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<p>This paper presents a novel stochastic carbon-aware planning framework for the optimal siting and sizing of renewable distributed generators (RDGs) and electric vehicle charging stations (EVCSs) in smart urban distribution networks. The proposed model jointly incorporates carbon emission costs and scenario-based uncertainty in renewable energy and EV charging demand using Monte Carlo simulation with K-means clustering. Four objectives, namely minimizing real power losses, voltage deviations, capital investment costs, and carbon emission costs, are aggregated using a fuzzy decision-making method with Analytic Hierarchy Process (AHP)-based weighting. The optimization is solved using the Snow Geese Algorithm (SGA), customized for the mixed discrete and continuous decision space, and benchmarked against the Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) under identical conditions. The framework is validated on the IEEE 33-bus and IEEE 69-bus systems under multiple realistic deployment scenarios. Results show that for the IEEE 33-bus system, the proposed strategy achieves up to 78% lower losses, 86% better voltage profile, and 4% lower investment cost compared to the base case. For the IEEE 69-bus system, the best-performing configuration reduces losses by 96%, improves the voltage profile by 97.3%, and slightly lowers investment cost by about 0.4%. Comparative analysis confirms that SGA outperforms GWO and PSO in convergence speed, solution quality. The stochastic approach ensures robust and resilient planning decisions, supporting sustainable and decarbonized urban energy systems.</p>

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Stochastic carbon-aware planning of renewable DGs and EV charging stations with demand flexibility in smart urban grids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月2日

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  • Shifeng Wang,
  • Qingji Tan,
  • Qishan Jiang,
  • Han Shao,
  • Mohammad Kouhi

摘要

This paper presents a novel stochastic carbon-aware planning framework for the optimal siting and sizing of renewable distributed generators (RDGs) and electric vehicle charging stations (EVCSs) in smart urban distribution networks. The proposed model jointly incorporates carbon emission costs and scenario-based uncertainty in renewable energy and EV charging demand using Monte Carlo simulation with K-means clustering. Four objectives, namely minimizing real power losses, voltage deviations, capital investment costs, and carbon emission costs, are aggregated using a fuzzy decision-making method with Analytic Hierarchy Process (AHP)-based weighting. The optimization is solved using the Snow Geese Algorithm (SGA), customized for the mixed discrete and continuous decision space, and benchmarked against the Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) under identical conditions. The framework is validated on the IEEE 33-bus and IEEE 69-bus systems under multiple realistic deployment scenarios. Results show that for the IEEE 33-bus system, the proposed strategy achieves up to 78% lower losses, 86% better voltage profile, and 4% lower investment cost compared to the base case. For the IEEE 69-bus system, the best-performing configuration reduces losses by 96%, improves the voltage profile by 97.3%, and slightly lowers investment cost by about 0.4%. Comparative analysis confirms that SGA outperforms GWO and PSO in convergence speed, solution quality. The stochastic approach ensures robust and resilient planning decisions, supporting sustainable and decarbonized urban energy systems.