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
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

ML Lineage Helper This library is a wrapper around the SageMaker SDK to support ease of lineage tracking across the ML lifecycle. Lineage artifacts in

AWS Samples 12 Nov 01, 2022
PyTorch evaluation code for Delving Deep into the Generalization of Vision Transformers under Distribution Shifts.

Out-of-distribution Generalization Investigation on Vision Transformers This repository contains PyTorch evaluation code for Delving Deep into the Gen

Chongzhi Zhang 72 Dec 13, 2022
Molecular AutoEncoder in PyTorch

MolEncoder Molecular AutoEncoder in PyTorch Install $ git clone https://github.com/cxhernandez/molencoder.git && cd molencoder $ python setup.py insta

Carlos Hernández 80 Dec 05, 2022
Randomizes the warps in a stock pokeemerald repo.

pokeemerald warp randomizer Randomizes the warps in a stock pokeemerald repo. Usage Instructions Install networkx and matplotlib via pip3 or similar.

Max Thomas 6 Mar 17, 2022
The implementation of DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 06, 2023
An NLP library with Awesome pre-trained Transformer models and easy-to-use interface, supporting wide-range of NLP tasks from research to industrial applications.

简体中文 | English News [2021-10-12] PaddleNLP 2.1版本已发布!新增开箱即用的NLP任务能力、Prompt Tuning应用示例与生成任务的高性能推理! 🎉 更多详细升级信息请查看Release Note。 [2021-08-22]《千言:面向事实一致性的生

6.9k Jan 01, 2023
Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

AgentFormer This repo contains the official implementation of our paper: AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecast

Ye Yuan 161 Dec 23, 2022
This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"

Learning to propose objects This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Ko

Philipp Krähenbühl 90 Sep 10, 2021
Boosted CVaR Classification (NeurIPS 2021)

Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar NeurIPS 2021 Table of Contents Quick Start Train

Runtian Zhai 4 Feb 15, 2022
Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

The value of international students to the United States. Probability of getting a non-immigrant visa. Project timeline: Jan 2021 - April 2021 Project

Zinaida Dvoskina 2 Nov 21, 2021
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

Vikash Sehwag 65 Dec 19, 2022
A python package simulating the quasi-2D pseudospin-1/2 Gross-Pitaevskii equation with NVIDIA GPU acceleration.

A python package simulating the quasi-2D pseudospin-1/2 Gross-Pitaevskii equation with NVIDIA GPU acceleration. Introduction spinor-gpe is high-level,

2 Sep 20, 2022
CTF challenges from redpwnCTF 2021

redpwnCTF 2021 Challenges This repository contains challenges from redpwnCTF 2021 in the rCDS format; challenge information is in the challenge.yaml f

redpwn 27 Dec 07, 2022
Experiments with the Robust Binary Interval Search (RBIS) algorithm, a Query-Based prediction algorithm for the Online Search problem.

OnlineSearchRBIS Online Search with Best-Price and Query-Based Predictions This is the implementation of the Robust Binary Interval Search (RBIS) algo

S. K. 1 Apr 16, 2022
Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks

This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi

Shunta Saito 255 Sep 07, 2022
Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Learning Canonical Representations for Scene Graph to Image Generation (ECCV 2020) Roei Herzig*, Amir Bar*, Huijuan Xu, Gal Chechik, Trevor Darrell, A

roei_herzig 24 Jul 07, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

Ilya Kostrikov 3k Dec 31, 2022
Video-based open-world segmentation

UVO_Challenge Team Alpes_runner Solutions This is an official repo for our UVO Challenge solutions for Image/Video-based open-world segmentation. Our

Yuming Du 84 Dec 22, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Dec 31, 2022
RIM: Reliable Influence-based Active Learning on Graphs.

RIM: Reliable Influence-based Active Learning on Graphs. This repository is the official implementation of RIM. Requirements To install requirements:

Wentao Zhang 4 Aug 29, 2022