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Found 19 papers, 2 papers with code
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations.
Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication
This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation.
Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty
In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations.
A Dynamic Equivalent Energy Storage Model of Natural Gas Networks for Joint Optimal Dispatch of Electricity-Gas Systems
The development of energy conversion techniques enhances the coupling between the gas network and power system.
Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network
Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality.
A Spatio-temporal Decomposition Method for the Coordinated Economic Dispatch of Integrated Transmission and Distribution Grids
In the spatial dimension, a multi-parametric programming projection based spatial decomposition algorithm is developed to coordinate the ED problems of TG and DNs in a distributed manner.
Parallel Computing Based Solution for Reliability-Constrained Distribution Network Planning
The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements.
Joint Chance-Constrained Economic Dispatch Involving Joint Optimization of Frequency-related Inverter Control and Regulation Reserve Allocation
The issues of uncertainty and frequency security could become significantly serious in power systems with the high penetration of volatile inverter-based renewables (IBRs).
Optimal Allocation of Virtual Inertia and Droop Control for Renewable Energy in Stochastic Look-Ahead Power Dispatch
To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes.
An Efficient Optimal Energy Flow Model for Integrated Energy Systems Based on Energy Circuit Modeling in the Frequency Domain
First, an energy circuit method (ECM) that algebraizes the PDEs of NGNs and DHNs in the frequency domain is introduced.
Energy Circuit-based Integrated Energy Management System: Theory, Implementation, and Application
Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years.
A Scenario-oriented Approach to Multi-period Energy-Reserve Joint Procurement and Pricing
In [1], a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism.
A Scenario-oriented Approach to Energy-Reserve Joint Procurement and Pricing
We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources.
Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices
Such VCC is formulated as a two-timescale optimization problem to jointly optimize FTCDs and STDDs in ADNs.
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service
As wind power penetration increases, the wind farms are required by newly released grid codes to provide frequency regulation service.
Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks
To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper.
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control
In this paper, we propose an online multi-agent reinforcement learning and decentralized control framework (OLDC) for VVC.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks
In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency.
Human-like machine thinking: Language guided imagination
Human thinking requires the brain to understand the meaning of language expression and to properly organize the thoughts flow using the language.
