Baseline powergrid model for NY

Related tags

Deep LearningNYgrid
Overview

Baseline-powergrid-model-for-NY

Table of Contents
  1. About The Project
  2. Usage
  3. License
  4. Contact
  5. Acknowledgements

About The Project

As the urgency to address climate change intensifies, the integration of distributed and intermittent renewable resources in power grids will continue to accelerate. To ensure the reliability and efficacy of the transformed system, researchers and other stakeholders require a validated representation of the essential characteristics of the power grid that is accurate for a specific region under study. For example, the Climate Leadership and Community Protection Act (CLCPA) in New York sets ambitious targets for transformation of the energy system, opening many interesting research and analysis questions. To provide a platform for these analyses, this paper presents an overview of the current NYS power grid and develops an open-source1 baseline model using only publicly available data. The proposed model is validated with real data for power flow and Locational Marginal Prices (LMPs) to demonstrate the feasibility, functionality and consistency of the model with hourly data of 2019 as an example. The model is easily adjustable and customizable for various analyses of future configurations and scenarios that require spatial-temporal information of the NYS power grid with data access to all the available historical data, and serves as a practical system for general methods and algorithms testing.

Built With

The code is written with Matlab and depends on the installation of Matpower. Please go to the following websties and follow the instructions to install Matlab and Matpower.

Usage

  1. git clone https://github.com/AndersonEnergyLab-Cornell/NYgrid
  2. Add the full folder and the subfolders to your Matlab Path
  3. Modify the main.m file to run a specific case

Main.m

Specify a year, and download and format the data in that year. Downlaoded data are stored in the "Prep" directory. Formatted data are stored in the "Data" directory. For example, to run for Jan 1st 2019 1:00 am, modify the test year, month, day and hour.

  testyear = 2019;
  testmonth = 1;
  testday = 1;
  testhour = 1;

Data sources include:

  1. NYISO:
    • hourly fuel mix
    • hourly interface flow
    • hourly real time price
  2. RGGI:
    • hourly generation for thermal generators larger than 25 MW
  3. NRC:
    • Daily nuclear capacity factor
  4. EIA:
    • Monthly hydro generation data for Niagara and St. Lawrence

The main function first update the operation condition for load and generators from the historical data and store the modified mpc struct in mpcreduced Then it automatically calls the Optimal Power Flow and Power Flow test and store the result in resultOPF and resultPF, respectively.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Vivienne Liu - [email protected]

Project Link: https://github.com/AndersonEnergyLab-Cornell/NYgrid

Acknowledgements

Owner
Anderson Energy Lab at Cornell
Cornell Research lab on sustainable energy, led by Prof. Lindsay Anderson
Anderson Energy Lab at Cornell
Notebooks, slides and dataset of the CorrelAid Machine Learning Winter School

CorrelAid Machine Learning Winter School Welcome to the CorrelAid ML Winter School! Task The problem we want to solve is to classify trees in Roosevel

CorrelAid 12 Nov 23, 2022
This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

Mahmoud Gamal Salem 166 Dec 06, 2022
Task-related Saliency Network For Few-shot learning

Task-related Saliency Network For Few-shot learning This is an official implementation in Tensorflow of TRSN. Abstract An essential cue of human wisdo

1 Nov 18, 2021
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022
NP DRAW paper released code

NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation This repo contains the official implementation for the NP-DRAW paper.

ZENG Xiaohui 22 Mar 13, 2022
Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

OG-SPACE Introduction Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE) is a computational framewo

Data and Computational Biology Group UNIMIB (was BI*oinformatics MI*lan B*icocca) 0 Nov 17, 2021
a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

pytorch-liteflownet This is a personal reimplementation of LiteFlowNet [1] using PyTorch. Should you be making use of this work, please cite the paper

Simon Niklaus 365 Dec 31, 2022
Dataset para entrenamiento de yoloV3 para 4 clases

Deteccion de objetos en video Este repo basado en el proyecto PyTorch YOLOv3 para correr detección de objetos sobre video. Construí sobre este proyect

1 Nov 01, 2021
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
ATAC: Adversarially Trained Actor Critic

ATAC: Adversarially Trained Actor Critic Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan

Microsoft 41 Dec 08, 2022
Semantic Segmentation Suite in TensorFlow

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

George Seif 2.5k Jan 06, 2023
SeqAttack: a framework for adversarial attacks on token classification models

A framework for adversarial attacks against token classification models

Walter 23 Nov 25, 2022
😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

------ Update September 2018 ------ It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such t

Hugging Face 865 Dec 24, 2022
Code for our CVPR 2021 paper "MetaCam+DSCE"

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21) Introduction Code for our CVPR 2021

FlyingRoastDuck 59 Oct 31, 2022
The official implementation code of "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction."

PlantStereo This is the official implementation code for the paper "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction".

Wang Qingyu 14 Nov 28, 2022
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
A code generator from ONNX to PyTorch code

onnx-pytorch Generating pytorch code from ONNX. Currently support onnx==1.9.0 and torch==1.8.1. Installation From PyPI pip install onnx-pytorch From

Wenhao Hu 94 Jan 06, 2023
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

Jacobi(Jiabo He) 147 Dec 05, 2022
Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Phil Wang 383 Jan 02, 2023
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation This is an unofficial PyTorch

MINDs Lab 170 Jan 04, 2023