Smart Technologies Energy Relevance Model#

Objectives

In this lab you will use the STERM model a tool to evaluate the payback time of smart systems

  • You will first discover the STERM tool through one provided use case: Smart light system

  • Evaluate the payback time of a second smart system: a Smart heating system

    • Collect the necessary data for this evaluation

    • Configure the tool to take into account those data

    • Analyze the obtained results

    • Provide a report presenting the system, the results and the analysis

1. Introduction#

1.1 Context of the STERM Project and model#

The Shift Project is a think tank that has been studying (among other things) the impact of digital technologies on climate change. In October 2020, along with a report on how to make digital technologies sober, they published a tool called STERM (Smart Technologies Energy Relevance Model) for evaluating the relevance of smart systems in terms of energy consumption.

To read the shift report on “Deploy numeric sobriety”

Details You can find:

The idea is to provide a methodology and a basic tool that can be reused to systematically evaluate the real impact of adding a connected layer to a system. To evaluate if a smart system can enable energy savings, it is important to quantify the possible gain and compare it to the energy needed for the smart layer. Three factors have to be examined:

  • The energy consumed by the smart layer, which is added to the energy consumption of the system itself ;

  • The grey energy or embodied energy: energy consumed in the production phase of the connected layer ;

  • The rebound effect, when a raise in the efficiency of a system makes this system increasingly used, which counterbalances the benefits of the amelioration. That can be modeled with an energy saving function that varies with time.

To evaluate the energetic efficiency of a system, the STERM model must be given data such as the baseline power for the components or their lifetime and then, based on certain hypothesis, it calculates the payback time of the system, which is the time when the energy saved thanks to the smart layer gets higher than the energy needed for the smart layer (production AND usage).

1.2 Three case studies#

The Shift Project team, who created the model, provides the testing of the model with three smart systems: a residential smart light , another smart light system but dedicated to a work office and a smart-meter system.

Smart light system for a residential space#

The first case study describes a connected “smart” light system for residential spaces. This kind of domotic system is becoming popular and could participate to an exponential increase of the number of connected devices in the next years (see this article).

The smart system necessitates the following additional devices:

  • one or several connected bulbs (which means they have an integrated communication device)

  • a movement sensor to detect if someone is entering the room,

  • a gateway, which centralises and distributes the communications between the different elements.

The purpose is to study the energetic relevance of such a system by finding whether the energetic cost of producing and using the “smart” infrastructure is absorbed by the energy savings that this infrastructure enables.

Smart light system for a working office#

The second case study is quite similar: it also describes a “smart” light system but in a working office. There are various scenarios, depending on the size of the office and on the number of workers using it.

Smart Grid#

The last case study is different from the two others since it describes a “smart meter”, an electricity meter with a connected layer that is supposed to give consumption data to the residents and to the electricity supplier, to impulse better consumption habits. The smart meter should have:

  • An electricity meter;

  • A display, which gives information to the residents;

  • A long range communication module (In France, it is a PLC -Power-line communication- over the energetic network, then GPRS to the administrator, Enedis);

  • Local communication modules to connected house equipments.

2. Understand the model#

2.0 Get the model#

The STERM model is a python implemented mathematical model that aims at evaluating the relevance of a « smart » system in terms of energy savings.

You have to download the STERM Python files.

mkdir -p $HOME/ENV4101 
cd $HOME/ENV4101

unzip ACV-IoT.zip
cd ACV-IoT/

2.1 The python files#

You have downloaded the following files:

  • The main file (TSP_SMARTX_MAIN.py) is the executor. It starts with the definition of the three systems already defined.

  • The config file (TSP_SMARTX_CONFIG_simple.py) is the core of the model, where the computations are done.

  • Finally, the system definition files (*_SYSTEM.py) are the data for each case study which give the useful values from the measurements (power, energy consumption, lifetime, …), there is one file by case study.

tp-sterm-files/model_implementation_schema.png

Python files and model variables#

2.2 The mathematical model#

To model the energetic relevance of a smart layer introduced in a system, we can define a global cost function:

\(G(t) = C*E_{savings}(t) - E_{smart}(t)\)

  • \(C\) is a factor that converts electric energy into primary energy, which is the naturally available energy before any transformation. Electricity is what we call a secondary energy source and we must take its transformation into account (Taylor, 2015 ). It has been set to 3, \(C=3\)

  • \(E_{smart}\) represents the energy needed by the smart layer and

  • \(E_{savings}\) is the energy saved thanks to it.

