Energy Data Hackdays 2022

16–17 September 2022, 09:00 @ Technopark Aargau, Badenerstrasse 13, 5200 Brugg, free Map

Organised by Opendata.ch, Hightech Zentrum Aargau AG, Zukunftsregion Argovia

The Energy Data Hackdays 2022 took place on 16 and 17 September 2022 at the Hightech Zentrum Aargau in the Technopark Aargau in Brugg. More than 110 participants and guests from over 30 organisations worked for 32 hours in 13 teams on challenges from energy utilities, research and administration – from e-mobility charging behaviour and solar production in real time to grid losses, heat demand and smart-meter interoperability.

Photos

See all 53 photosPhoto: Nadine Zielinski, Hightech Zentrum Aargau, Opendata.ch

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Documentation and recordings

• What we’ve learned at the Energy Data Hackdays 2022 – and what’s next? (opendata.ch, 20.3.2023)
• Results on dribdat (event 52)
• Aargauer Wirtschaft 13.10.2022: Smarte «Energy-Hacker» in Aktion
• Energie Rundschau 18.11.2022: «Hacker» im Einsatz für smarte Lösungen
• General-Anzeiger Brugg 22.9.2022: Energy Data Hackdays
• insidenews.ch 24.9.2022: Rückblende zu den Energy Data Hackdays
• Umwelttechnik Schweiz 31.10.2022: Energy Data Hackdays

Challenges

• Mapping of Swiss PV power production in real time
  Robin BühlerNicolas VetschMathis DedialIOIvan OlexynJoão EccelTill RichterSimon NeidhartMarc HauserUeli SchiltMMMatthias Meier
  Challenge description

  Based on real-time data from aliunid , develop a simple algorithm/logic to estimate the current solar radiation (Wh/m2) from PV production data (kW).
  Based on the obtained radiation data and the public PV data (installed power according to https://map.geo.admin.ch/ and roof area information from https://www.uvek-gis.admin.ch/BFE/sonnendach/ ) simulate the PV production in real time.

  Aim
  The aim is to map the total photovoltaic (PV) power produced in Switzerland in real-time without measuring each PV power plant individually. To achieve this, we want to simulate the PV power of all registered PV plants with PV plants for which aliunid has real-time data available.
  Idea
  It is safe to assume that two neighboring PV systems follow a very similar production curve. By measuring only one of them we could simulate the PV production of both. By extending this approach to the whole of Switzerland, the challenge is to simulate the PV output of all registered PV plants with data where aliunid already measures the real-time power output. The challenge consists of the following parts: 1. Generate location-specific irradiance data based on measured PV output all over Switzerland. 2. Apply the generated irradiance dataset to PV systems that are currently not measured based on basic information and open data (e.g., installed capacity, slope, orientation). 3. Present the results (e.g., the total amount of PV power currently produced in Switzerland) on a map, dashboard, or similar.
  Big picture
  Decentral Photovoltaic (PV) systems can be found all over Switzerland and their impact on the Swiss energy system is becoming increasingly relevant. Real-time information about each PV system helps to maximize its use and mitigate potential negative impacts. We will provide high-resolution data of the power output of PV systems all over Switzerland as well as historic weather data. Additionally, open data from geo.admin.ch and sonnendach.ch can be used

• Alternative pathways to net zero

  Visualization of energy scenarios

  Robert StantonStéphane BisingerBill ZollingerAdriana MarcucciLea RuefenachtTristan Gollmart
  Challenge description

  Final pitch slides

  The problem

  Currently, results from energy scenarios are presented in research papers or individual webpages. This makes it close to impossible for policy makers to get the full picture and understand the features of the net-zero Switzerland. How can policy-makers take decisions based on model-driven energy scenarios?

