Critical load profile estimation for sizing of battery storage system

Sharma, Desh Deepak; Singh, S. N.; Rajpurohit, Bharat Singh; Longatt, F. G.

doi:10.1109/pesgm.2015.7285947

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Indian Institute of Technology Kanpur (IITK) distribution system gets power supply from Panki power grid via 33 kV lines. One 10 MVA and two 5 MVA, 33 kV/11 kV transformers are installed in main substation [41]. The 10 MVA transformer (Tr-3) of main substation caters the major demand in IITK.…”

Section: Results With Proposed Methodsmentioning

confidence: 99%

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Identification and characterization of irregular consumptions of load data

Sharma

Singh

Lin

et al. 2017

J. Mod. Power Syst. Clean Energy

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The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

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Section: Results With Proposed Methodsmentioning

confidence: 99%

“…Based on the analysis of loading on a 10 MVA transformer at 33 kV/11 kV substation of IITK, in 2013, authors have identified a critical load profile using k-means algorithm [41] while utilizing complete load pattern data. This profile decides possible size of energy storage, without PV generation, for peak shaving operation.…”

Section: Size Of Energy Storagementioning

confidence: 99%

Identification and characterization of irregular consumptions of load data

Sharma

Singh

Lin

et al. 2017

J. Mod. Power Syst. Clean Energy

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“…Observations are assigned to the \ clusters to minimize the average Euclidean distance of the observations from the cluster centroid. The objective of the K-means algorithm is the Euclidean distance minimization as n data objects are separated into \ clusters [20].…”

Section: B Bess Sizingmentioning

confidence: 99%

Economic Viability of Distribution Network Upgrade Deferral through BESS Sizing from K-Means Clustered Annual Load Profile Data

Ondigo,

Wekesa

2024

Eng. Technol. Appl. Sci. Res.

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The augmented electricity demand requires electrical infrastructure upgrades with system operators instituting strategies to increase Distribution Network (DN) capacity in tandem with load growth. In this study, a simple method of deploying Li-ion Battery Energy Storage Systems (BESSs) to defer DN upgrades is presented by utilizing historical load profiles. The k-means algorithm is employed to cluster the annual load profiles obtained from a substation in groups of 15-minute intervals. The load data are min-max scaled and fed as input to the K-means algorithm. The NPV financial analysis method is followed in the DN upgrade deferral benefit determination with the acquired benefit depending on Li-ion BESS price and feeder upgrade cost. The results indicate economic viability of up to four years with a Net Present Value (NPV) of US$10k for Li-ion 2000kW/3000kWh BESS. More benefits and deferral years are achieved by varying Li-ion BESS and feeder upgrade costs to 80% and 120%, respectively with deferral years increased to six with an NPV of US$110k for Li-ion BESS of 3100kW/6000kWh.

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“…Alternatively, authors in [16] have reduced the data resolution by converting 10-s data to 15-min values in order to ease the computational burden over a year, which eliminates the high-frequency components of the power profile, and therefore, is not applicable for a hybrid ESS. To properly find a representative power profile for the whole data set, clustering techniques, such as K-means clustering [10], [17], [18] or fuzzy C-means clustering [19] have also been used. Using a clustering approach, daily power profiles with similar features (e.g., peak and mean value) are grouped together, and the cluster centres are selected for the sizing study as the representative power profile.…”

Section: Introductionmentioning

confidence: 99%