Customized Normalization Method to Enhance the Clustering Process of Consumption Profiles

The restructuring of electricity markets brought many changes to markets operation. To overcome these new challenges, the study of electricity markets operation has been gaining an increasing importance.With the emergence of microgrids and smart grids, new business models able to cope with new opportunities are being developed. New types of players are also emerging, allowing aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers. The virtual power player (VPP) facilitates their participation in the electricity markets and provides a set of new services promoting generation and consumption efficiency, while improving players` benefits. The contribution of this paper is a customized normalization method that supports a clustering methodology for the remuneration and tariffs definition from VPPs. To implement fair and strategic remuneration and tariff methodologies, this model uses a clustering algorithm, applied on normalized load values, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage.The present work was done and funded in the scope of the following projects: People Programme of the European Union's Seventh Framework Programme FP7/2007-2013/ project ELECON, REA grant agreement No 318912; EUREKA - ITEA2 Project M2MGrids with project number 13011info:eu-repo/semantics/publishedVersio

Customized normalization clustering meth-odology for consumers with heterogeneous characteristics

The increasing use and development of renewable energy sources and distributed generation, brought several changes to the power system operation. Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. This paper proposes a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, applied to load values, submitted to different types of normalization process, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage unit

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

A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm,leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10\% of the dataset, exhibit significant internal dissimilarity and thus it splits them even further to create nine clusters in total. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted Demand Response programs.Comment: 29 pages, 19 figure

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market. First, an adaptive clustering-based customer segmentation framework is proposed to categorize customers into different groups to enable the effective identification of usage patterns. Second, customized demand models with important market constraints which capture the price-demand relationship explicitly, are developed for each group of customers to improve the model accuracy and enable meaningful pricing. Third, the multiple pricing based demand response is formulated as a profit maximization problem subject to realistic market constraints. The overall aim of the proposed scalable and practical method aims to achieve 'right' prices for 'right' customers so as to benefit various stakeholders in the system such as grid operators, customers and retailers. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets

Coordinated Optimal Voltage Control in Distribution Networks with Data-Driven Methods

Voltage control is facing significant challenges with the increasing integration of photovoltaic (PV) systems and electric vehicles (EVs) in active distribution networks. This is leading to major transformations of control schemes that require more sophisticated coordination between different voltage regulation devices in different timescales. Except for conventional Volt/Var control (VVC) devices such on-load tap change (OLTC) and capacitor banks (CBs), inverter-based PVs are encouraged to participate in voltage regulation considering their flexible reactive power regulation capability. With the vehicle to grid (V2G) technology and inverter-based interface at charging stations, the charging power of an EV can be also controlled to support voltages. These emerging technologies facilitate the development of two-stage coordinated optimal voltage control schemes. However, these new control schemes pursue a fast response speed with local control strategies in shorter snapshots, which fails to track the optimal solutions for the distribution system operation. The voltage control methods mainly aim to mitigate voltage violations and reduce network power loss, but they seldom focus on satisfying the various requirements of PV and EV customers. This may discourage customer-owned resources from participating in ancillary services such as voltage regulation. Moreover, model-based voltage control methods highly rely on the accurate knowledge of power system models and parameters, which is sometimes difficult to obtain in real-life distribution networks. The goal of this thesis is to propose a data-driven two-stage voltage control framework to fill the research gaps mentioned above, showing what frameworks, models and solution methods can be used in the optimal voltage control of modern active distribution systems to tackle the security and economic challenges posed by high integration of PVs and EVs

Forecast-informed power load profiling: A novel approach

Power load forecasting plays a critical role in the context of electric supply optimization. The concept ofload characterization and profiling has been used in the past as a valuable approach to improve forecasting performance as well as problem interpretability. This paper proposes a novel, fully fledged theoretical framework for a joint probabilistic clustering andregression model, which is different from existing models that treat both processes independently. The clustering process is enhanced by simultaneously using the input data and the prediction targets during training. The model is thus capable of obtaining better clusters than other methods, leading to more informativedata profiles, while maintaining or improving predictive performance. Experiments have been conducted using aggregated load data from two U.S.A. regional transmission organizations, collected over 8 years. These experiments confirm that the proposed model achieves the goalsset for interpretability and forecasting performance.This work is partially supported by the National Science Foundation EPSCoR Cooperative Agreement OIA-1757207 and the SpanishMINECO grants TEC2014-52289-R and TEC2017-83838-R

ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at https://github.com/s-guenther/estss and https://zenodo.org/records/10213145