Optimal Active Power Control Research Articles

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

Optimal combined wake and active power control of large‐scale wind farm considering available power

AbstractThe high‐order nonlinear complex characteristics of traditional static wake model (SWM) restrict the fast optimization of wake effect. Therefore, this paper introduces a dynamic wake model (DWM) to describe the time‐varying wake characteristics of wind turbines (WTs) with low computational cost. The traditional SWM is linearized to derive the wake wind speed sensitivity coefficients, which represents the sensitivity of wake wind speed deficit with respect to the active power reference. Considering the natural propagation characteristics of wake effect, a wake delay function is added to realize the future wind speed prediction of different locations of the wind farm. And a joint control strategy of wake and active power based on model predictive control (MPC) is proposed to optimize the power contribution of each WT to minimize wake effects, and maximize total available power. Thus, the increased available power is able to satisfy the power demand from Transmission System Operator (TSO) for ancillary services and enhance power support capability. The control performance of the proposed control strategy is evaluated by simulations under constant and varying incoming wind speed.

Distributed cooperative fault tolerant optimal active power control in AC microgrid

In this paper, a new distributed and cooperative fault tolerant control is proposed for the double-function optimal active power control (APC) of distributed generators (DG) in an islanded AC microgrid. Double-function optimal APC consists of the economic dispatch which calculates the optimal active power set-points and coordinates control modules to receive and track the set-points. In the proposed control method, if there is a faulty DG whose capability to satisfy its optimum active power set-point is restricted due to a totally or partially DG failure, the task of reaching the global control goal is re-distributed among the fault-free DGs through designing dynamic feedback set-points. In addition, no central coordinator is considered for the MG, and the new set-points are calculated locally by the fault-free DGs through message exchange over the communication network. Redistribution is performed without requiring any coordination unit and only with the cooperation of DGs on redistributed set-points through local exchange information. The capability of the proposed method is evaluated by considering a microgrid with multiple DGs and analyzing its operational performance under a variety of fault scenarios, as well as providing a qualitative and quantitative comparison with existing literature to demonstrate its efficacy.

Optimal Power Control in Distributed Generation Units in an Islanded Microgrid

This article studies the effect of new optimization techniques with the aim of finding the best solution using a fuzzy approach for operating an independent microgrid. The power flow technique is used to model and solve the problem of optimal active and reactive power control of distributed generation units (DGs) in an island microgrid (MG), while taking into account the improvement of the voltage profile and the voltage stability index by regulating the droop characteristics of DG sources. The suggested algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS), Hybrid HS-GA, and Teaching-Learning Based Optimization, solve the optimization issue (TLBO). The numerical results of optimization algorithms are compared in terms of best solution, number of iterations, and convergence rate. Regarding multi-objective nature of the problem, the non-dominated sorting method is used to obtain the Pareto front for all algorithms. Finally, the best solution is selected from Pareto solutions using proposed fuzzy approach. Simulation results on the standard IEEE 33-bus network in MATLAB show that the TLBO algorithm has higher efficiency, and faster convergence rate compared to other methods in this study.

Optimal active power control of wind turbines in grid-forming mode

Together with the daily variation of load demand in power grids, the continuous integration of offshore and onshore wind farms to feed the grid with green energy has recently led to a severe frequency stability issue due to the extra variation in the generation side. Hence, wind turbines (WTs), among other renewable energy units are required to regulate their output power to mitigate the fluctuation in generated power and support frequency stability. In this paper, model predictive control (MPC) is implemented in an adaptive way to operate WT in de-loading mode under wind disturbances. In addition, to get comprehensive operation of WTs and meet grid code requirements; we present a new grid-forming controller in the WT rotor side converter to emulate the inherent synchronizing and load sharing property of conventional generators. The limitations of rotor speed and rotor side converter are considered in the proposed control approach.

