load-following-decision 0.1.0

This package performs an economic analysis of a micro-CHP system paired with thermal energy storage

Software Package: load_following_decision

Welcome to the load_following_decision README file!

This file will give a brief explanation of what this package does, when and why you should use it, how to install it, and basic guidelines for getting started. It will also delve into the limitations of this package and explain the assumptions this project is built on. Finally, we will delve into future work, aka the functionality I plan to add to this software in the future.

First, a brief note on nomenclature

In this package the following terms are used interchangeably:

• Heating demand, thermal demand, heating load, thermal load
• Energy demand, load profile

Overview: What this package does

In simple terms, this package answers the following question: if an apartment building owner replaces their conventional energy system with a micro-cogeneration (mCHP) unit paired with a thermal energy storage (TES) system, what would the costs, savings, and payback period be?

To answer that question, this software package needs the operating parameters of the proposed mCHP system, the building's existing boiler, and the proposed TES system. Once the user has that information, they can enter it into the .yaml file located in the package's /input_files folder. This software package also needs the energy demand of the building in the form of a .csv file. The package assumes that the energy demand has been modeled in an open source energy modeling software called "eQuest" and exported as a .csv file. You can find an example of such a file in the /input_files folder titled "default_file.csv". The data resolution is hourly and spans the length of one year for a total of 8760 data points for both the electrical demand of the building and the heating demand.

So why is the package called "load_following_decision"?

The mCHP system has two dispatch options: it can either operate so that the electricity produced by the system matches the electrical demand of the building (this is mode is called "electric load following", or ELF), or operate so that the thermal energy produced by the system matches the building’s thermal demands (which is called "thermal load following", or TLF). This software package is intended to perform an economic analysis of both operating modes and select the one that is most economically favorable (has the lowest payback period). Currently, the software only calculates the payback period for the ELF operating mode.

Further details can be found in /docs/project_motivation.md

When and why to use this package

This package is designed for my academic research project. I intend to eventually use this package to examine how the economic feasibility of using mCHP systems for mid-rise apartment buildings varies across the United States.

Eventually this package will be accessible for use by building owners who are interested in installing an mCHP system in their apartments and would like to perform a preliminary feasibility assessment using this tool. A note for these users: DO NOT make financial decisions based on these analysis results. Please consult with a professional before making a final decision on whether an mCHP system is right for you.

Installation Instructions

Installing from PyPI:

This project can be installed from PyPI by entering the following command in your terminal:

pip install load-following-decision

This will install all required dependencies automatically.

Installing from GitHub:

If installing from PyPI does not work, this project can also be installed from GitHub by entering the following command in your terminal:

pip install git+git://github.com/SoftwareDevEngResearch/load_following_decision.git

This package is dependent on a number of libraries that you will need to install before using this package. Here is a list of current known dependencies:

• python>=3.7.4
• pandas>=1.3.5
• PyYAML>=6.0
• pytest>=7.0.1
• tabulate>=0.8.9
• matplotlib>=3.5.1
• pint>=0.18

From your terminal, you can install these dependencies manually with the following command:

pip install package_name

For example, to install the pandas package, enter the following command:

pip install pandas>=1.3.5

Use pip freeze to double-check that the correct version of each dependency is installed.

Getting Started

Once the package and its dependencies have been installed, it is time to add the energy demand .csv file to the /input_files folder. Here you will find that there is already a .csv file named "default_file.csv". This has the electricity and thermal demand for a mid-rise apartment building modeled in eQuest for your convenience. If you would like to analyze your own hourly demand data, export it from eQuest as a .csv and add it to this folder.

Next, make a copy of the .yaml template (named "template.yaml") and change the file name of the copy as you see fit. Fill out the new .yaml file with the operating parameters of the mCHP and TES units you are interested in analyzing. Also add in the operating parameters of the building's existing boiler (please note that moving forward the boiler will be referred to as the "auxiliary boiler"). For your convenience I have added a pre-filled .yaml file called "default_file.yaml" with typical operating parameter values.

In addition to the operating parameters of the equipment, the .yaml file has entries for the cost of fuel and electricity in your area (these have also been pre-filled in the default_file.yaml file). Make sure to convert values to the correct units as indicated in the template.

Finally, enter the file name of the energy demand .csv file that you would like to analyze. For example, the default .csv file name would be entered as default_file.csv

Use case examples with step-by-step instructions and expected results are located in /docs/how_to_guide.md.

Limitations and Assumptions

This package operates under the following assumptions:

• mCHP Fuel = Natural gas
• Aux Boiler Fuel = Natural gas
• mCHP type = Internal combustion engine
• Building Type = Residential Apartment Building
• TES = Charge and discharge rate is not limited
• TES = Assumes no energy is lost during storage
• Net metering is not accounted for
• Building already has a boiler installed

This package also has the following limitations:

• Energy demand data resolution must be hourly
• Energy demand data must span the course of one year
• Weather, location, and building information (square footage, number of floors, etc) must be specified in eQuest when simulating the building's energy demand

Future Work

Features to be added to this package in the future include:

• Economic analysis for thermal load following (TLF) operating mode
• Calculation of mCHP capacity based on energy demand data
• Calculation of TES capacity based on energy demand data
• Generate results as a pdf file containing relevant tables and plots

Questions?

For questions regarding this package, please contact:

• Christopher Curl
• email: [e-mail]