A new scaled fuzzy method using PSO segmentation (SePSO) applied for two area power system

Hussein, Balasim M.

doi:10.11591/ijece.v9i2.pp815-825

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…For an unbiased comparison, we adopt the RN bidding strategy of the DER aggregator without considering the uncertainty in market price. In total, seven meta-heuristic algorithms are considered: genetic algorithm (GA) [39], artificial bee colony (ABC) [40], spider monkey (SM) [41], whale [42], enhanced GA (EGA) [39], security enhanced-PSO (SE-PSO) [43], and the proposed ELPSO algorithm. The simulations are implemented 15 times, and the results for the best, worst, mean, and corresponding average computation time (CPU) are obtained.…”

Section: Optimal Bidding Strategy Resultsmentioning

confidence: 99%

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Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit-enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Thenr, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed datadriven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

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Section: Optimal Bidding Strategy Resultsmentioning

confidence: 99%

“…There are two processes for solving the optimization problem considering market uncertainty [23,24]. First, an RN optimization problem is solved, as indicated by (43).…”

Section: ) Background Of Igdtmentioning

confidence: 99%

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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“…PSO was developed in 1995 as a swarm-based heuristic by [19], [20]. In the PSO, each particle is considered a potential solution, and the best particle in the solution space, p, is defined by the character, g. At each iteration step, the speed of the k th particle which is given as vɉ=(vɉ1, vɉ2, .…”

Section: Modelling Of Pso Algorithmmentioning

confidence: 99%

Evolutionary algorithm solution for economic dispatch problems

Hussein

2022

IJECE

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A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints; these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed; the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.

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“…Accordingly, analysts have been dealing with this issue for quite a whilst trying to find an answer. One of the influential techniques for improving transient strength is to make use of the bendy AC transmission gadget (FACTS) controller devices [4][5][6]. Anyway, influential damping will improve with the use of the damping stabilizer [7][8].…”

Section: Introductionmentioning

confidence: 99%

Enhancement the stability of power system using optimal location of FACTS devices

Abdullah

Ghadhban

Hameed

et al. 2020

IJEECS

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This paper proposes a steady-state of the Static Var Compensator (SVC) & Thyristor Controlled Series Capacitor (TCSC) set up for enhancing the damping overall performance and growing the integral clearing time (CCT) of a power network. The indispensable clearing time is carried out through increasing the time fault interval until the gadget loses stability. Increasing the CCT can be contribute to reliability of the safety gadget, decrease the protection machine ranking and cost. In order to attain most enhancement of machine stability via optimizing location, sizing and control modes of SVC and TCSC. Models and methodology for putting and designing shunt FACT’s units SVC (injected reactive strength Q) and series FACT’s devices TCSC (chose capacitive region) are examined in a 6-bus system. Performance factors are described to show validation of SVC and TCSC on extraordinary conditions. It is proven that the SVC is better than TCSC.

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A new scaled fuzzy method using PSO segmentation (SePSO) applied for two area power system

Cited by 4 publications

References 15 publications

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Evolutionary algorithm solution for economic dispatch problems

Enhancement the stability of power system using optimal location of FACTS devices

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