Twin-delayed deep deterministic policy gradient for enhanced power optimization in solar PV-integrated DFIG wind energy systems.

Deep Reinforcement Learning Framework for Adaptive Power Quality Management in Hybrid Microgrid

Hybrid microgrids integrating photovoltaic (PV) arrays, wind tur-binedriven PMSG units, fuel cells, and battery storage enhance sustainability but face serious power quality (PQ) challenges due to the intermittent and nonlinear behavior of renewable sources and loads. Traditional PI, PR, and hybrid intelligent controllers offer acceptable nominal performance but lack adaptability and predictive capability under rapidly varying disturbances. To overcome these limitations, this paper proposes a Deep Reinforcement Learning (DRL) frame-work based on the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm for real-time PQ management, where a multi-objective reward function guides optimal actions for voltage regulation, harmonic suppression, unbalance mitigation, and frequency stability. Vali-dation in a MATLAB/Simulink hybrid microgrid with nonlinear loads and renewable intermittency shows that the proposed DRL controller reduces THD from 8.42% to 2.11%, VUF from 3.9% to 0.7%, and frequency deviation from 0.42 to 0.08 Hz, while improving settling time by nearly 50%. Convergence and multi-run statistical analysis further confirm the robustness, stability, and reproducibility of the trained policy, demonstrating the effectiveness of DRL as an intelligent and scalable solution for next-generation microgrid PQ control.

Mother Algorithm Optimized Controller for Multi-Area Deregulated Power System Modelled with Solar/Wind/EV Sources: A Novel Approach

Due to increased system uncertainty, nonlinear dynamics, and marketdriven power exchanges, modern deregulated multi-area power systems with high penetration of renewable energy sources (RES) like wind and solar, and electric vehicle (EV) charging stations present serious challenges to automatic generation control (AGC). Degraded frequency regulation and tie-line power control under deregulated environments result from the limited robustness, slow dynamic response, poor handling of stochastic RES/EV variations, and susceptibility to local optimal solutions of both conventional PI/PID controllers and recently reported optimizationbased AGC schemes. However, with a view to addressing these issues, this study focuses on reducing area control errors (ACE) during different operational shifts, such as frequency fluctuation (f) and tie line variations (Ptie). The main objective of this study is to determine the optimal gain settings for the Fractional Order Proportional-Integral-Derivative controller (FOPIDC) using the mother optimization algorithm (MOA). The proposed control strategy looks at how generators 3-AMS behave in a deregulated environment and emphasises the significance of FOPIDC optimization in preserving system stability with the goal of reducing integral time and absolute error (ITAE). Furthermore, the effectiveness of the proposed method is verified by comparing it with the Walrus Optimization Algorithm (WOA). As case studies, the effectiveness of the suggested strategy is also evaluated under Poolco, bilateral agreements, stability, and sensitivity analysis. In terms of generator outputs, tie-line power variations, and frequencies across different locations, comparative data unequivocally demonstrate that the suggested MOA-adjusted FOPIDC performs better than alternative approaches. In case 1, by implementing the MOA optimized controller, the settling time is 8.5 s, and the value of the objective ITAE for the transient responses is 0.0005292. However, these values are less than the values obtained by WOA. Similarly, in case 2, the settling time is 11.5 s, and ITAE is 0.000395 less

Real-Time Energy Management of PV-ESS Integrated Active Distribution Networks Using Digital Twin-Enabled Deep Reinforcement Learning

