The increasing integration of solar energy into modern power grids presents both opportunities and challenges for achieving sustainable, reliable, and efficient energy systems. Variability in solar generation, driven by environmental and weather conditions, necessitates advanced strategies for grid management and optimization. The convergence of Internet of Things (IoT) technologies and Artificial Intelligence (AI) provides a transformative approach to address these challenges by enabling real-time monitoring, predictive analytics, and intelligent control of distributed energy resources. IoT-enabled sensors capture high-resolution data from generation units, storage systems, and consumer loads, while AI algorithms facilitate forecasting, anomaly detection, energy optimization, and adaptive decision-making. Large-scale deployment of integrated Solar–IoT–AI frameworks enhances grid stability, operational efficiency, and renewable energy utilization, while supporting demand-side management and storage coordination. Critical evaluation of system performance, including energy efficiency, reliability, economic feasibility, and environmental impact, demonstrates the potential of these technologies to enable resilient and low-carbon power infrastructures. Research gaps in interoperability, cybersecurity, scalability, and regulatory compliance highlight areas for future development, providing pathways toward intelligent, sustainable, and optimized power grids capable of accommodating high solar penetration.
Rapid growth in global electricity demand, coupled with the urgent requirement for decarbonized energy systems, has intensified attention on solar power as a strategic pillar of sustainable grid modernization [1]. Solar energy’s resource abundance and declining generation costs provide significant opportunities for large-scale clean power integration. Intermittency, temporal fluctuations, and location-dependent variability create operational complexities that conventional grid systems struggle to manage effectively [2]. These limitations encourage the deployment of advanced digital technologies capable of enabling continuous visibility, precise forecasting, and dynamic control of distributed solar assets. The transition from centralized electricity infrastructures to distributed and intelligent energy networks creates an environment where real-time information flow becomes essential [3]. Integration of digital sensing, communication, and intelligent analytical frameworks shapes a new operational model for modern grids. Continuous performance monitoring, enhanced resilience, and flexible dispatch of renewable energy resources form a central objective for global power sector transformation. Policy momentum, technological innovation, and sustainability-driven investment create favorable conditions for the emergence of solar-centric smart grid ecosystems [4]. The introduction of data-driven automation strengthens the transition toward cleaner generation portfolios by ensuring that operational challenges associated with variable solar output can be addressed through optimized control strategies. This evolving landscape positions solar energy as a critical component of next-generation power systems designed to support economic growth while reducing environmental burdens [5].
IoT-enabled sensing and communication infrastructures support the foundational requirements of digitally supervised solar networks by providing uninterrupted access to high-resolution operational data [6]. Grid components embedded with IoT devices generate continuous streams of information related to voltage conditions, equipment health, irradiance patterns, demand fluctuations, and environmental impacts. This pervasive monitoring framework enhances situational awareness across all operational layers, enabling distributed generation units, storage systems, and consumer-side loads to operate as coordinated entities [7]. The extensive connectivity supported by IoT platforms strengthens asset visibility and enables early detection of anomalies that can escalate into large-scale failures. Enhanced traceability of solar generation patterns assists in stabilizing grid behavior during abrupt changes in environmental conditions [8]. Communication protocols designed for interoperability allow solar assets to interact with supervisory control systems, enabling granular decision-making grounded in real-time data. Continuous flow of operational insights contributes to improved grid responsiveness, higher system reliability, and more efficient energy balancing [9]. Integration of IoT-based monitoring tools also supports advanced demand-side management strategies, creating opportunities for dynamic load adjustments and enhanced consumer participation. By providing a consistent digital layer over physical grid components, IoT infrastructure forms the core of an intelligent ecosystem where data acquisition and information transmission play a transformative role in shaping the reliability and stability of solar-dominated power networks [10].
AI technologies contribute a deeper analytical dimension to digitally interconnected solar grids by extracting meaningful insights from complex data streams produced by IoT systems. Forecasting models built on machine learning techniques support accurate prediction of solar irradiation, load patterns, equipment failures, and energy storage requirements [11]. These predictive capabilities enable power operators to plan resource allocation with greater confidence, reducing uncertainties inherent in intermittent renewable generation. Intelligent controllers execute optimized decision strategies for energy routing, storage utilization, and load coordination, creating an adaptive operational environment capable of adjusting to real-time grid conditions [12]. AI-driven optimization improves voltage regulation, minimizes line losses, and enhances energy dispatch efficiency by continuously analyzing interdependencies across distributed assets. Deep learning techniques provide strong pattern recognition capabilities that assist in identifying hidden relationships within vast datasets, improving maintenance planning and asset longevity [13]. Reinforcement learning models contribute to autonomous grid operation by enabling systems to learn optimal control policies through iterative feedback. Enhanced reliability, reduced operational cost, and improved grid flexibility arise from the integration of these advanced AI methods [14]. In solar-rich energy ecosystems, AI-based analytics ensure that fluctuations in power generation can be managed through proactive strategies, allowing cleaner energy sources to be employed efficiently while maintaining system stability [15].
