Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

Nojavan, Sayyad; Ajoulabadi, Vafa; Khalili, Tohid; Bidram, Ali

doi:10.48550/arxiv.2102.07828

Cited by 1 publication

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“…Examples of these schemes are direct-load control (DLC), interruptible/curtailable service (I/C), demand bidding/buy (DB), etc. [4]. Considering various end-user consumption behaviors, it required the utility companies to design reasonable strategies.…”

Section: Introductionmentioning

confidence: 99%

Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform

2022

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Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.

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Section: Introductionmentioning

confidence: 99%