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Web application for the simulation of day-ahead energy markets

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This repository contains all the work done for the Bachelor End Project (BEP) assignment: A Web Application For The Simulation Of Day-Ahead Energy Markets.
This project is made as an Bachelor Final End project assignment for the Electrical Energy Systems (EES) group of the departement of Electrical Engineering on the TU/e.
The project provides an web application where users may preform an simulation of the day ahead energy market , using an optimization model to dispatch the electricity in the most optimal way.
Users may choose multiple models for the optimization explained in models
The main goal of this project is to provide an easy to use (and modify) web application where users may play with market clearing problems where the overall cost of generators and load are minimized

Table of contents

Demo

Below is an gif which serves as an demonstration of how Stargazer works

Front-end vs back-end

Stargazer consists of two main parts namely the front-end written in React and the back-end is written in Python3 using Pyomo & PyPSA
NodeJs is further used to compile the React files to browser render-able Javascript files
In short the global overview of the application may be given by the figures below

Name origin

This project is named after a venomous fish called Stargazer
The fish has electricity conducting organs and is able to send an electric shock to its surroundings
This electric link is the reason the project is named after the Stargazer

Documentation

Documentation may be found in multiple forms code documentation can be found by going to docs an pdf and epub version of the code documentation may be found in the docs folder
A paper regarding the also be found in the docs folder and is written in the IEEE paper style

Optimization model

The web application is currently capable of optimizing using

  • Basic economic dispatch model
  • Network constrained unit commitment model

A third model, the stochastic programming joint market clearing model is proposed to be added to Stargazer.
For more in depth information about the inner workings of the model please read the paper

Basic economic dispatch model

The basic economic dispatch model preforms an optimization with an reduced set of constrains
The goal of the optimization proces is to minimize the overall cost of the powerplant, given the constrained parameters

$$ \text{minimize} } \quad & \sum_{t} \sum_{i} c_i \cdot p_{i,t} $$

$$ \text{subject to}\quad & \sum_{i} p_{i,t} = \sum_j l_{t,t} \hspace{.5cm} \forall\, t $$

$$ p_{i}^{min} \leq p_{i,t} \leq p_{i}^{max} \hspace{.5cm} \forall\,i,t $$

Network constrained unit commitment model

The network constrained model , models a more real world generator with limitations on the generating output regarding time i.e. ramp up, ramp down, minimum up time, minimum down time
The network model has been larlgey made around the PyPSA framework, and can be modeled using the following equations

$$ \text{minimize} }\quad & \sum_{t} \sum_i\sum_n c_{i} \cdot {p}_{i,t,n } + \sum_{t} \left(c_{i,t,n}^{sd} + c_{i,t,n}^{su} \right) $$

$$ \text{subject to}\quad & \sum_l K_{nl} f_{l,t} = \sum_i {p}_{i,t,n} - \sum_j {l}_{j,t,n} $$

$$ f_{l,t} = \left| \frac{\theta_{n,t} - \theta_{m,t} }{x_l} \right| $$

$$ f_{l,t} \leq F_l $$

$$ u_{i,t,n} \cdot p_{i,n}^{min} \leq p_{i,t,n} \leq u_{i,t,n} \cdot p_{i,n}^{max} \hspace{.5cm} \forall\, i,t,n $$

$$ \sum_{t'=t}^{t+t^{mu}} u_{i,t',n}\geq t^{mu} (u_{i,t,n} - u_{i,t-1,n}) \hspace{.5cm} \forall\, i,t,n $$

$$ \sum_{t'=t}^{t+t^{md}} (1-u_{i,t',n})\geq t^{md} (u_{i,t-1,n} - u_{i,t,n}) \hspace{.5cm} \forall\, i,t,n $$

$$ -rd_{i,n} \leq (p_{i,t,n} - p_{i,t-1,n}) \leq ru_{i,n} \hspace{.5cm} \forall\, i,n $$

$$ c_{i,t,n}^{sd} \geq c_{i,n}^{sd} (u_{i,t-1,n} - u_{i,t,n}) \hspace{.5cm} \forall\, i,t,n $$

$$ c_{i,t,n}^{su} \geq c_{i,n}^{su} (u_{i,t,n} - u_{i,t-1,n}) \hspace{.5cm} \forall\, i,t,n $$

