Develop Load Shape Dictionary Through Efficient Clustering Based on Elastic Dissimilarity Measure

Liang, Huishi; Jin, Ma

doi:10.1109/tsg.2020.3017777

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…A clustering is applied in [10] to load patterns represented as images, and then periods with similar consumption levels are identified by considering load variation and uncertainty. A bilevel load shape dictionary is developed in [11], where extracted features such as weekly and seasonal patterns and segment entropy characterize the energy usage of customers. Consumption dynamics for each enduser are formulated first in [12]; then, from a clustering process, an evaluation of variability in the resulting clusters is performed by an entropy analysis.…”

Section: Initial and Non-initial Cluster Centers Of Clustermentioning

confidence: 99%

Online Demand Response Characterization Based on Variability in Customer Behavior

Marrero,

Sbarbaro,

Garcia-Santander

2024

Journal of Modern Power Systems and Clean Energy

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This paper proposes an online framework to characterize demand response (DR) over time. The proposed framework facilitates obtaining and updating the daily consumption patterns of customers. The essential concept of response profile class (RPC) is introduced for characterization and complemented by the measure of the variability in customer behavior. This paper uses a modified version of the incremental clustering by fast search and find of density peaks (CFSFDP) algorithm for daily profiles, considering the multivariate normal kernel density estimator and incremental forms of the Davies-Bouldin (iDB) and Xie-Beni (iXB) validity indices. Case studies conducted using real-world and simulated daily profiles of residential and commercial Chilean end-users have demonstrated how the proposed framework can continuously characterize DR. The proposed framework is proven to achieve realistic customer models for effective energy management by estimating the customer response to price signals at the distribution system operator (DSO) level. Index Terms--Demand response (DR), incremental indice, online characterization, online clustering, response profile class (RPC). NOMENCLATUREShrink factor Product of ρ i and δ i Interval between two consecutive time points The minimum distance Electricity price at time point t Local density Auxiliary matrix [ p kl r 0 kv ]

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Section: Initial and Non-initial Cluster Centers Of Clustermentioning

confidence: 99%

Online Demand Response Characterization Based on Variability in Customer Behavior

Marrero,

Sbarbaro,

Garcia-Santander

2024

Journal of Modern Power Systems and Clean Energy

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“…There have also been several DSM studies with clustering of electricity consumption profiles. Liang et al [29] proposed a method for aggregating household load curves into a two-tier loading curve dictionary for the purpose of load forecasting. In their findings, two levels of load were used to obtain representative load profiles based on consumption information.…”

Section: Dsm and Nmmentioning

confidence: 99%

An energy demand-side management and net metering decision framework

Wen

Liu

Yang

et al. 2023

Energy

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“…DDTW employs the directional change information inherent in the sequences for alignment matching, with the change trend being encapsulated by the first-order derivative. Consequently, within the DDTW matching algorithm, the comparison entails evaluating the squared difference of derivatives between each pair of points [ 24 ]. The procedural sequence is delineated as follows:…”

Section: Ac Side Power Quality Zoning Strategy Considering Distribute...mentioning

confidence: 99%

A Collaborative Governance Strategy for Power Quality in AC/DC Distribution Networks Considering the Coupling Characteristics of Both Sides

Wang,

Qin,

Xiang

et al. 2023

Sensors

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The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis.

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Develop Load Shape Dictionary Through Efficient Clustering Based on Elastic Dissimilarity Measure

Cited by 19 publications

References 26 publications

Online Demand Response Characterization Based on Variability in Customer Behavior

Online Demand Response Characterization Based on Variability in Customer Behavior

An energy demand-side management and net metering decision framework

A Collaborative Governance Strategy for Power Quality in AC/DC Distribution Networks Considering the Coupling Characteristics of Both Sides

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