Robust Frequency Control in an Autonomous Microgrid: A Two-Stage Adaptive Fuzzy Approach

Highly intermittent power from renewable energy sources (RES) along with load and system perturbations in an autonomous microgrid (MG), results in large frequency fluctuations. Conventional controllers like PI controllers to be unable to provide acceptable performance over a wide range of operating conditions. To overcome this problem, present paper introduces a novel two-stage adaptive fuzzy logic based PI controller for frequency control of MG. In this proposed controller, particle swarm optimization (PSO) and grey wolf optimization (GWO) are used to optimize the membership functions (MFs) and rule base of fuzzy logic based PI controller. The proposed controller is examined on an MG test system, the robustness and performance of the proposed controller is tested in presence of different disturbance scenarios and parametric uncertainties. Finally, the superiority of the proposed controller is shown by comparing the results with various controllers available in literature like PSO tuned fuzzy logic based PI controller, fuzzy logic-based PI controller and also with the conventional PI controller.     

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.     

Frequency control of a wind-diesel system based on hybrid energy storage

Owing to the significant number of hybrid generation systems (HGSs) containing various energy sources, coordination between these sources plays a vital role in preserving frequency stability. In this paper, an adaptive coordination control strategy for renewable energy sources (RESs), an aqua electrolyzer (AE) for hydrogen production, and a fuel cell (FC)-based energy storage system (ESS) is proposed to enhance the frequency stability of an HGS. In the proposed system, the excess energy from RESs is used to power electrolysis via an AE for hydrogen energy storage in FCs. The proposed method is based on a proportional-integral (PI) controller, which is optimally designed using a grey wolf optimization (GWO) algorithm to estimate the surplus energy from RESs (i.e., a proportion of total power generation of RESs: Kn). The studied HGS contains various types of generation systems including a diesel generator, wind turbines, photovoltaic (PV) systems, AE with FCs, and ESSs (e.g., battery and flywheel). The proposed method varies Kn with varying frequency deviation values to obtain the best benefits from RESs, while damping the frequency fluctuations. The proposed method is validated by considering different loading conditions and comparing with other existing studies that consider Kn as a constant value. The simulation results demonstrate that the proposed method, which changes Kn value and subsequently stores the power extracted from the RESs in hydrogen energy storage according to frequency deviation changes, performs better than those that use constant Kn. The statistical analysis for frequency deviation of HGS with the proposed method has the best values and achieves large improvements for minimum, maximum, difference between maximum and minimum, mean, and standard deviation compared to the existing method.     

Impact of energy storage system on load frequency control for diverse sources of interconnected power system in deregulated power environment

This paper focuses Load Frequency Control (LFC) mechanism for multi-generating two areas interconnected power systems with energy storage system in a deregulated power environment. The two areas, demarcated as Area-I and Area-II, consist of thermal, hydro and gas power units. This paper also incorporates the economic load dispatch mechanism into the LFC for economical division of load during load deviation. Small signal stability analysis through participating factor has also been done to determine the oscillation state of the system, i.e., frequency deviation in both areas. Therefore, proper controller is required to reduce the oscillation of the system. The optimum value of the integral gain of the integral controller has to be selected to achieve the goal. Hence, Opposition-based Harmonic Search (OHS) technique is used for the optimization purpose. During major disturbance in the areas, primary and secondary controllers are not sufficient to reduce the frequency and tie-line power oscillation due to slow response of the governor mechanism. Therefore, energy storage system, i.e., Redox Flow Battery (RFB), is used for improvement of the dynamic response of the system which has very small time constant and quick response. The proposed control mechanism has been analyzed in a deregulated power environment with the help of different simulation case studies to find out improved dynamic performance over integral control strategies.     

