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Two parameters, voltage and frequency, are required for smooth delivery of quality power to consumer. Therefore, in this paper enhancement of frequency and voltage stability of multi microgrid system with a proposed hybrid fuzzy logic controller with two-degree of freedom tilt-integral derivative with filter (2DOFTIDFFUZZY) are analyzed. A novel algorithm known as artificial electric field algorithm (AEFA) technique is employed to obtain optimal controller parameters. Firstly, a two-area system widely used in literature is considered for proper comparison. Efficacy of 2DOFTIDFFUZZY controller is confirmed by comparing it with different controllers for the same system. Further, a multi microgrid system is considered for unified voltage-frequency control. Automatic Voltage Regulator (AVR) effect states a substantial improvement in system dynamics with the proposed controller. Under different unfavorable conditions like source and load variation, dynamic system performances are studied to enlighten robustness of proposed controller. Simulation results elucidate that proposed 2DOFTIDFFUZZY controller optimized with AEFA technique can achieve better system stability in case of any vulnerability. [ABSTRACT FROM AUTHOR]

In this work, two area systems of renewable energy sources (RES), such as tidal, wind and solar, are interconnected with the deep residual network (DRN) controller in a microgrid. The DRN controller is used to control load frequency, which is optimally designed by green anaconda optimization (GAO) algorithm. The GAO algorithm is used to optimally select the DRN layers that improve the control of a two-area interconnected power system. GAO algorithm is utilized for the parameter section of a DRN controller in the interconnected power system with two areas, which provides optimal solutions based on the natural behaviour of green anacondas. The reason for choosing the GAO method is that it has high capability in the exploration and exploitation phase, thus creating effective tuning of controllers. The DRN controller is chosen because it has fewer additional parameters and increases the accuracy level, thus enhancing control of the two areas. The proposed model is implemented in MATLAB tool, and the results are compared with the existing methods of proportional-integral derivative-whale optimization algorithm (PID-WOA), PID-Harris hawks optimization (PID-HHO) and two degrees of freedom (2DOF)-PID (2DOF-PID). The comparison results indicate that the proposed GAO-based DRN controller achieves 98% efficiency and 95% frequency deviation, thus providing high-performance over counterpart methods. [ABSTRACT FROM AUTHOR]

This paper thoroughly analyses energy, economic and environmental (3E) performance of using different battery (BAT) energy storage system like lead acid battery (LAB), lithium-ion battery (LIB), vanadium redox flow (VRF) battery and mechanical energy storage (MES) like flywheel and pumped hydro storage (PHS) using three different dispatch strategies (DS) such as cycle charging (CC), load following (LF) and generator order (GO), respectively. Andaman and Nicobar(A&N) Island of India have no main grid. Hybrid Optimization Model for Electric Renewable (HOMER) Pro software is used to study feasibility of power generation using various sources. Study incorporating tidal plant with wind turbine and Bio-DG in micro grid is considered first time for an Indian scenario as per authors' knowledge with application of various DS and energy storage technologies. The superiority of LIB over LAB and MES is proved in this study. Wind/Bio-DG /tidal/LIB have relatively least net present cost(NPC), cost of energy (COE) of $1.30M and $0.263, respectively among five energy storage devices using CC dispatch strategy compared to other two strategies. It is investigated that LIB uses only 250 units of batteries, which is 46% less than total utilized LAB units for catering same load. In terms of expected lifespan, energy losses, energy production and state of charge (SOC), performance of LIB are superior to LAB. SWOT and sensitivity analysis have been performed considering fuel price and wind speed. Different techno economic indicators like cost summary, cash flow, fuel summary, economics, electrical parameters, renewable fraction (RF), return of investment (ROI), lowest simple payback period, internal rate of return (IRR), pollutant emission are considered and compared with each other for LAB and LIB combinations. [ABSTRACT FROM AUTHOR]

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF1, ΔF2, and ΔPTie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

The fuel cell is vital in electrical distribution networks as a distributed generation in today's world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies' transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer's side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a comprehensive analytical study of conventional proportional‐integral‐derivative (PID) controller and fuzzy logic controller in a hybrid micro‐grid system (HMGS) for frequency stabilization. Conventional PID and fuzzy logic controllers are tuned by the competition over resources (COR) algorithm. The performance of proposed COR based fuzzy tilt‐integral‐derivative with filter (FTIDF) method is demonstrated on an interconnected HMGS. HMGS completes with variety of energy storage systems and different renewable generation sources. The system analysis is performed under number of scenarios. The scenarios include load fluctuations, variations in wind speed, and sun irradiance. The supremacy of FTIDF controller is established by comparing the results with CRO tuned conventional PID with filter (PIDF), fuzzy PIDF and fuzzy TIDF controllers. The effectiveness of the proposed system is investigated using simulation studies, which are performed using MATLAB/SIMULINK software. [ABSTRACT FROM AUTHOR]

