Energy planning based on economic and environmental indices with optimal power flow approach

Yong  Fu

doi:10.6180/jase.202411_27(11).0007

Energy planning based on economic and environmental indices with optimal power flow approach

Environmental Engineering

Yong FuThis email address is being protected from spambots. You need JavaScript enabled to view it.

College of Economics, Sichuan Agricultural University, Chengdu 611130, Sichuan, China

Received: August 21, 2023
Accepted: December 2, 2023
Publication Date: January 27, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202411_27(11).0007

Due to the growing trend of load and the high cost of electric energy production, and the limitation in the installation of new power plant units, planning and minimizing the cost of energy generation for active units in the power plants is necessary. The purpose of optimal power flow is to allocate the optimal contribution of the power plants, provide the required power of the network, and minimize the cost of power generation. In this article, the shuffled frog leaping algorithm (SFLA) is employed for the planning of the electrical energy by optimal power flow for minimizing costs and the emission pollution in the power plants. A quadratic objective function is expressed in terms of the generation of units in which constraints are modeled as linear equal and unequal equations. The proposed method is applied to a system of IEEE-30 bus test system. The obtained results show the ability of the proposed algorithm in the optimization of objective functions. Also, the results obtained from this algorithm have been compared with the results from other evolutionary algorithm methods.

Keywords: Optimal power flow; Shuffled frog leaping algorithm (SFLA); Costs and the emission pollution; Quadratic objective function