基于H2和H∞鲁棒优化的双馈感应发电机的二次调频策略

研究了双馈感应发电机（DFIG）接入电力系统后的频率稳定问题。当多个DFIG参与系统调频时,若只采用下垂控制分配各个DFIG参与调频的容量,无法使系统频率偏差为0。为解决这一问题,在DFIG下垂控制基础上采用H2 /H∞控制方法设计辅助控制器,提升系统动态性能及鲁棒性。为提高系统频率对风速变化的韧性,在该辅助控制器中将求解H2和H∞性能指标问题转化为含线性矩阵不等式（LMI）约束的优化问题。最后,采用算例对所提控制器的有效性进行验证。仿真结果表明,当出现负荷扰动时该控制器可把系统频率偏差控制到0;当风速在短时间大幅跌落时,该控制器仍能维持系统频率在允许范围内。     

基于滑模控制的感应耦合电能传输系统输出电压控制研究

感应耦合电能传输系统通过非接触的方式将系统能量从电源侧传递至负载侧。由于该系统具有负载参数变化、系统结构复杂、工作频率高等特点,系统控制器面临着设计难的问题。提出了一种基于滑模控制理论的输出电压控制器,采用移相控制方法实现了对输出电压的控制。该控制方法有效降低了系统建模难度,同时提高了系统输出电压响应特性。通过简化系统结构,推导出滑模控制器的具体表达式,并给出了滑模控制系数的选取方法。通过建立IPT实验系统,在不同负载工作工况条件下进行实验验证。对比传统PI控制方法和所提出的滑模控制方法,实验结果表明滑模控制方法响应更快、对系统负载参数变化不敏感、鲁棒性及动态性能更好,能够较好控制IPT系统的输出电压。     

Grey wolf optimization for combined heat and power dispatch with cogeneration systems

The Combined Heat and Power Dispatch (CHPD) is an important optimization task in power system operation for allocating generation and heat outputs to the committed units. This paper presents a Grey Wolf Optimization (GWO) algorithm for CHPD problems. The effectiveness of the proposed method is validated by carrying out extensive tests on three different CHPD problems such as static economic dispatch, environmental-economic dispatch and dynamic economic dispatch. Valve-point effects, ramp-rate limits and spinning reserve constraint along with network loss are considered. Standard test systems containing 4, 7, 11 and 24 units are used for demonstration purpose. To validate the performance of the GWO, statistical measures like best, mean, worst, standard deviation, epsilon, iter and sol-iter over 50 independent runs are taken. The simulation experiments reveal that GWO performs better in terms of solution quality and consistency.     

基于MVO算法与改进目标函数的电力系统负荷频率控制

针对风电并网时的随机波动功率、负荷频率控制(load frequency control, LFC)系统参数变化所引起的电力系统频率稳定问题,提出了一种基于智能优化算法与改进目标函数的互联电网LFC系统最优PID控制器设计方法。首先,分析了基于PID控制的含风电互联电力系统LFC闭环模型。其次,在时间乘误差绝对值积分(integral of time multiplied absolute error, ITAE)性能指标的目标函数中考虑了区域控制器的输出信号偏差,对优化目标函数进行改进。采用性能优良的多元宇宙优化(multi-verse optimizer, MVO)算法先计算后验证的思路,寻优获得最优PID控制器参数。最后,以两区域4机组互联电力LFC系统为例,仿真验证了基于MVO算法结合改进目标函数所获得的PID控制器,比基于MVO算法所获得的PID控制器,对阶跃负荷扰动、随机负荷扰动、风电功率偏差扰动以及系统的参数变化,具有相对较好的鲁棒性能。并且,对控制器参数也具有相对较好的非脆弱性指标。     

基于遗传算法的有源电力滤波器滑模控制

为改善滑模控制方式有源电力滤波器在高频处抖动的缺陷,提出用遗传算法对控制器参数进行优化,提高了控制器性能。简要说明了三相三线有源电力滤波器的工作原理,根据补偿电流和指令电流的误差构建滑模变结构控制器。详细给出了用遗传算法对控制器参数的优化过程,得到了控制器参数的确切值。通过对传统PI控制方式和优化后滑模控制方式进行仿真实验,结果表明,所提出的滑模控制方式响应速度快,算法简单,较好地抑制抖动,鲁棒性强,达到了优化的目的。     

