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Reactive Power Control in PV Systems Through (Explainable) AI
C. Utama, C. Meske, J. Schneider, C. Ulbrich
System, Grid Management, Voltage Stabilisation, Distributed System Operators (DSO)
Energy Transition – Integration, Storage, Sustainability, Policy, Economics, Energy Poverty, Society
Subtopic: Energy System Integration; Storage
Event: 8th World Conference on Photovoltaic Energy Conversion
Session: 5DV.2.9
ISBN: 3-936338-86-8
0,00 EUR
Document(s): poster


Across the world, efforts to support the energy transition and halt climate change have resulted in the significant growth of the number of renewable distributed generators (DGs) installed, among which PV systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. Existing strategies to determine optimal reactive power dispatch are either too simplistic, leading to sub-optimal solutions, or too complex to be practical. In this work, we propose the use of machine learning (ML) and explainable artificial intelligence (XAI) to learn approximate optimal mappings from AC optimal power flow (ACOPF) solutions, creating real-time distributed controllers which promote data privacy and reduce computational burden.