  Challenge 07-08 slides

  Current state

  many scenarios

  very detailed information

  not easy to extract conclusions

  The process

  Step-by-step

  get a project name
  get data ready
  brainstorm solutions
  divide the work
  calculate indicators
  visualise the data
  develop the tool / dashboard

  The goal

  Develop a decision-making tool to explore and compare multiple energy scenarios. The tool should visualise for each scenario key indicators and energy sources/carriers.

  Target audience
  policy-makers

  Key indicators

  CO2 emissions

  Foreign vs domestic

  Total costs

  The Energy Explorer

  The overview of different scenarios to compare

  Click on a scenario to get the detailed Energy-flow:

  Source code

  Tool is open source and accessible through GitHub

  The team

  Stephane Bisinger

  Bill Zollinger

  Tristan Gollmart

  Robert Stanton

  Adriana Marcucci

  Lea Ruefenacht

• E-Mobility Analytics – EKZ Angelos Selviaridis

  As the discussion around e-mobility grows help with the transition to an all-electric mobility world is needed.

  Angelos SelviaridisAndré EggliNicola RuchMarina GonzalezJBJeremy BuchmannAbdullah ShakilAdrian SwiridoffLucas JamanrCKChristian KöhlerBraulio BarahonaJulian Gander
  Challenge description

  Challenge

  Nowadays there is a lot of noise around electric mobility. As car manufacturers and the world turn towards this technology with the goal of lowering greenhouse gas emissions and as this technology gains momentum, a lot of discussions arise, bringing conflicting arguments, myths, assumptions and open questions to the surface. This we have in abundance… What we lack is real data and facts based on this data to support all these theoretical discussions.

  Presentation slides

  This is where it becomes interesting! We have the real data and we are looking for data enthusiasts, who can crunch the data and generate some very interesting facts. How do we charge? How much energy do we actually need in order to cover our monthly mobility needs? Do people wait for low tariff or do they charge during the day? How does the typical charging profile look like? How about charging at work or at other points of interest (supermarkets etc.)? These and many more questions are waiting for your analytics skills and talent to be answered!

  Data:
  Over two years of charging profiles (power consumption) timeseries in 15-minute resolution of more than 200 charging stations from the backend system of our e-mobility market product.

  Aim:
  Analyze the data set in order to visualize the behavior of e-mobility drivers, bust some myths and support arguments with facts.

  Idea:
  Here some interesting questions:
  How do we charge?
  How many people wait for low tariff?
  How many people charge their cars right away when returning home?
  How does the car’s charging logic work? How many cars are waiting for early morning to be fully charged (when drive-off time is preset by the driver)?
  Do people charge every day or only when the batteries are empty?
  Do people charge always on the same day? On which day of the week do people mostly charge?
  Typical charging profiles
  How does a typical charging profile look like?
  Which patterns are the most important ones?
  Place of charging
  How different is the charging behavior at work or at other places of interest?
  These and other questions need to be answered.

  Big Picture:
  As the discussion around e-mobility grows, facts are needed to support assumptions, deepen our knowledge and help with the transition to an all-electric mobility world. Understanding the behavior of charging will help us not only better understand e-mobility as a whole, but could also open new horizons for products and services.

• EMF Walks in Brugg

  An energetic tour to rediscover the town – Video: https://vimeo.com/750696951

  Markus LeutwylerGaston WeyOleg Lavrovsky
  Challenge description

  We walked around the town of Brugg with a Software-Defined radio receiver and antenna, running open source software on a laptop to capture electromagnetic field emissions - observing the interplay of radio frequencies invisible to the eye or ear. We then produced a short music video with the result. Call it data art, if you like - we had fun making this together!

