Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory

Jinsen Liu; Molin He; Jie Wang; Jie Lu

doi:10.4108/ew.10598

Authors

Jinsen Liu Power Grid Planning and Research Center of Guizhou Power Grid Co., Ltd
Molin He Guizhou Power Grid Co., Ltd
Jie Wang Power Grid Planning and Research Center of Guizhou Power Grid Co., Ltd
Jie Lu Zunyi Power Supply Branch of Guizhou Power Grid Co., Ltd.

DOI:

https://doi.org/10.4108/ew.10598

Keywords:

Distribution Network Planning, Fuzzy Optimization, Grey System Theory, Distributed Renewable Energy

Abstract

INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic methods fail to handle qualitative fuzziness (e.g., subjective reliability thresholds) and quantitative uncertainty (e.g., sparse historical data), leading to inflexible and inefficient solutions. This study addresses these challenges by developing a hybrid planning framework.

OBJECTIVES: This paper aims to solve the dual challenges of qualitative fuzziness and quantitative uncertainty in distribution network planning, providing a systematic solution to accommodate distributed renewable energy, handle load uncertainty, and balance conflicting stakeholder preferences through integrating fuzzy optimization theory and grey system theory.

METHODS: The hybrid algorithm combines fuzzy optimization and grey system theory. Fuzzy optimization uses triangular fuzzy numbers for load growth rates ([3%, 5%, 8%]) and trapezoidal fuzzy intervals for voltage constraints ([−10%, −5%, 5%, 10%]) with membership functions (threshold λ≥0.8) to convert qualitative requirements into solvable constraints. Grey system theory applies the GM(1,1) model for load forecasting (achieving 4.2% MAPE with 15-month data) and grey relational analysis (GRA) for data-driven objective weighting to eliminate expert bias. An improved particle swarm optimization (IPSO) algorithm is used for optimization, validated in a 33-node network with 8.5 MW PV and 6 MW wind capacity.

RESULTS: In the 33-node case study, compared to the deterministic genetic algorithm (D-GA), the hybrid algorithm reduces lifecycle costs by 19% (from $8.91M to $7.23M), increases renewable energy accommodation by 24% (from 9.8 MW to 12.3 MW), and improves the system average supply availability index (ASAI) from 99.92% to 99.95%. Under extreme uncertainties (±40% renewable output, ±30% load shifts), cost deviations remain within 6% and reliability metrics within 5%, demonstrating strong robustness.

CONCLUSION: This research presents a robust hybrid framework that bridges fuzzy qualitative reasoning and grey data efficiency, effectively addressing both qualitative fuzziness and quantitative uncertainty in distribution network planning. It provides a science-based tool for resilient grid design, with potential for extension to multi-energy system integration and real-time optimization in future work.

Downloads

Download data is not yet available.

References

[1] Yi J H, Cherkaoui R, Paolone M, et al. Expansion planning of active distribution networks achieving their dispatchability via energy storage systems. Appl Energy. 2022; 326: 119942.

[2] Khajehvand M, Fakharian A, Sedighizadeh M. A risk-averse decision based on IGDT/stochastic approach for smart distribution network operation under extreme uncertainties. Appl Soft Comput. 2021; 107: 107395.

[3] Osama R A, Zobaa A F, Abdelaziz A Y. A planning framework for optimal partitioning of distribution networks into microgrids. IEEE Syst J. 2019; 14(1): 916–926.

[4] Naderi E, Asrari A. Cyber-Physical Distribution Systems Resilience Against Cyberattacks via a Remediation Framework Based on Static VAR Compensators (SVCs). IEEE Access. 2024.

[5] Mohsenzadeh A, Pang C, Haghifam M R. Determining optimal forming of flexible microgrids in the presence of demand response in smart distribution systems. IEEE Syst J. 2017; 12(4): 3315–3323.

[6] Jia T, Yang G, Yao L. The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization. Energies. 2024; 17 (19).

[7] Wei M, Wen M, Zhang Y. A novel spatial electric load forecasting method based on LDTW and GCN. IET Gener Transm Distrib. 2024; 18(3): 491–505.

