New approach for the probabilistic power flow of distribution systems based on data clustering

Sehsalar, Omid Zare; Galvani, Sadjad; Farsadi, Mortaza

doi:10.1049/iet-rpg.2018.6264

Cited by 21 publications

(10 citation statements)

References 36 publications

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“…The efficient k-means method is used to divide the generated scenarios for each random variable into k sets (S 1 , S 2 , .., S K ), each of which has a centroid, stated generally by A k . In this algorithm, the following objective function, which is the total squared Euclidean distances between the samples and the associated centroids, is minimized [4,62].…”

Section: K-means Data Clustering Methodsmentioning

confidence: 99%

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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This paper presents a tri‐level stochastic coordinated transmission and distribution system expansion (CTDS) planning considering the deployment of energy hubs (EHs) across the distribution systems (DSs). The first level of the proposed optimization approach deals with transmission system expansion planning (TEP), while the second level develops distribution system expansion planning (DEP). Market clearing is done in the third level to update the locational marginal prices (LMPs) for different distribution system operators (DSOs). The EHs include photovoltaic cells (PVs), wind turbines (WTs), combined heating and power units (CHPs), boilers, and electrical and absorption chillers. The proposed tri‐level approach is solved with an iterative algorithm based on the diagonalization approach and tested on the modified IEEE 30‐bus transmission systems (TS) with several DSOs. Each DSO includes 18‐bus electrical, heating, and cooling energy systems. The numerical analysis shows that the total cost of the transmission system operator (TSO) is not changed with implementing the uncoordinated approach. However, the total cost of the whole system is reduced by 6.54% with accommodation of the EHs in the uncoordinated approach. Moreover, the total cost of the TSO as well as the whole system are reduced by 41.11% and 16.96%, respectively, with the proposed (CTDS) planning.

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Section: K-means Data Clustering Methodsmentioning

confidence: 99%

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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“…The output power of the PV module depends on the solar irradiance, ambient temperature, and parameters of the PV module. When the PV module operates at the maximum power point and at a solar irradiance s, the output power can be calculated as a function of s as follows (Sehsalar et al, 2019): Here, N denotes the number of PV modules, and FF is the fill factor obtained from Eq. ( 22); V MPP and I MPP are the voltage and current at the maximum power point in V and A, respectively; V oc and I SC are the open-circuit voltage and short-circuit current, respectively; T C , T a , and T n are the cell, ambient, and nominal operating temperatures of the PV cell, respectively (in °C); and K V and K i are the voltage and current temperature coefficients, respectively (in V/ °C and A/ °C, respectively).…”

Section: Modeling Of Variable Renewable Energy Source: Photovoltaic (Pv)mentioning

confidence: 99%

Probabilistic Optimal Power Flow-Based Spectral Clustering Method Considering Variable Renewable Energy Sources

et al. 2022

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Power system clustering is an effective method for realizing voltage control and preventing failure propagation. Various approaches are used for power system clustering. Graph-theory-based spectral clustering methods are widely used because they follow a simple approach with a short calculation time. However, spectral clustering methods can only be applied in system environments for which the power generation amount and load are known. Moreover, it is often impossible to sufficiently reflect the influence of volatile power sources (e.g., renewable energy sources) in the clustering. To this end, this study proposes a probabilistic spectral clustering algorithm applicable to a power system, including a photovoltaic (PV) model (for volatile energy sources) and a classification method (for neutral buses). The algorithm applies a clustering method that reflects the random outputs of PV sources, and the neutral buses can be reclassified via clustering to obtain optimal clustering results. The algorithm is verified through an IEEE 118-bus test system, including PV sources.

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“…In [36], the uncertainty of wind generation is addressed based on various probability distribution using the Kullback -Leibler divergence measure. On the other side, clustering techniques are used to decrease the complexity of problems with high renewable uncertainty [37]. However, practical considerations such as the environmental impact of ESUs, land requirement, and its cost and increasing RPOs are yet to be explored concerning optimal planning of ESUs.…”

Section: Hesumentioning

confidence: 99%

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

2020

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In recent days, aiming for power generation with less carbon emission, led to the high penetration of renewable energy into the distribution grid. To improve the intermittency caused by renewables and to increase the grid flexibility, grid integrated Energy Storage Units (ESUs) are proposed as the promising solution in the literature. However, considering the investment cost, ESUs are optimally placed by satisfying the network electrical constraints. On the other hand, consideration of environmental impacts and other practical constraints are also equally important. Therefore, in this article, on top of grid performance parameters, realistic parameters that may affect the location and its size such as (1) environmental impact, (2) land requirement & its associated cost for ESU installation and (3) renewable purchase obligation are formulated in the objective function. Decision making on ESU installation is a planning activity, which follows uncertainty. Consequently, it is essential to model the uncertainty parameters into the objective function, for better planning. In this article, optimal planning of hybrid ESUs based on realistic parameters along with uncertainty is addressed. For this study, a practical 156-bus distribution system of Dehradun district, India is considered. From the results obtained, it is evident that, formulating hybrid ESU constrained with the environmental impact has significantly decreased the emission of CO2 with maximum grid stability. INDEX TERMS Batteries, distributed power generation, environmental economics, power system planning, sustainable development.

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New approach for the probabilistic power flow of distribution systems based on data clustering

Cited by 21 publications

References 36 publications

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Probabilistic Optimal Power Flow-Based Spectral Clustering Method Considering Variable Renewable Energy Sources

Environmental Constrained Optimal Hybrid Energy Storage System Planning for an Indian Distribution Network

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