An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks

S Radhoush, BM Whitaker, H Nehrir - Energies, 2023 - mdpi.com
Distribution grids must be regularly updated to meet the global electricity demand. Some of
these updates result in fundamental changes to the structure of the grid network. Some …

Forecasting buying intention through artificial neural network: an algorithmic solution on direct-to-consumer brands

B Prasad, I Ghosal - FIIB Business Review, 2022 - journals.sagepub.com
The direct-to-consumer (DTC) brands are emerging to reach more number of consumers
with more potential to meet their expectations. They are characterized through their …

Data analytics in the electricity market: a systematic literature review

MH Imani, E Bompard, P Colella, T Huang - Energy Systems, 2023 - Springer
In the last decade, data analytics studies have covered a wide range of fields across the
entire value chain in the electricity sector, from production and transmission to the electricity …

Distribution locational marginal pricing (dlmp) for unbalanced three-phase networks

S Mohammadi, MR Hesamzadeh… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This paper applies the principles of distribution locational marginal pricing (DLMP) to
unbalanced three-phase distribution networks. We first propose a linear model for AC …

Econometric modeling of intraday electricity market price with inadequate historical data

S Mohammadi, MR Hesamzadeh - 2022 IEEE Workshop on …, 2022 - ieeexplore.ieee.org
The intraday (ID) electricity market has received an increasing attention in the recent EU
electricity-market discussions. This is partly because the uncertainty in the underlying power …

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

A Khodadadi, MR Hesamzadeh… - IEEE Systems Journal, 2022 - ieeexplore.ieee.org
This article presents a new approach for finding the optimal multimarket trading strategy of
cascaded hydropower plants (HPPs) in the sequential electricity markets. These markets are …

Safe Deep Reinforcement Learning for Power System Operation under Scheduled Unavailability

X Weiss, S Mohammadi, P Khanna… - 2023 IEEE Power & …, 2023 - ieeexplore.ieee.org
The electrical grid is a safety-critical system, since incorrect actions taken by a power system
operator can result in grid failure and cause harm. For this reason, it is desirable to have an …

A Perspective on Foundation Models for the Electric Power Grid

HF Hamann, T Brunschwiler, B Gjorgiev… - arXiv preprint arXiv …, 2024 - arxiv.org
Foundation models (FMs) currently dominate news headlines. They employ advanced deep
learning architectures to extract structural information autonomously from vast datasets …

Ancillary Services in Power System Transition Toward a 100% Non-Fossil Future: Market Design Challenges in the United States and Europe

L Viola, S Nordin, D Dotta, MR Hesamzadeh… - arXiv preprint arXiv …, 2023 - arxiv.org
The expansion of variable generation has driven a transition toward a 100\% non-fossil
power system. New system needs are challenging system stability and suggesting the need …

Bidding behavior analysis in joint electricity and carbon market by hybrid experimental learning

J Ye, Y Hu, J Liu, W Liu, G Liang - 2022 IEEE 5th International …, 2022 - ieeexplore.ieee.org
It is important to model the causality behind social behaviors in the electricity market.
Existing methods, including theoretical models and economic experiments, are difficult to be …