Critical review proposes a framework for improving energy management systems in renewable energy applications, highlighting security implications.
The rapid transition toward renewable energy integration necessitates dynamic control mechanisms that can effectively manage the volatility of sources such as solar photovoltaics. While the built-up environment accounts for approximately 40% of global energy consumption, systemic inefficiencies in control logic often undermine potential savings. This paper presents a critical review and a novel taxonomy of smart switching mechanisms for solar energy management, moving beyond descriptive hardware surveys to analyze the soft architecture of intelligent actuation. The existing technologies are classified into a three-layered framework: Control Logic (tracing the evolution from deterministic rule-based systems to Model Predictive Control and Deep Reinforcement Learning), Communication Architecture (evaluating the trade-offs between Zigbee mesh networks, Global System for Mobile wide-area control, and hybrid Internet of Things ecosystems), and Optimization Targets (distinguishing between grid stability (load shedding), economic optimization (Demand Response), and energy autonomy (self-harvesting)). Crucially, this study evaluates these layers through the lens of techno-economic viability, identifying how misalignment between peak generation and load profiles hampers return on investment and increases the levelized cost of electricity for prosumers. This analysis reveals a significant gap between simulation and reality in Artificial Intelligence-driven control, identifying critical vulnerabilities in cybersecurity, particularly the risk of False Data Injection. To address these gaps, this paper proposes the Integrated Smart Switching Framework, a novel technical blueprint that converges Security-by-Design (embedded Intrusion Detection System (IDS)), Graded Actuation Logic, and Human-Centric Optimization. This framework provides a structured pathway for transitioning from theoretical AI simulations to resilient, real-world solar microgrid infrastructure. It is concluded that future research must prioritize graded individual switching limits, gamified user engagement strategies, and embedded IDSs to ensure the resilience of next-generation microgrids.
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Akpan et al. (2026) studied this question.
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