Stochastic

The stochastic model is at this stage an proposed model and although it has been researched, it is not yet implemented.
And preforms an two-stage joint reserve and renewable energy optimization model
Where the uncertainty of renewable energy is optimized by clearing (in advanced) reserves for the renewable energy source
The model can be desrcibed by the following equations

$$ \underset{c}{\text{minimize} }\quad & SIC + \sum_w \pi_w \cdot SDC_w + \epsilon $$

$$ SIC = \sum_t \sum_i \Big( c_{i} \cdot p_{i,t} + c_i^{ru} \cdot r_{i,t}^{u} + c_i^{rd} \cdot r_{i,t}^d \Big) + \sum_t \sum_i \left(c_{i,t}^{sd} + c_{i,t}^{su} \right) $$

$$ SDC_w = \sum_t \Big( \sum_i c_{i} \cdot \left( r_{i,t,w}^u + r_{i,t,w}^d \right) + \sum_j v_j^{lol} \cdot l_{j,t}^{s} + \sum_w v_w^{ws} \cdot s_{w,t,s} \Big) $$

$$ \text{subject to}\quad & 0 \leq r_{i,t}^{u} \leq ru_{i} \cdot u_{i,t} \hspace{.5cm} \forall \, i,t $$

$$ 0 \leq r_{i,t}^{d} \leq rd_{i} \cdot u_{i,t} \hspace{.5cm} \forall \, i,t $$

$$ 0 \leq r_{i,t}^{u} \leq r_{i,t}^{u,max} \label{eq:schedu} \hspace{.5cm} \forall \, i ,t $$

$$ 0 \leq r_{i,t}^{d} \leq r_{i,t}^{d,max} \label{eq:schedd}\hspace{.5cm} $$

$$ 0 \leq l_{j,t}^{s} \leq l_{j,t} \label{eq:lvol} \hspace{.5cm} \forall \, j,t,w $$

$$ 0 \leq s_{i,t,w} \leq p_{i,t,w}^{ \varphi} \label{eq:wind} \hspace{.5cm} \forall \, i,t,s $$

$$ 0 \leq p_{w,t}^{wp} \leq + \infty \label{eq:pw} \hspace{.5cm} \forall \, w,t $$

$$ \sum_i p_{i,t} + \sum_w \left( p_{i,t,w}^{ \varphi} - s_{i,t,w} \right) = \sum_j \left( l_{j,t} - l_{j,t}^{s} \right) \label{eq:balance} $$

Getting started

Getting Python

This web application uses python 3 To install python on a debian based machines simply run

sudo apt-get install  python3

It's always helpful to use a virtual environment for your python installation.

Getting a MILP solver for the optimization

The web application is known to work with the free MILP solver GLPK and the Cbc solver.
The backend paper investigated the performance of these solvers.
For Debian-based systems you can get the Cbc solver with

sudo apt-get install coinor-cbc

There are similar packages for other GNU/Linux distributions. For Windows there is WinGLPK and Cbc

Installing the backend of Stargazer

If you have the Python package installer installed ( pip3) just run

sudo pip3 install -r requirements.txt

Please make sure to install the dependencies for Python 3, since the web application is only compatible with Python 3
If for some reason you want to manually install the packages that also possible just install :

  • pypsa
  • numpy
  • scipy
  • pandas
  • networkx
  • pyomo
  • moreitertools
  • Django
  • django-webpack-loader

Installing the front-end of Stargazer

NodeJs can be installed by going to NodeJs
To install NodeJs on debian based systems run

sudo apt-get install node

Make sure to install node version 4.x
Also webpack is needed wich may be installed using npm

npm install --save-dev webpack

After installing Node.Js and webpack run

npm install

Inside the asset (stargazer/assets) folder to compile the React to Javascript
After the dependencies have been installed make sure to run run

webpack --config webpack.config.js --watch

In side the asset folder

Running Stargazer

After you have installed the back-end and the front-end run

python3 manage.py runserver --nothreading --noreload

To run the web application after this just open localhost localhost on port 8000

To-do

  • Integrate the stochastic model
  • Rewrite export options

Changelog

  • version 1.0 : first public release

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Web application for the simulation of day-ahead energy markets

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react optimization modeling university-project web-application milp research-tool python-3 electricity power-systems-analysis electrical-engineering power-grids

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