The smart energy can be divided into two terms:

  • The embodied energy (the grey energy) needed to create the smart layer (material extraction, transport, etc…) and

  • The operation energy is the electric power needed for running it

  • \(G(t) = C*E_{savings}(t) - ( E_{smart_{embodied}}(t) + C*E_{smart_{operation}}(t) )\)

The embodied energy also includes the production energy for replacement pieces during the maintenance of the system. Therefore, it is also dependent on the time t.

We suppose that the energy needed for the operation of the smart layer can be estimated as a constant power through time:

  • \(E_{smart_{operation}}(t) = P_{smart_{operation}}*t\)

Question: Saved energy#

We suppose as well that the energy consumption of the system (without the smart layer) can be estimated by a baseline power through time:

  • \(E(t) = P_{baseline}*t*\beta\)

\(\beta\) represents the percentage of the time when the system is running (for example the average illumination time per day for a lamp).

The baseline power is a basic amount of power - in kilowatt hours (kWh) - that meets a significant amount of the “reasonable” energy needed on average. It has been experimentally measured. It can also be provided on the device description.

We estimate the savings by defining a certain percentage of the energy, \(\alpha\), that is not consumed thanks to the smart supervision.

How can you define \(E_{savings}(t)\) with \(\alpha\), \(P_{baseline}\) and \(t\)?

Solution

  • \(E_{savings}(t) = \alpha*\beta*P_{baseline}*t\)

Question: Maintenance#

The embodied energy is the sum of all the energies needed for raw materials extraction, transport, fabrication, end of life (recycling or destruction) and maintenance:

  • \(E_{embodied}(t) = E_{raw materials} + E_{manufacturing} + E_{transport} + E_{eol} + E_{maintenance}(t)\)

For the maintenance of the system, we consider only the replacement of defectuous components. If we suppose that the mean lifetime of a component is \(T_{replacement}\) and the energy needed for its replacement is \(E_{replacement}\), how can you express the energy \(E_{maintenance}(t)\)?

Solution

  • \(E_{maintenance}(t) = \frac{E_{replacement}*t}{T_{replacement}}\)

Question: Payback time#

Now that we have developed each term of the global cost \(G(t)\), calculate the payback time \(T_{PB}\): it is the time when the energy saved by the system will become superior to the energy needed for its fabrication and usage.

Tip

It means that:

  • \(G(t>T_{PB}) > 0 \land G(t<T_{PB}) < 0\)

Solution

  • \(T_{PB} = \frac{(E_{raw materials} + E_{manufacturing} + E_{transport} + E_{eol})}{(C*\alpha*\beta*P_{baseline} - C*P_{smart_{operation}} - \frac{E_{replacement}}{T_{replacement}})}\)

3. Run the model with the proposed use case#

3.1 Python implementation#

Choice of the system#

The choice of the system to study is made at line 80 in TSP_SMARTX_MAIN.py. Modify this file to choose HUE_system_LED.

System_sel = HUE_system_LED

This system describes a residential smart light setup. It contains one or several connected bulbs (connected means that they have an integrated communication device), a movement sensor and a gateway which organizes the communication between the elements of the system.

In the config file TSP_SMARTX_CONFIG_simple.py file, there are two kinds of functions: those who plot figures (such as plotTPB_AlphaRange and plotBulbsImpactHUE), which are called by the TSP_SMARTX_MAIN.py file. The others are only to be called inside the config file (such as TPB() which returns the payback time for a certain configuration).

Influence of the number of bulbs#

When you run the model, several kinds of diagrams may be plotted:

  • Plot the evolution of \(T_{PB}\) depending on the number of bulbs in the system (for \(\alpha\) fixed)

    • line 145 config.plotBulbsImpactHUE(System_sel)

  • Plot the evolution of \(T_{PB}\) depending on \(\alpha\) (and the savings depending on \(\alpha\))

    • line 153 config.plotTPB_AlphaRange(System_sel) (for \(nb_{bulbs}\) fixed).

