skip to main content

Integrated Rural Energy System Planning Based on POA and Stage Division under the Background of Economic Development

Student Affairs Department, Nanyang Institute of Technology, Nanyang, 47300, China, China

Received: 16 Apr 2026; Published: 10 Aug 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

To address the dynamic, multi-objective, and phased challenges in integrated rural energy system planning under economic development, this study proposes a dynamic planning framework that integrates macroeconomic stage division with an improved multi-objective Pelican optimization algorithm. The algorithm is enhanced using Sobol sequence initialization and an adaptive dynamic factor mechanism, and a two-stage stochastic programming model is developed to minimize the equivalent annual total cost and maximize annual net carbon emission reduction (CER). A case study of a typical rural area in northern China demonstrates that the proposed method outperforms comparative algorithms in convergence and solution distribution, achieving a Pareto distribution index of 0.082. The optimal plan reduces annual system cost to 14.856 million yuan and increases annual CER to 634.2 tons, with an energy efficiency of 76.8%. Overall, the framework effectively supports low-carbon and economical rural energy system planning aligned with regional development stages.

Article Metrics:

  1. Chander, N. & Upendra Kumar, M. (2024). Enhanced pelican optimization algorithm with ensemble-based anomaly detection in industrial internet of things environment. Cluster Computing, 27(5), 6491–6509; https://doi.org/10.1007/s10586-024-04303-y
  2. Chen, J., Mao, C., Liu, Z., Ma, C., Sha, G., Duan, Q., Fan, H., Qiu, S. & Wang, D. (2023). Stochastic planning of integrated energy system based on correlation scenario generation method via Copula function considering multiple uncertainties in renewable energy sources and demands. IET Renewable Power Generation, 17(12), 2978–2996; https://doi.org/10.1049/rpg2.12805
  3. Chen, W., Cao, Q., Cao, B. & Jin, B. (2025). An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm. Scientific Reports, 15(1), 1–20; https://doi.org/10.1038/s41598-025-95885-z
  4. Dong, W., Lu, Z., He, L., Zhang, J., Ma, T. & Cao, X. (2023). Optimal expansion planning model for integrated energy system considering integrated demand response and bidirectional energy exchange. CSEE Journal of Power and Energy Systems, 9(4), 1449–1459; https://doi.org/10.17775/CSEEJPES.2021.09220
  5. Fu, X., Wei, Z., Sun, H. & Zhang, Y. (2024). Agri-energy-environment synergy-based distributed energy planning in rural areas. IEEE Transactions on Smart Grid, 15(4), 3722–3738; https://doi.org/10.1109/TSG.2024.3364182
  6. He, J., Xiong, C., Li, Z., Mei, R., Zhao, X. & Wu, H. (2025). Research on aerial radioactive hotspot detecting based on an improved pelican optimization algorithm. Journal of Radioanalytical and Nuclear Chemistry, 334(6), 4159–4170; https://doi.org/10.1007/s10967-025-10176-1
  7. Irshad, A. S., Ueda, S., Furukakoi, M., Zakir, M. N., Ludin, G. A., Elkholy, M. H., Yona, A., Elias, S. & Senjyu, T. (2024). Novel integration and optimization of reliable photovoltaic and biomass integrated system for rural electrification. Energy Reports, 11, 4924–4939; https://doi.org/10.1016/j.egyr.2024.04.057
  8. Jabari, M., Ekinci, S., Izci, D., Bajaj, M. & Zaitsev, I. (2024). Efficient DC motor speed control using a novel multi-stage FOPD(1+PI) controller optimized by the Pelican optimization algorithm. Scientific Reports, 14(1), 1–25; https://doi.org/10.1038/s41598-024-73409-5
  9. Kocaturk, A., Orkcu, H. H. & Altunkaynak, B. (2025). Parameter optimization in biclustering algorithms for large datasets using a combined approach of NSGA-II and TOPSIS. International Journal of Data Science and Analytics, 20(6), 5499–5516; https://doi.org/10.1007/s41060-025-00782-3
  10. Lee, D., Kim, D. & Joo, S.-K. (2025). Interval-stochastic programming for integrated generation, transmission, and energy storage system (ESS) planning considering uncertainty in renewable energy sources. IEEE Access, 13, 30834–30844; https://doi.org/10.1109/ACCESS.2025.3538649
  11. Liu, J., Bai, Y. & He, Y. (2025). Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm. International Journal of Electrical Engineering and Education, 62(2), 144–163; https://doi.org/10.1177/0020720920983695
  12. Liu, P., Wu, Y., Sun, J. & Zhao, J. (2025). Coordinated optimization of control parameters for improving the stability of wind-PV hybrid power systems under improved pelican optimization algorithm. Electrical Engineering, 107(4), 5163–5186; https://doi.org/10.1007/s00202-024-02782-1
  13. Ma, G., Hu, S., Pang, N. & Zhou, Q. (2025). Strategy improved pelican algorithm optimization ELM for short-term electricity load forecasting. Distributed Generation and Alternative Energy Journal, 40(1), 85–108; https://doi.org/10.13052/dgaej2156-3306.4014
