Probabilistic load flow analysis of active distribution network adopting improved sequence operation methodology

Liu, Hong; Tang, Chong; Han, Jun Hyun; Li, Teng; Li, Jifeng; Zhang, Kun

doi:10.1049/iet-gtd.2016.0406

Cited by 25 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An EGH-IES composed of a 33-bus electricity network [26], an 11-node gas network [7] and a 13-node heating network [13] operating in the "electricity/gas following heat" mode is used as a test system to verify the proposed PEF analysis method. The configuration of the test system is shown in Figure 5.…”

Section: Test System Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems

2018

View full text Add to dashboard Cite

The existing studies on probabilistic steady-state analysis of integrated energy systems (IES) are limited to integrated electricity and gas networks or integrated electricity and heating networks. This paper proposes a probabilistic steady-state analysis of integrated electricity, gas and heating networks (EGH-IES). Four typical operation modes of an EGH-IES are presented at first. The probabilistic energy flow problem of the EGS-IES considering its operation modes and correlated uncertainties in wind/solar power and electricity/gas/heat loads is then formulated and solved by the Monte Carlo method based on Latin hypercube sampling and Nataf transformation. Numerical simulations are conducted on a sample EGH-IES working in the "electricity/gas following heat" mode to verify the probabilistic analysis proposed in this paper and to study the effects of uncertainties and correlations on the operation of the EGH-IES, especially uncertainty transmissions among the subnetworks.

show abstract

Section: Test System Descriptionmentioning

confidence: 99%

“…The original electricity/gas/heat loads in Refs. [7,13,26] are taken as the mean values, and the SDs of loads are set to be 5% of their mean values.…”

Section: Test System Descriptionmentioning

confidence: 99%

Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems

2018

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 5, an IEEE-30 bus system is modified for simulation analyses. Nodes 14,15,16,17,18 are linked to a wind farm (WF), and the rated capacity of each WF is The correlation coefficient matrix of the five WFs is:…”

Section: The Setting Of Desired Rangementioning

confidence: 99%

“…However, these algorithms are not accurate enough for low sampling times. In Reference [16], a sequence method was applied to reduce the iteration times; however, correlation was not considered in this method. In Reference [17], a uniform sampling method was adopted to solve the correlation problem, but the Copula function became very complex when the correlation nodes exceeded two and vitally limited the computation efficiency.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Rong

2018

Energies

View full text Add to dashboard Cite

The growing amount of distributed generation has brought great uncertainty to power grids. Traditional probabilistic load flow (PLF) algorithms, such as the Monte-Carlo method (MCM), can no longer meet the needs of efficiency and accuracy in large-scale power grids. Latin Hypercube Sampling (LHS) develops a sampling efficiency and solves the correlation problem of distributed generation (DG) access nodes for accuracy analyses. In this paper, a modified Latin Hypercube-Important Sampling method is proposed for higher efficiency and precision by using the importance sampling method before LHS and the Cholesky decomposition method in correlation calculations. The simulation results are presented using a modified IEEE 30-bus system and are compared with traditional MCM and LHS.

show abstract

“…In [20], a firstorder second-moment method (FOSMM) was applied to obtain the mean and standard deviation of load flow solutions. The sequence operation methodology [21] is one of the analytical methods. It has a great advantage in terms of efficiency by sequence operation.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic load flow method considering large-scale wind power integration

Deng

Zhang

Jin

et al. 2019

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

The increasing penetration of wind power brings great uncertainties into power systems, which poses challenges to system planning and operation. This paper proposes a novel probabilistic load flow (PLF) method based on clustering technique to handle large fluctuations from large-scale wind power integration. The traditional cumulant method (CM) for PLF is based on the linearization of load flow equations around the operating point, therefore resulting in significant errors when input random variables have large fluctuations. In the proposed method, the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster. With such pre-processing, the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy. The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples. The proposed method is validated on modified IEEE 9-bus and 118-bus test systems with additional wind farms. Compared with the traditional CM, 2m?1 point estimate method (PEM), Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS) based MCS, the proposed method can achieve a better performance with consideration of both computational efficiency and accuracy.

show abstract

Probabilistic load flow analysis of active distribution network adopting improved sequence operation methodology

Cited by 25 publications

References 20 publications

Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems

Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems

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

Probabilistic load flow method considering large-scale wind power integration

Contact Info

Product

Resources

About