Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

Jun Li; Qi Fu; Pei Ruan

doi:10.4108/ew.5824

Authors

Jun Li Department of electronic engineering ,North China Electric Power University ,Baoding, Hebei, 071000, China / Zhejiang Huayun Information Technology Co., LTD，Hangzhou, Zhejiang，310000, China
Qi Fu Zhejiang Huayun Information Technology Co., LTD，Hangzhou, Zhejiang，310000, China
Pei Ruan Zhejiang University school of continuing education，Hangzhou, Zhejiang, 310000, China

DOI:

https://doi.org/10.4108/ew.5824

Keywords:

Power grid (PG), multiple areas, dispatch issue, mutable galaxy-based search-tuned flexible deep convolutional neural network (MGS-FDCNN)

Abstract

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.

Downloads

Download data is not yet available.

References

Lipu, M.H., Hannan, M.A., Karim, T.F., Hussain, A., Saad, M.H.M., Ayob, A., Miah, M.S. and Mahlia, T.I., 2021. Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges, and future outlook. Journal of Cleaner Production, 292, p.126044.https://doi.org/10.1016/j.jclepro.2021.126044 DOI: https://doi.org/10.1016/j.jclepro.2021.126044

Rocchetta, R., Bellani, L., Compare, M., Zio, E. and Patelli, E., 2019. A reinforcement learning framework for optimal operation and maintenance of power grids. Applied energy, 241, pp.291-301. https://doi.org/10.1016/j.apenergy.2019.03.027 DOI: https://doi.org/10.1016/j.apenergy.2019.03.027

Jiang, W., Wang, X., Huang, H., Zhang, D. and Ghadimi, N., 2022. Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm. Journal of Energy Storage, 55, p.105311. https://doi.org/10.1016/j.est.2022.105311 DOI: https://doi.org/10.1016/j.est.2022.105311

Chapaloglou, S., Nesiadis, A., Iliadis, P., Atsonios, K., Nikolopoulos, N., Grammelis, P., Yiakopoulos, C., Antoniadis, I. and Kakaras, E., 2019. Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island's power system. Applied Energy, 238, pp.627-642. https://doi.org/10.1016/j.apenergy.2019.01.102 DOI: https://doi.org/10.1016/j.apenergy.2019.01.102

Butt, O.M., Zulqarnain, M. and Butt, T.M., 2021. Recent advancement in smart grid technology: Prospects in the electrical power network. Ain Shams Engineering Journal, 12(1), pp.687-695. https://doi.org/10.1016/j.asej.2020.05.004 DOI: https://doi.org/10.1016/j.asej.2020.05.004

Ahmad, T., Madonski, R., Zhang, D., Huang, C. and Mujeeb, A., 2022. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, p.112128. https://doi.org/10.1016/j.rser.2022.112128 DOI: https://doi.org/10.1016/j.rser.2022.112128

Roy, K., Mandal, K.K. and Mandal, A.C., 2019. Ant-Lion Optimizer algorithm and recurrent neural network for energy management of microgrid connected system. Energy, 167, pp.402-416. https://doi.org/10.1016/j.energy.2018.10.153 DOI: https://doi.org/10.1016/j.energy.2018.10.153

Suresh, V., Muralidhar, M. and Kiranmayi, R., 2020. Modeling and optimization of an off-grid hybrid renewable energy system for electrification in rural areas. Energy Reports, 6, pp.594-604. https://doi.org/10.1016/j.egyr.2020.01.013 DOI: https://doi.org/10.1016/j.egyr.2020.01.013

Panda, D.K. and Das, S., 2021. Smart grid architecture model for control, optimization, and data analytics of future power networks with more renewable energy. Journal of Cleaner Production, 301, p.126877. https://doi.org/10.1016/j.jclepro.2021.126877 DOI: https://doi.org/10.1016/j.jclepro.2021.126877

Khan, M.W., Wang, J., Ma, M., Xiong, L., Li, P. and Wu, F., 2019. Optimal energy management and control aspects of distributed microgrid using multi-navigator systems. Sustainable Cities and Society, 44, pp.855-870. https://doi.org/10.1016/j.scs.2018.11.009 DOI: https://doi.org/10.1016/j.scs.2018.11.009

Sun, S., Fu, J., Wei, L. and Li, A., 2020. Multi-objective optimal dispatching for a grid-connected micro-grid considering wind power forecasting probability. IEEE Access, 8, pp.46981-46997. https://doi.org/10.1109/ACCESS.2020.2977921 DOI: https://doi.org/10.1109/ACCESS.2020.2977921

Karagiannopoulos, S., Aristidou, P. and Hug, G., 2019. Data-driven local control design for active distribution grids using off-line optimal power flow and machine learning techniques. IEEE Transactions on Smart Grid, 10(6), pp.6461-6471. https://doi.org/10.1109/TSG.2019.2905348 DOI: https://doi.org/10.1109/TSG.2019.2905348

Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D. and Tzovaras, D., 2021. Machine learning and deep learning in smart manufacturing: The smart grid paradigm. Computer Science Review, 40, p.100341. https://doi.org/10.1016/j.cosrev.2020.100341 DOI: https://doi.org/10.1016/j.cosrev.2020.100341

Babar, M., Tariq, M.U. and Jan, M.A., 2020. Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid. Sustainable Cities and Society, 62, p.102370. https://doi.org/10.1016/j.scs.2020.102370 DOI: https://doi.org/10.1016/j.scs.2020.102370

Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y. and Wen, J., 2020. Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges, and future directions. Applied Energy, 278, p.115733. https://doi.org/10.1016/j.apenergy.2020.115733 DOI: https://doi.org/10.1016/j.apenergy.2020.115733

Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S. and Lai, C.S., 2020. A multi-navigator reinforcement learning-based data-driven method for home energy management. IEEE Transactions on Smart Grid, 11(4), pp.3201-3211.https://doi.org/10.1109/TSG.2020.2971427 DOI: https://doi.org/10.1109/TSG.2020.2971427

Du, Y. and Li, F., 2019. Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning. IEEE Transactions on Smart Grid, 11(2), pp.1066-1076. https://doi.org/10.1109/TSG.2019.2930299 DOI: https://doi.org/10.1109/TSG.2019.2930299

Nallaperuma, D., Nawaratne, R., Bandaragoda, T., Adikari, A., Nguyen, S., Kempitiya, T., De Silva, D., Alahakoon, D. and Pothuhera, D., 2019. Online incremental machine learning platform for big data-driven smart traffic management. IEEE Transactions on Intelligent Transportation Systems, 20(12), pp.4679-4690. https://doi.org/10.1109/TITS.2019.2924883 DOI: https://doi.org/10.1109/TITS.2019.2924883