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

Distributed optimal active and reactive power control for wind farms based on ADMM

In this paper, a distributed optimal active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for wind farms (WFs). The WFs operate in a distributed manner to minimize the network power loss, voltage deviations of buses from the rated voltage, and active power output deviations of WTs from their proportional distribution (PD)-based power reference. An optimization problem is formulated as a quadratic programming (QP) problem by using the linearized DistFlow model. The ADMM-based solution is used to decompose the centralized optimization problem to several subproblems which are solved in individual local controllers with exchanged information from their practical neighbor controllers. Compared with existing distributed/hierarchical optimal control, the impacts of the active power injection from the WTs are taken into consideration and optimized while meeting the dispatch command from the transmission system operator (TSO). The ADMM-based distributed solution eliminates the requirement of a central unit. Compared with conventional centralized optimal control, the scalability of the WFs is improved. A WF consisting of 20 WTs is simulated in MATLAB/Simulink to test the control effectiveness of the proposed DARPC strategy.

Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm Based on Alternating Direction Method of Multipliers

An ADMM-based hierarchical optimal active power control (HOAPC) scheme is proposed for synthetic inertial response of large-scale wind farms. The proposed scheme coordinates the active power outputs of wind turbines (WTs) inside the wind farm during the synthetic inertial response process, aiming to minimize the differences in the rotor speed of the WTs and the wind energy loss caused by the decrease in the rotor speed of the WTs. The optimal control is formulated based on the model predictive control (MPC) according to the local WT wind conditions. A hierarchical solution method based on the alternating direction method of multipliers (ADMM) is developed to solve the MPC-based optimization problem in a fast way without the loss of optimality. With the HOAPC, the wind farm controller guarantees the rotor speed stability of WTs inside the wind farm and reduces the wind energy losses during the synthetic inertial response process. Moreover, the optimization problem is decomposed to several optimization subproblems solved in parallel. As such, the computation burden of the wind farm central controller is significantly reduced, and the protection of the information privacy of the wind farm is improved. A wind farm with 100 WTs was used to validate the proposed HOAPC scheme.

Event-Triggered Optimal Active Power Control in Islanded Microgrid With Variable Demand and Time-Varying Communication Topology

For islanded microgrids, traditional droop-based control leads to frequency deviation and low economic efficiency. To resolve this issue, a novel event-triggered optimal active power control strategy, which achieves frequency regulation and economic operation in a fully distributed way, is proposed in this paper. Frequency deviations are monitored and used to trigger the load estimation algorithm to track changes in power demand. Based on estimated demand values, a distributed economic dispatch (ED) algorithm, which only requires that the communication topology satisfies the jointly connected condition, is proposed to calculate the optimal generation references for the generators. The ED algorithm has the finite-time convergence property, which is rigorously proved by Lyapunov theory, LaSalle’s invariance principle and homogeneous property. Based on optimal generation references, the parameters of droop controller are reset in real time, which makes microgrid achieve frequency restoration and economic operation simultaneously. Finally, several case studies are presented to illustrate the effectiveness of the proposed control strategy.

Optimal active power control based on MPC for DFIG-based wind farm equipped with distributed energy storage systems

An optimal active power control scheme based on model predictive control (MPC) is proposed for a doubly-fed induction generator (DFIG)-based wind farm equipped with distributed energy storage systems (ESSs). A two-stage optimal control scheme is proposed. In the first stage, the power reference of each WT and the total power command of ESSs are generated, aiming to reduce the fatigue load of WTs by minimizing variations of thrust force and shaft torque. In the second stage, the charge/discharge power of ESSs are optimized to achieve the fair power sharing and maximize the capacity margin. An MPC based optimization problem is formulated for the constrained multiple input and multiple output (MIMO) wind farm system. The dynamics of converters and WTs are taken into account by the MPC. With the proposed control scheme, the active power references are optimized between WTs and ESSs according to their local wind conditions. Fatigue loads of WTs are reduced efficiently by coordinating the DFIG-based WTs and distributed ESSs. A wind farm with 10 DFIG-based WTs was used to validate the control performance of the proposed optimal active power control scheme.