[8] Ibrahim I A, Hossain M J. Low voltage distribution networks modeling and unbalanced (optimal) power flow: A comprehensive review. IEEE Access. 2021; 9: 143026–143084.

[9] Liang C, Gu D, Bichindaritz I, et al. Integrating gray system theory and logistic regression into case-based reasoning for safety assessment of thermal power plants. Expert Syst Appl. 2012; 39(5): 5154–5167.

[10] Liu S, Tao Y, Xie N, et al. Advance in grey system theory and applications in science and engineering. Grey Syst Theor Appl. 2022; 12(4): 804–823.

[11] Jahani F, Mohammadi M, Pourdaryaei A, et al. A Novel Multi-Criteria Decision-Making Framework in Electrical Utilities Based on Gray Number Approach. IEEE Access. 2022; 10: 121508–121519.

[12] Chen W H, Tsai M S, Kuo H L. Distribution system restoration using the hybrid fuzzy-grey method. IEEE Trans Power Syst. 2005; 20(1): 199–205.

[13] Zhong Z, Yang C, Cao W, et al. Short‐Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization. Math Probl Eng. 2017; 2017(1): 5812394.

[14] Chen X, Xu L, Li X, et al. Risk assessment method of power communication network based on gray cloud theory and matter-element extension model coupling. IEEE Access. 2023; 12: 47581–47593.

[15] Cai J, Li Q, Li L, et al. A fuzzy adaptive chaotic ant swarm optimization for economic dispatch. Int J Electr Power Energy Syst. 2012; 34(1): 154–160.

[16] Sun Z, Wang N, Srinivasan D, et al. Optimal tunning of type-2 fuzzy logic power system stabilizer based on differential evolution algorithm. Int J Electr Power Energy Syst. 2014; 62: 19–28.

[17] Berrazouane S, Mohammedi K. Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system. Energy Convers Manag. 2014; 78: 652–660.

[18] El-Zonkoly A M, Khalil A A, Ahmied N M. Optimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization. Expert Syst Appl. 2009; 36(2): 2097–2106.

[19] Gafar M G, El-Sehiemy R A, Hasanien H M. A novel hybrid fuzzy-JAYA optimization algorithm for efficient ORPD solution. IEEE Access. 2019; 7: 182078–182088.

[20] Liu X, Cui J. Fuzzy economic model predictive control for thermal power plant. IET Control Theory Appl. 2019; 13(8): 1113–1120.

[22] Sambariya D K, Prasad R. Optimal tuning of fuzzy logic power system stabilizer using harmony search algorithm. Int J Fuzzy Syst. 2015; 17: 457–470.

[23] Bernstein A, Reyes-Chamorro L, Le Boudec J Y, et al. A composable method for real-time control of active distribution networks with explicit power setpoints. Part I: Framework[J]. Electric Power Systems Research, 2015, 125: 254-264.

[24] Dall’Anese E, Simonetto A. Optimal power flow pursuit[J]. IEEE Transactions on Smart Grid, 2016, 9(2): 942-952.

[25] Li Q, Dong F, Zhou G, et al. Co-optimization of virtual power plants and distribution grids: Emphasizing flexible resource aggregation and battery capacity degradation[J]. Applied Energy, 2025, 377: 124519.

[26] Fugui D, Yuzhu H, Wanying L, et al. Intelligent decision-making of distribution network planning scheme with distributed wind power generations [J][J]. International Journal of Electrical Power and Energy Systems, 2022, 136.

[27] Palomino A, Giraldo J, Parvania M. Graph-based interdependent cyber-physical risk analysis of power distribution networks[J]. IEEE transactions on power delivery, 2022, 38(3): 1510-1520.

[28] Braik A M, Salman A M, Li Y. Reliability-based assessment and cost analysis of power distribution systems at risk of tornado hazard[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2020, 6(2): 04020014.

[29] Sha Y, Li W, Yan J, et al. Research on investment scale calculation and accurate management of power grid projects based on three-level strategy[J]. IEEE Access, 2021, 9: 67176-67185.

[30] Jiang D, Zhou X, Ai Q, et al. A decentralized optimization framework for multi-MGs in distribution network considering parallel architecture[J]. International Journal of Electrical Power & Energy Systems, 2025, 164: 110252.