  🎶 Credits 🎶

  Music by Static In Verona - Etcetera Ra Ra (© 2021 Gin Rubby Music)

  Penguin illustration by George Shaw (CC BY 4.0)

  Maps and open data from swisstopo and SFOE

  Data visualization made with Gqrx running on Fedora Linux

  Radio signals measured with an RTL-SDR V3 receiver

  Video recorded with Open Broadcaster Software and produced with Pitivi

  📡 Why? 📡

  The town hosting our event has a turbulent industrial past and many energy-related infrastructure and historic sites, most of it well hidden behind the façades of residence and commerce. Noticing sensors, transformers, mobile phone antennas, and other sources of radio interference, we were also much more aware of the technology that surrounds us even within a short radius. Our aim is to passively observe the variety and mystery of the patterns that we encounter during our walk. With a bit of practice, we find interesting frequencies, and learn to localize sources of interference. We may start to "see" the city through a new lens, even though in our case it is just a 1-dimensional waterfall-like visualization called a Spectrogram .

  We planned our walk using swisstopo open data shown on the GeoAdmin map shown below. Based on these locations of interesting sites, we are creating an Actionbound quest to visit the most interesting industrial sites around Brugg. We can recommend frequencies to tune into, such the ones typically emitted by public works. In the future we could offer these "high tech" tours of the smart city to school classes or tourists.

  ##🚶Who?🚶

  Markus ( make.human.technology )

  Gaston ( glam.opendata.ch )

  Oleg ( dat.alets.ch )

  🙇 Inspired by ⚡

  https://hack.opendata.ch/project/844

• Energy (Heat Demand) modelling in Sankt Gallen with AI methods – St.Galler Stadtwerke (SGSW)

  Heat demand modelling and prediction for the buildings of the city of Sankt Gallen with AI methods

  Esther LinderTaeJun MoonClara EsteveRaffael CorrodiPhilipp DitzelWolfgang KorosecLaure StadlerNicola WullschlegerAndy Gubser
  Challenge description

  Demo: heatsg.energy.opendata.ch

  Challenge

  The department “Environment and Energy” of the city of St.Gallen is tracking and modeling the energy consumption in buildings for 20 years. The existing model is an "handmade" Microsoft Access-based model. Now the department wants to improve the heat demand model using AI methods.

  Challenge presentation

  Participants should bring knowledge or interest in one or several of these areas: AI, classification methodologies, data analysis, energy modeling.

  Available Data:

• Energy Data Access Control – VIA Science, Ray Neubauer

  Monetisation for Small-Scale Consumers in CH

  Challenge description

  Supply and demand always have to be balanced for the electricity grid to operate reliably. Situations like high or low demand, but also under and oversupply have to be managed actively by distribution system operators (DSOs) and transmission system operators (TSOs) using ancillary services such as balancing energy and demand response mechanisms.

  For industrial and commercial consumers that account for 55.4% (32.18TWh in 2021) of Switzerland’s electricity consumption, there are sufficient measures in place to react to imbalances in the electricity grid by providing balancing energy and flexibility to TSOs and DSOs.

  However, for private households that are still accountable for 34.6% (20.1TWh in 2021) of Switzerland’s annual electricity consumption, there are so far no mechanisms to:

  trigger and incentivize grid-serving behavior of private household end-consumers

  enable bi-directional real-time communication between private households and utilities

  enable utilities to request and gain access to electricity data from a large number of end-consumers and to use their data for a different purpose than invoicing of electricity consumption

  Task:
  Based on live consumption data of 2-3 households in Switzerland and the VIA SkylightTM framework (including a variety of Web3 technologies like blockchain smart contracts, zero-knowledge proofs, and privacy-preserving encryption) supplied by VIA Science, we ask the participants to develop an application that enables:

  end-consumers (data creators) to share real-time electricity data in a data privacy preserving and cyber secure way by being able to grant or revoke data access permissions for utilities or other energy service providers

  utilities and energy service providers (insights buyers) to remunerate end-consumers for sharing their electricity data from end-consumers (e.g. with VIA SkylightTM tokens)

  Goals:
  The solution shall be based on the VIA SkylightTM framework and its underlying functionality. It shall establish direct communication between end-consumers and utilities / DSOs by enabling third parties to compensate (data creators) in real-time for their energy data and actions while at the same time end-consumers remain in full control of their data and its usage.