基于SOGWO的电力系统稳定器参数优化

电力系统稳定器(Power System Stabilizer, PSS)是抑制电力系统低频振荡的主要手段。提出选择反向运算灰狼优化(Selected Opposition-Based Grey Wolf Optimizer, SOGWO)算法对PSS进行参数优化。首先,选择典型的PSS实现类型,并设置优化过程的目标函数。其次,利用选择反向学习算法加快搜索速度,增强灰狼算法的全局搜索性能。最后,应用IEEE四机两区域系统模型验证所提方法的有效性。此外,分别对PSS参数进行PSO、GWO、SOGWO的100次优化,由统计出的阻尼比最大值、最小值、平均值以及标准差数据可知：三种优化算法均能较好地避免陷入局部最优并快速收敛,而SOGWO优化PSS参数的鲁棒性更好。     

Bat inspired algorithm based optimal design of model predictive load frequency control

Bat inspired algorithm (BIA) has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, BIA-based design of model predictive controllers (MPCs) is proposed for load frequency control (LFC) to enhance the damping of oscillations in power systems. The proposed model predictive load frequency controllers are termed as MPLFCs. Two-area hydro-thermal system, equipped with MPLFCs, is considered to accomplish this study. The suggested power system model considers generation rate constraint (GRC) and governor dead band (GDB). Time delays imposed to the power system by governor-turbine, thermodynamic process, and communication channels are accounted for as well. BIA is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed controller has been compared to those of the conventional PI controller based on integral square error (ISE) technique and the PI controller optimized by genetic algorithms (GA), in order to demonstrate the superior efficiency of the BIA-based MPLFCs. Simulation results emphasis on the better performance of the proposed MPLFCs compared to conventional and GA-based PI controllers over a wide range of operating conditions and system parameters uncertainties.     

Adaptive coordination control strategy of renewable energy sources, hydrogen production unit, and fuel cell for frequency regulation of a hybrid distributed power system

The ‘mismatch losses’ problem is commonly encountered in distributed photovoltaic (PV) power generation systems. It can directly reduce power generation. Hence, PV array reconfiguration techniques have become highly popular to minimize the mismatch losses. In this paper, a dynamical array reconfiguration method for Total-Cross-Ties (TCT) and Series–Parallel (SP) interconnected PV arrays is proposed. The method aims to improve the maximum power output generation of a distributed PV array in different mismatch conditions through a set of inverters and a switching matrix that is controlled by a dynamic and scalable reconfiguration optimization algorithm. The structures of the switching matrix for both TCT-based and SP-based PV arrays are designed to enable flexible alteration of the electrical connections between PV strings and inverters. Also, the proposed reconfiguration solution is scalable, because the size of the switching matrix deployed in the proposed solution is only determined by the numbers of the PV strings and the inverters, and is not related to the number of PV modules in a string. The performance of the proposed method is assessed for PV arrays with both TCT and SP interconnections in different mismatch conditions, including different partial shading and random PV module failure. The average optimization time for TCT and SP interconnected PV arrays is 0.02 and 3 s, respectively. The effectiveness of the proposed dynamical reconfiguration is confirmed, with the average maximum power generation improved by 8.56% for the TCT-based PV array and 6.43% for the SP-based PV array compared to a fixed topology scheme.     

