Influence of Renewable Energy Sources on Day Ahead Optimal Power Flow Based on Meteorological Data Forecast Using Machine Learning: A case study of Johor Province

Haider Jouma Touma, Muhamad Mansor, Muhamad Safwan Abd Rahman, Hazlie Mokhlis, Yong Jia Ying


This article investigates a day ahead optimal power flow considering the intermittent nature of renewable energy sources that involved with weather conditions. The article integrates the machine learning into power system operation to predict precisely day ahead meteorological data (wind speed, temperature and solar irradiance) that influence directly on the calculations of generated power of wind turbines and solar photovoltaic generators. Consequently, the power generation schedulers can make appropriate decisions for the next 24 hours. The proposed research uses conventional IEEE -30-bus as a test system running in Johor province that selected as a test location. algorithm designed in Matlab is utilized to accomplish the day ahead optimal power flow. The obtained results show that the true and predicted values of meteorological data are similar significantly and thus, these predicted values demonstrate the feasibility of the presented prediction in performing the day ahead optimal power flow. Economically, the obtained results reveal that the predicted fuel cost considering wind turbines and solar photovoltaic generators is reduced to 645.34 USD/h as compared to 802.28 USD/h of the fuel cost without considering renewable energy sources. Environmentally, CO2 emission is reduced to 340.9 kg/h as compared to 419.37 kg/h of the conventional system. To validate the competency of the whale optimization, the OPF for the conventional system is investigated by other 2 metaheuristic optimization techniques to attain statistical metrics for comparative analysis.


Machine learning Regression models Meteorological data Forecasting Renewable energy Optimization


Balaji V, Gurgenci H. Search for optimum renewable mix for Australian off-grid power generation. Energy. 2019 May 15;175:1234-45.

Konneh DA, Howlader HO, Shigenobu R, Senjyu T, Chakraborty S, Krishna N. A multi-criteria decision maker for grid-connected hybrid renewable energy systems selection using multi-objective particle swarm optimization. Sustainability. 2019 Jan;11(4):1188.

Luta DN, Raji AK. Optimal sizing of hybrid fuel cell-supercapacitor storage system for off-grid renewable applications. Energy. 2019 Jan 1;166:530-40.

Abhyankar N, Lin J, Liu X, Sifuentes F. Economic and environmental benefits of market-based power-system reform in China: A case study of the Southern grid system. Resources, Conservation and Recycling. 2020 Feb 1;153:104558.

Timilsina GR, Pang J, Yang X. Macroeconomic impacts of power sector reforms in China. Energy Policy. 2021 Oct 1;157:112509.

Li M, Gao H, Abdulla A, Shan R, Gao S. Combined effects of carbon pricing and power market reform on CO2 emissions reduction in China's electricity sector. Energy. 2022 Jul 6:124739.

Calise F, Cappiello FL, d’Accadia MD, Vicidomini M. Dynamic simulation, energy and economic comparison between BIPV and BIPVT collectors coupled with micro-wind turbines. Energy. 2020 Jan 15;191:116439.

Calise F, Cappiello FL, d’Accadia MD, Vicidomini M. Dynamic modelling and thermoeconomic analysis of micro wind turbines and building integrated photovoltaic panels. Renewable Energy. 2020 Nov 1;160:633-52.

Abbaszadeh MA, Ghourichaei MJ, Mohammadkhani F. Thermo‐economic feasibility of a hybrid wind turbine/PV/gas generator energy system for application in a residential complex in Tehran, Iran. Environmental Progress & Sustainable Energy. 2020 Jul;39(4):e13396.

Steffen B, Beuse M, Tautorat P, Schmidt TS. Experience curves for operations and maintenance costs of renewable energy technologies. Joule. 2020 Feb 19;4(2):359-75.

Ciupageanu DA, Barelli L, Lazaroiu G. Real-time stochastic power management strategies in hybrid renewable energy systems: A review of key applications and perspectives. Electric Power Systems Research. 2020 Oct 1;187:106497.

Foley AM, McIlwaine N, Morrow DJ, Hayes BP, Zehir MA, Mehigan L, Papari B, Edrington CS, Baran M. A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation. Energy. 2020 Aug 15;205:117671.

Mohammadi F, Neagoe M. Emerging issues and challenges with the integration of solar power plants into power systems. Solar Energy Conversion in Communities. 2020:157-73.

Alshawaf M, Poudineh R, Alhajeri NS. Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty. Renewable and Sustainable Energy Reviews. 2020 Dec 1;134:110346.

Rathor SK, Saxena D. Energy management system for smart grid: An overview and key issues. International Journal of Energy Research. 2020 May;44(6):4067-109.

