Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

Li, Quan; Wang, Xin; Rong, Shuaiang

doi:10.3390/en11113171

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…LHS sampling [30,31] Suitable for uniform sampling in multi-dimensional space and small samples; high sampling efficiency…”

Section: Discussionmentioning

confidence: 99%

“…In many cases, Latin hypercube sampling (LHS) is considered to be an improvement on rough MCS. In [30], LHS is used to improve sampling efficiency and deal with the relevance problem of DRE generation access nodes, while the methods of MCS and LHS are analyzed to generate WP scenarios in [31]. At present, many scholars have verified that the copula function in statistical science is effective in the correlation modeling between random variables.…”

Section: Power Uncertaintymentioning

confidence: 99%

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Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

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The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

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“…LHS sampling [30,31] Suitable for uniform sampling in multi-dimensional space and small samples; high sampling efficiency…”

Section: Discussionmentioning

confidence: 99%

Section: Power Uncertaintymentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

show abstract

“…In view of the advantages of the LHS method, some scholars have used this method to model randomness problems. Li, Q. et al proposed an improved LHS method applied to the grid system with distributed generation, which improved the efficiency and accuracy of the probabilistic power flow algorithm [43]. Zhao, W. et al proposed an LHS method suitable for random variables and demonstrated the superiority of the proposed method by comparing the computational performance of Monte Carlo (MC) continuous sampling, MC non-continuous sampling, and the proposed method [44].…”

Section: Latin Hypercube Samplingmentioning

confidence: 99%

Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control

Chu

2022

Energies

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With the increasing severity of climate change, the power industry, as one of the main sources of carbon emissions, is playing an extremely important role in the process of low-carbon energy transformation. The purpose of this paper is to try to find a general method to solve the optimal path for the low-carbon evolution of the power supply structure so as to meet the challenges faced by the low-carbon transformation of the power industry in the future. This paper first uses the capacity coefficient index (CCI) to represent the power generation ability of different technologies and proposes a forecasting method for the CCI of renewable energy generation. In this paper, a two-layer optimization model considering multiple constraints is established and solved using the MPC method. The results show that China’s installed capacity of renewable power could account for more than 50% in 2030, while the carbon emissions will decrease after reaching a peak in 2023. On the premise of ensuring sufficient reserve adjustment capacity of thermal power units, increasing the proportion of renewable energy generation is an important way to realize emission reduction in the power industry.

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“…Some of these new methods include the fast continuation method, the extended Newton-Raphson method, the extended current injection load flow method, the extended backward & forward sweep [17], etc. [14][15][16][17][18][19]. All those methods are increasing the coplexity of the problems.…”

Section: Introductionmentioning

confidence: 99%

Acceleration Factor Optimization of Gauss-Seidel Numerical Analysis for Power Flow Using FA

Yahyia,

Abbas,

Jaber

2023

J. Phys.: Conf. Ser.

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Load flow analysis is one of the essential ways used by electrical power engineers to achieve high system quality owing to the relevance of load flow in economic optimization, planning, and stability. This issue can be formulated as a set of nonlinear algebraic equations, which can be solved by iterative algorithms. Gauss-Seidel is one of the simple ways of determining load flow in a power system, with the acceleration factor being the most considerable modification when aiming at improving the accuracy of this method. This article reports the determination of the optimal acceleration factor using PSO and Firefly (FA) algorithms. The simulation was done in MATLAB using a 4-bus, 6-bus, and 9-bus of IEEE standard system for the determination of the optimal acceleration factor. The proposed methods were evaluated and benchmarked against the conventional value of the factor using the number of iterations as the fitness function. The outcome of the study showed the FA method as the most preferable method of identifying the optimum acceleration factor.

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Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

Cited by 15 publications

References 26 publications

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control

Acceleration Factor Optimization of Gauss-Seidel Numerical Analysis for Power Flow Using FA

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