7,347 research outputs found

    Variability in electricity consumption by category of consumer: the impact on electricity load profiles

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    Residential electrification of transport and heat is changing consumption and its characteristics significantly. Previous studies have demonstrated the impact of socio-techno-economic determinants on residential consumption. However, they fail to capture the distributional characteristics of such consumer groups, which impact network planning and flexibility assessment. Using actual residential electricity consumption profile data for 720,000 households in Denmark, we demonstrate that heat pumps are more likely to influence aggregated peak consumption than electric vehicles. At the same time, other socio-economic factors, such as occupancy, dwelling area and income, show little impact. Comparing the extrapolation of a comprehensive rollout of heat pumps or electric vehicles indicates that the most common consumer category deploying heat pumps has 14% more maximum consumption during peak load hours, 46% more average consumption and twice the higher median compared to households owning an electric vehicle. Electric vehicle show already flexibility with coincidence factors that ranges between 5-15% with a maximum of 17% whereas heat pumps are mostly baseload. The detailed and holistic outcomes of this study support flexibility assessment and grid planning in future studies but also the operation of flexible technologies.Comment: 37 pages, 18 figures, journal articl

    Review of Non-Technical Losses Identification Techniques

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    Illegally consumption of electric power, termed as non-technical losses for the distribution companies is one of the dominant factors all over the world for many years. Although there are some conventional methods to identify these irregularities, such as physical inspection of meters at the consumer premises etc, but it requires large number of manpower and time; then also it does not seem to be adequate. Now a days there are various methods and algorithms have been developed that are proposed in different research papers, to detect non-technical losses. In this paper these methods are reviewed, their important features are highlighted and also the limitations are identified. Finally, the qualitative comparison of various non-technical losses identification algorithms is presented based on their performance, costs, data handling, quality control and execution times. It can be concluded that the graph-based classifier, Optimum-Path Forest algorithm that have both supervised and unsupervised variants, yields the most accurate result to detect non-technical losses

    A novel feature set for low-voltage consumers, based on the temporal dependence of consumption and peak demands

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    This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands

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

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    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

    Power Grid Network Evolutions for Local Energy Trading

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    The shift towards an energy Grid dominated by prosumers (consumers and producers of energy) will inevitably have repercussions on the distribution infrastructure. Today it is a hierarchical one designed to deliver energy from large scale facilities to end-users. Tomorrow it will be a capillary infrastructure at the medium and Low Voltage levels that will support local energy trading among prosumers. In our previous work, we analyzed the Dutch Power Grid and made an initial analysis of the economic impact topological properties have on decentralized energy trading. In this paper, we go one step further and investigate how different networks topologies and growth models facilitate the emergence of a decentralized market. In particular, we show how the connectivity plays an important role in improving the properties of reliability and path-cost reduction. From the economic point of view, we estimate how the topological evolutions facilitate local electricity distribution, taking into account the main cost ingredient required for increasing network connectivity, i.e., the price of cabling