Das P, Mathuria P, Bhakar R, Mathur J, Kanudia A, Singh A. Flexibility requirement for large-scale renewable energy integration in Indian power system: Technology, policy and modeling options. Energy Strategy Reviews. 2020 May 1;29:100482.

Elattar EE, Shaheen AM, Elsayed AM, El-Sehiemy RA. Optimal power flow with emerged technologies of voltage source converter stations in meshed power systems. IEEE Access. 2020 Sep 9;8:166963-79.

Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GA. Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms. Soft Computing. 2020 Feb;24(4):2999-3023.

Gupta S, Kumar N, Srivastava L. Solution of optimal power flow problem using sine-cosine mutation based modified Jaya algorithm: a case study. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2021 Aug 8:1-24.

Duman S, Li J, Wu L, Yorukeren N. Symbiotic organisms search algorithm-based security-constrained AC–DC OPF regarding uncertainty of wind, PV and PEV systems. Soft Computing. 2021 Apr 27:1-38.

Maheshwari A, Sood YR. Solution approach for optimal power flow considering wind turbine and environmental emissions. Wind Engineering. 2021 Jul 23:0309524X211035152.

Riaz M, Hanif A, Hussain SJ, Memon MI, Ali MU, Zafar A. An optimization-based strategy for solving optimal power flow problems in a power system integrated with stochastic solar and wind power energy. Applied Sciences. 2021 Jan;11(15):6883.

Biswas PP, Suganthan PN, Amaratunga GA. Optimal power flow solutions incorporating stochastic wind and solar power. Energy conversion and management. 2017 Sep 15;148:1194-207.

Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GA. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach. Applied Soft Computing. 2019 Feb 1;75:616-32.

Kayal P, Chanda CK. Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network. Renewable energy. 2015 Mar 1;75:173-86.

Awad NH, Ali MZ, Mallipeddi R, Suganthan PN. An efficient differential evolution algorithm for stochastic OPF based active–reactive power dispatch problem considering renewable generators. Applied Soft Computing. 2019 Mar 1;76:445-58.

Morshed MJ, Hmida JB, Fekih A. A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems. Applied energy. 2018 Feb 1;211:1136-49.

Roy R, Jadhav HT. Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. International Journal of Electrical Power & Energy Systems. 2015 Jan 1;64:562-78.

Wang Z, Hong T, Piette MA. Building thermal load prediction through shallow machine learning and deep learning. Applied Energy. 2020 Apr 1;263:114683.

[30] Chaabene WB, Flah M, Nehdi ML. Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials. 2020 Nov 10;260:119889.

J. Radosavljević, ‘‘Optimal power flow in transmission networks,’’ inMetaheuristic Optimization in Power Engineering, 1th ed. London, U.K.: IET, 2018, ch. 6, secs. 6.1–6.6, pp. 177–233.

NASA power data, “Data Access Viewer”. [online ].

A. Kumar and A. Biswas,Techno-Economic Optimization of a Stand-alone PV/PHS/Battery Systems for very low load Situation,Int. J. Renew. Energy Res, 2017,vol. 7, no. 2, pp. 844–856.

Borhanazad H, Mekhilef S, Ganapathy VG, Modiri-Delshad M, Mirtaheri A. Optimization of micro-grid system using MOPSO. Renewable Energy. 2014 Nov 1;71:295-306.

]Mirjalili S, Lewis A. The whale optimization algorithm. Advances in engineering software. 2016 May 1;95:51-67.

Mirjalili S, Mirjalili SM, Saremi S, Mirjalili S. Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters. Nature-Inspired Optimizers. 2020:219-38.

Alimi OA, Ouahada K, Abu-Mahfouz AM. A review of machine learning approaches to power system security and stability. IEEE Access. 2020 Jun 19;8:113512-31.

J. Zhang and X. Wang, Quickest Detection of Time-Varying False Data Injection Attacks in Dynamic Linear Regression Models, arXiv Preprint arXiv: , 2018,1811.05423.

Chen, T.; Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; ACM: San Francisco, CA, USA, 2016;Volume 13–17, pp. 785–794.

Miraftabzadeh SM, Longo M, Foiadelli F, Pasetti M, Igual R. Advances in the application of machine learning techniques for power system analytics: A survey. Energies. 2021 Jan;14(16):4776.

Mirjalili S. The ant lion optimizer. Advances in engineering software. 2015 May 1;83:80-98.

Eskandar H, Sadollah A, Bahreininejad A, Hamdi M. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures. 2012 Nov 1;110:151-66.

Full Text: PDF


  • There are currently no refbacks.


Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats