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Found 16 papers, 6 papers with code
Quantifying the Aggregate Flexibility of EV Charging Stations for Dependable Congestion Management Products: A Dutch Case Study
In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service.
Stochastic Mean Field Game for Strategic Bidding of Consumers in Congested Distribution Networks
The rapid increase of photovoltaic cells, batteries, and Electric Vehicles (EVs) in electric grids can result in congested distribution networks.
Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs
In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand.
Representative Days and Hours with Piecewise Linear Transitions for Power System Planning
Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability.
Capturing Chronology and Extreme Values of Representative Days for Planning of Transmission Lines and Long-Term Energy Storage Systems
The proposed method is examined in a complex co-planning model for transmission lines, wind power plants (WPPs), short-term battery and long-term pumped hydroelectric energy storage systems.
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.
Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles
Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact.
Reinforcement Learning by Guided Safe Exploration
Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses.
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.
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.
Coordination of Heterogeneous Deferrable Loads using the F-MBC Mechanism
Increasing participation of prosumers in the electricity grid calls for efficient operational strategies for utilizing the flexibility offered by Distributed Energy Resources (DER) to match supply and demand.
Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method
Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face.
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios.
Low-complexity decentralized algorithm for aggregate load control of thermostatic loads
Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource.
Sample-Derived Disjunctive Rules for Secure Power System Operation
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems.
