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This article presents a new version of the salp swarm inspired algorithm (SSIA) for the optimal design of the microgrid droop controller. The new version of SSIA is originated from the hybridizing of SSIA with the updating features of the particle swarm optimization (PSO). The development of SSIA is achieved by applying referential integrity betwee...
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... (AVR) are employed to maintain the voltage as well as the frequency within limits. Unfortunately, both TG and AVR is not suitable for PVS and FC. For these reasons, the tendency to search for another alternative solution becomes mandatory. Thanks to droop control that can help in keeping the voltage and frequency during any change in load. Fig. 3 shows the topology of the microgrid control strategy, including three layers with their components that present a computational complexity issue for the controllers' designers and control practitioners. The three control layers with their computational complexity will be discussed in detail in the following subsections. ...
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... While the proposed intelligent and robust controller for islanded microgrids (MGs) showed promise in achieving optimal transient response and power quality, its applicability and effectiveness in diverse operational scenarios under dynamic operation cases remain untested. Droop control of the DC microgrid is achieved by using a hybrid SSIA optimization with PSO in (Ebrahim et al., 2020). The limitation of this paper is its narrow focus on droop control only, without considering the primary and secondary control layers of microgrid operation. ...
... The integral of absolute error (IAE), integral of square error (ISE), integral of time absolute error (ITAE), and integral of time square error (ITSE) are the four types of error benchmark objective functions (Govind et al., 2023). Paper (Ebrahim et al., 2020) shows that the usage of ITAE is the most appropriate for comparing the performance of PI controller with different optimization system. ...
The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques. This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights. HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities. The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600 V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally , precise regulation of voltage and frequency is achieved with + − 0.19%− + deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 s. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility
... The output frequency can be calculated from Eq. (27). ...
... 98 . The application of ITAE is the most appropriate for comparing the performance of PI controller with different optimization system 27 . The parameters of the test system are summarized in Table 2 102 . ...
The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.
... In [22], an intelligent voltage/frequency control scheme was proposed for primary and secondary microgrids to ensure a stable and reliable power supply. This is complemented by the work in [23], which presented a new version of the salp swarm optimization algorithm for optimal design of the microgrid controller. Energy management is another critical aspect of microgrid operation. ...
... Equations (22)(23) illustrate the modelling of EVs in the microgrid. The net energy of battery charging and discharging (e net_ev n,t ) in EVs according to the efficiency of each battery is shown in Equation (22). ...
... In Equation (22), p dis_ev n,t and p ch_ev n,t are equal to the discharge and charging power of the EV battery, respectively, as well as ev n illustrates the battery charging efficiency of the EV. Equation (23) illustrates the rate of injected and received power from or to the microgrid by EVs, which is calculated by the charging and discharging value of the battery: ...
The development of microgrids is progressing due to intelligent load demands, clean energy, batteries and electric vehicles. The presence of such systems in microgrids causes power balance inconsistency, leading to increased power losses and deviation in voltage. In this paper, a mixed-integer non-linear programming model is proposed for modelling island microgrid energy management considering smart loads, clean energy resources, electric vehicles and batteries. Similarly, a flexible distributed AC transmission system device is proposed to prevent voltage deviation and reduce power losses. A scenario-based multi-objective function has been proposed to decrease energy losses and voltage deviations and energy outages of clean energy resources, reduce emissions from fossil-fired distributed generation and finally decrease load outages to reduce the vulnerability of the islanded microgrid. Regarding the proposed mixed-integer non-linear model and the high number of variables and constraints, a modified evolutionary algorithm based on particle swarm optimization has been proposed to solve the proposed model, which can be more efficient than other algorithms to achieve global optimal solutions. The model presented is implemented on a 33-node island microgrid and the results illustrate that the proposed algorithm and model are effective in reducing energy losses and voltage deviation, as well as reducing the vulnerability of the microgrid. The simulation results demonstrate that the proposed approach can lead to significant improvements in the performance of the microgrid. Specifically, the approach can result in a 27% reduction in losses, a 6% reduction in pollution and a 31% improvement in voltage. Additionally, the approach allows maximum utilization of renewable energy sources, making it a promising solution for sustainable energy management.
... Intelligent optimization algorithms have been widely used in the scheduling of the microgrid. Ebrahim et al. (2020), Monteiro et al. (2020), Moradi et al. (2015) and Vivek et al. (2017) used the particle swarm optimization (PSO) algorithm to solve several optimization problems related to the microgrid. The PSO algorithm has fast convergence speed; however, because all particles fly in the direction of the optimal solution during convergence, the particles tend to become identical and lose their diversity, which makes the algorithm fall into the local optimum easily and thus do not yield the global optimal solution (Chen et al., 2013). ...
