K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

Characterising Domestic Electricity Demand for Customer Load Profile Segmentation

The aim of this research was to characterise domestic electricity patterns of use on a diurnal, intra-daily and seasonal basis as a function of customer characteristics. This was done in order to produce a library of representative electricity demand load profiles that are characteristic of how households consume electricity. In so doing, a household’s electricity demand can be completely characterised based solely on their individual customer characteristics. A number of different approaches were investigated as to their ability to characterise domestic electricity use. A statistical regression approach was evaluated which had the advantage of identifying key dwelling, occupant and appliance characteristics that influence electricity use within the home. An autoregressive Markov chain method was applied which proved to be effective at characterising the magnitude component to electricity use within the home but failed to adequately characterise the temporal properties sufficiently. Further time series techniques were investigated: Fourier transforms, Gaussian processes, Neural networks, Fuzzy logic, and Wavelets, with the former two being evaluated fully. Each method provided disparate results but proved to be complimentary to each other in terms of their ability to characterise different patterns of electricity use. Both approaches were able to sufficiently characterise the temporal characteristics satisfactorily, however, were unable to adequately associate customer characteristics to the load profile shape. Finally clustering based approaches such as: k-means, k-medoid and Self Organising Maps (SOM) were investigated. SOM showed the greatest potential and when combined with statistical and regression techniques proved to be an effective way to completely characterise electricity use within the home and their associated customer characteristics. A library of domestic electricity demand load profiles representing common patterns of electricity use on a diurnal, intra-daily and seasonal basis within the home in Ireland and their associated household characteristics are then finally presented

Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing

Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer's personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attri

A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

© 2019 Elsevier B.V. The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use

Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy

A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin note: text overlap with arXiv:2111.1050