  Resources Available to Participants:

  Real-time electricity consumption data of 2-3 households in Switzerland

  VIA SkylightTM framework (including Web3 technologies like blockchain smart contracts, zero-knowledge proofs, and privacy-preserving encryption)

  On-call DevOps of VIA Science to answer technical questions

  API documentation

• Enriching Point Clouds (LiDAR) with Meta Data – Axpo Grid AG Tobias Schmocker

  In densely populated Switzerland, high-voltage overhead lines are in many cases located in the vicinity of existing buildings.

  NWNicolas WenkPascal LeimerJWJens WitzigTSTobias SchmockerMax KapustinMatthias Burger
  Challenge description

  In densely populated Switzerland, high-voltage overhead lines are in many cases located in the vicinity of existing buildings. To protect the population from non-ionizing radiation, we want to classify all buildings near power lines using public available landscape models and register of buildings. Linking this data with LiDAR data helps us to discover any non-conforming constructions and to identify buildings which should be specially protected from non-ionizing radiation.

  Challenge slides

• Gamification of building energy performance with human digital twins – EMPA Philipp Heer, Cai Hanmin, Khayatian Fazel

  Playing games where people interact and observe their impacts for data-driven modeling of occupant behavior

  Oleg Lavrovsky
  Challenge description

  Aim

  This challenge uses an open-source digital twin developed at Empa (https://github.com/Khayatian/nestli) with real-life systems and data. The participants are expected to build on top of the research results, develop ways to incorporate social/occupants' behavior and build the foundation to design a game that would raise public awareness.

  Idea

  data-driven modeling of occupant behavior: In this challenge, we value the modeling of stochastic human behaviors. Given the digitalization of the energy systems, occupants' behavior is taking a more central role in designing automation and control systems. However, occupants are considered (and modeled) passive recipients of the control system to date. To obtain a more realistic assessment of occupants' interaction with building automation and control, this task shall model occupants as active agents that react and occasionally override control decisions, forcing their preferences into the building operation, which the controller has to deal with as disturbance.

  if time permits, design a game framework based on the digital twin and the developed human twin: Energy end-users are currently taking a passive role in the energy transition. However, they have a much higher impact by changing their behaviors. For example, lowering thermal comfort voluntarily during extremely cold weather would help the energy system to avoid using fossil fuel-based energy production. This awareness may be raised by playing games where people interact and observe their impacts. Our open-source digital twin supports an open-source game for such a purpose. Code is appreciated, but conceptual level development is also sufficient.

  Big picture

  Building energy demand accounts for a large share of global energy consumption and carbon emission. Advanced building energy management systems can help to reduce energy consumption and carbon footprint. Although the technical design has been much investigated, the impacts of occupant behaviors are rarely considered. In addition, occupants’ behavior can be influenced by great awareness of their impacts and contribution to the energy transition.

  See also: https://github.com/khayatian/nestli

  Keywords: game design, machine learning, social behavior

  Appreciated skills/background: python/game design/data analytics/social science

• Graph Neural Network of a Wind Farm – OST Sarah Barber, Francesca Paoletti

  Modelling Wake Interactions within a large windfarm based on a Graph Neural Network.

  TLThomas LosingerTobias JuckerFlorian Hammer
  Challenge description

  The goal of this challenge is to model wake interactions within a large windfarm based on a Graph Neural Network.
  With the Graph Neural Network the turbines that have the largest impact on the Annual Energy Production of the whole wind farm due to wake interactions should be identified.
  This will allow wind farm operators to make specific changes to certain turbines in order to optimize the power production.

