Efficient utilisation of existing grid infrastructure empowering smart communities

Einfalt, Alfred; Brunner, Helfried; Prüggler, Wolfgang; Schultis, Daniel-Leon; Herbst, Daniel; Beidinger, Thomas; Hauer, Daniel; Lugmaier, Andreas

doi:10.1109/isgt45199.2020.9087640

Cited by 3 publications

(1 citation statement)

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“…Within the Austrian flagship project, 'Power System Cognification (PoSyCo)' [28] are developed-among many others-algorithms that manage congestions at LV level by the centralized coordination of EVCSs. The focus of this study is set on easy-to-implement but suboptimal solutions that bypass the modeling, monitoring, and communication challenges associated with the state-of-art coordination schemes.…”

Section: Introductionmentioning

confidence: 99%

Sparse Measurement-Based Coordination of Electric Vehicle Charging Stations to Manage Congestions in Low Voltage Grids

Schultis

2020

Smart Cities

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The increasing use of distributed generation and electric vehicle charging stations provokes violations of the operational limits in low voltage grids. The mitigation of voltage limit violations is addressed by Volt/var control strategies, while thermal overload is avoided by using congestion management. Congestions in low voltage grids can be managed by coordinating the active power contributions of the connected elements. As a prerequisite, the system state must be carefully observed. This study presents and investigates a method for the sparse measurement-based detection of feeder congestions that bypasses the major hurdles of distribution system state estimation. Furthermore, the developed method is used to enable congestion management by the centralized coordination of the distributed electric vehicle charging stations. Different algorithms are presented and tested by conducting load flow simulations on a real urban low voltage grid for several scenarios. Results show that the proposed method reliably detects all congestions, but in some cases, overloads are detected when none are present. A minimal detection accuracy of 73.07% is found across all simulations. The coordination algorithms react to detected congestions by reducing the power consumption of the corresponding charging stations. When properly designed, this strategy avoids congestions reliably but conservatively. Unnecessary reduction of the charging power may occur. In total, the presented solution offers an acceptable performance while requiring low implementation effort; no complex adaptations are required after grid reinforcement and expansion.

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Section: Introductionmentioning

confidence: 99%