Optimal Design of PV/WT/Battery Based Microgrid for Rural Areas in Leh Using Dragonfly Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.3922Keywords:
Photovoltaic, wind energy, battery energy storage, greenhouse gas emissions, microgrid, levelized cost of energyAbstract
This study proposes an optimal microgrid design for rural electrification in India’s Leh and Ladakh regions, using wind energy, solar, energy, and battery energy storage system. The Dragonfly Algorithm (DA) is used to calculate the optimal number of microgrid units, and results are compared with popular optimization algorithms such as Grey Wolf optimization (GWO), Differential Evolution (DE), and Discrete Harmony Search (DHS). The optimal design is based on an objective function to minimize the Levelized cost of energy (LCOE) while keeping the loss of power supply probability (LOPSP) as a reliability constraint. Three configuration studies are carried out, with three cases, each with a different maximum permissible LOPSP (LOPSPmax) value. The results show that optimal design and efficient energy management reliably meet the load demand. The energy generated from the proposed microgrid is clean compared to the grid supply, and the amount of greenhouse gas (GHG) emissions is reduced by 91.2% from Configuration-I, Case-I, which is the most economical configuration. The LCOE obtained from Configuration-I, Case-I is 0.129 $/kWh, the lowest among similar systems available in the literature. To determine the parameter cost with supply, the LCOE and Total life cycle cost (TLCC) sensitivity to LOPSPmax are considered. Furthermore, statistical analysis shows that DA outperforms GWO, DE, and DHS in terms of accuracy and convergence rate.
Downloads
References
R. D. Daniel Schnitzer, Deepa Shinde Lounsbury, Juan Pablo Carvallo and D. M. K. Jay Apt. Microgrids for Rural Electrification: A critical review of best practices based on seven case studies. United Nations Found. 1–122, 2014.
K. Kusakana. Techno-economic analysis of off-grid hydrokinetic-based hybrid energy systems for onshore/remote area in South Africa. Energy, 68:947–957, 2014. https://doi:10.1016/j.energy.2014.01.100.
P. Paliwal, N. P. Patidar, and R. K. Nema. Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization. Renew. Energy, 63:194–204, 2014. https://doi:10.1016/j.renene.2013.09.003.
M. S. Ismail, M. Moghavvemi, and T. M. I. Mahlia. Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems. Energy Convers. Manag., 85:120–130, 2014. https://doi:10.1016/j.enconman.2014.05.064.
A. Chauhan and R. P. Saini. Techno-economic optimization based approach for energy management of a stand-alone integrated renewable energy system for remote areas of India. Energy, 94:138–156, 2016. https://doi:10.1016/j.energy.2015.10.136.
A. Sunil and D. M. Venkaiah, Chintham, Vinod Kumar. Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits. Distrib. Gener. Altern. Energy J., 38(5):1531–1558, 2023. https://doi.org/10.13052/dgaej2156-3306.3858.
P. Kumar, V. Das, A. K. Singh, and P. Karuppanan. Levelized Cost of Energy-Based Economic Analysis of Microgrid Equipped with Multi Energy Storage System. Distrib. Gener. Altern. Energy J., 38(4):1331–1356, 2023. https://doi.org/10.13052/dgaej2156-3306.38411.
K. Anoune, M. Ghazi, M. Boyua, A. Laknizi, M. Ghazouani, A.B. Abdellah and A. Astito. Optimization and techno-economic analysis of photovoltaic-wind-battery based hybrid system. J. Energy Storage, 32:101878, 2020. https://doi:10.1016/j.est.2020.101878.
N. Alshammari and J. Asumadu. Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms. Sustain. Cities Soc., 60:102255, 2020. https://doi:10.1016/j.scs.2020.102255.
B. A. Bhayo, H. H. Al-Kayiem, S. I. U. Gilani, and F. B. Ismail. Power management optimization of hybrid solar photovoltaic-battery integrated with pumped-hydro-storage system for standalone electricity generation. Energy Convers. Manag., 215:112942, 2020. https://doi:10.1016/j.enconman.2020.112942.
Y. He, S. Guo, J. Zhou, F. Wu, J. Huang, and H. Pei. The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies. Energy Convers. Manag., 229: 113779, 2021. https://doi:10.1016/j.enconman.2020.113779.
