Advancing Hybrid Renewable Energy Systems Through AI and Classical Management

A comprehensive review by Hassan II University researchers compares classical and AI-driven strategies for managing Hybrid Renewable Energy Systems, highlighting AI’s superior adaptability, predictive power, and optimization potential. While AI offers transformative benefits for efficiency and sustainability, its deployment faces challenges in computation, data quality, cybersecurity, and integration with legacy systems.

In a sweeping review from the Research Group of Smart Control, Diagnostic and Renewable Energy (SCDRE) at the Laboratory of Complex Physical Systems (LCCPS), ENSAM, Hassan II University in Casablanca, Morocco, a team of researchers delves into how classical and AI-driven energy management strategies are reshaping Hybrid Renewable Energy Systems (HRES). These systems combine solar photovoltaic panels, wind turbines, diesel backup, grid connections, and energy storage to mitigate the inherent variability of renewable energy sources. The study highlights that well-designed energy and demand-side management can cut CO₂ emissions by up to 25%, reduce net present costs by nearly 15%, and lower the cost of energy by 14%, while improving both renewable fraction and system stability. The researchers aim to map the full spectrum of management processes, ranging from rule-based control and optimization techniques to advanced AI solutions, evaluating their strengths, limitations, and potential for future enhancement through predictive and real-time decision-making.

Mapping the Landscape of Hybrid Energy Management

Following a PRISMA-based systematic literature review, the authors examined studies published between 2015 and 2024, ultimately narrowing 60 initial papers to 18 that met rigorous selection criteria. They begin by outlining the anatomy of HRES, explaining how configurations balance renewable intermittency with storage and grid interaction. Central to this is the Energy Management System (EMS), which surpasses the capabilities of basic Supervisory Control and Data Acquisition (SCADA) by integrating forecasting, optimization, and decision-making functions. The authors present energy management as a constrained optimization problem, seeking to minimize costs while satisfying technical and environmental constraints such as power balance, battery state-of-charge limits, and capacity restrictions. The EMS workflow, illustrated as a continuous cycle of data acquisition, forecasting, optimization, and dispatch, enables adaptive system control in real time.

Classical Versus AI-Driven Approaches

The review classifies strategies into classical and AI-driven categories. Classical methods encompass rule-based control, valued for simplicity and reliability but limited in adaptability; schedule-based control, using day-ahead, intraday, or rolling schedules to match supply with demand; and load shedding, which disconnects non-critical loads to prevent overload. While dependable, these methods often fail to respond effectively to rapid changes in generation or consumption. AI-driven strategies, on the other hand, embrace complexity to deliver precision and flexibility. Forecasting tools such as Long Short-Term Memory (LSTM) neural networks and ARIMA models predict fluctuations in renewable generation and load demand. AI-based switching dynamically reallocates energy sources in response to real-time data, while machine learning techniques, including neural networks, regression analysis, clustering, support vector machines, and reinforcement learning, enable predictive control, pattern detection, and optimized storage management. AI-based optimization methods such as genetic algorithms, particle swarm optimization, simulated annealing, ant colony optimization, and fuzzy logic address intricate scheduling and resource allocation challenges under uncertainty.

Trends and Real-World Applications

Analysis of research trends from 2010 to 2025 shows a marked shift away from manual and rule-based methods toward AI forecasting, optimization, and reinforcement learning. Case studies from India, Australia, the UAE, Italy, and elsewhere provide practical demonstrations: rural electrification in India using AI-based optimization with artificial neural networks; industrial load management in the UAE with fuzzy logic; microgrid battery scheduling in Italy using rule-based and linear programming approaches; and improved forecasting in Tuscany with advanced neural models. Comparative results indicate that AI-based strategies excel at reducing power curtailment, improving grid stability, and leveraging market conditions to buy low and sell high, optimizing operational costs. However, these benefits come with demands for high-quality data, substantial computational resources, skilled personnel, and advanced infrastructure.

Challenges on the Road to Implementation

The review does not shy away from addressing the pitfalls of both approaches. Classical methods suffer from low adaptability, limited predictive capability, and scalability challenges. AI strategies, despite their superior performance in adaptability and optimization, are hindered by high computational costs, data dependency, cybersecurity risks, and integration hurdles with legacy systems. The introduction of AI creates new vulnerabilities, data manipulation, adversarial attacks, and model tampering, necessitating advanced security protocols such as encryption, authentication, anomaly detection, and federated learning, which in turn add complexity. Another challenge lies in the explainability of AI decisions, especially for safety-critical systems like microgrids, where transparency is essential.

The Future: Smarter, More Resilient Systems

The authors see the future of energy management as a convergence of AI, IoT-enabled smart grids, and edge computing, allowing for faster, decentralized control. The rise of “prosumers”, households that both consume and generate energy, demands residential energy management systems capable of two-way communication with the grid. The expansion of electric vehicles adds new layers to storage and load balancing needs, while microgrids and advanced energy storage systems create opportunities for more resilient, decentralized networks. Key research priorities include reinforcement learning for real-time adaptive control, scalable and cost-effective AI deployment, greater explainability, and robust cybersecurity. The study concludes that while AI-driven EMS has transformative potential, success hinges on bridging the gap between advanced algorithmic capabilities and the operational realities of energy infrastructure worldwide. By combining the stability of classical methods with the adaptability of AI, hybrid approaches can offer scalable, resilient, and economically viable solutions for accelerating the global transition to sustainable energy systems.