Extreme heat events increasingly stress power distribution networks and reduce their ability to safely accommodate distributed solar generation. Traditional hosting capacity assessments fail to account for the cumulative thermal damage that sustained high temperatures inflict on transformers, cables, and other temperature-sensitive equipment. This limitation constrains solar deployment in regions experiencing frequent heat waves.
Researchers have developed a coordinated planning approach that optimizes distributed photovoltaic hosting capacity specifically under extreme temperature scenarios. The method models how accumulated heat degrades network components over time, capturing realistic thermal aging effects that static models miss. It simultaneously addresses the inherent variability in solar output and electricity demand through probabilistic analysis, acknowledging that renewable integration introduces complex uncertainties.
The core innovation combines an improved multi-objective artificial bee colony algorithm with advanced sampling techniques to balance two competing goals: maximizing solar hosting capacity while minimizing the cost of grid upgrades and control systems needed to achieve it. The algorithm considers three coordinated control strategies: reactive power management from solar inverters, distributed var compensation devices, and incentive-based demand response that shifts consumption away from peak heat periods.
Testing on a standard 33-node distribution network under extreme heat conditions revealed remarkable results: coordinated optimization increased solar hosting capacity by approximately 70 percent compared to baseline scenarios. This substantial gain emerges not from expensive infrastructure rebuilding but from intelligent coordination of existing and emerging grid technologies.
The practical implications are significant. As climate change intensifies heat extremes, utilities need robust methods to assess how much distributed solar their networks can accommodate safely. This work bridges that gap, providing a computational framework that accounts for thermal realities while leveraging modern control capabilities. The approach supports accelerated renewable integration without compromising grid reliability during the most challenging operating conditions.
