Optimal Unit Commitment for Renewable Energy and Energy Storage Integrated Power System

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dc.contributor.author Apon, Hasan Jamil
dc.date.accessioned 2025-06-20T07:06:09Z
dc.date.available 2025-06-20T07:06:09Z
dc.date.issued 2024-11-30
dc.identifier.citation [1] A. Singh and A. Khamparia, “A hybrid whale optimization-differential evolu tion and genetic algorithm based approach to solve unit commitment scheduling problem: Wodega,” Sustainable Computing: Informatics and Systems, vol. 28, p. 100442, 2020. [2] F. P. Mahdi, P. Vasant, V. Kallimani, J. Watada, P. Y. Fai, and M. Abdullah Al-Wadud, “A holistic review on optimization strategies for combined eco nomic emission dispatch problem,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 3006–3020, 2018. [3] D. Maradin, “Advantages and disadvantages of renewable energy sources utiliza tion,” International Journal of Energy Economics and Policy, vol. 11, no. 3, pp. 176–183, 2021. [4] K. R. Voorspools and W. D. D’haeseleer, “Long-term unit commitment optimi sation for large power systems: Unit decommitment versus advanced priority listing,” Applied Energy, vol. 76, no. 1-3, pp. 157–167, 2003. [5] W. L. Snyder, H. D. Powell, and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE Power Engineering Review, vol. PER-7, no. 5, pp. 41–42, 1987. [6] W. Ongsakul and N. Petcharaks, “Transmission and ramp constrained unit com mitment using enhanced adaptive lagrangian relaxation,” in 2005 IEEE Russia Power Tech, vol. 2, 2005, pp. 1–8. [7] G. W. Chang, Y. D. Tsai, C. Y. Lai, and J. S. Chung, “A practical mixed integer linear programming based approach for unit commitment,” in IEEE Power Engi neering Society General Meeting, vol. 2, 2004, pp. 221–225. [8] M. Younes and F. Benhamida, “Genetic algorithm-particle swarm optimization (ga-pso) for economic load dispatch,” Przeglad Elektrotechniczny, vol. 4, pp. 369–372, 2011. 56 [9] H. Q. Truong and C. Jeenanunta, “Fuzzy mixed integer linear programming model for national level monthly unit commitment under price-based uncertainty: A case study in thailand,” Electric Power Systems Research, vol. 209, p. 107963, 2022. [10] F. Feng, P. Zhang, M. A. Bragin, and Y. Zhou, “Novel resolution of unit commit ment problems through quantum surrogate lagrangian relaxation,” IEEE Trans actions on Power Systems, vol. 38, no. 3, pp. 2460–2471, 2023. [11] M. S. Rifat, M. A. Niloy, M. F. Rizvi, A. Ahmed, R. Ahshan, S. H. Nengroo, and S. Lee, “Application of binary slime mould algorithm for solving unit commit ment problem,” IEEE Access, vol. 11, pp. 45 279–45 300, 2023. [12] S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Transactions on Power Systems, vol. 11, no. 1, pp. 83–92, 1996. [13] T. Logenthiran and D. Srinivasan, “Particle swarm optimization for unit com mitment problem,” in 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, 2010, pp. 642–647. [14] S. P. Simon, N. P. Padhy, and R. S. Anand, “Ant colony system based unit com mitment problem with gaussian load distribution,” in 2006 IEEE Power Engi neering Society General Meeting, vol. 4, 2006. [15] S. S. Sakthi, R. K. Santhi, N. Murali Krishnan, S. Ganesan, and S. Subramanian, “Wind integrated thermal unit commitment solution using grey wolf optimizer,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2309–2320, 2017. [16] S. Kigsirisin and H. Miyauchi, “Short-term operational scheduling of unit com mitment using binary alternative moth-flame optimization,” IEEE Access, vol. 9, pp. 12 267–12 281, 2021. [17] J.-S. Pan, P. Hu, and S.