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
Definition of a business problem according to Wilson Lower Bound Score and Time Based Average Rating

Wilson Lower Bound Score, Time Based Rating Average In this study I tried to calculate the product rating and sorting reviews more accurately. I have

3 Sep 30, 2021
People Interaction Graph

Gihan Jayatilaka*, Jameel Hassan*, Suren Sritharan*, Janith Senananayaka, Harshana Weligampola, et. al., 2021. Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Id

University of Peradeniya : COVID Research Group 1 Aug 24, 2022
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl

idealo 4k Jan 08, 2023
FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At

Yu-Che Tsai 64 Dec 13, 2022
[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver

Shape As Points (SAP) Paper | Project Page | Short Video (6 min) | Long Video (12 min) This repository contains the implementation of the paper: Shape

394 Dec 30, 2022
Chinese clinical named entity recognition using pre-trained BERT model

Chinese clinical named entity recognition (CNER) using pre-trained BERT model Introduction Code for paper Chinese clinical named entity recognition wi

Xiangyang Li 109 Dec 14, 2022
Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

NeRF++ Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields" Work with 360 capture of large-scale unbounded scenes. Sup

Kai Zhang 722 Dec 28, 2022
Just Go with the Flow: Self-Supervised Scene Flow Estimation

Just Go with the Flow: Self-Supervised Scene Flow Estimation Code release for the paper Just Go with the Flow: Self-Supervised Scene Flow Estimation,

Himangi Mittal 50 Nov 22, 2022
State-of-the-art data augmentation search algorithms in PyTorch

MuarAugment Description MuarAugment is a package providing the easiest way to a state-of-the-art data augmentation pipeline. How to use You can instal

43 Dec 12, 2022
GPU Programming with Julia - course at the Swiss National Supercomputing Centre (CSCS), ETH Zurich

Course Description The programming language Julia is being more and more adopted in High Performance Computing (HPC) due to its unique way to combine

Samuel Omlin 192 Jan 03, 2023
Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London

POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection Source code related to the article submitted to the Internati

Tomasz Szczepański 1 Apr 29, 2022
ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Introduction PyTorch code for the ICLR 2021 paper [i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning]. @inproceedings{lee2021i

Kibok Lee 68 Nov 27, 2022
A collection of random and hastily hacked together scripts for investigating EU-DCC

A collection of random and hastily hacked together scripts for investigating EU-DCC

Ryan Barrett 8 Mar 01, 2022
Simple (but Strong) Baselines for POMDPs

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Tianwei V. Ni 172 Dec 29, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning This is a small repo illustrating how to use WebDataset on ImageNet. usi

50 Dec 16, 2022
A simple image/video to Desmos graph converter run locally

Desmos Bezier Renderer A simple image/video to Desmos graph converter run locally Sample Result Setup Install dependencies apt update apt install git

Kevin JY Cui 339 Dec 23, 2022
Like a cowsay but without cows!

Foxsay This is a simple program that generates pictures of a cute fox with a message. It is like a cowsay but without cows! Fox girls are better! Usag

Anastasia Kim 28 Feb 20, 2022
Rethinking Transformer-based Set Prediction for Object Detection

Rethinking Transformer-based Set Prediction for Object Detection Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiD

Zhiqing Sun 62 Dec 03, 2022
Deep generative models of 3D grids for structure-based drug discovery

What is liGAN? liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grid

Matt Ragoza 152 Jan 03, 2023
an Evolutionary Algorithm assisted GAN

EvoGAN an Evolutionary Algorithm assisted GAN ckpts

3 Oct 09, 2022