N. S. Attemene, K. S. Agbli, S. Fofana, and D. Hissel. Optimal sizing of a wind, fuel cell, electrolyzer, battery and supercapacitor system for off-grid applications. Int. J. Hydrogen Energy, 45(8):5512–5525, 2020. https://doi:10.1016/j.ijhydene.2019.05.212.
M. M. Samy, M. I. Mosaad, and S. Barakat. Optimal economic study of hybrid PV-wind-fuel cell system integrated to unreliable electric utility using hybrid search optimization technique. Int. J. Hydrogen Energy,46(20):11217–11231, 2021. https://doi:10.1016/j.ijhydene.2020.07.258.
S. Barakat, H. Ibrahim, and A. A. Elbaset. Multi-objective optimization of grid-connected PV-wind hybrid system considering reliability, cost, and environmental aspects. Sustain. Cities Soc., 60:102178, 2020. https://doi:10.1016/j.scs.2020.102178.
D. Temene, N. Donatien, T. Konchou, F. Armel, and T. Ren. Techno-economic and environmental feasibility study with demand-side management of photovoltaic/wind/hydroelectricity/Battery/ diesel:A case study in Sub-Saharan Africa. Energy Convers. Manag., 258:115494, 2022. https://doi:10.1016/j.enconman.2022.115494.
W. Jung, J. Jeong, J. Kim, and D. Chang. Optimization of hybrid off-grid system consisting of renewables and Li-ion batteries. J. Power Sources, 451:227754, 2020. https://doi:10.1016/j.jpowsour.2020.227754.
O. M. Babatunde, J. L. Munda, and Y. Hamam. Off-grid hybrid photovoltaic – micro wind turbine renewable energy system with hydrogen and battery storage: Effects of sun tracking technologies. Energy Convers. Manag., 255:115335, 2022.
N. Ghorbani, A. Kasaeian, A. Toopshekan, L. Bahrami, and A. Maghami. Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability. Energy,154:581–591, 2018. https://doi:10.1016/j.energy.2017.12.057.
M. S. Javed, T. Ma, J. Jurasz, S. Ahmed, and J. Mikulik. Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems. Energy, 210:118599, 2020. https://doi:10.1016/j.energy.2020.118599.
M. Najafi Ashtiani, A. Toopshekan, F. Razi Astaraei, H. Yousefi, and A. Maleki. Techno-economic analysis of a grid-connected PV/battery system using the teaching-learning-based optimization algorithm. Sol. Energy, 203:69–82, 2020. https://doi:10.1016/j.solener.2020.04.007.
F. A. Khan, N. Pal, S. H. Saeed, and A. Yadav. Techno-economic and feasibility assessment of standalone solar Photovoltaic/Wind hybrid energy system for various storage techniques and different rural locations in India. Energy Convers. Manag., 270: 116217, 2022. https://doi:10.1016/j.enconman.2022.116217.
M. Jahannoosh, S. A. Nowdeh, A. Naderipour, H. Kamyab, I. F. Davoudkhani, and J. J. Klemeš. New hybrid meta-heuristic algorithm for reliable and cost-effective designing of photovoltaic/wind/fuel cell energy system considering load interruption probability. J. Clean. Prod., 278:123406, 2021. https://doi:10.1016/j.jclepro.2020.123406.
D. Fares, M. Fathi, and S. Mekhilef. Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system. Appl. Energy, 305: 117823, 2022. https://doi:10.1016/j.apenergy.2021.117823.
M. Chennaif, H. Zahboune, M. Elhafyani, and S. Zouggar. Electric System Cascade Extended Analysis for optimal sizing of an autonomous hybrid CSP/PV/wind system with Battery Energy Storage System and thermal energy storage. Energy, 227:120444, 2021. https://doi:10.1016/j.energy.2021.120444.
H. M. Sultan, A. S. Menesy, S. Kamel, A. Korashy, S. A. Almohaimeed, and M. Abdel-akher. An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV / WT / FC energy system. Alexandria Eng. J., 60:1001–1025, 2021. https://doi:10.1016/j.aej.2020.10.027.
