Random-Fuzzy Chance-Constrained Programming Optimal Power Flow of Wind Integrated Power Considering Voltage Stability

Ma, Rui; Li, Xuan; Gao, Weicheng; Lu, Peng; Wang, Tieqiang

doi:10.1109/access.2020.3040382

Cited by 14 publications

(13 citation statements)

References 29 publications

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“…Stochastic Chance Constraint Programming is utilized to convert a stochastic mathematical model into a deterministic counterpart [53] . This approach is a well-known widely used method introduced by Charnes and Cooper [54] .…”

Section: Solution Methodsmentioning

confidence: 99%

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

Shirazi

Kia

Ghasemi

2021

Applied Soft Computing

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As of March 24, 2020, the Food and Drug Administration (FDA) authorized to bleed the newly recovered from Coronavirus Disease 2019 (COVID-19), i.e., the ones whose lives were at risk, separate Plasma from their blood and inject it to COVID-19 patients. In many cases, as observed the plasma antibodies have cured the disease. Therefore, a four-echelon supply chain has been designed in this study to locate the blood collection centers, to find out how the collection centers are allocated to the temporary or permanent plasma-processing facilities, how the temporary facilities are allocated to the permanent ones, along with determining the allocation of the temporary and permanent facilities to hospitals. A simulation approach has been employed to investigate the structure of COVID-19 outbreak and to simulate the quantity of plasma demand. The proposed bi-objective model has been solved in small and medium scales using -constraint method, Strength Pareto Evolutionary Algorithm II (SPEA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi Objective Invasive Weed Optimization algorithm (MOIWO) approaches. One of the novelties of this research is to study the system dynamic structure of COVID-19’s prevalence so that to estimate the required plasma level by simulation. Besides, this paper has focused on blood substitutability which is becoming increasingly important for timely access to blood. Due to shorter computational time and higher solution quality, MOIWO is selected to solve the proposed model for a large-scale case study in Iran. The achieved results indicated that as the plasma demand increases, the amount of total system costs and flow time rise, too. The proposed simulation model has also been able to calculate the required plasma demand with 95% confidence interval.

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Section: Solution Methodsmentioning

confidence: 99%

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

Shirazi

Kia

Ghasemi

2021

Applied Soft Computing

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“…In the PLF method, PDFs of power system injected variables (power generation and load) are known, then the moments of the injected are obtained from (11) or (12). According to the equations of moments and the cumulants as shown in ( 13), the injected cumulants are obtained by the injected moments [30].…”

Section: Cumulants and Pdfs Of Wind Generation Based On Dfigsmentioning

confidence: 99%

“…In order to consider the uncertainties arising from the stochastic nature of loads and generators, the method of probabilistic load flow (PLF) is proposed [9]. Currently, wind generation (WG) is one of the premier renewable power generations [10]- [12]. Considering the uncertainties arising from WG in steady-state operation, the PLF method is further studied to analyze the power system integrated with WG [13], [14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

2021

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The increase of wind generation has challenged the conventional way of probabilistic load flow (PLF) calculation. A reliable and efficient PLF method is required to face the stochastic nature of various power systems with wind generation (WG). Firstly, the paper analyzes several typical cumulant methods (CMs) for PLF, such as Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), and maximum entropy (ME). Then, an improved integrated CM by probability distribution pre-identification is proposed for power systems with WG based on doubly fed induction generations (DFIGs). The skewness and kurtosis are used as probability distribution pre-identification indices in the CM framework. Meanwhile, the influence of the DFIG control strategy on reactive power is considered in the load flow model and the moment calculation. Finally, the accuracy and efficiency of the proposed method are validated with the IEEE test system. In various scenarios, suitable CM is selected and applied to the PLF based on pre-identifying distribution characteristics. Results reveal that probabilistic density functions (PDFs) of bus voltages and line flows obtained by the proposed method have both accuracy and efficiency.INDEX TERMS Probabilistic load flow (PLF), integrated cumulant method (integrated CM), doubly fed induction generation (DFIG), Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), maximum entropy (ME).

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“…The method was applied to solve OPF of IEEE 30-bus and IEEE-57 bus with stochastic wind and solar energy. In [51], the random-fuzzy chance-constrained programing was employed to consider the nature of wind speed in OPF. Moreover, many researches solved OPF considering the advanced reactive power sources such as TCSC [52], and STATCOM [53].…”

Section: Introductionmentioning

confidence: 99%

Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems

et al. 2022

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The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

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Random-Fuzzy Chance-Constrained Programming Optimal Power Flow of Wind Integrated Power Considering Voltage Stability

Cited by 14 publications

References 29 publications

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems

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