Ensemble learning for optimal active power control of distributed energy resources and thermostatically controlled loads in an islanded microgrid

To achieve an effective coordination between the secondary control and the tertiary control of load frequency control (LFC), a new optimal active power control (OAPC) is constructed for real-timely changing the operating points of distributed energy resources (DERs) and thermostatically controlled loads (TCLs) in an islanded microgrid. A large number of TCLs are integrated as a load aggregator (LA) for participating the secondary control of LFC, which can enhance the dynamic response performance due to their much faster response speeds compared with that of distributed generators. Since OAPC is a nonsmooth and nonlinear optimization with a quite short implementation period, a novel model-free ensemble learning (EL) is proposed to rapidly obtain a high-quality optimal solution for it. EL based OAPC is composed of multiple sub-optimizers and a learning concentrator, where each sub-optimizer is responsible for providing the exploitation and exploration samples to the learning concentrator, while the reinforcement learning based concentrator is mainly used for knowledge learning and knowledge transfer. Case studies are thoroughly carried out to verify the performance of EL based OAPC in an islanded microgrid with 12 DERs and 900 TCLs.

Network Partition-Based Zonal Voltage Control for Distribution Networks With Distributed PV Systems

As the penetration level of distributed photovoltaic (PV) systems keeps increasing in distribution networks, overvoltage due to reverse power flow is an urgent issue to be addressed. This paper proposes a voltage regulation method by utilizing the voltage control capability of PV inverters. A novel network partition approach based on a community detection algorithm is presented to realize zonal voltage control in a shorter control response time using the minimum amount of reactive power compensation and active power curtailment. An improved modularity index that considers local reactive power balance is introduced to partition a distribution network into several clusters/communities with PVs based on the node voltage sensitivity analysis. An optimal reactive and active power control strategy is proposed for voltage control in each cluster. The voltage management of the overall system can be achieved by controlling each cluster separately. The proposed approach is applied to the voltage control of a practical 10 kV, 37-node feeder. Case studies on the real distribution network and a modified IEEE 123-node system are carried out to verify the feasibility and effectiveness of the proposed method.

Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization

The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3 kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

This research presents a novel approach for optimal active and reactive power control using permanent magnet synchronous generator with fully rated converter (PMSG-FRC). The power output variations and wind energy intermittent performance create significant challenges for power system integration, operation, and control. A fast and reliable sensorless optimal wind speed (WS) control system-based extreme learning machines and complete ensemble empirical mode decomposition with adaptive noise is developed. Also, an approximate entropy-based complexity measure is used for online WS estimation. The fuzzy controller system is used for calculating the optimal wind turbine (WT) speed, power, and torque, and then adjusts WT by the optimal speed value. Moreover, the wind farm (WF) active and reactive power controller is utilised for obtaining the active and reactive power reference values for every region/feeder. The system is applied and integrated to power system grid by using IEEE standard models, and the speed of WT is adjusted by the required active power level and the estimated WS value to increase the maximum power capture from WF. The simulations results using different cases studies and power system voltage levels have confirmed the validity and accuracy of the proposed control algorithms for practical applications and demonstrated excellent performance for power system integration.

