An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties

Tolulope David Makanju (), Ali N. Hasan, Oluwole John Famoriji and Thokozani Shongwe
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Tolulope David Makanju: Department of Electrical and Electronics Engineering, Faculty of Engineering and Built Environment, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
Ali N. Hasan: Department of Electrical and Electronics Engineering, Faculty of Engineering and Built Environment, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
Oluwole John Famoriji: Department of Electrical and Electronics Engineering, Faculty of Engineering and Built Environment, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
Thokozani Shongwe: Department of Electrical and Electronics Engineering, Faculty of Engineering and Built Environment, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa

Energies, 2025, vol. 18, issue 13, 1-21

Abstract: The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times.

Keywords: distributed energy resources; PV energy; voltage-regulating devices; coordination control; uncertainties (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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