M. H. Jahangir and R. Cheraghi. Economic and environmental assessment of solar-wind-biomass hybrid renewable energy system supplying rural settlement load. Sustain. Energy Technol. Assessments, 42:100895,2020. https://doi:10.1016/j.seta.2020.100895.
A. F. Altun and M. Kilic. Design and performance evaluation based on economics and environmental impact of a PV-wind-diesel and battery standalone power system for various climates in Turkey. Renew. Energy, vol. 157:424–443, 2020. https://doi:10.1016/j.renene.2020.05.042.
M. M. Samy, S. Barakat, and H. S. Ramadan. Techno-economic analysis for rustic electrification in Egypt using multi-source renewable energy based on PV/ wind/ FC. Int. J. Hydrogen Energy, 45(20):11471–11483, 2020. https://doi:10.1016/j.ijhydene.2019.04.038.
R. Khezri, A. Mahmoudi, and H. Mohammed. A Demand Side Management Approach for Optimal Sizing of Standalone Renewable-Battery Systems. IEEE Trans. Sustain. Energy, 12(4):2184–2194, 2021. https://doi:10.1109/TSTE.2021.3084245.
X. Xu, Z. Zhang, J. Yuan, and J. Shao. Design and multi-objective comprehensive evaluation analysis of PV-WT-BG-Battery hybrid renewable energy systems in urban communities. Energy Convers. Manag., 18:100357, 2023.
G. Fosso, P. Tiam, F. Lenine, and D. Koffi. Techno-economic investigation of an environmentally friendly small-scale solar tracker-based PV/wind/Battery hybrid system for off-grid rural electrification in the mount bamboutos, Cameroon. Energy Strateg. Rev., 48:101107, 2023. https://doi.org/10.1016/j.esr.2023.101107.
J. Kumagai. Lights for the enlightened. IEEE Spectr., 53(12):32–39, 2016.
N. J. Williams, P. Jaramillo, J. Taneja, and T. S. Ustun. Enabling private sector investment in microgrid-based rural electrification in developing countries: A review. Renew. Sustain. Energy Rev.,52:1268–1281, 2015. https://doi:10.1016/j.rser.2015.07.153.
P. K. Sharma, S. Ahmed, and V. Warudkar. A preliminary study of wind–solar hybrid systems potential in Jammu and Kashmir. Int. J. Ambient Energy, 41(9):1026–1030, 2020. https://doi:10.1080/01430750.2018.1501743.
A. report On Wind/Solar Resource Assessment studies in Leh-Ladakh UT 6. 2019.
S. Mirjalili. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl., 27(4): 1053–1073, 2016. https://doi:10.1007/s00521-015-1920-1.
S. Mirjalili, S. M. Mirjalili, and A. Lewis. Grey Wolf Optimizer. Adv. Eng. Softw., 69:46–61, 2014. https://doi:10.1016/j.advengsoft.2013.12.007.
R. Storn and K. Price. Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Glob. Optim., 11(4):341–359, 1997. https://doi:10.1023/A:1008202821328.
A. Askarzadeh. A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of PV / wind hybrid system. Sol. Energy, 97:93–101, 2013. https://doi:10.1016/j.solener.2013.08.014.
A. G. Hussien, M. Amin, M. Wang, G. Liang, A. Alsanad, A. Gumaei and H. Chen. Crow Search Algorithm: Theory, Recent Advances, and Applications. IEEE Access, 8:173548, 2020. https://doi:10.1109/ACCESS.2020.3024108.
J. & K. Census of India 2011. District census handbook Leh 2011. 2011.
A. Askarzadeh and A. Rezazadeh. Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy, 86: 3241–3249, 2012. https://doi:0.1016/j.solener.2012.08.018.
S. Singh, M. Singh, and S. C. Kaushik. Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system. Energy Convers. Manag., 128:178–190, 2016. https://doi:10.1016/j.enconman.2016.09.046.
A. Askarzadeh and L. dos Santos Coelho. A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran. Sol. Energy,112:383–396, 2015. https://doi:10.1016/j.solener.2014.12.013.
Yong Yang and Rong Li. Techno-Economic Optimization of an Off-Grid Solar/Wind/Battery Hybrid System with a Novel Multi-Objective Differential Evolution Algorithm. Energies, 13:1–16, 2020. https://doi:10.3390/en13071585.
