Journal of Advanced Digital Communications

Downloads

Jember, A. G., Bao, R., Yao, Z., Wang, Z., Zhou, Z., & Wang, X. Supply and Demand Forecasting-Based Optimal Energy Sharing Strategies for Grid-Integrated Community Networks. Journal of Advanced Digital Communications. 2024. doi: Retrieved from https://test.sciltp.com/testj/jadc/article/view/583
Forthcoming Issue
Copyright (c) 2024 by the authors.
Creative Commons License

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

Article

Supply and Demand Forecasting-Based Optimal Energy Sharing Strategies for Grid-Integrated Community Networks

Adugna Gebrie Jember 1, Ruiyu Bao 1, Zijia Yao 1, Zhao Wang 1,*, Zhenyu Zhou 1 and Xiaoyan Wang 2

1 Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

2 Graduate School of Science and Engineering, Ibaraki University, Ibaraki 310-8512, Japan

* Correspondence: zhaow@ncepu.edu.cn

Received: 23 July 2024; Revised: 17 October 2024; Accepted: 17 October 2024; Published: 5 November 2024

Abstract: This research introduces a model with two distinct stages for accurate forecasting and efficient energy sharing within grid-integrated community networks (GICNs), which combines residential power loads, generators of wind and photovoltaic (PV) power, and energy storage (ES). In the first-stage system model, we employ a machine learning (ML) algorithm for day-ahead supply and demand forecasting to improve the forecasting accuracy. Specifically, we develop a hybrid convolutional neural network with long short-term memory (CNN-LSTM), which effectively includes both spatial and temporal dimensions of time series data. In the second stage, a bidirectional real-time energy sharing strategy is designed based on forecasted data, facilitating the efficient distribution of surplus energy among communities. The two-stage system model integrates forecasting and energy sharing to accurately predict supply and demand, as well as effective energy sharing among GICN participants. The measurements including mean absolute error (MAE), root mean squared error (RMSE), and generation utilization rate are defined to evaluate prediction accuracy and energy sharing effectiveness thereby ensuring optimal grid operation and sustainability. Finally, the proposed hybrid CNN-LSTM algorithm is compared with the single LSTM and CNN models to demonstrate the performance superiority of the hybrid CNN-LSTM model. Numerical simulation experiments indicate that the proposed model CNN-LSTM achieved high accuracy in predicting user PV, wind, and load demand. Additionally, the implemented real-time energy sharing strategy efficiently manages energy distribution within GICNs.

Keywords:

supply and demand forecasting energy sharing convolutional neural network with long short-term memory (CNN-LSTM) networks grid-integrated networks self-sufficiency ratio (SSR) self-consumption rate (SCR)