Load frequency control of an autonomous hybrid power system by quasi-oppositional harmony search algorithm

This paper deals with a novel quasi-oppositional harmony search algorithm (QOHSA) based design of load frequency controller for an autonomous hybrid power system model (HPSM) consisting of multiple power generating units and energy storage units. QOHSA is a novel improved version of music inspired harmony search algorithm for obtaining the best solution vectors and faster convergence rate. In this paper, the efficacy of the proposed QOHSA is adjudged for optimized load frequency control (LFC) of an autonomous HPSM. The studied HPSM consists of renewable/non-renewable energy based generating units such as wind turbine generator, solar photovoltaic, solar thermal power generator, diesel engine generator, fuel cell with aqua-electrolyzer while energy storage units consists of battery energy storage system, flywheel energy storage system and ultra-capacitor. Gains of the conventional controllers such as integral (I) controller, proportional–integral (PI) controller and proportional–integral–derivative (PID) controller (installed as frequency controller one at a time in the proposed HPSM) is optimized using QOHSA to mitigate any frequency deviation owing to sudden generation/load change. In order to corroborate the efficacy of QOHSA, performance of QOHSA to design optimal LFC is compared with that of other well-established technique such as teaching learning based optimization algorithm (TLBOA). The comparative performances of the HPSM under the action of QOHSA/TLBOA based optimized conventional controllers (I or PI or PID) are investigated and compared in the present work. It is found that the QOHSA tuned frequency controllers improves the overall dynamic response in terms of settling time, overshoot and undershoot in the profile of frequency deviation and power deviation of the studied HPSM.     

A new smart charging method for EVs for frequency control of smart grid

Nowadays, due to the shortage of fossil fuels on the one hand and their high prices on the other hand, using electric vehicles (EVs) has been increased. Charging of EVs has imposed new loads on power systems. These new and major loads have faced the frequency control and stability of power systems with new challenges. One way to deal with this new challenge is smart charging of EVs. In this method, grid condition is a key parameter that affects the charging of EV. In other words, in smart charging method, charging is performed with respect to power system parameters such as frequency. In this paper, a smart charging method based on fuzzy controller is proposed, in which charging process is performed with respect to the frequency deviation of grid and state of charge (SOC) of EV battery. To evaluate the performance of the proposed controller in control of grid frequency, IEEE 39-bus system in the presence of renewable energy sources is considered as test system. In order to the frequency analysis, this system is converted into a three-area system and, for each area, several EV categories with different numbers of EVs, battery capacity, start time of charging, and initial SOC are supposed. Moreover performance of proposed method is compared with an optimized PI controller in terms of frequency control. To investigate performance of proposed method in charging of EVs, a two area system is assumed and charging of EVs is verified by applying step loads to both areas. Simulations are carried out in MATLAB/SIMULINK environment. Results of the simulations reveal the good performance of the proposed controller in terms of frequency control of grid and charging of EVs.     

Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems

This paper presents a new population based parameter free optimization algorithm as teaching learning based optimization (TLBO) and its application to automatic load frequency control (ALFC) of multi-source power system having thermal, hydro and gas power plants. The proposed method is based on the effect of the influence of teacher on the output of learners and the learners can enhance their knowledge by interactions among themselves in a class. In this extensive study, the algorithm is applied in multi area and multi-source realistic power system without and with DC link between two areas in order to tune the PID controller which is used for automatic generation control (AGC). The potential and effectiveness of the proposed algorithm is compared with that of differential evolution algorithm (DE) and optimal output feedback controller tuning performance for the same power systems. The dynamic performance of proposed controller is investigated by different cost functions like integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE) and integral of time multiplied absolute error (ITAE) and the robustness of the optimized controller is verified by its response toward changing in load and system parameters. It is found that the dynamic performance of the proposed controller is better than that of recently published DE optimized controller and optimal output feedback controller and also the proposed system is more robust and stable to wide changes in system loading, parameters, size and locations of step load perturbation and different cost functions.     