    Residential Sector Energy Consumption at the Spotlight: From Data to Knowledge

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    Energy consumption is at the core of economic development, but its severe impacts on resources depletion and climate change have justified a call for its general reduction across all economic activities. Lowering households’ energy demand is a key factor to achieve carbon dioxide emission reductions as it has an important energy-saving potential. Households in the European Union (EU28) countries have a significant weight (25%) in the total final energy consumption. However, a wide range of variation is observed within the residential sector from 7.6 to 37.4 GJ per capita/annum, with the lowest consumption indicator observed in Southern EU countries. Energy consumption in the residential sector is a complex issue, explained by a combination of different factors. To pinpoint how to reduce energy consumption effectively while deliver energy services, we need to look not just at technology, but also to the factors that drive how and in what extent people consume energy, including the way they interact with technology (i.e., energy efficiency). The main objective of this research is to understand the differences in energy consumption arising from different socio-demographic, technologic, behavioral and economic characteristics of residential households. This research brings to the spotlight the needs and benefits of looking deeper into residential sector energy consumption in a southern European country. Portugal and the municipality of Évora, in particular, were selected as case studies. Residential sector consumption is a moving target, which increase the complexity of adequate policies and instruments that have to address the bottleneck between increase demand for e.g. climatization due to current lack of thermal comfort and to comply with objectives of increased energy efficiency which ultimately intend to reduce energy consumption. This calls for different levels of knowledge to feed multiscale policies. This dissertation expands the understanding of energy consumption patterns at households, consumers’ role in energy consumption profiles, indoor thermal comfort, and the levels of satisfaction from energy services demand. In a country potentially highly impacted by climate change, with low levels of income and significant lower energy consumption per capita compared to the EU28 average, looking into these issues gains even more importance. The work combines detailed analysis at different spatial (national, city and consumers level) and time scales (hour to annual) taking advantage of diverse methods and datasets including smart meters’ data, door to door surveys and energy simulation and optimization modelling. The results identify (i) ten distinct residential sector consumer groups (e.g., under fuel poverty); (ii) daily and annual consumption patterns (W, U and flat); (iii) major energy consumption determinants such as the physical characteristics of dwellings, particularly the year of construction and floor area; climatization equipment ownership and use, and occupants’ profiles (mainly number and monthly income). It is (iv) recognized that inhabitants try to actively control space heating, but without achieving indoor thermal comfort levels. The results also show (v) that technology can overweight the impact of practices and lifestyle changes for some end-uses as space heating and lighting. Nevertheless, important focus should be given to the evolution in the future of uncertain parameters related with consumer behavior, especially those on climatization, related to thermal comfort and equipment’s use. Furthermore, the research work presents a (vi) bottom-up methodology to project detailed energy end-uses demand, and (vii) an integrated framework for city energy planning. This work sets the ground for the definition of tailor-made policy recommendations for targeted consumer groups (e.g., vulnerable consumers) and climatization behavior/practices to reduce peak demand, social support policies, energy efficiency instruments and measures, renewable energy sources integration, and energy systems planning

    Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

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    Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

    Demand Response in Smart Grids

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    The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

    Regional And Residential Short Term Electric Demand Forecast Using Deep Learning

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    For optimal power system operations, electric generation must follow load demand. The generation, transmission, and distribution utilities require load forecasting for planning and operating grid infrastructure efficiently, securely, and economically. This thesis work focuses on short-term load forecast (STLF), that concentrates on the time-interval from few hours to few days. An inaccurate short-term load forecast can result in higher cost of generating and delivering power. Hence, accurate short-term load forecasting is essential. Traditionally, short-term load forecasting of electrical demand is typically performed using linear regression, autoregressive integrated moving average models (ARIMA), and artificial neural networks (ANN). These conventional methods are limited in application for big datasets, and often their accuracy is a matter of concern. Recently, deep neural networks (DNNs) have emerged as a powerful tool for machine-learning problems, and known for real-time data processing, parallel computations, and ability to work with a large dataset with higher accuracy. DNNs have been shown to greatly outperform traditional methods in many disciplines, and they have revolutionized data analytics. Aspired from such a success of DNNs in machine learning problems, this thesis investigated the DNNs potential in electrical load forecasting application. Different DNN Types such as multilayer perception model (MLP) and recurrent neural networks (RNN) such as long short-term memory (LSTM), Gated recurrent Unit (GRU) and simple RNNs for different datasets were evaluated for accuracies. This thesis utilized the following data sets: 1) Iberian electric market dataset; 2) NREL residential home dataset; 3) AMPds smart-meter dataset; 4) UMass Smart Home datasets with varying time intervals or data duration for the validating the applicability of DNNs for short-term load forecasting. The Mean absolute percentage error (MAPE) evaluation indicates DNNs outperform conventional method for multiple datasets. In addition, a DNN based smart scheduling of appliances was also studied. This work evaluates MAPE accuracies of clustering-based forecast over non-clustered forecasts
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