A. Kashefi Kaviani, G. H. Riahy, and S. M. Kouhsari. Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages. Renew. Energy, 34(11):2380–2390, 2009. https://doi:10.1016/j.renene.2009.03.020.
H. Jinglin, H. Ping, Z. Hui, H. Chunguang, R. Hou, and L. Bo. Multi-objective Collaborative Planning Method for Micro-energy Systems Considering Thermoelectric Coupling Clusters. Distrib. Gener. Altern. Energy J., 38(5):1677–1706, 2023. https://doi:10.13052/dgaej2156-3306.38514.
Y. P. Xu, P. Ouyang, S. M. Xing, L. Y. Qi, M. khayatnezhad, and H. Jafari. Optimal structure design of a PV/FC HRES using amended Water Strider Algorithm. Energy Reports,7:2057–2067, 2021.
A. Maleki and A. Askarzadeh. Optimal sizing of a PV/wind/diesel system with battery storage for electrification to an off-grid remote region: A case study of Rafsanjan, Iran. Sustain. Energy Technol. Assessments,7:147–153, 2014. https://doi:10.1016/j.eta.2014.04.005.
M. Kharrich, O. H. Mohammed, N. Alshammari, and M. Akherraz. Multi-objective optimization and the effect of the economic factors on the design of the microgrid hybrid system. Sustain. Cities Soc., 65:102646, 2021. https://doi:10.1016/j.scs.2020.102646.
Gunnar Myhre and Drew Shindell. Anthropogenic and Natural Radiative Forcing. 2014.
M. Brander, A. Sood, C. Wylie, A. Haughton, J. Lovell. Electricity-specific emission factors for electricity. Ecometrica, 1–22, 2011.
M. Brander, A. Sood, C. Wylie, A. Haughton, and J. Lovell. Electricity-specific emission factors for electricity. Ecometrica, 1–22, 2011.
J. Peng, L. Lu, and H. Yang. Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems. Renew. Sustain. Energy Rev., 19:255–274, 2013. https://doi:10.1016/j.rser.2012.11.035.
T. Ackermann. Distributed generation: a definition. Electr. Power Syst. Res., 57:195–204, 2001.
P. Denholm and G. L. Kulcinski. Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems. Energy Convers. Manag.,45(13): 2153–2172, 2004. https://doi:10.1016/j.enconman.2003.10.014.
C. Mostert, B. Ostrander, S. Bringezu, and T. M. Kneiske. Comparing electrical energy storage technologies regarding their material and carbon footprint. Energies, 11(12), 2018.
L. Xingjun, S. Zhiwei, C. Hongping, and B. O. Mohammed. A new fuzzy-based method for load balancing in the cloud-based Internet of things using a grey wolf optimization algorithm. Int. J. Commun. Syst.,33(8):1–19, 2020. https://doi:10.3390/en11123386.
K. P. Wong and Z. Dong. Differential Evolution, an Alternative Approach to Evolutionary Algorithm. 73–83, 2005.
A. Naderipour and H. Kamyab. Optimal design of hybrid grid-connected photovoltaic/ wind/battery sustainable energy system improving reliability, cost and emission. Energy, 257:124679, 2022. https://doi.org/10.1016/j.energy.2022.124679.
M. Kharrich, S. Kamel, M. Abdel-akher, and A. Eid. Optimization based on movable damped wave algorithm for design of photovoltaic/wind/diesel/biomass/battery hybrid energy systems. Energy Reports, 8:11478–11491, 2022. https://doi:10.1016/j.egyr.2022.08.278.
L. Xu, X. Ruan, C. Mao, B. Zhang, and Y. Luo. An improved optimal sizing method for wind-solar-battery hybrid power system. IEEE Trans. Sustain. Energy, 4(3):74–785, 2013. https://doi:10.1109/TSTE.2012.2228509.
K. Vamba, O. Bode, and M. M. Gamil. A scenario-based multi-attribute decision making approach for optimal design of a hybrid off-grid system. Energy, 265:125663, 2023. https://doi.org/10.1016/j.energy.2022.125663.