article · Frontiers in Artificial Intelligence
Integrating wind turbines and solar photovoltaics into radial power distribution networks requires coordination between capacity planning and grid stability under unpredictable weather. A hybrid artificial neural network and particle swarm optimisation framework addresses this challenge by incorporating smart-inverter voltage control alongside renewable uncertainty modelling. Tested with long-term meteorological data from Algeria across stratified probability scenarios, the method optimises equipment sizing and voltage regulation at selected network buses. Verification against a standard distribution network confirmed accurate power-flow calculations. The resulting design substantially lowered network energy losses and reduced voltage deviations while raising minimum grid voltages compared to an existing benchmark technique. Analysis indicates that while renewable capacity sizing drives the majority of energy loss reductions, smart-inverter reactive power support is critical for securing voltage stability.
Renewable energy sources like wind and solar fluctuate with the weather, creating voltage instability and energy waste on local electricity grids. Developing automated planning tools that simultaneously determine renewable capacity and utilise smart inverters helps power utilities integrate clean energy more reliably without triggering costly line overhauls or power disruptions.
This work is relevant to electricity distribution utilities, renewable project developers, and grid-planning software vendors seeking to optimise distributed generation. Operating currently as an early-stage, benchmark-calibrated proof-of-concept, the computational framework requires further direct validation across full real-world feeder networks before it can be packaged into commercial network planning tools.
AI-generated from the published abstract. Always read the original work before citing.
Introduction: Radial distribution grids require renewable planning methods that jointly account for meteorological generation, WT/PV capacity allocation, and smart-inverter voltage support. Methods: An ANN-PSO-VoltVAR framework was developed using 2012-2021 NASA POWER irradiance, wind-speed, and temperature data for In Salah, Algeria. Renewable uncertainty was represented by 12 stratified Weibull-Beta scenarios and an expected-cost/CVaR objective. The mixed decision vector optimized WT units, PV strings, and voltage-dependent inverter support at four preselected IEEE 85-bus locations. An exact backward/forward-sweep module on the standard 33-bus feeder independently verified the load-flow implementation. Results: The tuned ANN achieved an RMSE of 1.6323 kW, an MAE of 1.2314 kW, and an R-squared value of 0.99999. The verified five-start mean-profile solution reduced mean active loss to 15.9900 kW and voltage deviation to 0.051082 p.u., while increasing minimum voltage to 0.98213 p.u. and minimum VSI to 0.96307. Relative to IFLO, the respective improvements were 54.57%, 22.49%, 1.21%, and 8.41%. Discussion: The ablation analysis showed that capacity sizing accounted for most of the loss reduction, whereas Volt-VAR support produced the clearer additional voltage-security gains. Because the IEEE 85-bus electrical response is benchmark-calibrated rather than fully reconstructed, the findings should be interpreted as a reproducible proof-of-concept. Direct full-feeder validation on the IEEE 85-bus system and other networks remains necessary.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3389/frai.2026.1896551
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
