Impact of solar panel orientation on the integration of solar energy in low-voltage distribution grids

In Belgium, and many other countries, rooftop solar panels are becoming a ubiquitous form of decentralised energy production. The increasing share of these distributed installations however imposes many challenges on the operators of the low-voltage distribution grid. They must keep the voltage levels and voltage balance on their grids in check and are often regulatory required to provide sufficient reception capacity for new power producing installations. By placing solar panels in different inclinations and azimuth angles, power production profiles can possibly be shifted to align more with residential power consumption profiles. In this article, it is investigated if the orientation of solar panels can have a mitigating impact on the integration problems on residential low voltage distribution grids. An improved simulation model of a solar panel installation is constructed, which is used to simulate the impact on a residential distribution grid. To stay as close to real-life conditions as possible, real irradiation data and a model of an existing grid are used. Both the developed model as the results on grid impact are evaluated

C-Vine copula mixture model for clustering of residential electrical load pattern data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method

An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings

Buildings are responsible for over 30% of global final energy consumption and nearly 40% of total CO2 emissions. Thus, rapid penetration of renewable energy technologies (RETs) in this sector is required. Integration of renewable energy sources (RESs) into residential buildings should not only guarantee an overall neutral energy balance over long term horizon (nZEB concept), but also provide a higher flexibility, a real-time monitoring and a real time interaction with end-users (smart-building concept). Thus, increasing interest is being given to the concepts of Hybrid Renewable Energy Systems (HRES) and Multi-Energy Buildings, in which several renewable and nonrenewable energy systems, the energy networks and the energy demand optimally interact with each other at various levels, exploring all possible interactions between systems and vectors (electricity, heat, cooling, fuels, transport) without them being treated separately. In this context, the present paper gives an overview of functional integration of HRES in Multi-Energy Buildings evidencing the numerous problems and potentialities related to the application of HRESs in the residential building sector. Buildingintegrated HRESs with at least two RESs (i.e., wind–solar, solar–geothermal and solar–biomass) are considered. The most applied HRES solutions in the residential sector are presented, and integration of HRES with thermal and electrical loads in residential buildings connected to external multiple energy grids is investigated. Attention is focused on the potentialities that functional integration can offer in terms of flexibility services to the energy grids. New holistic approaches to the management problems and more complex architectures for the optimal control are described

Residential Electrical Load Model based on Mixture Model Clustering and Markov Models

Detailed large-scale simulations require a lot of data. Residential electrical load profiles are well protected by privacy laws. Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations. A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper. The load profiles are clustered by a Mixed Model to group similar ones. Within the group, a behavior model is constructed with a Markov model. The states of the Markov models are based on the probability distribution of the electrical power. A second Markov model is created to randomize the behavior. A load profile is created by first performing a random-walking of the Markov models to get a sequence of states. The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power. © 2005-2012 IEEE.status: publishe

MACHINERY ANOMALY DETECTION UNDER INDETERMINATE OPERATING CONDITIONS

Anomaly detection is a critical task in system health monitoring. Current practice of anomaly detection in machinery systems is still unsatisfactory. One issue is with the use of features. Some features are insensitive to the change of health, and some are redundant with each other. These insensitive and redundant features in the data mislead the detection. Another issue is from the influence of operating conditions, where a change in operating conditions can be mistakenly detected as an anomalous state of the system. Operating conditions are usually changing, and they may not be readily identified. They contribute to false positive detection either from non-predictive features driven by operating conditions, or from influencing predictive features. This dissertation contributes to the reduction of false detection by developing methods to select predictive features and use them to span a space for anomaly detection under indeterminate operating conditions. Available feature selection methods fail to provide consistent results when some features are correlated. A method was developed in this dissertation to explore the correlation structure of features and group correlated features into the same clusters. A representative feature from each cluster is selected to form a non-correlated set of features, where an optimized subset of predictive features is selected. After feature selection, the influence of operating conditions through non-predictive variables are removed. To remove the influence on predictive features, a clustering-based anomaly detection method is developed. Observations are collected when the system is healthy, and these observations are grouped into clusters corresponding to the states of operating conditions with automatic estimation of clustering parameters. Anomalies are detected if the test data are not members of the clusters. Correct partitioning of clusters is an open challenge due to the lack of research on the clustering of the machinery health monitoring data. This dissertation uses unimodality of the data as a criterion for clustering validation, and a unimodality-based clustering method is developed. Methods of this dissertation were evaluated by simulated data, benchmark data, experimental study and field data. These methods provide consistent results and outperform representatives of available methods. Although the focus of this dissertation is on the application of machinery systems, the methods developed in this dissertation can be adapted for other application scenarios for anomaly detection, feature selection, and clustering