Control Strategies for Grid-Connected Renewable Energy Systems

Grid-connected renewable energy systems increasingly underpin modern power networks, combining wind, solar and storage assets to deliver low-carbon electricity at scale. Effective control strategies maintain power quality, ensure stability under fluctuating generation and demand, and support ancillary services such as frequency regulation and voltage support. Contemporary approaches range from classical proportional-integral controllers to advanced model-based and data-driven schemes. Model predictive control frameworks anticipate future operating conditions, optimising set-points across multiple objectives such as power maximisation, mechanical load reduction and fault ride-through. Reinforcement learning techniques adapt to real-time dynamics by continuously updating control policies based on observed system behaviour. Hybrid architectures integrate digital twins or neural networks with conventional controllers to enhance robustness against uncertainties and grid disturbances. Emerging methods also exploit coordinated control of converters, energy storage and network interfaces to balance local generation with wider grid requirements. Collectively, these developments aim to secure reliable, resilient and high-performance operation of large-scale renewable plants and distributed resources within increasingly stringent grid codes worldwide.

Research from Nature Portfolio

Recent studies have demonstrated a real-time adaptive control scheme for photovoltaic inverters that employs a digital twin to refine controller parameters under rapidly varying irradiance and temperature conditions. This architecture achieves faster transient response and reduced overshoot compared to fixed-gain designs, while complying with tight grid-code requirements for voltage support during faults. Another work has introduced a data-driven predictive control algorithm for wind turbines, which merges machine-learning-based system identification with nonlinear model predictive control to optimise pitch and torque settings. The combined approach enhances power capture in turbulent wind regimes and mitigates structural loading across all operating regions. A further contribution explores decentralised droop and virtual synchronous machine control for converter-interfaced generators, demonstrating improved inertia emulation and smoother power sharing in high-penetration scenarios.

Control Strategies for Grid-Connected Renewable Energy Systems publication trend

The graph below shows the total number of articles in control strategies for grid-connected renewable energy systems across all publications each year (not limited to Nature Index journals).

Technical terms

Model predictive control: An optimisation-based strategy that uses a mathematical model to predict future system behaviour and compute control actions that minimise a cost function over a receding horizon.

Reinforcement learning: A data-driven method in which a control agent iteratively learns optimal policies through trial-and-error interactions with the system, guided by reward signals.

Matrix converter: A direct AC-to-AC power-electronic device that enables variable voltage and frequency conversion without intermediate energy storage, offering compactness and energy efficiency.

Low-voltage ride-through (LVRT): A grid-code requirement obliging inverters to remain connected and support the network by injecting current during short-term voltage sags or faults.

Digital twin: A virtual replica of a physical system that mirrors real-time operating conditions, used to optimise controller tuning and predict future performance under various scenarios.

References

1. Data-driven torque and pitch control of wind turbines via reinforcement learning. Renewable Energy (2023).
2. VOLTAGE CONTROL OF WIND SYSTEM USING ADAPTIVE FUZZY SLIDING METHOD WITH IOT MONITORING. International Journal of Advances in Signal and Image Sciences (2023).
3. A Comprehensive Review of Control Strategies to Overcome Challenges During LVRT in PV Systems. IEEE Access (2021).