Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

Sedighizadeh, Mostafa; Esmaili, Masoud; Eisapour-Moarref, Amir

doi:10.1016/j.asoc.2016.12.031

Cited by 45 publications

(44 citation statements)

References 28 publications

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“…However, the study did not consider the voltage profile, which is one of the very important parameters in the islanded mode of MG. Authors in the references [11,12] developed an AI-based controller for the voltage regulation of an islanded MG using Pareto based BB-BC and hybrid big-bang big-crunch (BB-BC) algorithms, respectively. In both case studies, the frequency declined to 59.7 Hz from its rated value (60 Hz) during DG insertion and load change conditions.…”

Section: Control Architecturesmentioning

confidence: 99%

“…Most recently, Qazi et al [13] used the whale optimization algorithm (WOA) for regulating frequency and voltage independently by formulating two different fitness functions in an islanded MG. However, it may be noted that the voltage and frequency are two interrelated parameters [12], and therefore, it is not practically possible to independently optimize these two inter-dependent parameters. Most recently, in November 2018, the authors in reference [14] explored the grasshopper optimization algorithm (GOA) in order to regulate voltage and frequency along with the optimal transient response of an islanded MG.…”

Section: Control Architecturesmentioning

confidence: 99%

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Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

et al. 2019

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The islanded mode of the microgrid (MG) operation faces more power quality challenges as compared to grid-tied mode. Unlike the grid-tied MG operation, where the voltage magnitude and frequency of the power system are regulated by the utility grid, islanded mode does not share any connection with the utility grid. Hence, a proper control architecture of islanded MG is essential to control the voltage and frequency, including the power quality and optimal transient response during different operating conditions. Therefore, this study proposes an intelligent and robust controller for islanded MG, which can accomplish the above-mentioned tasks with the optimal transient response and power quality. The proposed controller utilizes the droop control in addition to the back to back proportional plus integral (PI) regulator-based voltage and current controllers in order to accomplish the mentioned control objectives efficiently. Furthermore, the intelligence of the one of the most modern soft computational optimization algorithms called salp swarm optimization algorithm (SSA) is utilized to select the best combination of the PI gains (kp and ki) and dc side capacitance (C), which in turn ensures optimal transient response during the distributed generator (DG) insertion and load change conditions. Finally, to evaluate the effectiveness of the proposed control approach, its outcomes are compared with that of the previous approaches used in recent literature on basis of transient response measures, quality of solution and power quality. The results prove the superiority of the proposed control scheme over that of the particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA) based MG controllers for the same operating conditions and system configuration.

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Section: Control Architecturesmentioning

confidence: 99%

Section: Control Architecturesmentioning

confidence: 99%

Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

et al. 2019

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“…In order to avoid scaling problem and to transform objective functions into the same range, the fuzzification method is used (Esmaili and Goldoust 2015). All objective functions are fuzzified and transformed into the same range of [0, 1] using the trapezoidal fuzzy membership function which is expressed as (Sedighizadeh et al 2017): represent the ideal and nadir values for objective function i, respectively; f i is objective function value; and q i is its fuzzy membership value. Ideal and nadir values (Akorede et al 2011) represent the best and worst accessible values of each objective function, respectively, in the solution space of the problem.…”

Section: Fuzzy-based Combination Of Objective Functionsmentioning

confidence: 99%

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

2017

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Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multiobjective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

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“…It was found that the proposed multi-objective PSO could be validated for multi variable EMS of a factory. The optimal power control strategy for a standalone microgrid was developed and presented in [20] where optimized gains of a proportional-integrator controller of inverter-based DGs using hybrid big bang-big crunch (BB-BC) algorithm were found.With the aim of adjusting microgrid generation to energy demand, numerous technical studies have been carried out to improve energy management with penetration of renewable energy sources combined with energy storage devices in microgrids [21][22][23][24][25]. Alvarez et al [26] conducted an optimization of micro sources in a DG microgrid in terms of emission and fuel consumption costs.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

2019

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Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.Sustainability 2019, 11, 3839 2 of 28 algorithms were used to optimize power sharing among active DGs. In order to determine the optimal component sizing in a microgrid, the mixed integer linear programming (MILP) method has been used to model the microgrid components with consideration of demand response [5]. In another study [6], similar sizing problems of microgrid components were solved using a genetic algorithm (GA) and the energy management issue was formulated using MILP. In addition to component sizing, system configuration was optimized in [7] using the multi-objective PSO by taking into account production cost, reliability, and environmental impact. The studied system comprised of a diesel generator, solar panels, wind turbines, and battery storage. Several methods have also been used for optimizing multi variable problems of energy management systems (EMS) in microgrids [8][9][10][11][12][13]. Wang et al. [14] investigated EMS of a microgrid with multi period optimization problems using an MPI based PSO algorithm and concluded that the proposed algorithm could be effectively used to improve the operation time.Four techniques were used in [15] to determine the optimized operational strategy for an entire day based on least operating cost and minimum carbon emissions. These techniques were a non-dominated sorting genetic algorithm (NSGA), multi-objective PSO, multi-objective uniform water cycle algorithm (WCA), and normal constraint algorithm. Reference [16] presented a real time EMS that had the tendency to optimally minimize carbon emissions and energy cost, and simultaneously maximized the power coming from renewable DGs using binary PSO. An improved binary PSO with double-structure coding was applied to optimize a microgrid operatio...

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Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

Cited by 45 publications

References 28 publications

Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources

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