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Found 14 papers, 6 papers with code
On Future Power Systems Digital Twins: Towards a Standard Architecture
This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain.
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions.
Quantum Neural Networks for Power Flow Analysis
This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis.
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective
The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models.
Towards Scalable FMI-based Co-simulation of Wind Energy Systems Using PowerFactory
Due to the increased deployment of renewable energy sources and intelligent components the electric power system will exhibit a large degree of heterogeneity, which requires inclusive and multi-disciplinary system assessment.
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation.
Impact of Dynamic Tariffs for Smart EV Charging on LV Distribution Network Operation
With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement.
Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm
To overcome this, in this paper, a DRL algorithm (namely MIP-DQN) is proposed, capable of \textit{strictly} enforcing all operational constraints in the action space, ensuring the feasibility of the defined schedule in real-time operation.
Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder
Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical data.
Generating Contextual Load Profiles Using a Conditional Variational Autoencoder
Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient.
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling
This trade-off introduces extra hyperparameters that impact the DRL algorithms' performance and capability of providing feasible solutions.
Generating Multivariate Load States Using a Conditional Variational Autoencoder
In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed.
Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system.
Detection of False Data Injection Attacks Using the Autoencoder Approach
State estimation is of considerable significance for the power system operation and control.