If you run the model, it draws the evolution of the payback time \(T_{PB}\) depending on the number of bulbs in the system:

python TSP_SMARTX_MAIN.py

Questions

What observations can you make?

  • If we have less than 5 bulbs, does the smart system may provide energy savings?

  • How many bulbs do you need for getting eventually energy savings?

  • For 10 bulbs, how many years is the \(T_{PB}\)?

  • What is the value of \(\alpha\) and \(\beta\)? Does it seem realistic?

  • What is the system life time? Does it seem realistic?

  • At which lines of code are set \(\alpha\), \(\beta\), system life time?

Tips

We can see on the graph

  • Below 5 bulbs there is no possible energy saving

  • We need at least 8 bulbs for potential energy saving

  • For 10 bulbs, \(T_{PB}\approx 2.2~years\)

  • \(\alpha=0,7\) and \(\beta=0,11\)

  • The system lifetime is of 5 years

We can see in the code

  • TSP_SMARTX_CONFIG_simple.py line 52 that those data are provided in the definition of the system

  • TSP_SMARTX_MAIN.py is where the systems are defined

    • line 36-43 the values provided for the HUE_system_LED system (see the comments lines 27-34)

      • Line 38: List of alphas \(\alpha=[0.17, 0.70, 0.90]\)

      • Line 39: \(\beta= (1000/365.25)/24=0,114077116\) : light on 1000 hours/year

      • Line 40: System lifetime in year: 5

Evolution of \(T_{PB}\) with \(\alpha\)#

Now, we want to plot the evolution of the payback time \(T_{PB}\) when \(\alpha\) evolves.

For this purpose, you have to remove the comment at line 153 in file TSP_SMARTX_MAIN.py The second plot appears when you close the first one.

config.plotTPB_AlphaRange(System_sel)  # comment to remove Q 3.1

Questions

What is the minimal \(\alpha\) the system needs to reach if we want the payback time to be inferior to the system’s lifetime?

Solution

  • In the figure, we can see that the Typical boundary of the potential savings cross the system’s lifetime limit for \(\alpha = 64\%\)

    • For \(\alpha \gt 64\%\) the payback time will be above the system lifetime

  • We can also notice that for \(\alpha \lt 60\%\), the system has no payback time at all, whatever the system lifetime is. The energy spent for the smart system is too high to obtain a gain

Interpretation of \(\alpha\)#

The system is supposed to be running a certain percentage (\(\beta\)) of the time. The savings made thanks to the smart system are related to the time when the system turns itself off to save power. We saw that

\(E_{savings}(t) = \alpha*\beta*P_{baseline}*t\)

So we can see the \(\alpha\) coefficient as a percentage of the running time of the system ( \(\beta\)*24 hours a day ) when the smart layer avoids useless illumination.

Questions

How many hours a day should it save for \(\alpha=0,4\) and \(\alpha=0,7\)?

Solution

For \(\alpha = 0,4\) \((40\%)\) (lower boundary) and \(\beta = 0,11\) \((11\%)\), we must save \(\alpha*(\beta*24)\) about one hour per day.

For \(\alpha =0,7\) \((70\%)\) (higher boundary), we must save almost two hours of illumination each day.

In the configuration, according to the chosen \(\beta\), the light was on \(2.74\) hours a day. For \(\alpha =0,7\), it means that the home light is turned on uselessly two hours each day and that our smart system is able to correct this…

3.2 Influence of the variables#

We still work with the HUE case study.

Modify the baseline power of one bulb and reload the evolution of payback time with \(\alpha\).

You can try with those values : 11, 7, 5

What observation can you make?

Try and change the value of several other parameters (such as the lifetime, the embodied energy or even the recycling energy) to understand how much influence each one has on the system. Can you see a good way of improvement for the system? On the contrary, is there something with a very weak influence?

3.3 What do you have to do to define a new system, the OFFICE_LIGHT system#

Choice of the system#

Line 80 in the main file, select the OFFICE_LIGHT_system.

System_sel = OFFICE_LIGHT_system

Draw the evolution of the payback time when alpha evolves#

Draw the evolution of the payback time when alpha evolves. Can you make the same conclusions as for the HUE system?