-C. Chu, “Binary fish migration optimization for solving unit commitment,” Energy, vol. 226, p. 120329, 2021. [18] M. Moghimi Hadji and B. Vahidi, “A solution to the unit commitment problem using imperialistic competition algorithm,” IEEE Transactions on Power Sys tems, vol. 27, no. 1, pp. 117–124, 2012. [19] X. Yuan, A. Su, H. Nie, Y. Yuan, and L. Wang, “Unit commitment problem using enhanced particle swarm optimization algorithm,” Soft Computing, vol. 15, no. 1, pp. 139–148, 2010. 57 [20] J. Ebrahimi, S. H. Hosseinian, and G. B. Gharehpetian, “Unit commitment prob lem solution using shuffled frog leaping algorithm,” IEEE Transactions on Power Systems, vol. 26, no. 2, pp. 573–581, 2011. [21] D. Simopoulos, S. Kavatza, and C. Vournas, “Unit commitment by an enhanced simulated annealing algorithm,” in 2006 IEEE PES Power Systems Conference and Exposition, vol. 23, 2006, pp. 193–201. [22] X. Yuan, B. Ji, S. Zhang, H. Tian, and Y. Hou, “A new approach for unit commit ment problem via binary gravitational search algorithm,” Applied Soft Comput ing, vol. 22, pp. 249–260, 2014. [23] K. A. Juste, H. Kita, E. Tanaka, and J. Hasegawa, “An evolutionary programming solution to the unit commitment problem,” IEEE Transactions on Power Systems, vol. 14, no. 4, pp. 1452–1459, 1999. [24] A. Bhadoria and S. Marwaha, “Economic energy scheduling through chaotic go rilla troops optimizer,” International Journal of Energy and Environmental Engi neering, vol. 14, no. 4, pp. 803–827, 2022. [25] S. Shabir and R. Singla, “A comparative study of genetic algorithm and the parti cle swarm optimization,” International Journal of Electrical Engineering, vol. 9, no. 2, pp. 215–223, 2016. [26] V. Kumar and D. Kumar, “Binary whale optimization algorithm and its applica tion to unit commitment problem,” Neural Computing and Applications, vol. 32, no. 7, pp. 2095–2123, 2018. [27] E. S. Ali, S. M. A. Elazim, and A. S. Balobaid, “Implementation of coyote op timization algorithm for solving unit commitment problem in power systems,” Energy, vol. 263, p. 125697, 2023. [28] V. K. Kamboj, “A novel hybrid pso–gwo approach for unit commitment prob lem,” Neural Computing and Applications, vol. 27, no. 6, pp. 1643–1655, 2015. [29] C.-P. Cheng, C.-W. Liu, and C.-C. Liu, “Unit commitment by lagrangian relax ation and genetic algorithms,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 707–714, 2000. [30] H. H. Balci and J. F. Valenzuela, “Scheduling electric power generators using particle swarm optimization combined with the lagrangian relaxation method,” International Journal of Applied Mathematics and Computer Science, vol. 14, no. 3, pp. 411–421, 2004. [31] M. A. Awadallah, A. I. Hammouri, M. A. Al-Betar, M. S. Braik, and M. A. Elaziz, “Binary horse herd optimization algorithm with crossover operators for feature selection,” Computers in Biology and Medicine, vol. 141, p. 105152, 2022. 58 [32] I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, and H. Chen, “Run beyond the metaphor: An efficient optimization algorithm based on runge kutta method,” Expert Systems with Applications, vol. 181, p. 115079, 2021. [33] J. Tu, H. Chen, M. Wang, and A. H. Gandomi, “The colony predation algorithm,” Journal of Bionic Engineering, vol. 18, no. 3, pp. 674–710, 2021. [34] I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, and A. H. Gandomi, “Info: An efficient optimization algorithm based on weighted mean of vectors,” Expert Systems with Applications, vol. 195, p. 116516, 2022. [35] H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, and H. Chen, “Rime: A physics-based optimization,” Neurocomputing, vol. 532, pp. 183–214, 2023. [36] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures opti mization algorithm: A new nature-inspired metaheuristic algorithm for global op timization problems,” Computers & Industrial Engineering, vol. 158, p. 107408, 2021. [37] E. E. Elattar, “Modified harmony search algorithm for