables in mixed integer linear programming based unit commitment in large-scale electricity markets. Electr Power Syst Res 160: 429–438. doi: 10.1016/j.epsr.2018.03.019
[17]	Liu G, Tomsovic K (2015) Robust unit commitment considering uncertain demand response. Electr Power Syst Res 119: 126–137. doi: 10.1016/j.epsr.2014.09.006
[18]	Alabedin AZ, El-Saadany EF, Salama MMA (2012) Generation scheduling in microgrids under uncertainties in power generation. In 2012 IEEE Electrical Power and Energy Conference 133–138.
[19]	Kia M, Nazar MS, Sepasian MS, et al. (2017) Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system. Energy 120: 241–252. doi: 10.1016/j.energy.2016.11.079
[20]	Nemati M, Zöller T, Tenbohlen S, et al. (2015) Optimal energy management system for future microgrids with tight operating constraints. In 2015 12th International Conference on the European Energy Market (EEM) 1–6.
[21]	Haddadian G, Khalili N, Khodayar M, et al. (2015) Security-constrained power generation scheduling with thermal generating units, variable energy resources, and electric vehicle storage for V2G deployment. Int J Electr Power Energy Syst 73: 498–507. doi: 10.1016/j.ijepes.2015.05.020
[22]	Koltsaklis NE, Dagoumas AS (2018) Incorporating unit commitment aspects to the European electricity markets algorithm: An optimization model for the joint clearing of energy and reserve markets. Appl energy 231: 235–258. doi: 10.1016/j.apenergy.2018.09.098
[23]	Rostami R, Khoshnava SM, Lamit H, et al. (2017) An overview of Afghanistan's trends toward renewable and sustainable energies. Renewable Sustainable Energy Rev 76: 1440–1464. doi: 10.1016/j.rser.2016.11.172
[24]	Asumadu-Sarkodie S,Owusu PA (2016) A review of Ghana's solar energy potential. AIMS Energy 4: 675–696. doi: 10.3934/energy.2016.5.675
[25]	Aminjonov F (2016) Afghanistan's energy security. Tracing Central Asian countries'contribution 1–30.
[26]	December (2014) Renewable energy development. Asian Development Bank Technical Assistance Report 1–11.
[27]	November (2014) Afghanistan national renewable energy policy. Islamic Republic of Afghanistan, Ministry of Energy and Water 1–20.
[28]	April (2013) Power sector master plan. Islamic Republic of Afghanistan, Ministry of Energy and Water.
[29]	Ibrahimi AM, Howlader HOR, Danish MSS, et al. (2019) Optimal unit commitment with concentrated solar power and thermal energy storage in Afghanistan electrical system. Int J Emerging Electr Power Syst 20: 1–16.
[30]	Elliott D (2011) Wind resource assessment and mapping for Afghanistan and Pakistan. National Renewable Energy Laboratory. Golden, Color, USA.
[31]	Milbrandt A, Overend R (2011) Assessment of biomass resources in Afghanistan. National Renewable Energy Lab (NREL), Golden, CO (United States).
[32]	Anwarzai MA, Nagasaka K (2017) Prospect area mapping for geothermal energy exploration in Afghanistan. J Clean Energy Technol 5: 501–506. doi: 10.18178/JOCET.2017.5.6.424
[33]	Jahangiri M, Haghani A, Mostafaeipour A, et al. (2019) Assessment of solar-wind power plants in Afghanistan: A review. Renewable Sustainable Energy Rev 99: 169–190. doi: 10.1016/j.rser.2018.10.003
[34]	Salvatore J (2013) World energy perspective: cost of energy technologies. World Energy Council.
[35]	Kost C, Shammugam S, Julch V, et al. (2018) Levelized cost of electricity renewable energy technologies. Fraunhofer Institute for Solar Energy Systems ISE.
[36]	Ilas A, Ralon P, Rodriguez A, et al. (2018) Renewable power generation costs in 2017. International Renewable Energy Agency, Abu Dhabi, UAE.
[37]	Biegler T, Zhang DK (2009) The hidden costs of electricity: externalities of power generation in Australia. Australian Academy of Technological Science and Engineering.
[38]	Lai CS, McCulloch MD (2017) Levelized cost of electricity for solar photovoltaic and electrical energy storage. Appl Energy 190: 191–203. doi: 10.1016/j.apenergy.2016.12.153
[39]	Mcconnell D (2011) Renewable energy technology cost review. Melbourne Energy Inst.
[40]	Branker K, Pathak MJ, Pearce JM (2011) A review of solar photovoltaic levelized cost of electricity. Renewable sustainable energy rev 15: 4470–4482. doi: 10.1016/j.rser.2011.07.104
[41]	Celik AN, Muneer T, Clarke P (2009) A review of installed solar photovoltaic and thermal collector capacities in relation to solar potential for the EU-15. Renewable Energy 34: 849–856. doi: 10.1016/j.renene.2008.05.025
[42]	Chung D, Davidson C, Fu R, et al. (2015) US photovoltaic prices and cost breakdowns. Q1 2015 benchmarks for residential, commercial, and utility-scale systems. National Renewable Energy Lab (NREL), Golden, CO (United States).