In the case of smart light, we saw that the energy savings are related to a capacity of the light to ‘turn itself off’ when unnecessary. Its efficiency is therefore supposed to lower when the behavior of the users change (if they start turning themselves the light off). The « smart » layer brings a sort of supervision, so we can imagine that the more vast the lightning zone is, the more relevant this kind of service can be.

Defining the variables of the system#

In the file Annexe-STERM-UseCase-param-2020.xlsx, you can find the values used in the system definition for the three use cases and different scenarios.

If you look at the Smart Light - Office sheet, you can remark that

  • The first part of the table contains the data for the OFFICE_system.py file

  • The other parts represent four scenarios.

Questions

  • What scenario is currently implemented in the model?

  • Which file and lines do you need to change to modify:

    • The value for the grey energy or embodied energy (bulbs, sensors, hub)?

    • The value for the baseline power of a light?

    • The value for the sleep power of a light?

4. Create a new use case: Smart Heating system#

4.1 Description of the heating system#

In this last part, we will be using the model on a new case: a Smart heating system.

The technology has been chosen. The whole heating system will be reinstalled:

  • Brand Thermor heaters (one has to be installed in a room of \(15m^{2}\), 2 for \(30m^{2}\) …), each heater has a power of 1000W

  • Cozy touch Bridge (one bridge for 20 heaters, range 20 meters)

  • Two Presence sensors in each room.

Read in the notice

Auto mode enables smart control. You set the desired temperature when you are in the room and the device optimizes your comfort while saving money. The device :

  • records your rhythm of life and changes during the week to manage its own programming,

  • anticipates heating for you to have the right temperature when you return to the room,

  • detects your presence and unplanned absences to adjust the temperature automatically,

  • detects window opening and closing to lower the temperature to 7°C when you let some fresh air in the room

4.2 Gather useful Data#

We gathered some information about a connected heater of the Thermor brand that you can find in data_heating_system.xlsx.

Details Some of the data used here come from this article for the heater energetic data, from the constructor notice for the gateway here and for the heater here.

The rest of the data is extrapolated (when possible) from the case studies of the shift project (for the movement sensor for example) or set to zero when nothing else is possible. The hypothesis made here should be examined further for a real study. The goal of this part is mostly to learn how to use the model for a new case, but not to give a definitive conclusion about this system.

Question

Complete the sheet

  • What is the power of the Cozy bridge (you can find useful information on page 6 of the Hub Notice (variable POWER_HUB_ACTIVE_SPECS)?

  • What is the embodied energy of one heater, what is the raw material necessary to produce one heater (estimated with table 4 of this article)?

Bravo!

Congratulations, you have been able to find the necessary information for doing a little life cycle assessment.

4.3 Fill the Heating system python file HEAT_system.py#

There are lines with a TODO comment (19, 31, 32, 33, 43), they correspond to the parameters you had to fill in the excel sheet.

4.5 Scenario#

Question

  • Choose the house in which the system is installed. The size of the house, the number of heaters, the number of bridges, the number of sensors.

  • Define the system

    • Go to the main file and create a SmartSystem object called HEATING_system. There is a reminder of which arguments the constructor takes in the main file. The lines to be modified are 66-73. The args field is a list that will contain the number of sensors (os) first, then the number of heaters and finally the number of bridges/hubs.

  • The value for

    • \(\beta\) depends on the weather as well as the comfort and night temperature. We will use the following values: approximately 8 hours per day for 150 days per year

    • \(\alpha\) depends on the presence of the users, it is estimated at \(23\%\) of saving read on (palmetto).

4.6 Running the model#

Finally, you can go to the system selection line in the main file and select your new system. Run the model and comment the results. You should save the obtained figures. You can run with several scenarios.

5 Return on moodle#

You should return on moodle by pair

  • The excel sheet with your chosen values (data_heating-system.xlsx)

  • The modified python files

  • A pdf file that includes

    • The description of the house (size and number of rooms) and the explanation of the choices in terms of number of heaters, number of bridges, number of sensors

    • The figure representing the Evolution of \(T_{PB}\) with \(\alpha\) (you can present different scenarios, with different variables)

    • A discussion on the interest to change one existing heating system with the smart heating system

    • Any useful comment.