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Found 45 papers, 3 papers with code
N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network
Case studies prove the proposed AHGNN and the associated N-1 ROPF are highly effective in reducing computation time while preserving solution quality, highlighting the promising potential of ML, particularly GNN in enhancing power system operations.
Optimal Planning of PV and Battery Resources in Remote Microgrids Considering Degradation Costs: An Iterative Post-Optimization Correction-based Approach
The optimal sizing and energy scheduling of PV and BESS pose significant importance for minimal investment and operational cost.
Power and Hydrogen Hybrid Transmission for Renewable Energy Systems: An Integrated Expansion Planning Strategy
This paper introduces an expansion strategy for electric power and hydrogen transmission systems, tailored for future renewable energy-enriched grids.
Transmission Planning for Climate-impacted Renewable Energy Grid: Data Preparation, Model Improvement, and Evaluation
As renewable energy is becoming the major resource in future grids, the weather and climate can have higher impact on the grid reliability.
Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting
Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.
Linearization of ReLU Activation Function for Neural Network-Embedded Optimization:Optimal Day-Ahead Energy Scheduling
Each method employs a set of linear constraints to replace the ReLU function, effectively linearizing the optimization problem, which can overcome the computational challenges associated with the nonlinearity of the neural network model.
Decentralized Micro Water-Energy Co-Optimization for Small Communities
The water-energy nexus encompasses the interdependencies between water and energy resources identifying the existing links between the production and distribution of these resources.
Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model
To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles.
Evaluation of Battery Storage to Provide Virtual Transmission Service
An immediate need in the transmission system is to find alternative solutions that improve system operation and defer the need for new transmission lines.
Convolutional Neural Network-based RoCoF-Constrained Unit Commitment
This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency.
Optimal Sizing of On-site Renewable Resources for Offshore Microgrids
A cost optimization renewable sizing (CORS) model is proposed to optimize the sizes of the generation and storage resources.
Optimal Dynamic Reconfiguration of Distribution Networks
Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost.
Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment
An active data sampling method is proposed to maintain the bindingness of the frequency related constraints.
Graph Neural Network-based Power Flow Model
A comprehensive performance analysis is conducted, comparing the proposed GNN-based power flow model with the traditional DC power flow model, as well as deep neural network (DNN) and convolutional neural network (CNN).
Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment
Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing.
Privacy-Preserving Decentralized Energy Management for Networked Microgrids via Objective-Based ADMM
This paper also presents a centralized energy management (CEM) model as a benchmark, and a post-processing proportional exchange algorithm (PEA) to balance the economic benefit of each microgrid.
Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile
Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power.
A Synthetic Texas Backbone Power System with Climate-Dependent Spatio-Temporal Correlated Profiles
Most power system test cases only have electrical parameters and can be used only for studies based on a snapshot of system profiles.
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr.
Cost-benefit Analysis And Comparisons For Different Offshore Wind Energy Transmission Systems
The other two methods utilize a hydrogen supercenter (HSC) and transmit offshore energy to and onshore substation through hydrogen pipelines.
Microgrid Optimal Energy Scheduling with Risk Analysis
We assume the infrastructure to set up an ad-hoc microgrid is already in place for a residential neighborhood with power sources such as photovoltaic (PV), diesel, and battery energy storage system (BESS).
Machine Learning Assisted Inertia Estimation using Ambient Measurements
The proposed LRCN and GCN based inertia estimation models achieve an accuracy of 97. 34% and 98. 15% respectively.
Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data
The installation capacity of energy storage system, especially the battery energy storage system (BESS), has increased significantly in recent years, which is mainly applied to mitigate the fluctuation caused by renewable energy sources (RES) due to the fast response and high round-trip energy efficiency of BESS.
Annual Benefit Analysis of Integrating the Seasonal Hydrogen Storage into the Renewable Power Grids
In this paper, an annual scheduling model (ASM) for energy hubs (EH) coupled power grids is proposed to investigate the annual benefits of seasonal hydrogen storage (SHS).
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems
Conventional synchronous generators are gradually being replaced by inverter-based resources, such transition introduces more complicated operation conditions.
Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems
RoCoF predictor is trained to predict the highest locational RoCoF based on a high-fidelity simulation dataset.
A 100% Renewable Energy System: Enabling Zero CO2 Emission Offshore Platforms
The total electricity consumption from offshore oil/gas platforms is around 16 TWh worldwide in 2019.
Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations
The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high-quality solutions.
Centralized Networked Micro Water-Energy Nexus with Proportional Exchange Among Participants
This paper proposes a Networked Micro Water-Energy Nexus (NetMicroWEN) capable of co-optimizing and simultaneously supplying water and energy to local consumers in nearby communities.
An Alternative Method for Solving Security-Constrained Unit Commitment with Neural Network Based Battery Degradation Model
When incorporating the NNBD model into security-constrained unit commitment (SCUC), we can establish a battery degradation based SCUC (BD-SCUC) model that can consider the equivalent battery degradation cost precisely.
The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitment
In this paper, hydrogen pipeline networks, combined with power-to-hydrogen (P2H) and hydrogen-to-power (H2P) facilities, are organized to form a Hydrogen Energy Transmission and Conversion System (HETCS).
Reduced Optimal Power Flow Using Graph Neural Network
It is concluded that the application of GNN for ROPF is able to reduce computing time while retaining solution quality.
Resilient Operational Planning for Microgrids Against Extreme Events
Moreover, the proposed ROP algorithm is able to obtain a greater SR overall compared to that achieved by the traditional MEM, and this benefit of using the proposed ROP increases as the inverter failure probabilities increase.
Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation
When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model.
Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model
Electricity price is a key factor affecting the decision-making for all market participants.
Neural Network-based Power Flow Model
The DC power flow model is a popular linear power flow model that is widely used in the power industry.
Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements
The increasing integration of renewable energy resources imports different dynamics into traditional power systems; therefore, the estimation of system inertia using mathematical model becomes more difficult.
Machine Learning Assisted Approach for Security-Constrained Unit Commitment
Security-constrained unit commitment (SCUC) is solved for power system day-ahead generation scheduling, which is a large-scale mixed-integer linear programming problem and is very computationally intensive.
Quantitative Analysis of Demand Response Using Thermostatically Controlled Loads
The flexible power consumption feature of thermostatically controlled loads (TCLs) such as heating, ventilation, and air-conditioning (HVAC) systems makes them attractive targets for demand response (DR).
Optimal Allocation of Virtual Inertia Devices for Enhancing Frequency Stability in Low-Inertia PowerSystems
In this study, a state-space model for the power system network is developed with VI as a frequency regulation method.
Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids
With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia.
Optimal Skeleton Network Reconfiguration considering Topological Characteristics and Transmission Path
Power system restoration can be divided into three stages: black-start, network reconfiguration, and load restoration.
A Novel Real-Time Energy Management Strategy for Grid-Supporting Microgrid: Enabling Flexible Trading Power
Numerical simulations demonstrate the performance of the proposed GSEM strategy which enables the grid operator to have a dispatch choice of trading power with MG and enhance the reliability and resilience of the main grid.
A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS).
Dynamic Estimation of Power System Inertia Distribution Using Synchrophasor Measurements
Integration of intermittent renewable energy sources in modern power systems is increasing very fast.