The increasing penetration of photovoltaic (PV) generation and battery energy storage systems (BESSs) has significantly increased the operational complexity of active distribution networks, where real-time energy management must simultaneously address renewable uncertainty, voltage regulation, and battery lifetime preservation. Existing Digital Twin-based energy management approaches primarily support monitoring and visualization, whereas deep reinforcement learning (DRL) controllers are commonly developed independently of real-time system synchronization, limiting their adaptability under rapidly changing operating conditions. To overcome these limitations, this paper proposes a Digital Twin-enabled Deep Reinforcement Learning (DT-DRL) framework for coordinated PV–BESS energy management in active distribution networks. The proposed framework establishes a closed-loop cyber–physical architecture in which continuously synchronized Digital Twin states are directly incorporated into a Proximal Policy Optimization (PPO)-based decision-making process. A multi-objective formulation is developed to jointly minimize operating cost, voltage deviation, and battery degradation while satisfying network operational constraints. Renewable generation and load uncertainties are represented using Monte Carlo-based stochastic scenarios to improve policy robustness under practical operating conditions. The proposed framework is validated on the IEEE 33-bus distribution system and compared with rule-based control (RBC), optimal power flow (OPF), and conventional DRL approaches. Simulation results demonstrate that the proposed method reduces the daily operating cost by 22.2%, decreases the maximum voltage deviation to 0.039 p.u., and achieves more stable BESS operation with lower operational variability under uncertain conditions. Furthermore, the complete Digital Twin synchronization and PPO decision-making process requires only 0.41 s per control interval, satisfying the timing requirements of distribution-level energy management systems. These results demonstrate that the proposed DT-DRL framework provides an accurate, computationally efficient, and practically deployable solution for real-time energy management in renewable-rich active distribution networks.

Deep Reinforcement Learning-Assisted Universal Active Filter for Power Quality Enhancement in a SyRG-Based Standalone Renewable Energy System with Hybrid Energy Storage

The rapid deployment of standalone renewable energy systems has increased the need for intelligent power quality enhancement techniques capable of operating under highly dynamic and nonlinear conditions. Conventional proportionalintegral (PI) controllers and recently developed Artificial Neural Network (ANN)-based controllers improve voltage regulation and harmonic mitigation; however, their performance depends on prior training and fixed learning structures. This paper proposes a Deep Reinforcement Learning (DRL)-assisted Universal Active Filter (UAF) for a Synchronous Reluctance Generator (SyRG)-based standalone renewable energy system integrated with a photovoltaic array and hybrid battery energy storage system. Unlike conventional controllers, the proposed DRL controller continuously learns the optimal switching policy from real-time operating conditions without requiring repeated controller tuning. The controller coordinates the operation of the series and shunt voltage source converters to suppress harmonic currents, compensate reactive power, stabilize the DC-link voltage, and maintain sinusoidal load voltage under varying renewable generation and nonlinear load conditions. MATLAB/Simulink simulations demonstrate significant improvements in dynamic response, voltage regulation, power factor correction, and harmonic suppression compared with conventional PI with ANN controller. The proposed intelligent controller effectively minimizes Total Harmonic Distortion (THD), improves converter efficiency, enhances system robustness against disturbances, and satisfies IEEE-519 power quality standards, making it suitable for next-generation standalone renewable microgrids.

Hierarchical Proximal Policy Optimization for Wide-Area Damping Control of Inter-Area Oscillations in Power Systems with DFIG Wind Integration

Reinforcement Learning for Smart Grid Energy Optimization

Frequent network of renewable energy sources, electric cars, and distributed generation stations has changed traditional power systems into complicated smart grids. This change puts in place considerable uncertainty, non-linear and dynamic decision-making problems regarding energy management. Conventional optimization methods are usually unable to adapt effectively to the stochastic and time sensitive nature of contemporary smart grids. A recent development in machine learning has been presented as a means of solving these problems by use or Reinforcement Learning (RL), a branch of machine learning, which allows intelligent agents to acquire an optimal control policy by interacting with the environment. The paper will be a detailed report on the implementation of reinforcement learning to solve smart grid optimized energy. The framework proposed is based on the demand-side control, scheduling of energy storage and integration of renewable energy to reduce the operational cost without affecting the grid stability and reliability. The different RL paradigms such as Q-learning, Deep Q-networks (DQN) as well as Policy Gradients are discussed in their applications in the context of the smart grid. An elaborated methodology is constructed, with its system modelling, the design of state space, design of reward functions, and processes of training. The simulated experiments prove that the RL-based management strategy is much more effective in terms of minimization of costs and peak loads and its use of renewable energy sources in comparison with traditional rule-based and optimization-based strategies. The findings indicate the versatility and scability of reinforcement learning techniques in complex power system settings. The study concludes that reinforcement learning will be a highly robust and versatile solution to next-generation optimization of cyber grids with regard to data-based and autonomous, data-based grid management systems.