combined economic emis sion dispatch of microgrid incorporating renewable sources,” Energy, vol. 159, pp. 496–507, 2018. [38] A. A. Abou El Ela, R. A. El-Sehiemy, A. M. Shaheen, and A. S. Shalaby, “Ap plication of the crow search algorithm for economic environmental dispatch,” in 2017 Nineteenth International Middle East Power Systems Conference (MEP CON), 2017, pp. 1–6. [39] A. Esmat, A. Magdy, W. ElKhattam, and A. M. ElBakly, “A novel energy man agement system using ant colony optimization for micro-grids,” in 2013 3rd In ternational Conference on Electric Power and Energy Conversion Systems, 2013, pp. 1–6. [40] I. N. Trivedi, P. Jangir, M. Bhoye, and N. Jangir, “An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm,” Neural Computing and Applications, vol. 30, no. 7, pp. 2173– 2189, 2016. [41] K. Nagarajan, A. Rajagopalan, S. Angalaeswari, L. Natrayan, and W. D. Mammo, “Combined economic emission dispatch of microgrid with the incorporation of renewable energy sources using improved mayfly optimization algorithm,” Com putational Intelligence and Neuroscience, pp. 1–22, 2022. [42] W.-K. Hao, J.-S. Wang, X.-D. Li, Y. Liu, J.-H. Zhu, M. Zhang, and M. Wang, “Multi-objective arithmetic optimization algorithm with random searching strate 59 gies to solve combined economic emission dispatch problem,” Computers & In dustrial Engineering, vol. 195, p. 110434, 2024. [43] S. K. Mishra and S. K. Mishra, “Solution of the combined environmental eco nomic dispatch problem using multi-objective cat swarm optimization,” Interna tional Journal on Electrical Engineering & Informatics, vol. 13, no. 2, 2021. [44] W. Lai, X. Zheng, Q. Song, F. Hu, Q. Tao, and H. Chen, “Multi-objective mem brane search algorithm: A new solution for economic emission dispatch,” Applied Energy, vol. 326, p. 119969, 2022. [45] J. Sun and C. Liu, “A multi-objective particle swarm optimization algorithm for combined economic emission dispatch problem,” in 2021 4th International Con ference on Artificial Intelligence and Pattern Recognition, 2021, pp. 555–560. [46] M. Sutar and H. T. Jadhav, “An economic/emission dispatch based on a new multi-objective artificial bee colony optimization algorithm and nsga-ii,” Evolu tionary Intelligence, vol. 17, no. 2, pp. 1127–1162, 2022. [47] S. Y. Abujarad, M. W. Mustafa, and J. J. Jamian, “Recent approaches of unit com mitment in the presence of intermittent renewable energy resources: A review,” Renewable and Sustainable Energy Reviews, vol. 70, pp. 215–223, 2017. [48] L. Moretti, E. Martelli, and G. Manzolini, “An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids,” Applied Energy, vol. 261, p. 113859, 2020. [49] M. Melamed, A. Ben-Tal, and B. Golany, “A multi-period unit commitment prob lem under a new hybrid uncertainty set for a renewable energy source,” Renew able Energy, vol. 118, pp. 909–917, 2018. [50] M. Pezic and V. M. Cedres, “Unit commitment in fully renewable, hydro-wind energy systems,” in 2013 10th International Conference on the European Energy Market (EEM), vol. 21, 2013, pp. 1–8. [51] Y. Ikeda, T. Ikegami, K. Kataoka, and K. Ogimoto, “A unit commitment model with demand response for the integration of renewable energies,” in 2012 IEEE Power and Energy Society General Meeting, vol. 35, 2012, pp. 1–7. [52] J. Pan and T. Liu, “Optimal scheduling for unit commitment with electric vehicles and uncertainty of renewable energy sources,” SSRN Electronic Journal, 2022. [53] M. Shahbazitabar and H. Abdi, “A novel priority-based stochastic unit commit ment considering renewable energy sources and parking lot cooperation,” Energy, vol. 161, pp. 308–324, 2018. 