Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Valdés, Javier; Macía, Yunesky Masip; Dorner, Wolfgang; Camargo, Luis Ramirez

doi:10.1016/j.energy.2020.118962

Cited by 18 publications

(7 citation statements)

References 33 publications

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“…Regarding the geographical dimension, because of the lack of granular data on the geography, location, and demand loads, researchers and institutions have filled this gap by developing databases that offer an aggregated or typical view of the daily behavior of a consumer (Standard Load Profiles or SLP), the typical annual consumption of different consumers, and the consumption in a region based on the top-down calculations based on economic activities [22]. These databases are usually developed from disaggregation of data by class, and they do not always coincide with reality [23].…”

Section: Research Problemmentioning

confidence: 99%

Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria

et al. 2022

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In this study, the possibility of sector coupling with biological Power-to-Methane to support and stabilize the energy transition of the three major sectors of electricity, heat, and gas was addressed. For this purpose, the energy cell simulation methodology and the Calliope tool were utilized for energy system optimization. This combination provides detailed insights into the existing dependencies of consumers and fossil and renewable energy suppliers on a local scale. In this context, Power-to-Methane represents an efficient technology for quickly and effectively exploiting unused electricity potential for various sectors and consumers. It was found that, even in regions with low wind levels, this surplus electricity potential already exists and depends on various influencing factors in very different ways. The solar influence on these potentials was considered in connection with gas-fired cogeneration plants for district heating. It was found that the current heat demand for district heating produces a large amount of electricity and can generate surplus electricity in the winter. However, in the summer, large amounts of usable waste heat are dissipated into the environment, owing to the low consumption of district heat. This problem in the heat sector could be reduced by the expansion of photovoltaics, but this would require further expansion of storage or conversion systems in the electricity sector. This demonstrates that the consideration of several sectors is necessary to reflect the complexity of the sector coupling with Power-to-Methane properly.

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Section: Research Problemmentioning

confidence: 99%

Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria

et al. 2022

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“…Energy storages with high energy density, such as electrochemical batteries and LH2 tanks, can address the energy mismatch issue and provide power supply reliability. Furthermore, demand-side management strategies in accordance with renewable power characteristics and dynamic grid information can provide energy flexibility from the multi-energy systems [14]. The aggregated electric vehicles can become flexible sources and enhance system resilience, with the deferrable load and large storage capacity [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Low-carbon transition in smart city with sustainable airport energy ecosystems and hydrogen-based renewable-grid-storage-flexibility

Zhou

2022

Energy Reviews

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“…Using smart meter data, a classification method based on an agglomerative clustering algorithm aiming at detecting customers with photovoltaic units has been presented in [19]. In [20], a combination of partitioning and hierarchical clustering algorithms aiming at calculating demand-side management flexibility potential of industries using hourly electricity consumption data of smart meters has been presented.…”

Section: Introductionmentioning

confidence: 99%

Selecting and prioritizing the electricity customers for participating in demand response programs

Asghari

Zakariazadeh

Siano

2022

IET Generation Trans & Dist

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Demand response (DR) provides an opportunity for customers to play an important role in the operation of the electricity grid by reducing or shifting their electricity usage during peak periods. However, selecting customers to participate in DR programs is challenging. To solve this problem, typical load profiles should be characterized by data mining techniques such as clustering algorithms. Traditional clustering algorithms manually determine the centre of clusters and that the selected centre of clusters may fall into a local optimum. Here, to overcome these issues, a new clustering algorithm based on the Density Peak Clustering algorithm (DPC) and Artificial Bee Colony algorithm (ABC) which is called A‐DPC, is implemented to optimally determine the representative load curves. Moreover, by introducing a new priority index, the eligible residential customers are selected for participating in DR programs. Also, to meet the sufficient load reduction in a DR event, the proposed approach suggests a plenty number of residential customers to be called. The result evidence that A‐DPC has a stronger global search ability to optimally select the centre of clusters if compared to other clustering algorithms.

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Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Cited by 18 publications

References 33 publications

Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria

Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria

Low-carbon transition in smart city with sustainable airport energy ecosystems and hydrogen-based renewable-grid-storage-flexibility

Selecting and prioritizing the electricity customers for participating in demand response programs

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