With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.
... The authors of Jumani et al. (2018) employed the grasshopper optimization algorithm to find the best PI controllers' gains for one control level-based islanded microgrid. M. A. Ebrahim, et al. used a self-adaptive salp swarm optimization algorithm in an isolated microgrid for choosing its PI controllers' coefficients (Ebrahim et al., 2020). In contrast to the PI controllers, the design of PR controllers and their HCs is insufficiently studied in the literatures. ...
In this paper, new optimal procedures are introduced to design the finest controllers and harmonic compensators (HCs) of three-level cascaded control for three-phase grid-supporting inverters based-AC microgrid. The three control levels, comprising primary, secondary and synchronization control levels, are developed in stationary αβ-frame and based on the proportional–integral (PI) controllers and the proportional-resonant controllers along with additional HCs. The new optimal design guidelines of microgrid’s controllers and HCs are aimed to fulfill the study requirements. The optimization objectives and constraints are employed to minimize both the total harmonic distortion (THD) and individual harmonics of microgrid’s voltage to enhance the quality of microgrid’s output power. The THD of microgrid’s voltage can be reduced to 0.19% under the nonlinear loads. Moreover, the microgrid’s voltage and frequency can be perfectly regulated with zero deviations. Furthermore, these new optimal procedures accelerate the speed of synchronization process between the external power grid and the microgrid to be accomplished in time less than 20 ms. Additionally, an accurate power-sharing among paralleled operated inverters can be achieved to avoid overstressing on any one. Also, seamless transitions can be guaranteed between grid-tied and isolated operation mode. The optimal controllers and HCs are designed by a new optimization algorithm called H-HHOPSO, which is created by hybridizing between Harris hawks optimization and particle swarm optimization algorithms. The effectiveness and robustness of the H-HHOPSO-based controllers and HCs are compared with other meta-heuristic optimization algorithms-based controllers and HCs. A microgrid, including two grid-supporting inverters based optimal controllers and optimal HCs, are modeled and carried out using MATLAB/SIMULINK to test the performance under linear and nonlinear loads, and also during the interruption of any one of two inverters. The performance is investigated according to IEC/IEEE harmonic standards, and compared with the conventional control strategy developed in synchronous dq-frame and based on only PI controllers.
... Many challenges are confronted when carrying out the test procedures such as cost for testing, cost of failure, system variations, repeatability and availability. In this situation, HIL real-time simulator is a compelling strategy [25,26]. ...
This paper proposes new analytical and optimal design procedures of the proportional-
resonant (PR) controller and its harmonic compensators (HCs) for three-phase grid-
connected voltage source inverters (VSIs) powered by renewable energy resources. The
modeling and analysis based on stationary reference frame are performed for VSIs collab-
orated with an L-type filter. The theoretical verification and simulation validation of the
proposed design guidelines are done to approve its effectiveness and robustness. Particle
swarm, grey wolf and Harris hawks’ optimization techniques are applied and compared
for a proper selection of the parameters of the proposed PR controller and its HCs. To
accomplish this study, multi-objective error functions are employed and compared to min-
imize the total harmonic distortion of the grid output current. The proposed PR con-
troller and its HCs are tested, using MATLAB/Simulink, along with the allowable changes
of inverter output active and reactive powers, and also under the grid voltage distortion.
Moreover, their performance is evaluated according to IEEE and IEC harmonics stan-
dards, and compared with the conventional PI controller based on reference frame. Fur-
thermore, the experimental validation for the proposed controllers is done based on the
hardware-in-the-loop real-time simulator using C2000TM-microcontroller-LaunchPadXL-
TMS320F28377S kit.
... PI controllers with automatic tuning of controller gains have also been introduced in the literature. A self-adaptive Salp Swarm optimization-based tuning of the PI controller was applied for microgrid control [17]. A hybrid Harris hawks and particle swarm optimization algorithm (H-HHOPSO)-based tuning of the PI controller was given for the microgrid control [18]. ...
... From system Equation (17) and the high-gain observer Equation (12), the estimation error dynamics are as follows:ζ ...