References

  1. Blesslin, S.T.; Wessley GJ, J.; Kanagaraj, V.; Kamatchi, S.; Radhika, A.; Janeera, D.A. Microgrid Optimization and Integration of Renewable Energy Resources: Innovation, Challenges and Prospects. Integr. Renew. Energy Sources Smart Grid 2021, 2021, 239–262.
  2. Liao, H.; Zhou, Z.; Jia, Z.; Shu, Y.; Tariq, M.; Rodriguez, J.; Frascolla, V. Ultra-Low AoI Digital Twin-Assisted Resource Allocation for Multi-Mode Power IoT in Distribution Grid Energy Management. IEEE J. Sel. Areas Commun. 2023, 41, 3122–3132.
  3. Kabeyi, M.J.B.; Olanrewaju, O.A. Smart grid technologies and application in the sustainable energy transition: A review. Int. J. Sustain. Energy 2023, 42, 685–758.
  4. Akinte, O.O.; Prasartkaew, B. Grid Integrated Renewable Energy Network in Variety Sources. In Proceedings of the 2023 International Conference on Power, Energy and Innovations (ICPEI), Phrachuap Khirikhan, Thailand, 18–20 October 2023; pp. 46–51.
  5. Zhou, Z.; Dong, M.; Ota, K. Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: Integration of Blockchain and Edge Computing. IEEE Trans. Syst. Man Cybern. Syst. 2020, 50, 43–57.
  6. Maheswari, K.L.; Sathya, B.; Jeylani AM, A. Mitigating Measures to Address Challenges of Renewable Integration—Forecasting, Scheduling, Dispatch, Balancing, Monitoring, and Control. Integr. Renew. Energy Sources Smart Grid 2021, 2021, 281–304.
  7. Li, P.; Wang, J.; Zhang, C.; Wang, N.; Dou, Z.; Zhou, X.; Wang, G. Day-ahead Time-sharing Optimal Scheduling for Community Integrated Energy System Based on Multi-energy Time-series Analysis. In Proceedings of the 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Shanghai, China, 8–11 July 2022; pp. 738–743.
  8. Liao, H.; Zhou, Z.; Liu, N.; Zhang, Y.; Xu, G.; Wang, Z.; Mumtaz, S. Cloud-Edge-Device Collaborative Reliable and Communication-Efficient Digital Twin for Low-Carbon Electrical Equipment Management. Trans. Ind. Inform. 2023, 19, 1715–1724.
  9. Han, M.E.; Alston, M.; Gillott, M. A multi-vector community energy system integrating a heating network, electricity grid and PV production to manage an electrified community. Energy Build. 2022, 266, 112105.
  10. Minuto, F.D.; Lanzini, A. Energy-sharing mechanisms for energy community members under different asset ownership schemes and user demand profiles. Renew. Sustain. Energy Rev. 2022, 168, 112859.
  11. Zhou, Z.; Jia, Z.; Liao, H.; Lu, W.; Mumtaz, S.; Guizani, M.; Tariq, M. Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids. IEEE Trans. Ind. Inform. 2022, 18, 4933–4943.
  12. Liu, N.; Yu, X.; Fan, W.; Hu, C.; Rui, T.; Chen, Q.; Zhang, J. Online Energy Sharing for Nanogrid Clusters: A Lyapunov Optimization Approach. IEEE Trans. Smart Grid 2017, 9, 4624–4636.
  13. Ayoub, N.; Musharavati, F.; Pokharel, S.; Gabbar, H.A. ANN Model for Energy Demand and Supply Forecasting in a Hybrid Energy Supply System. In Proceedings of the 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 12–15 August 2018; pp. 25–30.
  14. Golder, A.; Jneid, J.; Zhao, J.; Bouffard, F. Machine Learning-Based Demand and PV Power Forecasts. In Proceedings of the 2019 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada, 16–18 October 2019; pp. 1–6.
  15. Zhang, X.; Wang, Z.; Liao, H.; Zhou, Z.; Ma, X.; Yin, X.; Lv, G. Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations. Int. J. Electr. Power Energy Syst. 2023, 147, 108816.
  16. Dimitropoulos, N.; Sofias, N.; Kapsalis, P.; Mylona, Z.; Marinakis, V.; Primo, N.; Doukas, H. Forecasting of short-term PV production in energy communities through Machine Learning and Deep Learning algorithms. In Proceedings of the 2021 12th International Conference on Information, Intelligence, Systems and Applications (IISA), Chania Crete, Greece, 12–14 July 2021; pp. 1–6.
  17. Wen, L.; Zhou, K.; Yang, S.; Lu, X. Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting. Energy 2019, 171, 1053–1065.
  18. Qayyum, F.; Jamil, H.; Jamil, F.; Kim, D. Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network. IEEE Access 2022, 10, 23593–23604.
  19. Wang, K.; Qi, X.; Liu, H. Photovoltaic power forecasting based LSTM-Convolutional Network. Energy 2019, 189, 116225.
  20. Zhang, X.; Wang, Z.; Zhou, Z.; Liao, H.; Ma, X.; Yin, X.; Liu, Y. Optimal Dispatch of Multiple Photovoltaic Integrated 5G Base Stations for Active Distribution Network Demand Response. Front. Energy Res. 2022, 10, 919197.
  21. Shi, M.; Xu, K.; Wang, J.; Yin, R.; Wang, T.; Yong, T. Short-Term Photovoltaic Power Forecast Based on Long Short-Term Memory Network. In Proceedings of the 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), Beijing, China, 7–9 September 2019; pp. 2110–2116.
  22. Mejdi, L.; Kardous, F.; Grayaa, K. Experimental Validation of PV Power Prediction with ML Models for Improved Grid Integration. In Proceedings of the 2023 20th International Multi-Conference on Systems, Signals and Devices (SSD), Mahdia, Tunisia, 20–23 February 2023; pp. 439–445.
  23. Zhang, X.; Chau, T.K.; Chow, Y.H.; Fernando, T.; Iu HH, C. A Novel Sequence to Sequence Data Modelling Based CNN-LSTM Algorithm for Three Years Ahead Monthly Peak Load Forecasting. IEEE Trans. Power Syst. 2024, 39, 1932–1947.