Optimal Control to Increase Energy Production of Wind Farm Considering Wake Effect and Lifetime Estimation

In a wind farm, the upstream wind turbine may cause power loss to the downstream wind turbines due to the wake effect. Meanwhile, the energy production is determined by the power generation and the lifetime of the wind turbine. In this paper, an optimal active power control method is proposed to maximize the energy production of wind farms by considering the wake effect and the lifetime of wind turbine. It starts with the analysis of the pitch angle curve and active power curve seen from the Maximum Power Point Tracking (MPPT) of individual wind turbines. Taking the wake effect into account, the pitch angle curve and active power curve are optimized with the aim of Maximum Power Generation (MPG) of the wind farm. Afterwards, considering the lifetime of wind turbines, a comparison is offered between the MPPT method and the MPG method for energy production using a simplified two-turbine wind farm as an example. Due to the small range of the effective wake area, it is found that the energy production is almost the same. Finally, the pitch angle curve and active power curve are optimized according to the Maximum Energy Production (MEP) of a wind farm. Upon considering and contrasting the MPPT method and the MEP method, it can be seen that the energy production of wind farms can be increased even in the case of there not being an effective wake area.

Optimal active and reactive power control of wind turbine driven DFIG using TLBO algorithm and artificial neural networks

This paper investigates the optimal active and reactive power control capabilities for typical wind turbine (WT) driven doubly fed induction generator (DFIG). The main objective is to determine the optimal rotor voltage to extract certain active and reactive power from the DFIG over wide ranges of wind speed. Teaching learning-based optimisation (TLBO) algorithm is a new heuristic optimisation technique, used to obtain the optimum rotor voltages to achieve reference active and reactive powers overall operating points. Artificial neural network (ANN) controller is used as an adaptive controller to predict the value of rotor voltage for all operating points. The ideal power curve of a 2 MW wind turbine has been estimated to design the active power controller. The stator reactive power control capability with the range of ±1.6 MVAR is developed. With the proposed control strategy, the DFIG-based wind farm provides maximum power point tracking (MPPT), fully active and reactive powers control. For all operated wind speeds, the adaptive proposed controller develops useful network support compared to the conventional DFIG-based wind farm. The proposed system is developed in MATLAB/Simulink environment.

Optimal active and reactive power control of wind turbine driven DFIG using TLBO algorithm and artificial neural networks

Optimal active and reactive power control of wind turbine driven DFIG using TLBO algorithm and artificial neural networks

Optimal active power control of a wind farm equipped with energy storage system based on distributed model predictive control

This study presents the distributed model predictive control (D-MPC) of a wind farm equipped with fast and short-term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D-MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS.

Distributed Optimal Active Power Control of Multiple Generation Systems

A novel coordinated power controller design framework is proposed to optimize the active power output of multiple generators in a distributed network. Each bus in the distributed generation systems includes two function modules: a distributed economic dispatch (DED) module and a cooperative control (CC) module. By virtue of the distributed consensus theory, a DED algorithm is proposed and utilized to calculate the optimal active power generation references for each generator. The CC module receives and tracks the active power generation references such that the generation–demand balance is guaranteed at minimum operating cost while satisfying all generation constraints. The distributed control and management strategies enhance the redundancy and the plug-and-play capability in microgrids. Optimal properties and convergence rates of the proposed distributed algorithms are strictly proved. Simulation studies further demonstrate the effectiveness of the proposed approach.

Distributed Model Predictive Control of a Wind Farm for Optimal Active Power ControlPart II: Implementation With Clustering-Based Piece-Wise Affine Wind Turbine Model

This paper presents distributed model predictive control (D-MPC) of a wind farm for optimal active power control using the fast gradient method via dual decomposition. The objectives of the D-MPC control of the wind farm are power reference tracking from the system operator and wind turbine mechanical load minimization. The optimization of the active power control of the wind farm is distributed to the local wind turbine controllers. The D-MPC developed was implemented using the clustering-based piece-wise affine wind turbine model. With the fast gradient method, the convergence rate of the D-MPC has been significantly improved, which reduces the iteration numbers. Accordingly, the communication burden is reduced. A wind farm with ten wind turbines was used as the test system. Case studies were conducted and analyzed, which include the operation of the wind farm with the D-MPC under low and high wind conditions, and the dynamic performance with a wind turbine out of service. The robustness of the D-MPC to errors and uncertainties was tested by case studies with consideration of the errors of system parameters.