SITUATIONAL AWARENESS AND DECISION SUPPORT FRAMEWORK FOR DISTRIBUTION SYSTEMS WITH HIGH PENETRATION OF BEHIND-THE-METER PHOTOVOLTAIC SYSTEMS AND ELECTRIC VEHICLE FLEET CHARGING

Date

2025-8-06

Author

Küçükaslan, Özgür Arda

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With advancements in photovoltaic panels and inverter technologies, behind-the-meter rooftop photovoltaic (rPV) systems contribute up to 4% of the total electricity generation. However, monitoring these systems is neither economically nor computationally feasible due to high infrastructure costs and overwhelming data volumes. This lack of visibility creates operational challenges for Distribution System Operators (DSO), including difficulties in managing voltage fluctuations and ensuring network stability. Additionally, high-volume EV fleet charging can put a strain on the distribution network. To address these challenges, a comprehensive situational awareness and decision support framework is proposed. It estimates rPV generation using a limited number of strategically placed measurements, improving accuracy under uncertainty. A Probabilistic Load Flow analysis incorporates the stochastic nature of solar generation and the proposed estimation method to enhance situational awareness. Decision-making methods such as distribution network reconfiguration, which employs a nonlinear optimization algorithm with preprocessing, and battery optimization, which determines optimal inverter operating points, help mitigate voltage violations and improve power quality. Additionally, a smart charging scheduling method for electric bus fleets is introduced. This method not only manages the integration of large, fast DC charging loads but also leverages the flexibility of charging schedules to assist in voltage regulation. By proactively identifying potential voltage issues and implementing corrective actions, the proposed system ensures more stable and reliable distribution network operation. Ultimately, this system enhances the efficiency and seamless integration of rPV systems into distribution networks, improving grid resilience and enabling greater renewable energy adoption.

Subject Keywords

Renewable energy integration, Decision support systems, Situational awareness, Network optimization, EV Charging integration

URI

https://hdl.handle.net/11511/115563

Collections

Graduate School of Natural and Applied Sciences, Thesis