Automatic Generation Control Strategies in Power Systems

Automatic Generation Control (AGC) is a cornerstone of modern power system operation, tasked with maintaining system frequency and scheduled power exchanges between interconnected areas. Traditional AGC schemes rely on proportional‐integral control to adjust generator setpoints in response to area control error, thereby ensuring real‐time balance between generation and load. The evolving portfolio of control strategies encompasses robust and adaptive designs, model predictive control and intelligent methods such as fuzzy logic, evolutionary algorithms and deep learning. These advances address challenges posed by high penetrations of renewable energy, low‐inertia networks and distributed resources in microgrids. Coordination among multi‐area AGC units is achieved through frequency bias settings, deadband compensation and tie‐line power management. Recent trends emphasise decentralised and multi‐agent frameworks to enhance scalability and privacy, while data‐driven approaches leverage real‐time measurements to tune controller parameters dynamically. Practical implementations demonstrate improved frequency quality, reduced control cost and faster disturbance rejection, underscoring AGC’s global significance in securing resilient, low‐carbon power systems.

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Automatic Generation Control Strategies in Power Systems publication trend

The graph below shows the total number of articles in automatic generation control strategies in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

Area Control Error (ACE): The difference between actual and scheduled net interchange power plus the product of frequency deviation and frequency bias factor in a control area.

Tie‐line Power: The power exchanged over transmission lines connecting neighbouring control areas, crucial for inter‐area balancing.

Frequency Bias Factor: A predefined coefficient that represents a control area’s sensitivity to frequency deviations during AGC.

Deep Reinforcement Learning (DRL): A machine learning paradigm combining reinforcement learning with deep neural networks to learn control policies in complex dynamic environments.

Twin Delayed Deep Deterministic Policy Gradient (TD3): An actor–critic DRL algorithm that mitigates overestimation bias by using two critics and delayed policy updates for stability.

Multi‐Agent System (MAS): A decentralised control architecture in which multiple autonomous agents cooperate or compete to achieve global control objectives.

Model Predictive Control (MPC): A receding‐horizon optimisation method that computes future control actions by solving a constrained optimisation problem at each time step.

References

1. Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach. IEEE Access (2022).
2. Data‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning. IET Renewable Power Generation (2021).
3. Multi-Objective Mayfly Optimization-Based Frequency Regulation for Power Grid With Wind Energy Penetration. Frontiers in Energy Research (2022).