[43]	Zhang HL, Baeyens J, Degrève J, et al. (2013) Concentrated solar power plants: Review and design methodology. Renewable sustainable energy rev 22: 466–481. doi: 10.1016/j.rser.2013.01.032
[44]	Gielen D (2012) Renewable energy technologies: Cost analysis series. Volume 1: Power Sector, Issue 2/5, Concentrating Solar Power [2014-11-20]. Available from: http://www. researchgate. net/profile/Dolf Gielen/publications/6.
[45]	Díaz-González F, Sumper A, Gomis-Bellmunt O, et al. (2012) A review of energy storage technologies for wind power applications. Renewable sustainable energy rev 16: 2154–2171. doi: 10.1016/j.rser.2012.01.029
[46]	Herbert GJ, Iniyan S, Sreevalsan E, et al. (2007) A review of wind energy technologies. Renewable sustainable energy rev 11: 1117–1145. doi: 10.1016/j.rser.2005.08.004
[47]	Kaldellis JK, Zafirakis D (2011) The wind energy (r) evolution: A short review of a long history. Renewable energy 36: 1887–1901. doi: 10.1016/j.renene.2011.01.002
[48]	Leung DY, Yang Y (2012) Wind energy development and its environmental impact: A review. Renewable sustainable energy rev 16: 1031–1039. doi: 10.1016/j.rser.2011.09.024
[49]	Miketa A, Merven B (2013) West African power pool: Planning and prospects for renewable energy. IRENA, Abu Dhabi.
[50]	Askarzadeh A, dos Santos Coelho L (2015) A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran. Solar Energy 112: 383–396. doi: 10.1016/j.solener.2014.12.013
[51]	Sediqi MM, Howlader HOR, Ibrahimi AM, et al. (2017) Development of renewable energy resources in Afghanistan for economically optimized cross-border electricity trading. AIMS Energy 5: 691–717. doi: 10.3934/energy.2017.4.691
[52]	Palmintier BS, Webster MD (2015) Impact of operational flexibility on electricity generation planning with renewable and carbon targets. IEEE Trans Sust Energy 7: 672–684.
[53]	Howlader HO, Sediqi MM, Ibrahimi AM, et al. (2018) Optimal thermal unit commitment for solving duck curve problem by introducing CSP, PSH and demand response. IEEE Access 6: 4834–4844. doi: 10.1109/ACCESS.2018.2790967
[54]	Chakraborty S, Senjyu T, Saber AY, et al. (2009) Optimal thermal unit commitment integrated with renewable energy sources using advanced particle swarm optimization. IEEJ Trans Electr Electron Eng 4: 609–617. doi: 10.1002/tee.20453
[55]	Sivasakthi S, Santhi RK, Krishnan NM, et al. (2017) Optimal thermal unit commitment solution integrating renewable energy with generator outage. Iran J Electr Electron Eng 13: 112–122.
[56]	Marneris I, Biskas P, Bakirtzis A (2017) Stochastic and deterministic unit commitment considering uncertainty and variability reserves for high renewable integration. Energies 10: 140. doi: 10.3390/en10010140
[57]	Sediqi MM, Furukakoi M, Lotfy ME, et al. (2017) An optimization approach for unit commitment of a power system integrated with renewable energy sources: A case study of afghanistan. J Energy Power Eng 11: 528–536.
[58]	Furukakoi M, Adewuyi OB, Matayoshi H, et al. (2018) Multi objective unit commitment with voltage stability and PV uncertainty. Appl Energy 228: 618–623. doi: 10.1016/j.apenergy.2018.06.074
[59]	Carrión M, Arroyo JM (2006) A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans power syst 21: 1371–1378. doi: 10.1109/TPWRS.2006.876672
[60]	Zhang Q, Ishihara KN, Mclellan BC, et al. (2012) Scenario analysis on future electricity supply and demand in Japan. Energy 38: 376–385. doi: 10.1016/j.energy.2011.11.046
[61]	Sooriyaarachchi TM, Tsai IT, El Khatib S, et al. (2015) Job creation potentials and skill requirements in, PV, CSP, wind, water-to-energy and energy efficiency value chains. Renewable Sustainable Energy Rev 52: 653–668. doi: 10.1016/j.rser.2015.07.143
[62]	Akella AK, Saini RP, Sharma MP (2009) Social, economical and environmental impacts of renewable energy systems. Renewable Energy 34: 390–396. doi: 10.1016/j.renene.2008.05.002
[63]	Available from: https://www.adb.org/countries/afghanistan/poverty.
[64]	Available from: https://www.tolonews.com/business/afghanistan-has-highest-unemployed-work-force-ilo.

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