An Optimal Control Scheme for Load Bus Voltage Regulation and Reactive Power-Sharing in an Islanded Microgrid

This paper presents an optimal control scheme for an islanded microgrid (MG), which performs reactive power-sharing and voltage regulation. Two-fold objectives are achieved, i.e., the load estimation strategy, firstly, approximates the MG’s impedance and transmits this information through a communication link. Based on approximated impedance information, an optimal regulator is then constructed to send optimal control commands to respective local power controllers of each distributed generation unit. An optimal regulator is a constraints optimized problem, mainly responsible to restore the buses’ voltage magnitudes and realize power-sharing proportionally. The important aspect of this control approach is that the voltage magnitude information is only required to be transferred to each inverter’s controller. In parallel, a secondary control layer for frequency restoration is implemented to minimize the system frequency deviations. The MATLAB/Simulink and experimental results obtained under load disturbances show the effectiveness for optimizing the voltage and power. Modeling and analysis are also verified through stability analysis using system-wide mathematical small-signal models.

Optimal Distribution Coefficients of Energy Resources in Frequency Stability of Hybrid Microgrids Connected to the Power System

The continuous stability of hybrid microgrids (MGs) has been recently proposed as a critical topic, due to the ever-increasing growth of renewable energy sources (RESs) in low-inertia power systems. However, the stochastic and intermittent nature of RESs poses serious challenges for the stability and frequency regulation of MGs. In this regard, frequency control ancillary services (FCAS) can be introduced to alleviate the transient effects during substantial variations in the operating point and the separation from main power grids. In this paper, an efficient scheme is introduced to create a coordination among distributed energy resources (DERs), including combined heat and power, diesel engine generator, wind turbine generators, and photovoltaic panels. In this scheme, the frequency regulation signal is assigned to DERs based on several distribution coefficients, which are calculated through conducting a multi-objective optimization problem in the MATLAB environment. A meta-heuristic approach, known as the artificial bee colony algorithm, is deployed to determine optimal solutions. To prove the efficiency of the proposed scheme, the design is implemented on a hybrid MG. Various operational conditions which render the system prone to experience frequency fluctuation, including switching operation, load disturbance, and reduction in the total inertia of hybrid microgrids, are studied in PSCAD software. Simulation results demonstrate that this optimal control scheme can yield a more satisfactory performance in the presence of grid-following and grid-forming resources during different operational conditions.

MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application.

Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

<div>The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown</div><div>and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic</div><div>two-wheel robot where the obtained results demonstrate its</div><div>effectiveness.</div>

Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

<div>The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown</div><div>and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic</div><div>two-wheel robot where the obtained results demonstrate its</div><div>effectiveness.</div>

Robust control of interlinking converter using PSO and ABC algorithms

Objective: In this paper, an optimal control scheme for the Interlinking Converter (IC) system is achieved by the proper regulation of its gate switching functions through appropriate optimal feedback controller design. Methods: Proportional-Integral-Derivative (PID), Fractional Order Proportional-Integral-Derivative (FOPID) and Hinfinity loop shaping controller have been designed for the two-fold control objective of simultaneous improvement in system robustness and reduced tracking error using parameter tuning via Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms. Results: The controller parameters are obtained by optimization algorithms. The comparative analysis of the controller performance is carried out through simulation in MATLAB platform to validate the effectiveness in the controller design under various changing situations. Conclusion: The optimized controller parameters obtained through ABC algorithm are better than that obtained through PSO algorithm in terms of both objective function values and execution time. The resultant robust control strategy for IC system obtained through H-infinity loop shaping controller provides reduced tracking error and improved stability as compared to PID and FOPID controller, as proved by the simulation results.