Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation

Ma, Chenjie; Menke, Jan‐Hendrik; Dasenbrock, Johannes; Braun, Martin; Hayek, Matthias; Schmid, Karl-Heinz

doi:10.1016/j.apenergy.2019.113907

Cited by 28 publications

(17 citation statements)

References 35 publications

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“…washing appliances after preparation or looking at the TV. The basic concept of every model relies on the design pattern growth or frequent pattern development approach, using a depth-first divide-and-conquer technique strategy [20][21][22]. This operation was not usually being carried out, but it was detached, which is obviously not applicable to well-being applications involving fast dynamic response.…”

Section: The Forecasting Framework Of Proposed Approachesmentioning

confidence: 99%

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

2021

KSII TIIS

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The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

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Section: The Forecasting Framework Of Proposed Approachesmentioning

confidence: 99%

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

2021

KSII TIIS

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“…However, for distribution system operators to monitor photovoltaic diffusion, the use of internal information from consumer units already opting for photovoltaic systems is useful in forecasting adoption. It is because the electricity distributor can map its consumer units and indicate which consumers are affected by this type of influence, taking into account their operating characteristics [119].…”

Section: The Dimension Of Behavioral Viabilitymentioning

confidence: 99%

Factors for Measuring Photovoltaic Adoption from the Perspective of Operators

et al. 2020

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The diffusion of photovoltaic distributed generation is relevant for addressing the political, economic, and environmental issues in the electricity sector. However, the proliferation of distributed generation brings new administrative and operational challenges for the sustainability of electric power utilities. Electricity distributors operate in economies of scale, and the high photovoltaic penetration means that these companies have economic and financial impacts, in addition to influencing the migration of other consumers. Thus, this paper aims to systematically identify and evaluate critical factors and indicators that may influence electricity distributors in predicting their consumers' adoption of photovoltaic technology, which were subjected to the analysis of 20 industry experts. Results show that the cost of electricity, generation capacity, and cost of the photovoltaic systems are the most relevant indicators, and it is possible to measure a considerable part of them using the internal data of the electricity distributors. The study contributes to the understanding of the critical factors for the forecast of the adoption of consumers to distributed photovoltaic generation, to assist the distribution network operators in the decision making, and the distribution sustainability. Also, it establishes the theoretical, political, and practical implications for the Brazilian scenario and developing countries.

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“…It was also used for testing new approaches to contingency analysis [44], curtailment minimization of distributed generators [45], state estimation [46] and fault occurrence evaluation [47]. Its special potential lies in the application within studies that automatedly analyze large grid areas, such as grid integration studies [10,48] or energy loss studies [49,50]. Due to its similar design and performance, pandapipes is capable of performing similar studies with respect to piping grids, and in combination with pandapower also to MEGs.…”

Section: Multi-energy Grid Simulation: Overview Of Approaches and Reqmentioning

confidence: 99%

pandapipes: an Open Source Piping Grid Calculation Package for the Application in Coupled Multi-Energy Grid Simulations

Lohmeier¹,

Cronbach²,

Drauz³

et al. 2020

Preprint

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The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.

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Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation

Cited by 28 publications

References 35 publications

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

Factors for Measuring Photovoltaic Adoption from the Perspective of Operators

pandapipes: an Open Source Piping Grid Calculation Package for the Application in Coupled Multi-Energy Grid Simulations

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