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Found 13 papers, 6 papers with code
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e. g., photovoltaic and electric vehicles) are increasingly adopted.
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.
Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems
In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in distribution systems.
PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks
Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks' operation and planning.
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.
Volt/VAR Optimization in the Presence of Attacks: A Real-Time Co-Simulation Study
This paper develops a real-time co-simulation setup to assess the effect of cyberattacks on VVO.
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.
Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF
This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty.
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.
Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-Physical Microgrid Systems
We investigate the rootkits' precompromise stage involving the deployment to multiple system locations and aggregation of system-specific information to build a neural network-based virtual data-driven model (VDDM) of the system.