60 [54] H. Quan, D. Srinivasan, A. M. Khambadkone, and A. Khosravi, “A computational framework for uncertainty integration in stochastic unit commitment with inter mittent renewable energy sources,” Applied Energy, vol. 152, pp. 71–82, 2015. [55] P. Nikolaidis, S. Chatzis, and A. Poullikkas, “Renewable energy integration through optimal unit commitment and electricity storage in weak power net works,” International Journal of Sustainable Energy, vol. 38, no. 4, pp. 398–414, 2018. [56] M. Y. Nassar, M. N. Abdullah, and A. Ahmwed, “A review of optimization meth ods for economic and emission dispatch considering pv and wind energy,” in IOP Conference Series: Materials Science and Engineering, vol. 1127, no. 1, 2021, p. 012035. [57] M. Azeem, T. N. Malik, H. A. Muqeet, M. M. Hussain, A. Ali, B. Khan, and A. u. Rehman, “Combined economic emission dispatch in presence of renewable energy resources using cissa in a smart grid environment,” Electronics, vol. 12, no. 3, p. 715, 2023. [58] B. Dey, S. K. Roy, and B. Bhattacharyya, “Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms,” Engineering Science and Technology, an International Journal, vol. 22, no. 1, pp. 55–66, 2019. [59] G. Wang, Y. Zha, T. Wu, J. Qiu, J. Peng, and G. Xu, “Cross entropy optimization based on decomposition for multi-objective economic emission dispatch consid ering renewable energy generation uncertainties,” Energy, vol. 193, p. 116790, 2020. [60] A. Elbaz and M. T. Güne¸ser, “Multi-objective optimization of combined eco nomic emission dispatch problem in solar pv energy using hybrid bat-crow search algorithm,” International Journal of Renewable Energy Research, vol. 11, no. 1, 2021. [61] M. Premkumar, R. Sowmya, C. Ramakrishnan, P. Jangir, E. H. Houssein, S. Deb, and N. Manoj Kumar, “An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties,” Energy Reports, vol. 9, pp. 1029– 1053, 2023. [62] A. Rezaee Jordehi, “An improved particle swarm optimisation for unit com mitment in microgrids with battery energy storage systems considering bat tery degradation and uncertainties,” International Journal of Energy Research, vol. 45, no. 1, pp. 727–744, 2020. 61 [63] N. Nasiri, M. Reza Banaei, and S. Zeynali, “A hybrid robust-stochastic approach for unit commitment scheduling in integrated thermal electrical systems consid ering high penetration of solar power,” Sustainable Energy Technologies and As sessments, vol. 49, p. 101756, 2022. [64] P. P. Gupta, V. Kalkhambkar, P. Jain, K. C. Sharma, and R. Bhakar, “Battery en ergy storage train routing and security constrained unit commitment under solar uncertainty,” Journal of Energy Storage, vol. 55, p. 105811, 2022. [65] Y.-Y. Hong, G. F. Apolinario, T.-K. Lu, and C.-C. Chu, “Chance-constrained unit commitment with energy storage systems in electric power systems,” Energy Reports, vol. 8, pp. 1067–1090, 2022. [66] J. Lu and X. Li, “The benefits of hydrogen energy transmission and conversion systems to the renewable power grids: Day-ahead unit commitment,” in 2022 North American Power Symposium (NAPS), vol. 9, 2022, pp. 1–6. [67] L. Moretti, E. Martelli, and G. Manzolini, “An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids,” Applied Energy, vol. 261, p. 113859, 2020. [68] M. J. Anita, “Solution of unit commitment problem using shuffled frog leaping algorithm,” IOSR Journal of Electrical and Electronics Engineering, vol. 1, no. 4, pp. 09–26, 2012. [69] Y.-W. Jeong, J.-B. Park, S.