[1] H. Li, T. Zheng, S. Huang, Z. Tang, and H. Cao, (2020) “A fault ride through strategy of unified power flow controller and its coordination with protection" Electric Power Systems Research 184: 106323. DOI: 10.1016/j.epsr.2020.106323.
[2] G. Wu, P. Ju, X. Song, C. Xie, and W. Zhong, (2016) “Interaction and coordination among nuclear power plants, power grids and their protection systems" Energies 9: 306. DOI: 10.3390/en9040306.
[3] M. Amin and B. Wollenberg, (2005) “Toward A Smart Grid,„IEEE Power & Energy Magazine”" Toward a SmartGrid-IEEE power and Energy Magazine, September/October2005:
[4] H. Khalilnezhad, M. Popov, L. van der Sluis, J. A. Bos, J. P. W. de Jong, and A. Ametani, (2017) “Countermeasures of zero-missing phenomenon in (E) HV cable systems" IEEE Transactions on power delivery 33: 1657–1667.
[5] A. Askarzadeh, (2016) “Capacitor placement in distribution systems for power loss reduction and voltage improvement: a new methodology" IET Generation, Transmission & Distribution 10: 3631–3638.
[6] M. Montazeri and A. Askarzadeh, (2019) “Capacitor placement in radial distribution networks based on identification of high potential busses" International Transactions on Electrical Energy Systems 29: e2754.
[7] Z. Zhou and L. Ge, (2019) “Operation of stand-alone microgrids considering the load following of biomass power plants and the power curtailment control optimization of wind turbines" IEEE Access 7: 186115–186125.
[8] H.-J. Kim, H.-S. Lee, S.-W. Lee, D.-H. Jung, and D.-S. Moon. “Mitigation of environmental impact of power-plant discharge by use of Ocean Thermal Energy Conversion system”. In: IEEE, 2010, 1–4.
[9] A. Mazandarani, T. M. I. Mahlia, W. T. Chong, and M. Moghavvemi, (2011) “Fuel consumption and emission prediction by Iranian power plants until 2025" Renewable and Sustainable Energy Reviews 15: 1575–1592.
[10] R. Billinton, S. Kumar, N. Chowdhury, K. Chu, L. Goel, E. Khan, P. Kos, G. Nourbakhsh, and J. OtengAdjei, (1990) “A reliability test system for educational purposes-basic results" IEEE Transactions on Power Systems 5: 319–325.
[11] M. Fan, K. Sun, D. Lane, W. Gu, Z. Li, and F. Zhang, (2018) “A novel generation rescheduling algorithm to improve power system reliability with high renewable energy penetration" IEEE Transactions on Power Systems 33: 3349–3357.
[12] I. de J Silva, M. J. Rider, R. Romero, A. V. Garcia, and C. A. Murari, (2005) “Transmission network expansion planning with security constraints" IEE ProceedingsGeneration, Transmission and Distribution 152: 828–836.
[13] S. D. L. Torre, A. J. Conejo, and J. Contreras, (2008) “Transmission expansion planning in electricity markets" IEEE transactions on power systems 23: 238–248. DOI: 10.1109/TPWRS.2007.913717.
[14] S. Mohseni, A. C. Brent, and D. Burmester, (2019) “A demand response-centred approach to the long-term equipment capacity planning of grid-independent micro-grids optimized by the moth-flame optimization algorithm" Energy Conversion and Management 200: 112105.
[15] Y. M. Alsmadi, A. M. Abdel-hamed, A. E. Ellissy, A. S. El-Wakeel, A. Y. Abdelaziz, V. Utkin, and A. A. Uppal, (2019) “Optimal configuration and energy management scheme of an isolated micro-grid using Cuckoo search optimization algorithm" Journal of the Franklin Institute 356: 4191–4214.
[16] Z. Movahediyan and A. Askarzadeh, (2018) “Multiobjective optimization framework of a photovoltaic-diesel generator hybrid energy system considering operating reserve" Sustainable Cities and Society 41: 1–12.
[17] M. Farrokhabadi, C. A. Cañizares, and K. Bhattacharya, (2015) “Frequency control in isolated/islanded microgrids through voltage regulation" IEEE Transactions on Smart Grid 8: 1185–1194.
[18] M. Farrokhabadi, C. A. Cañizares, and K. Bhattacharya, (2016) “Unit commitment for isolated microgrids considering frequency control" IEEE Transactions on Smart Grid 9: 3270–3280.
[19] A. Ahmad and J. Y. Khan, (2018) “Roof-top stand-alone PV micro-grid: A joint real-time BES management, load scheduling and energy procurement from a peaker generator" IEEE Transactions on Smart Grid 10: 3895–3909. DOI: 10.1109/TSG.2018.2842757.
[20] N. Zhao, X. Yu, K. Hou, X. Liu, Y. Mu, H. Jia, H. Wang, and H. Wang, (2021) “Full-time scale resilience enhancement framework for power transmission system under ice disasters" International Journal of Electrical Power & Energy Systems 126: 106609.
[21] N. L. Dehghani, A. B. Jeddi, and A. Shafieezadeh, (2021) “Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning" Applied energy 285: 116355.
[22] M. Mahzarnia, M. P. Moghaddam, P. Siano, and M.-R. Haghifam, (2020) “A comprehensive assessment of power system resilience to a hurricane using a two-stage analytical approach incorporating risk-based index" Sustainable Energy Technologies and Assessments 42: 100831.
[23] J. Najafi, A. Peiravi, and J. M. Guerrero, (2018) “Power distribution system improvement planning under hurricanes based on a new resilience index" Sustainable cities and society 39: 592–604.
[24] H. Rasay, F. Naderkhani, and A. M. Golmohammadi, (2020) “Designing variable sampling plans based on lifetime performance index under failure censoring reliability tests" Quality Engineering 32: 354–370. DOI: 10.1080/08982112.2020.1754426.
[25] L. Tang, J. Qu, Z. Mi, X. Bo, X. Chang, L. D. Anadon, S. Wang, X. Xue, S. Li, and X. Wang, (2019) “Substantial emission reductions from Chinese power plants after the introduction of ultra-low emissions standards" Nature Energy 4: 929–938. DOI: 10.1038/s41560-019-0468-1.
[26] R. J. Campbell and S. Lowry. “Weather-related power outages and electric system resiliency”. In: Congressional Research Service, Library of Congress Washington, DC, 2012.
[27] S. Dorahaki, A. Abdollahi, M. Rashidinejad, and M. Moghbeli, (2021) “The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost" Journal of Energy Storage 33: 102022. DOI: 10.1016/j.est.2020.102022.
[28] E. Kiani, H. Doagou-Mojarrad, and H. Razmi, (2020) “Multi-objective optimal power flow considering voltage stability index and emergency demand response program" Electrical Engineering 102: 2493–2508. DOI: 10.1007/s00202-020-01051-1.
[29] A. Alghazi, S. Z. Selim, and A. Elazouni, (2012) “Performance of shuffled frog-leaping algorithm in financebased scheduling" Journal of computing in civil engineering 26: 396–408. DOI: 10.1061/(ASCE)CP.1943-5487.0000157.
[30] M. Eusuff, K. Lansey, and F. Pasha, (2006) “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization" Engineering optimization 38: 129– 154. DOI: 10.1080/03052150500384759.
[31] M. Shahidehpour and Y. Wang, (2003) “Appendix C: IEEE30 bus system data":