Day Ahead Unit Commitment with High Penetration of Renewable Energy Sources and Electric Vehicle Charging Stations
Day Ahead Unit Commitment with High Penetration of Renewable Energy Sources and Electric Vehicle Charging Stations |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-6 |
||
| Year of Publication : 2024 | ||
| Author : Diaa Salman, Abdulaziz Ahmed Siyad, Mehmet Kusaf, Yonis Elmi |
||
| DOI : 10.14445/22315381/IJETT-V72I6P133 | ||
How to Cite?
Diaa Salman, Abdulaziz Ahmed Siyad, Mehmet Kusaf, Yonis Elmi, "Day Ahead Unit Commitment with High Penetration of Renewable Energy Sources and Electric Vehicle Charging Stations," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 361-379, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P133
Abstract
Unit Commitment (UC) is a power system nonlinear programming with mixed integers issue. As Electric Vehicles (EVs) and renewable energy sources are incorporated into the power system, the UC problem becomes more challenging. With the continued increase of wind and solar-based renewable energy in the utility power system on the supply side, the random features of the supply and demand sides of the power grid will become increasingly apparent, affecting the system's security, stability, and economical operation. In that sense, UC has theoretical and practical importance. The optimal scheduling of thermal, wind, solar, and EV units has been studied. The purpose of optimal scheduling is to minimize unit operating expenses. This study examines how the integration of a large percentage of renewable energy sources like wind and solar affects the effectiveness of short-term power system planning and control in urban areas where EVs charging stations and conventional demand coexist. Particle Swarm Optimization (PSO)is used to minimize the system's operational costs. The IEEE 24-bus test system is used to evaluate the study. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared and used to forecast the day ahead performance of the load demand, wind and solar energy, and EVs stations demand to be used in the proposed case study. It has been found that in forecasting the load demand, solar power, and EVs charging demand, LSTM performs better than GRU with MSE of 5.2%, 3.6%, and 8.6%, respectively, and for wind power prediction, GRU outperforms LSTM with MSE of 3.9%. Moreover, the results show the robustness of the proposed methodology with optimal production costs of $340686.
Keywords
Deep learning, Economic dispatch, Forecasting, Optimization, Unit commitment.
References
[1] Aniket Agarwal, and Kirti Pal, “Optimization of Unit Commitment Problem Using Genetic Algorithm,” International Journal of System Dynamics Applications, vol. 10, no. 3, pp. 21-37, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Wenting Hou, and Hua Wei, “Data-Driven Robust Day-Ahead Unit Commitment Model for Hydro/Thermal/Wind/Photovoltaic/Nuclear Power Systems,” International Journal of Electrical Power & Energy Systems, vol. 125, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jinwen Wang et al., “Comparison of Efficient Procedures for Hydropower Unit Commitment,” Energy Reports, vol. 8, pp. 2472-2479, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Saleh Y. Abujarad, M.W. Mustafa, and J.J. Jamian, “Recent Approaches of Unit Commitment in the Presence of Intermittent Renewable Energy Resources: A Review,” Renewable and Sustainable Energy Reviews, vol. 70, pp. 215-223, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Renewables 2019 Global Status Report, Paris: REN21 Secretariat, pp. 1-336, 2019. [Online]. Available: https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf
[6] Idriss Abdou, and Mohamed Tkiouat, “Unit Commitment Problem in Electrical Power System: A Literature Review,” International Journal of Electrical and Computer Engineering, vol. 8, no. 3, pp. 1357-1372, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] W. Van Ackooij et al., “Large-Scale Unit Commitment under Uncertainty: An Updated Literature Survey,” Annals of Operations Research, vol. 271, pp. 11-85, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Luis Montero, Antonio Bello, and Javier Reneses, “A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods,” Energies, vol. 15, no. 4, pp. 1-40, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] James Ostrowski, Miguel F. Anjos, and Anthony Vannelli, “Tight Mixed Integer Linear Programming Formulations for the Unit Commitment Problem,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 39-46, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Huaizhi Wang et al., “A Review of Deep Learning for Renewable Energy Forecasting,” Energy Conversion and Management, vol. 198, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Mohammad Javad Feizollahi et al., “Large-Scale Decentralized Unit Commitment,” International Journal of Electrical Power & Energy Systems, vol. 73, pp. 97-106, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ioannis K. Bazionis, and Pavlos S. Georgilakis, “Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research,” Electricity, vol. 2, no. 1, pp. 13-47, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ahmad