-H. Jang, and K. Y. Lee, “A new quantum-inspired binary pso for thermal unit commitment problems,” in 2009 15th International Conference on Intelligent System Applications to Power Systems, vol. 28, 2009, pp. 1–6. [70] L. K. Panwar, K. S. Reddy, A. Verma, B. K. Panigrahi, and R. Kumar, “Binary grey wolf optimizer for large scale unit commitment problem,” Swarm and Evo lutionary Computation, vol. 38, pp. 251–266, 2018. [71] A. Alshareef and A. Y. Saber, “An application of particle swarm optimization (pso) to dynamic unit commitment problem for the western area of saudi arabia,” Journal of King Abdulaziz University: Engineering Sciences, vol. 149, no. 642, pp. 1–34, 2012. [72] B. Vedik, C. K. Shiva, and K. Vamshidhar, “Solution to economic load dispatch using quasi-oppositional based code by considering transmission line losses,” IOP Conference Series: Materials Science and Engineering, vol. 981, no. 4, p. 042056, 2020. [Online]. Available: https://doi.org/10.1088/1757-899x/981/4/ 042056 62 [73] G. V. Chary and K. M. Rosalina, “Analysis of transmission line modeling routines by using offsets measured least squares regression ant lion optimizer in orpd and eld problems,” Heliyon, vol. 9, no. 3, p. e13387, 2023. [Online]. Available: https://doi.org/10.1016/j.heliyon.2023.e13387 [74] R. J. CHINASAMY HEMPARUVA, S. P. SIMON, S. KINATTINGAL, and S. R. PANUGOTHU, “Gravitational search algorithm-based dynamic economic dispatch by estimating transmission system losses using a loss coefficients,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, vol. 24, pp. 3769–3781, 2016. [Online]. Available: https://doi.org/10.3906/elk-1412-152 [75] K. Selvakumar, K. Vijayakumar, and C. Boopathi, “Demand response unit com mitment problem solution for maximizing generating companies’ profit,” Ener gies, vol. 10, no. 10, p. 1465, 2017. [76] B. Ahmadi, O. Ceylan, A. Ozdemir, and M. Fotuhi-Firuzabad, “A multi-objective framework for distributed energy resources planning and storage management,” Applied Energy, vol. 314, p. 118887, 2022. [77] L. Fernández, “Solar pv installation cost worldwide 2022,” November 2023, accessed on: 2024-10-06. [Online]. Available: https://www.statista.com/ statistics/809796/global-solar-power-installation-cost-per-kilowatt/ [78] A. X. Mah, W. S. Ho, M. H. Hassim, H. Hashim, G. H. Ling, C. S. Ho, and Z. A. Muis, “Optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads,” Energy, vol. 235, p. 121218, 2021. [79] D. Dey and B. Subudhi, “Design, simulation and economic evaluation of a 90 kw grid connected photovoltaic system,” Energy Reports, vol. 6, pp. 1778–1787, 2020. [80] A. Shrestha and F. Gonzalez-Longatt, “Parametric sensitivity analysis of rotor angle stability indicators: Simulation case,” Energy Reports, vol. 8, pp. 727–735, 2022. [81] JRC Photovoltaic Geographical Information System, “Photovoltaic geographical information system (pvgis),” accessed on: 2024-10-06. [Online]. Available: https://re.jrc.ec.europa.eu/pvg_tools/en/ [82] A. Costa, T. S. Ng, and B. Su, “Long-term solar pv planning: An economic driven robust optimization approach,” Applied Energy, vol. 335, p. 120702, 2023. [83] N. Khodadadi, F. Soleimanian Gharehchopogh, and S. Mirjalili, “Moavoa: A new multi-objective artificial vultures optimization algorithm,” Neural Comput ing and Applications, vol. 34, no. 23, pp. 20 791–20 829, 2022. 63 [84] B. Zhao, C. X. Guo, B. R. Bai, and Y. J. Cao, “An improved particle swarm optimization algorithm for unit commitment,” International Journal of Electrical Power & Energy Systems, vol. 28, no. 7, pp. 482–490, 2006. [85] Y.-W. Jeong, J.-B. Park, S.-H. Jang, and K. Y. Lee, “A new quantum-inspired binary pso for thermal unit commitment problems,” in 2009 15th International Conference on Intelligent System Applications to Power Systems, vol. 28, 2009, pp. 1–6. [86] L. K. Panwar, K. Srikanth Reddy, and R. Kumar, “Binary fireworks algorithm based thermal unit commitment,” International Journal of Swarm Intelligence Research, vol. 6, no. 2, pp. 87–101, 2015. [87] N. A. Saber, M. Salimi, and D. Mirabbasi, “A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm,” Energy, vol. 117, pp. 272–280, 2016. [88] S. B. BUKHARI, A. AHMAD, S. A. RAZA, and M. N. SIDDIQUE, “A ring crossover genetic algorithm for the unit commitment problem,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, pp. 3862–3876, 2016. [89] K. Srikanth, L. K. Panwar, B. Panigrahi, E. Herrera-Viedma, A. K. Sangaiah, and G.