基于改进功率环的微电网对等控制策略研究

基于传统下垂控制方法存在的不足,同时考虑减小微电网依赖于通信系统,使负荷和分布式电源能够即插即用,提出一种基于改进功率环的微电网对等控制策略。传统的下垂控制方法会造成系统频率和交流母线电压的偏差,针对该问题,引入电压补偿环节和频率补偿环节,构建改进的功率环反馈控制器。利用该控制策略对由2台同容量分布式电源构成的微电网进行仿真分析,并和采用传统下垂控制方法所得结果进行比较,此外,在并网/孤网切换模式和负荷投切模式下,分析该控制策略下的微电网运行特性,仿真结果表明了基于改进功率环的微电网对等控制策略能够有效降低系统频率和交流母线电压的偏差。     

Design of biogeography optimization based dual mode gain scheduling of fractional order PI load frequency controllers for multi source interconnected power systems

This paper presents the effect on application of biogeography optimization (BBODMFOPI) based dual mode gain scheduling of fractional order proportional integral controllers for load frequency control (LFC) of a multi source multi area interconnected power systems. This controller has three parameters to be tuned. Thus, it provided one more degree of freedom in comparison with the conventional proportional integral (PI) controller. For proper tuning of the controller parameters, Biogeography-Based Optimization (BBO) was applied. BBO is a novel evolutionary algorithm which involves the methodology of making the system effectively by using mathematical techniques. The dual mode concept is also incorporated in this work, because it can improve the system performance. In this work, simulation investigations were taken out on a two-area power system with different generating units. The simulation results show that the proposed biogeography optimization based dual mode gain scheduling of fractional order PI controllers, provide better transient as well as steady state response. It is also found that the proposed controller is less sensitive to the changes in system parameters and robust under different operating condition of the power systems.     

初值优化的电力系统戴维南等值跟踪算法研究

在电力系统分析计算时,需要对电网进行等值处理。戴维南等值是常用的等值手段,它能够反应系统运行状况并且具有模型清晰、简单快速的特点。针对现有方法求取戴维南等值参数时,存在参数漂移和参数跟踪问题,提出基于初值优化的偏差校正跟踪算法,利用相邻运行点的戴维南等值电压幅值不变、相角变化,进而获取等值跟踪初值,采用偏差校正方式进行跟踪电网运行点的变化。初值优化算法与偏差校正方法相结合能够快速跟踪等值参数,又能够避免参数数值计算不稳定。通过跟踪性能测试和四机两区域系统仿真算例,证明了所提算法的可靠性、准确性与有效性,能够快速跟踪实际电网运行方式。     

基于AHP / GA的储能装置参与电网调频控制器参数优化

针对电力系统调频过程中火电机组响应速度慢、不适合参与短周期调频的问题,提出一种基于层次分析法(AHP)和遗传算法(GA)相结合的优化算法用于电池储能控制器参数优化,使控制器能更好地控制电池储能装置并参与调频。通过AHP确定最大偏差幅值、稳态偏差、调节时间之间的权重大小,构造出一个GA适应度函数,再由GA进行寻优计算得到最佳的控制器参数。借助MATLAB/Simulink对储能装置参与电网调频的两区域系统进行仿真。仿真结果表明,优化后的控制器可以有效地控制储能装置并辅助AGC进行调频,能够及时响应扰动,相较于传统以时间绝对偏差乘积积分(ITAE)准则作为适应度函数的参数优化效果更好。     

含抽水蓄能电站的互联电网负荷频率自抗扰优化控制研究

针对发电能源结构的多元化发展给互联电网负荷频率的稳定性控制带来较大的挑战,建立含抽水蓄能电站的两区域互联电网多元混合发电的负荷频率控制模型,提出一种基于粒子群优化算法的负荷频率线性自抗扰控制器参数整定优化策略,通过粒子群算法的迭代寻优计算获得最优的线性自抗扰控制器参数。考虑互联电网各区域发生不同的负荷扰动,在抽水蓄能电站的抽水和发电2种工况下,对所提出的控制方法进行系统仿真。仿真结果表明,通过粒子群算法优化的负荷频率线性自抗扰控制器,与传统PI控制器对比,前者具有更强的抗扰动能力和适应性,系统动态稳定性更好。