Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach

Aien, Morteza; Rashidinejad, Masoud; Firuzabad, Mahmud Fotuhi

doi:10.1016/j.rser.2014.09.012

Cited by 71 publications

(38 citation statements)

References 41 publications

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“…In 2014, Sebastián et al [97] presented a probabilistic AC optimal power flow (POPF) that takes into account load variation, wind's stochastic behaviour, and variable line's thermal rating which is usually used as a deterministic value in several studies. In 2015, Aien et al [98] reviewed the probabilistic techniques used for probabilistic optimal power flow (POPF) and proposed a method by using the unscented transformation (UT) technique. In 2013, Arabali et al [134] presented a technical outline to evaluate the energy storage application, optimal placement, and economic advantage, for the social cost and transmission congestion.…”

Section: 4mentioning

confidence: 99%

“…In 2014, Panda and Tripathy [104] presented an OPF solution for modified power system in which three conventional generators are replaced by wind-energy conversion systems (WECS). To justify the limitation of reactive power generation capability of WECS, genetic algorithm (GA) and a modified bacteria [92][93][94][95][96][97][98][99][100][101][102][103][104][105][106] foraging algorithm are employed independently, for determining the optimal schedule. In 2015, Panda and Tripathy [105] presented a modified bacteria foraging algorithm, which is capable of handling multiobjective optimization problems.…”

Section: 4mentioning

confidence: 99%

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Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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Section: 4mentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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“…By employing statistical analysis, the created alterations can be attained. Since electric consumption changes continually with a high level of uncertainty, in this paper, load demand is considered as a normal distribution function with a mean value and a standard deviation as follows [35].…”

Section: Uncertainties In Solar Radiation and Loadmentioning

confidence: 99%

Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties

et al. 2017

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Abstract:A novel method for a day-ahead optimal operation of a hybrid microgrid system including fuel cells, photovoltaic arrays, a microturbine, and battery energy storage in order to fulfill the required load demand is presented in this paper. In the proposed system, the microgrid has access to the main utility grid in order to exchange power when required. Available municipal waste is utilized to produce the hydrogen required for running the fuel cells, and natural gas will be used as the backup source. In the proposed method, an energy scheduling is introduced to optimize the generating unit power outputs for the next day, as well as the power flow with the main grid, in order to minimize the operational costs and produced greenhouse gases emissions. The nature of renewable energies and electric power consumption is both intermittent and unpredictable, and the uncertainty related to the PV array power generation and power consumption has been considered in the next-day energy scheduling. In order to model uncertainties, some scenarios are produced according to Monte Carlo (MC) simulations, and microgrid optimal energy scheduling is analyzed under the generated scenarios. In addition, various scenarios created by MC simulations are applied in order to solve unit commitment (UC) problems. The microgrid's day-ahead operation and emission costs are considered as the objective functions, and the particle swarm optimization algorithm is employed to solve the optimization problem. Overall, the proposed model is capable of minimizing the system costs, as well as the unfavorable influence of uncertainties on the microgrid's profit, by generating different scenarios.

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“…For a power system integrated with wind farms, the deterministic OPF model can be described by the following non-linear optimization problem with equality and inequality constraints [5,25]:…”

Section: Traditional CM For P-opfmentioning

confidence: 99%

“…References [5,6] provided critical reviews on the probabilistic methods studying the uncertainties in power systems. The methods of solving P-OPF problems could be classified into three categories: simulation methods, approximation methods and analytical methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables

Deng

Zhang

2017

Energies

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Abstract:The traditional cumulant method (CM) for probabilistic optimal power flow (P-OPF) needs to perform linearization on the Karush-Kuhn-Tucker (KKT) first-order conditions, therefore requiring input variables (wind power or loads) varying within small ranges. To handle large fluctuations resulting from large-scale wind power and loads, a novel P-OPF method is proposed, where the correlations among input variables are also taken into account. Firstly, the inverse Nataf transformation and Cholesky decomposition are used to obtain samples of wind speeds and loads with a given correlation matrix. Then, the K-means algorithm is introduced to group the samples of wind power outputs and loads into a number of clusters, so that in each cluster samples of stochastic variables have small variances. In each cluster, the CM for P-OPF is conducted to obtain the cumulants of system variables. According to these cumulants, the moments of system variables corresponding to each cluster are computed. The moments of system variables for the total samples are obtained by combining the moments for all grouped clusters through the total probability formula. Then, the moments for the total samples are used to calculate the corresponding cumulants. Finally, Cornish-Fisher expansion is introduced to obtain the probability density functions (PDFs) of system variables. IEEE 9-bus and 118-bus test systems are modified to examine the proposed method. Study results show that the proposed method can produce more accurate results than traditional CM for P-OPF and is more efficient than Monte Carlo simulation (MCS).

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Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach

Cited by 71 publications

References 41 publications

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties

A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables

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