RESEARCH PAPER
Presenting a stochastic framework for resilient self-healing active distribution networks with integrated distributed generation
1
School of Cyber Science and Engineering, Southeast University, China
2
Inner Mongolia Power Group Mengdian Information & Telecommunication Industry Co., Ltd., China
Submission date: 2024-10-22
Final revision date: 2024-12-22
Acceptance date: 2025-02-19
Online publication date: 2025-02-22
Publication date: 2025-02-22
Corresponding author
Changsheng Wan
School of Cyber Science and Engineering, Southeast University, 211189, Nanjing, China
School of Cyber Science and Engineering, Southeast University, 211189, Nanjing, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):202095
HIGHLIGHTS
- Proposing a stochastic model to self-healing active networks with graph restructuring.
- Minimizing short-circuit capacity in reconfiguration to enhance resilience to faults.
- Integrating loadability enhancement to prevent voltage collapse and ensure stability.
- Adapting the pelican algorithm for faster convergence & superior optimization results.
- Achieving recovery, loss reduction, and stability via multi-objective optimization.
KEYWORDS
TOPICS
ABSTRACT
This study introduces a novel stochastic framework for self-healing active distribution networks, addressing critical challenges posed by the integration of distribution networks (DNs), such as increased short-circuit capacity, voltage instability, and load variability. By employing graph theory for optimal network restructuring and incorporating stochastic programming to account for load uncertainties, the proposed method ensures robust and efficient fault recovery. The framework integrates innovative security enhancements, including adaptive short-circuit capacity minimization and dynamic loadability improvement. Using the pelican optimization algorithm (POA), the method achieves superior performance in real-world scenarios. The proposed framework’s unique integration of stochastic modeling, adaptive security mechanisms, and optimization techniques sets a new benchmark for self-healing smart grids. Simulations on IEEE 33-bus and 83-bus networks demonstrate the framework’s efficacy. The results highlight a 20% reduction in fault recovery time and a 12.5% decrease in power losses.
REFERENCES (54)
1.
Liu K, Zou T, Xin M. Comparative Analysis of Stochastic and Uncertain Process Degradation Modeling Based on RQRL. Eksploatacja i.
3.
Ahmadi S, Vahidinasab V, Ghazizadeh MS, Giaouris D. A stochastic framework for secure reconfiguration of active distribution networks.
5.
Zheng Z, Yang J, Hu Y, Wang X. Open-source software reliability modeling with stochastic impulsive differential equations. Eksploatacja.
7.
Zhao R, Tan Y, Lu J, Guo W, Du H. A resilient self‐healing approach for active distribution networks considering dynamic microgrid.
9.
Gholami M, Karimi Ghaleh Jough F, Gholami A. Streamlining Smart Grids Reliability Assessment: An Innovative Mapping Approach.
10.
Eksploatacja i Niezawodność – Maintenance and Reliability 2024. https://doi.org/10.17531/ein/1....
11.
Mohammad Zaheri D, Nazerian Salmani S, Shahnia F, Wang H, Su X. A Two-Stage Hybrid Stochastic–Robust Coordination of Combined.
12.
Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids. Energies (Basel) 2024;17:4281.
14.
Takenobu Y, Yasuda N, Kawano S, Minato S-I, Hayashi Y. Evaluation of annual energy loss reduction based on reconfiguration scheduling.
16.
Huang S, Wu Q, Cheng L, Liu Z. Optimal reconfiguration-based dynamic tariff for congestion management and line loss reduction in.
17.
distribution networks. IEEE Trans Smart Grid 2015;7:1295–303. https://doi.org/10.1109/TSG.20....
18.
Arasteh H, Sepasian MS, Vahidinasab V. An aggregated model for coordinated planning and reconfiguration of electric distribution.
20.
Amanulla B, Chakrabarti S, Singh SN. Reconfiguration of power distribution systems considering reliability and power loss. IEEE.
22.
AlKuwaiti RH, El-Sayed WT, Farag HEZ, Al-Durra A, El-Saadany EF. Power system resilience against climatic faults: An optimized selfhealing approach using conservative voltage reduction. International Journal of Electrical Power & Energy Systems 2024;155:109519.
23.
Arefifar SA, Alam MS, Hamadi A. A review on self-healing in modern power distribution systems. Journal of Modern Power Systems and.
25.
Chen H, Wang J, Zhu J, Xiong X, Wang W, Yang H. A two-stage stochastic mixed-integer programming model for resilience enhancement.
26.
of active distribution networks. Journal of Modern Power Systems and Clean Energy 2023;11:94–106.
28.
Tang L, Han Y, Zalhaf AS, Zhou S, Yang P, Wang C, et al. Resilience enhancement of active distribution networks under extreme disaster.
29.
scenarios: A comprehensive overview of fault location strategies. Renewable and Sustainable Energy Reviews 2024;189:113898.
30.
Nowbandegani MT, Nazar MS, Javadi MS, Catalão JPS. Demand response program integrated with self-healing virtual microgrids for.
31.
enhancing the distribution system resiliency. International Journal of Electrical Power & Energy Systems 2024;157:109898.
32.
Lv C, Liang R, Chai Y. Decentralized bilateral risk-based self-healing strategy for power distribution network with potentials from central.
33.
energy stations. Journal of Modern Power Systems and Clean Energy 2023;11:179–90. https://doi.org/10.35833/MPCE.....
34.
Kalantari A, Lesani H. Operation Scheduling of Distribution Network with Photovoltaic/Wind/Battery Multi‐Microgrids and.
35.
Reconfiguration considering Reliability and Self‐Healing. Int J Energy Res 2024;2024:5724653.
36.
Zakaryaseraji M, Ghasemi-Marzbali A. Evaluating congestion management of power system considering the demand response program.
37.
and distributed generation. International Transactions on Electrical Energy Systems 2022;2022:5818757.
38.
Botea A, Rintanen J, Banerjee D. Optimal reconfiguration for supply restoration with informed A $^{\ast} $ Search. IEEE Trans Smart.
40.
Bollobás B. Graph theory: an introductory course. vol. 63. Springer Science & Business Media; 2012.
41.
Bondy JA, Murty USR. Graph theory with applications. vol. 290. Macmillan London; 1976. https://doi.org/10.1007/978-1-....
42.
Odetayo B, MacCormack J, Rosehart WD, Zareipour H. A chance constrained programming approach to integrated planning of distributed.
43.
power generation and natural gas network. Electric Power Systems Research 2017;151:197–207.
46.
LIU Y. Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach. Eksploatacja i Niezawodność –.
48.
Ahmadi S, Vahidinasab V, Ghazizadeh MS, Mehran K, Giaouris D, Taylor P. Co‐optimising distribution network adequacy and security.
49.
by simultaneous utilisation of network reconfiguration and distributed energy resources. IET Generation, Transmission & Distribution.
51.
Elmitwally A, Elsaid M, Elgamal M, Chen Z. A fuzzy-multiagent self-healing scheme for a distribution system with distributed generations.
53.
Trojovský P, Dehghani M. Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors.
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| ISSN: | 1507-2711 |
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