This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous (dq) reference frame via a sliding mode control strategy. The control strategy required only output voltages; other states of the system were estimated by using a high-gain observer. The power-sharing among multiple inverters was achieved by solving power flow equations of the electrical network. The stability analysis showed that the error was ultimately bound in the case of the real PWM inverter and/or with a nonlinear load in the electrical network. The microgrid was simulated using the SimPowerSystems Toolbox from MATLAB/Simulink. The simulation results show the effectiveness of the proposed control scheme. The output voltage regulation of the inverter and power-sharing was achieved with the ultimately bounded error for the linear load. Later, the nonlinear load was also included in the electrical network and the error was shown to remain ultimately bounded. The output voltage regulation and power-sharing were achieved with the nonlinear load in the system.
... In order to prove the similarity of the proposed system with the existing systems, a methodical comparison was made with the literature. 50 The implementation was done by connecting in series 10 PV panels of 35 W maximum power of each panel with varying irradiance of 100 to 1000 W/m 2 to confirm and validate the proposed ISSPSO algorithm. Another input of MSX-60 W four series two parallel (4S2P) panels configuration under partial conditions has been utilized. ...
In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fast-tracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex-MSX-60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck-boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast-tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10 C,15 C,20 C,25 C,30 C,35 C ½. K E Y W O R D S buck-boost converter, particle swarm optimization, photovoltaics, salp swarm algorithm Abbreviations: GMPPT, global maximum power point tracking; LMPPT, local maximum power point tracking; SOC, state of charge; PSC, partial shading condition; C 1 , C 2 , acceleration coefficients; l and L, current iteration and maximum number of iterations; ω, inertia weight; ub j , lb j , upper and lower bounds; R s , R sh , solar cell series and shunt resistances; η Tr , tracking efficiency (%); P Tr , P max , tracked output power and maximum real power; q, charge of the electron (C); T, absolute temperature (K); k, Boltzmann constant (J/K); g, best value; G, solar irradiance (W/m 2); D, duty cycle; Pbest, i, personal best solution; Gbest, global best of Pbest, i; xi k, position vector; STC, standard test condition; vi k, velocity vector; PV, photovoltaic; SSA, salp swarm algorithm; PSO, particle swarm optimization; MPPT, maximum power point tracking; a, ideality factor of a single solar cell constant; Vt, PV thermal voltage; Voc, PV cell open-circuit voltage at reference temperature; Tamb, ambient temperature (25) at STC; Ns, number of series cells constant; Np, number of parallel cells constant; α I , short-circuit current temperature coefficient constant.
... In order to prove the similarity of the proposed system with the existing systems, a methodical comparison was made with the literature. 50 The implementation was done by connecting in series 10 PV panels of 35 W maximum power of each panel with varying irradiance of 100 to 1000 W/m 2 to confirm and validate the proposed ISSPSO algorithm. Another input of MSX-60 W four series two parallel (4S2P) panels configuration under partial conditions has been utilized. ...
In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fast‐tracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex‐MSX‐60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck‐boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast‐tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10°C,15°C,20°C,25°C,30°C,35°C.
... e: error. The multi-objective function is used in this case study to recognize both the frequency and voltage errors via the property of the accumulative sum. Figure 6 illustration the test system diagram consists of two solar PV array systems (SPVAS), a DC-DC boost converter, two battery stations (BSs), a supercapacitor (SC), a three-phase VSI, a load, and a transmission line [31]. Owing to their fast charging and discharging characteristics, supercapacitors are also used to boost the microgrid's dynamic response. ...
... The detailed comparative analysis is given in Table 2 for three optimization techniques. The findings indicate that SSIA Test system diagram [31]. succeeded with minimal voltage and frequency errors in achieving the assigned control task. ...
... The swarm of Salps (Salps chain)[31]. ...
The collaboration of the various distributed generation (DG) units is required to meet the increasing electricity demand. To run parallel-connected inverters for microgrid load sharing, several control strategies have been developed. Among these methods, the droop control method was widely accepted in the research community due to the lack of important communication links between parallel-connected inverters to control the DG units within a microgrid. To help to solve the power-sharing process, keep to frequency and voltage constrained limits in islanded mode microgrid system. The parameter values must therefore be chosen accurately by using the optimization technique. Optimization techniques are a hot topic of researchers; hence This paper discusses the microgrid droop controller during islanding using the salp swarm inspired algorithm (SSIA). To obtain a better fine microgrid output reaction during islanding, SSIA-based droop control is used to optimally determine the PI gain and the coefficients of the prolapse control. The results of the simulation show that the SSIA-based droop control can control the power quality of the microgrid by ensuring that the keep to frequency and voltage constrained limits and deviation and proper power-sharing occurs during the microgrid island mode during a load change.