  14. Mary, V. B., Narmadha, T. V. & William Christopher, I. (2025). Hybrid power generating system sources and load synchronization using adaptive-neural-fuzzy-inference-system and pelican optimization algorithm technique. IETE Journal of Research, 71(1), 259–271; https://doi.org/10.1080/03772063.2024.2404254
  15. Mohan, Y., Yadav, R. K. & Manjul, M. (2025). EECRPOA: energy efficient clustered routing using pelican optimization algorithm in WSNs. Wireless Networks, 31(5), 3743–3769; https://doi.org/10.1007/s11276-025-03970-y
  16. Mu, Y., Guo, H., Wu, Z., Jia, H., Jin, X. & Qi, Y. (2024). A two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub. Energy and Artificial Intelligence, 18(4), 291–308; https://doi.org/10.1016/j.egyai.2024.100435
  17. Nassar, S. M., Saleh, A. A., Eisa, A. A., Abdallah, E. M. & Nassar, I. A. (2025). Optimal planning of integrated nuclear-hybrid renewable energy systems for electrical distribution networks based on artificial intelligence. Scientific Reports, 15(1), 1–24; https://doi.org/10.1038/s41598-025-11049-z
  18. Qin, M., Xu, Q., Liu, W. & Xu, Z. (2024). Low-carbon economic optimal operation strategy of rural multi-microgrids based on asymmetric Nash bargaining. IET Generation, Transmission and Distribution, 18(1), 24–38; https://doi.org/10.1049/gtd2.12959
  19. Serat, Z., Fatemi, S. A. Z. & Shirzad, S. (2023). Design and economic analysis of on-grid solar rooftop PV system using PVsyst software. Archives of Advanced Engineering Science, 1(1), 63–76; https://doi.org/10.47852/bonviewAAES32021177
  20. Shahrom, S. F., Aviso, K. B., Tan, R. R., Saleem, N. N., Ng, D. K. S. & Andiappan, V. (2023). Regional planning and optimization of renewable energy sources for improved rural electrification. Process Integration and Optimization for Sustainability, 7(4), 785–804; https://doi.org/10.1007/s41660-023-00323-0
  21. Song, H. M., Wang, J. S., Hou, J. N., Wang, Y. C., Song, Y. W. & Qi, Y. L. (2025). Multi-strategy fusion pelican optimization algorithm and logic operation ensemble of transfer functions for high-dimensional feature selection problems. International Journal of Machine Learning and Cybernetics, 16(7), 4433–4470; https://doi.org/10.1007/s13042-024-02517-5
  22. Sun, Q., Wu, Z., Gu, W., Dong, Z., Liu, P., Qiu, H., Ghias, A., Lu, Y. & Zheng, Y. (2025). Seismic-resilient planning for integrated energy system: a risk-economic coordination perspective. IEEE Transactions on Power Systems, 40(3), 2568–2583; https://doi.org/10.1109/TPWRS.2024.3468393
  23. Wang, D., Li, H., Li, J., Yu, Y. & Zhao, Y. (2024). Optimal configuration of rural integrated energy storage systems considering multiple demand-side responses. Journal of Nanoelectronics and Optoelectronics, 19(11), 1186–1194; https://doi.org/10.1166/jno.2024.3681
  24. Wang, Q., Zhang, X., Xu, Y., Yi, Z. & Xu, D. (2025). Planning of stationary-mobile integrated battery energy storage systems under severe convective weather. IEEE Transactions on Sustainable Energy, 16(2), 1253–1268; https://doi.org/10.1109/TSTE.2024.3513295
  25. Wu, C. (2025). Carbon emission evaluation and low carbon economy optimization scheduling of rural integrated energy system based on LCA method. IEEE Access, 13, 17182–17194; https://doi.org/10.1109/ACCESS.2025.3533099
  26. Xu, Y., Sang, B. & Zhang, Y. (2025). Application of an improved pelican optimization algorithm based on comprehensive strategy in PV parameter identification. Scientific Reports, 15(1), 1–25; https://doi.org/10.1038/s41598-025-04396-4
  27. Yi, L., Cheng, S., Wang, Y., Ma, H., Luo, B. & Hu, Y. (2024). A multi-objective pelican optimization algorithm for dynamic reconfiguration of multi-type rural rooftop PV array. Journal of Intelligent and Fuzzy Systems, 47(5–6), 393–409; https://doi.org/10.3233/JIFS-236528
  28. Yu, Y. S., Chen, C., Wang, Y., Yu, H., Pei, C., Ren, J., Yang, L. & Lin, Z. (2025). Station-network cooperative optimization planning of urban integrated energy system considering heat storage capacity of heat network. IEEE Transactions on Sustainable Energy, 16(3), 1956–1976; https://doi.org/10.1109/TSTE.2025.3542549
  29. Zhang, J., Chang, X., Xue, Y., Bai, X., Li, Z., Wang, P. & Sun, H. (2025). Optimal planning for electricity-gas-hydrogen integrated energy systems considering intertemporal long-term hydrogen storage and multiple uncertainties. IEEE Transactions on Power Systems, 40(6), 4660–4674; https://doi.org/10.1109/TPWRS.2025.3577703
  30. Zhang, X., Ding, C., Liang, G., Yang, P. & Wang, X. (2024). Research on integrated energy system planning based on the correlation between wind power and photovoltaic output. IET Renewable Power Generation, 18(11), 1771–1782; https://doi.org/10.1049/rpg2.13054
  31. Zhu, J., He, C., Cheung, K., Luo, F., Liu, Y., Guo, T. & Li, S. (2023). Coordination planning of integrated energy system and electric vehicle charging station considering carbon emission reduction. IEEE Transactions on Industry Applications, 59(6), 7555–7569; https://doi.org/10.1109/TIA.2023.3298330

Last update:

No citation recorded.

Last update: 2026-08-13 23:58:19

No citation recorded.