-G. Wang, “Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem,” Computers & Electrical Engineering, vol. 70, pp. 243–260, 2018. [90] T. Alquthami, H. Ravindra, M. O. Faruque, M. Steurer, and T. Baldwin, “Study of photovoltaic integration impact on system stability using custom model of pv arrays integrated with pss/e,” in Proceedings of the North American Power Symposium. Arlington, TX, USA: IEEE, 2010, pp. 1–8. [91] A. Jawad, S. A. Naim, C. Saha, and N. A. Masood, “Frequency stability enhance ment of a large-scale pv integrated grid,” in Proceedings of the 2020 11th Inter national Conference on Electrical and Computer Engineering (ICECE). Dhaka, Bangladesh: IEEE, 2020, pp. 290–293. [92] M. Z. ul Abideen, O. Ellabban, S. S. Refaat, H. Abu-Rub, and L. Al-Fagih, “A novel methodology to determine the maximum pv penetration in distribution net works,” in 2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE). IEEE, 2019. en_US
dc.identifier.uri http://hdl.handle.net/123456789/2438
dc.description Supervised by Prof. Dr. Ashik Ahmed, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Master of Science in Electrical and Electronic Engineering, 2024 en_US
dc.description.abstract As the demand for electricity rises, effective management of generating units becomes essential for ensuring a reliable and sustainable energy supply. The Unit Commitment Problem (UCP) plays a critical role in scheduling generation units to meet fluctuating load demands while minimizing operational costs. Concurrently, the Combined Economic Emission Dispatch (CEED) problem addresses the dual objectives of reducing fuel costs and harmful emissions from power plants. This research focuses on optimizing these interconnected problems within the IEEE 39-bus system, featuring 10 generating units, while incorporating renewable energy sources (RESs) and energy storage systems (ESSs). The study employs the African Vulture Optimization Algorithm (AVOA) for solving the UCP to achieve an operational cost of $559,791.23 and emissions totalling 26,608.40 tons per day without considering the associated transmission losses. Utilizing the Multi-Objective African Vulture Optimization Algorithm (MOAVOA) for the CEED problem resulted in a significant reduction of emissions to 22,748.63 tons per day, illustrating the trade-offs between economic efficiency and environmental impact. Additionally, the integration of PV systems at 10% and 20% penetration levels showed substantial emissions reductions, even as operational costs increased slightly. The research further explores the role of ESSs in managing variability in PV output, thereby enhancing grid reliability and reducing the risk of load shedding. Overall, this study highlights the importance of developing balanced strategies that integrate cost considerations, emissions reduction, and renewable technologies. It advocates for continued investment in innovative energy solutions and supportive regulatory frameworks to facilitate the transition to cleaner, more resilient energy systems. en_US
dc.language.iso en en_US
dc.publisher Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh en_US
dc.subject Unit Commitment Problem, Economic Load Dispatch, Combined Economic Emission Dispatch, African Vulture Optimization Algorithm, Photovoltaic Integration. en_US
dc.title Optimal Unit Commitment for Renewable Energy and Energy Storage Integrated Power System en_US
dc.type Thesis en_US


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