[HTML][HTML] Aggregated demand-side energy flexibility: A comprehensive review on characterization, forecasting and market prospects

F Plaum, R Ahmadiahangar, A Rosin, J Kilter - Energy Reports, 2022 - Elsevier
Existing grids have been designed with traditional large centralized generation in mind;
however, with the ever-increasing utilization of renewable distributed energy resources, the …

Industry 4.0 and demand forecasting of the energy supply chain: A literature review

AR Nia, A Awasthi, N Bhuiyan - Computers & Industrial Engineering, 2021 - Elsevier
The number of publications in demand forecasting of the energy supply chain augmented
meaningfully due to the 2008 global financial crisis and its consequence on the global …

Let's wait awhile: How temporal workload shifting can reduce carbon emissions in the cloud

P Wiesner, I Behnke, D Scheinert, K Gontarska… - Proceedings of the …, 2021 - dl.acm.org
Depending on energy sources and demand, the carbon intensity of the public power grid
fluctuates over time. Exploiting this variability is an important factor in reducing the emissions …

[HTML][HTML] Blockchain based decentralized local energy flexibility market

C Antal, T Cioara, M Antal, V Mihailescu, D Mitrea… - Energy Reports, 2021 - Elsevier
Large-scale deployment of renewable energy sources brings new challenges for smart grid
management requiring the development of decentralized solutions and active participation …

Virtual power plant optimization in smart grids: A narrative review

B Goia, T Cioara, I Anghel - Future Internet, 2022 - mdpi.com
Virtual power plants (VPPs) are promising solutions to address the decarbonization and
energy efficiency goals in the smart energy grid. They assume the coordination of local …

[HTML][HTML] A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

S Pelekis, IK Seisopoulos, E Spiliotis… - … Energy, Grids and …, 2023 - Elsevier
Short-term load forecasting (STLF) is vital for the effective and economic operation of power
grids and energy markets. However, the non-linearity and non-stationarity of electricity …

In search of deep learning architectures for load forecasting: A comparative analysis and the impact of the Covid-19 pandemic on model performance

S Pelekis, E Karakolis, F Silva… - … & Applications (IISA), 2022 - ieeexplore.ieee.org
In power grids, short-term load forecasting (STLF) is crucial as it contributes to the
optimization of their reliability, emissions, and costs, while it enables the participation of …

[HTML][HTML] A tri-layer optimization framework for day-ahead energy scheduling based on cost and discomfort minimization

P Koukaras, P Gkaidatzis, N Bezas, T Bragatto… - Energies, 2021 - mdpi.com
Over the past few decades, industry and academia have made great strides to improve
aspects related with optimal energy management. These include better ways for efficient …

A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations

M Srivastava, PK Tiwari - Energy, 2024 - Elsevier
The operation of generating resources connected to VPPs during peak demand is crucial for
determining the economic sustainability of participants in a competitive electricity market and …

[HTML][HTML] Blockchain-based distributed federated learning in smart grid

M Antal, V Mihailescu, T Cioara, I Anghel - Mathematics, 2022 - mdpi.com
The participation of prosumers in demand-response programs is essential for the success of
demand-side management in renewable-powered energy grids. Unfortunately, the …