A three unequal area interconnected power system under deregulated environment having multi-sources of generation is considered. The first area consists of solar thermal and thermal unit, the second area consists of thermal and hydro unit, and the third area contains thermal and gas units as generating units with proper generation rate constraint (GRC). Cascaded proportional integral-proportional derivative with filter coefficient (CPIPDF) is employed as a secondary controller. A recent meta-heuristic algorithm called moth flame optimization (MFO) is considered to optimize the controller parameters. System performances are compared with the classical controllers like proportional-integral (PI), proportional-integral-derivative (PID), proportional-integral-derivative with filter coefficient (PIDF) and cascaded proportional-integral-proportional-derivative (CPIPD) controller. MFO tuned CPIPDF controller performs better than all other controllers considered in this paper for different electrical transactions. Robustness of the optimum gains of CPIPDF controller obtained at poolco transaction condition is evaluated. In addition, system performance is studied with variation in disco participation matrix (DPM). [ABSTRACT FROM AUTHOR]

Summary: In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional‐integral (PI)‐proportional‐derivative (PD) controller with filter (PI‐PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two‐area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO‐tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI‐PDF controller performs better than PI controller does. Then, the study is extended to a three‐area interconnected hybrid system with proper generation rate constraint. Area‐1 consists of solar thermal‐thermal unit; area‐2 consists of thermal‐hydro unit and area‐3 thermal‐gas unit as generating sources. Performance of PI‐PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI‐PD controller without filter. Result analysis divulges that MFO‐tuned PI‐PDF controller performs better than all other controllers considered in this article. Robustness of the PI‐PDF controller is evaluated using parameter variations and random load variation. [ABSTRACT FROM AUTHOR]

A new evolutionary optimisation technique, namely hybrid flower pollination and pattern search (hFPA-PS) technique is developed to tune output feedback sliding mode controller (OFSMC)for a multi-sources interconnected deregulated automatic generation control (AGC) system. The developed algorithm is justified considering popular benchmark functions. The developed algorithm is applied first time for the AGC system, to tune parameters of different classical controllers. The supremacy of recommended approach is established by competing the dynamic system performances with FPA, fruit fly optimization (FOA) and particle swarm optimization (PSO) technique for different power scenario for different classical controllers. Dynamic response of the system with hFPA-PS optimized OFSMC is established to be better than the classical controllers. The dynamic response of the system is analysed in the presence of generation rate constraint (GRC), governor dead band (GDB) and time delay. Additionally, the supremacy of recommended approach is analysed with sensitivity analysis, under uncertainty of system parameters. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made for comprehensive study of proportional-integral-double-derivative(PIDD) controller to solve automatic generation control (AGC) problem by applying moth flame optimization algorithm (MFOA). At first two unequal areas of thermal system is considered and the gains of PID/IDD/PIDD controller are optimized using MFOA technique. Simulation study depicted that MFOA-optimized PIDD controller provides better system performances considering settling time, overshoot and undershoot of area frequency and deviations in tie-line power as compared to other optimization techniques considered in this paper. The generation rate constraint (GRC) is included for two-area thermal system and dynamic stability of the system is investigated and compared with recent competitive algorithms. Further, the study is extended to non-linear AGC system with diverse source of generation. Generating unit in each control area consists of hydro, thermal and nuclear generation. Sensitivity analysis reveals that the MFOA-optimized PIDD controller parameter obtained at nominal condition need not necessary to change for wide changes in system parameters and with variation in random step load perturbation. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design of two‐degree‐of‐freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two‐area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional‐integral, proportional‐integral‐derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system. [ABSTRACT FROM AUTHOR]

Summary: A new algorithm called moth‐flame optimization (MFO) algorithm is proposed to optimize a dual‐mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2‐area nonreheat system is considered. The optimum gains of DMC and proportional‐integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6‐unit hybrid‐sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching‐learning–based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters. [ABSTRACT FROM AUTHOR]

A new swarm optimisation algorithm, that is, fruit fly optimisation algorithm is proposed to tuned several classical controllers for multi-area multi-source interconnected power system under deregulated environment. In deregulated environment, for multi-source combination of reheat thermal, hydro and nuclear generating units in each control area with appropriate generation rate constraint and AC/DC link. The performance of several controller such as integral (I), proportional-integral, proportional-integral-derivative, integral-double derivative (IDD) and proportional-integral-double derivative (PIDD) are compared under different power system scenario and it is found that PIDD controller performs better than other controllers. Then sensitivity analysis is performed with system parameter variation from their nominal values. Sensitivity analysis reveals that optimum gains of PIDD controller need not be reset for wide variation in system parameters. [ABSTRACT FROM AUTHOR]

Summary: In this work, a novel optimization technique named quasi‐oppositional whale optimization algorithm (QOWOA) is employed for the LFC problem. In this study to improve the convergence speed, accuracy, and reliability of WOA, WOA is integrated with quasi‐oppositional–based learning theory and applied first time for the LFC problem. To show the efficacy of the QOWOA technique, at first, it is applied to an interconnected two‐area system with governor dead band nonlinearity then expanded to a realistic three‐area hybrid power system, and another system with two‐area four‐unit hydro‐thermal system is considered. Proportional‐integral and dual‐mode PI controllers are considered as secondary controller for system analysis. The optimization supremacy of the QOWOA technique is validated contrasting the outcomes with original WOA, moth flame optimization algorithm, other recent competitive algorithms, and fuzzy PI controller for an identical system. Robustness of the projected approach is tested with random load perturbation at area‐1 and step load perturbation at diverse location at diverse instant that establishes the efficacy of QOWOA. The critical system analysis demonstrates that the proposed QOWOA technique may be effectively applied for the LFC problem. [ABSTRACT FROM AUTHOR]