Mitigating Solar Curtailment in High-Penetration Interconnections: An AI-Driven Approach to Dynamic Load Balancing

Abstract

This study addresses the persistent problem of solar curtailment in high-penetration interconnections, where available photovoltaic output is reduced because grid systems cannot absorb, transfer, or balance solar generation efficiently during peak periods. The purpose of the research was to examine whether AI-driven dynamic load balancing can mitigate solar curtailment by improving forecasting accuracy, balancing efficiency, system flexibility, and operational responsiveness within renewable-rich grid environments. Using a quantitative, cross-sectional, case-based design, the study collected data from 188 usable respondents drawn from cloud-enabled and enterprise-scale electricity and grid-operation cases, including grid operators, utility engineers, renewable energy managers, dispatch analysts, planners, and technical staff. The key variables examined were AI Forecasting Accuracy, Dynamic Load Balancing Efficiency, Grid Flexibility, Interconnection Capacity, AI Operational Responsiveness, Operational Trust in AI, and Solar Curtailment Mitigation. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong positive respondent agreement across the core variables, with mean scores of 4.18 for AI Forecasting Accuracy, 4.24 for Dynamic Load Balancing Efficiency, 4.11 for Grid Flexibility, 4.06 for Interconnection Capacity, 4.21 for AI Operational Responsiveness, 3.89 for Operational Trust in AI, and 4.27 for Solar Curtailment Mitigation. Reliability was high across all constructs, with Cronbach’s alpha values ranging from 0.811 to 0.896. Correlation results revealed that Dynamic Load Balancing Efficiency had the strongest association with Solar Curtailment Mitigation (r = .708, p < .01), followed by AI Forecasting Accuracy (r = .651, p < .01) and AI Operational Responsiveness (r = .624, p < .01). The regression model explained 62.4% of the variance in solar curtailment mitigation (R² = .624), and the model was statistically significant (F = 31.480, p < .001). Dynamic Load Balancing Efficiency emerged as the strongest predictor (β = .310, p < .001), followed by AI Forecasting Accuracy (β = .270, p = .002), Grid Flexibility (β = .220, p = .006), AI Operational Responsiveness (β = .190, p = .011), Interconnection Capacity (β = .160, p = .018), and Operational Trust in AI (β = .140, p = .031). The study implies that reducing solar curtailment requires not only better AI tools but also flexible infrastructure, responsive interconnections, and institutional trust to support practical adoption in modern electricity systems.