PRE2023 1 Group1

Group members

Name	Student number	Major
Sven Bendermacher	1726803	BAP
Marijn Bikker	1378392	BAP
Jules van Gisteren	1635530	BAP
Lin Wolter	1726927	BAP

Problem statement

The problem that will be addressed in this project is the development of an algorithm that finds the moment the price for electricity is the lowest for electric devices, reducing the amount of money one loses to its yearly electricity bill. With the variable fee of the energy contract, the timing of the use of your electric device matters for the price one pays for the electricity. At moments when the energy generation exceeds the demand of electricity, the price will be low. An smart algorithm putting on the dish washer or washing machine at the moment the price is the best during the night could be profitable. Today, people can already take advantage of the hourly fluctuating prices by watching the costs of a kilowatt in an app, and then setting the time of an high electricity demanding device at an inexpensive moment. This is a practice for some people, but not everyone likes put in the effort to do this consistently. For those this algortihm combined with an app that does the work for you can come in handy, in particular if the algoritm can be connected to smart electric devices to operate them, making sure the system operates on its own without the need of human activity.

Project Requirements, Preferences, and Constraints

Creating RPC criteria

Setting requirements

Setting preferences

Setting constraints

RPC-list

Requirements

• Safety
• User feedback/interaction
  • App should be easy to use
• Implementable
  • Relatively cheap
  • No infrastructural changes in the electric circuit for a device that is able to turn on electric device
  • Algorithm should be reliable

Preferences

• App should be easy to use
• App is looking good
• App tracking the total amount of saved money

Constraints

• Environment (house)
  • Not-smart electric devices
  • Times when the electric devices cannot be turned on, a not ready dishwater for example.

Users

The possible users for the algorithm for low energy pricing are quite vast, ranging from private homeowners to businesses. Private homeowners can use this app to lower their expenses on energy, which is especially important due to the surge in energy prices due to everything happening in geopolitics. Homeowners could thus use this to turn on their appliances at the right times leading to huge chances on saving large sums of money. Next to private homeowners even factories or businesses could look into using the algorithm, their sometimes-intensive use of energy could then also be better placed at more beneficial times. Energy intensive procedures needed to for example fabricate a certain product could then be done at better times lowering the costs of production leading to higher profits, which is in capitalism of course one of the main drivers in business. Thus, the users for the algorithm spread almost everyone, since almost no one lives without using electricity in this current era.

Private homeowners

As described above one of the most important users would be private homeowners, since the developed app/algorithm would enable them to save large sums of money. This users most important wish would encompass mostly a good working app which is easy to navigate as well as a trustable algorithm. The algorithm should be trustable since if the algorithm is wrong very often there would be no need to use the app, some errors can be accepted though since in no case would using the app cost more money than when using electricity on chosen times, only a perfect human being would be able to spread the electricity usage better than the algorithm.

Companies

The companies would similarly to the homeowners also want a good working app, with again a huge focus on the accuracy of the algorithm. Companies would also benefit from other built in functions such as overviews of electricty use as well as a method to make sure the responsible people are informed, which should thus need everyone to be connected to a single account. Companies would thus make profit from the use of the app/algorithm but would not need major alterations to the normal version for private homeowners.

Other institutions

Since the use of electricity is such a general thing in life, any group of people, company or institution having control over their electricity use could use the app to lower their electricty prices. An important question that then arises however is, in the case that many people start using the app, can the algorithm adapt to correctly spread the use of electricity. Since the increase in usage would also lead to an increase in electricity use on times that would otherwise be seen as off peak.

Deliverables

The deliverables of this project will exist of an algorithm that is implanted in an app. This algorithm will be substantiated with documentation in which a literature review is embedded and the capabilities/results of the algorithm will be compared with other energy pricing options.

State-of-the-art

Papers on algorithms for optimal energy consumption.

• Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning.
• Residential demand response: Dynamic energy management and time-varying electricity pricing.
• Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts.
• Impact of dynamic energy pricing schemes on a novel multi-user home energy management system.

Appendix

Milestones and logbook

Milestones

Week	Day	Date	Occasion	Contents
1	Monday	04-09	Instruction meeting
1	Thursday	07-09	Group meeting
1	Sunday	10-09	Deadline
2	Monday	11-09	Feedback meeting
2	Thursday	14-09	Group meeting
2	Sunday	17-09	Deadline
3	Monday	18-09	Feedback meeting
3	Thursday		Group meeting
3	Sunday		Deadline
4	Monday		Feedback meeting
4	Thursday		Group meeting
4	Sunday		Deadline
5	Monday		Feedback meeting
5	Thursday		Group meeting
5	Sunday		Deadline
6	Monday		Feedback meeting
6	Thursday		Group meeting
6	Sunday		Deadline
7	Monday		Feedback meeting
7	Thursday		Group meeting
7	Sunday		Deadline
8	Monday		Presentation

Logbook

Week	Name	Break-down of hours	Total hours spent
1	Sven Bendermacher	Searing for ideas (2h), Meeting about subject (1h), Writing deliverables section and mail teachers (0.5h), finding/scanning some promising literature [1-4] (2.5h).	6
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
2	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
3	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
4	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
5	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
6	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
7	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter
8	Sven Bendermacher
	Marijn Bikker
	Jules van Gisteren
	Lin Wolter