Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman, Serhat; Rivera, Sergio; Li, Jie; Wu, Lei

doi:10.1002/2050-7038.12270

Cited by 54 publications

(32 citation statements)

References 60 publications

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“…In this paper, to test the performance of the proposed MODE-OPF, an updated IEEE 30-bus system has been considered in line to the recent papers published in [12,39,49,38] as depicted in Figure 7. In this modified system, the conventional thermal generators are substituted for wind turbines on bus 5, 11 and the solar generators on bus 13.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Hossain¹,

Sallam²,

Elsayed³

et al. 2021

Preprint

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In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Hossain¹,

Sallam²,

Elsayed³

et al. 2021

Preprint

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“…In the last decade, some studies also have been documented [43][44][45][46][47][48][49][50][51][52][53] to find optimal solution to the OPF problems in the thermal, wind, and solar energy sources-based hybrid power systems. In recent studies [43][44][45][46][47][48][49][50][51][52][53], different metaheuristic approaches including grey wolf optimizer (GWO) [43], fuzzy membership function based PSO (FMF-PSO) [44], improved adaptive DE (IADE) [45], modified imperialist competitive algorithm based on sequential quadratic programming (MICA-SQP) [46], modified JAYA [47], hybrid of phasor PSO and GSA [48], barnacles mating optimization (BMO) [49], PSO [50], Hybrid of DE and PSO [51], MBFA [52], and sunflower optimization (SFO) [53] have been proposed for solving the OPF problems in hybrid power systems.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In studies [43][44][45][46][47][48][49][50][51][52][53], mostly Lognormal PDF and Weibull PDF have been applied for modeling uncertainty of the stochastic solar irradiance and wind speed, respectively.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Table 2 represents the summary of all of the aforementioned studies [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] have been conducted on thermal and wind or thermal, wind, and solar energy sources-based hybrid power systems. As specified in Table 2, OPF problem primary objective -quadratic fuel cost ξ q (electricity generation cost) minimization is considered in all studies.…”

Section: B Literature Reviewmentioning

confidence: 99%

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A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Javaid¹,

Niaz

Almogren

et al. 2021

IEEE Access

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In recent studies, emphasis has been placed on optimal power flow (OPF) problems in traditional thermal, wind, and solar energy sources-based hybrid power systems. Various metaheuristic algorithms have been proposed to find optimal solutions to the OPF problems in the hybrid power system. The OPF, due to the quadratic nature of its primary objective function, is a nonlinear, nonconvex, and quadratic optimization problem. In this study, we have proposed a bio-inspired bird swarm algorithm (BSA) to find an optimal solution to the OPF problem in the hybrid power system because it performs well in the case of optimizing the well-known Rastrigin quadratic benchmark function. In this study, uncertainty of utility load demand and stochastic electricity output from renewable energy resources (RESs) including wind and solar are incorporated into the hybrid power system for achieving accuracy in operations and planning of the system. We have used a modified IEEE-30 bus test system to verify and measure the performance of BSA and a comparison is made with well-known evolutionary metaheuristic algorithms. The proposed BSA consistently achieves more accurate and stable results than other metaheuristic algorithms. Simulation-based optimization results have shown the superiority of BSA approach to solve the OPF problems by satisfying all constraints and minimum power generation cost 863.121 $/h is achieved in case study 1. Simulation-based experiment results have indicated that by imposing the carbon tax (ton/h) the power generation from RESs was increased. In case study 2, the proposed BSA approach has also outperformed and minimum electricity cost 890.728 $/h is achieved as compared to other algorithms.INDEX TERMS Deterministic optimal power flow, Uncertainty of utility load demand, Bio-inspired bird swarm algorithm, Stochastic solar and wind power.

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“…The Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO) [37] is a single-objective algorithm based on the EPSO (Evolutionary Particle Swarm Optimization) [50] and Differential Evolution (DE). C-DEEPSO is an improvement of DEEPSO (see [51,52]). C-DEEPSO has been successfully applied to several optimization problems in power systems, such as: minimization of costs in power production [53], active power dispatch in large scale grids [37], cascade operation in hydropower plants [39], study of energy generation via renewable sources and energy storage systems [41], hybrid microgrid systems operation [43], minimization of active power losses [40], control optimization in hydraulic power plants [44], and pattern classification [54].…”

Section: The C-deepso Algorithmmentioning

confidence: 99%

Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

et al. 2021

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In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.

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Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Cited by 54 publications

References 60 publications

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

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