Alzahrani et al., “Solar Irradiance Forecasting Using Deep Neural Networks,” Procedia Computer Science, vol. 114, pp. 304-313, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Adel Mellit et al., “Advanced Methods for Photovoltaic Output Power Forecasting: A Review,” Applied Sciences, vol. 10, no. 2, pp. 1-22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Can Wan et al., “Photovoltaic and Solar Power Forecasting for Smart Grid Energy Management,” CSEE Journal of Power and Energy Systems, vol. 1, no. 4, pp. 38-46, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Abdul Azeem et al., “Electrical Load Forecasting Models for Different Generation Modalities: A Review,” IEEE Access, vol. 9, pp. 142239-142263, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jiaan Zhang, Chenyu Liu, and Leijiao Ge, “Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN,” Energies, vol. 15, no. 7, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Haixin Wang et al., “Optimal Dispatch Based On Prediction of Distributed Electric Heating Storages in Combined Electricity and Heat Networks,” Applied Energy, vol. 267, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mariz B. Arias, and Sungwoo Bae, “Electric Vehicle Charging Demand Forecasting Model Based On Big Data Technologies,” Applied Energy, vol. 183, pp. 327-339, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jian Pan, and Tingzhang Liu, “Optimal Scheduling for Unit Commitment with Electric Vehicles and Uncertainty of Renewable Energy Sources,” Energy Reports, vol. 8, pp. 13023-13036, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jianhui Wang, Mohammad Shahidehpour, and Zuyi Li, “Security-Constrained Unit Commitment with Volatile Wind Power Generation,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1319-1327, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Wei-Tzer Huang et al., “Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch,” Energies, vol. 11, no. 2, pp. 1-19, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Lili A. Wulandhari, Siti Komsiyah, and Wisnu Wicaksono, “Bat Algorithm Implementation on Economic Dispatch Optimization Problem,” Procedia Computer Science, vol. 135, pp. 275-282, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Hatim S. Madraswala, “Modified Genetic Algorithm Solution to Unit Commitment Problem,” 2017 International Conference on Nascent Technologies in Engineering, Vashi, India, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Venkata Silpa Borra, and K. Debnath, “Comparison Between the Dynamic Programming and Particle Swarm Optimization for Solving Unit Commitment Problems,” 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, Amman, Jordan, pp. 395-400, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Vikram Kumar Kamboj, “A Novel Hybrid PSO–GWO Approach for Unit Commitment Problem,” Neural Computing and Applications, vol. 27, pp. 1643-1655, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Divya Ananthan, and S. Nishanthinivalli, “Unit Commitment Solution Using Particle Swarm Optimisation (PSO),” IOSR Journal of Engineering, vol. 4, no. 3, pp. 1-9, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Md. Sajid Alam, B. Durga Hari Kiran, and Matam Sailaja Kumari, “Priority List and Particle Swarm Optimization Based Unit Commitment of Thermal Units Including Renewable Uncertainties,” 2016 IEEE International Conference on Power System Technology, Wollongong, NSW, Australia, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Chao-Rong Chen, and Chien-Nan Chen, “Application of Ant Colony System to Optimal Thermal Unit Commitment,” 2019 International Conference on Machine Learning and Cybernetics, Kobe, Japan, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Majid Moazzami et al., “SCUC Considering Loads and Wind Power Forecasting Uncertainties Using Binary Gray Wolf Optimization Method,” Majlesi Journal of Electrical Engineering, vol. 12, no. 4, pp. 15-24, 2018.
[Google Scholar] [Publisher Link]
[31] Sheraz Aslam et al., “A Survey on Deep Learning Methods for Power Load and Renewable Energy Forecasting in Smart Microgrids,” Renewable and Sustainable Energy Reviews, vol. 144, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Pratima Kumari, and Durga Toshniwal, “Deep Learning Models for Solar Irradiance Forecasting: A Comprehensive Review,” Journal of Cleaner Production, vol. 318, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Kyunghyun Cho et al., “Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1724-1734, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Yong Yu et al., “A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures,” Neural Computation, vol. 31, no. 7, pp. 1235-1270, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Wanjun Huang, “Historical Data in Simulation (Data in France),” Figshare Dataset, 2019.
[CrossRef] [Publisher Link]
[36] Francesco Bellesini, “EVs/Charging Stations Data,” Zenodo, 2020.
[CrossRef] [Publisher Link]
[37] Banalaxmi Brahma, and Rajesh Wadhvani, “Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data,” Symmetry, vol. 12, no. 11, pp. 1-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]