  Challenge slides

• New interface to estimate heat demand in buildings – Dr Stefano Cozza Dr Jonathan Chambers Prof Matthias Sulzer Dr Pierre Höllmüller Xiang Li

  Development of a novel user interface/user experience for estimating the heat demand in buildings

  Jonathan ChambersVSVildana SmajicDTDeniz TepeSamuel GilliéronPhilipp SchützLPLucien Porta
  Challenge description

  Imagine the next generation of building modelling tool experiences

  Aim:

  To develop new concept for how a user could interact with our heat demand estimation API.
  Idea:

  We have good models of heat demands in buildings, but there are many possible ways we can bring this to users. We want to imagine a user and use case and develop an interface that meets quickly their specific needs.
  Big picture:

  Heating is the biggest consumer of energy in Swiss homes and is still 60% coming from fossil fuels. This needs to change very quickly to meet climate targets and end fossil fuel dependence. Knowing the heat demand of a building is the first step towards improving efficiency and changing heating systems.
  Challenge slides

  Dr Jonathan Chambers
  Prof Matthias Sulzer
  Dr Pierre Höllmüller
  Xiang Li

• Optimizing PV usage in a turbulent time
  
  Challenge description

  Photovoltaic (PV) supply is time-dependent. Given the current geopolitical situation, there are large changes in the relative generation shares of fossil technologies, and this has impacts on both the environmental and economic costs of electricity from the grid. This project will use measured data from PV generation in the region of Brugg, and explore the possibilities of optimizing this clean electricity with dispatchable household loads and the use of batteries. Life cycle assessment will be used to assess the impacts of adding batteries or control electronics to make more household loads dispatchable.

• Optimizing short-term active power losses forecast – Swissgrid AG Liu Xiying

  Optimizing the forecasting based on the results of these models by designing an optimization algorithm

  Flavio MüllerMarius GigerKPKathrin PuthiyedathuCRChristoph RothertCLConstantin Le CleïBeatriz AlvarezXiying LiuRMRalph MachunzeNataliia Kovalchuk
  Challenge description

  Optimizing short-term active power losses forecast

  Currently we use a group of models to forecast the short-term (day-ahead & intraday) active power losses on the transmission grid, using statistical, machine learning, and deep learning models.

  The goal of this challenge is to:
  Optimizing the forecasting based on the results of these models by designing an optimization algorithm, to minimize the expected forecasting error and the procurement costs
  Designing the visualization for presenting the final forecasting results and the performance evaluation charts

  Challenge slides

• Regional Energy Information in High Resolution – GeoImpact/BfE Digital Office

  Collecting, analyzing and visualizing energy policy data from Swiss communities

  Jan-Philipp SasseMMManuel MeyerBaptiste JordanThilo WeberATAlexander ThommenMatthias PauliPeter JanesLukas OberholzerEmilie BoillatGiuseppe Giulio CaponettoLucas TochtermannPeter Robineau
  Challenge description

  National energy policy goals are known and monitored, but how is the progress of the Swiss energy transition on regional level?

  Working on our challenges will contributing to the current, highly relevant discussion on security of energy supply in Switzerland. You will support us to develop cutting-edge energy policy design.

  Challenge slides

  Step 1: Web scraping energy policy goals

  Find and gather information about energy policy goals and activities of municipalities.
  Identify ambitious and less ambitious municipalities
  Optional: Cluster municipalities that follow same/close goals and benchmark

  Step 2: regionalize renewable energy production

  How much production in Swiss municipalities?
  Data wrangling and fusion

  Project Team

  Who has which skills and is in which team - policy (P), data (D), scraping (S)

  

  Emilie Boillat

  Guiseppe Caponetto

  Peter Janes («Piet»)

  Baptiste Jordan

  Manuel Meyer

  Lukas Oberholzer

  Matthias Pauli

  Peter Robineau

  Jan-Philipp Sasse

  Alexander Thommen

  Lucas Tochtermann

  Thilo Weber

  

  Team 1 - Policy and Data

  Which energy policy keywords and categories are relevant?

  Step 1 - Brainstorming Keywords

  

  Step 2 - Structuring Keywords

  Clustering brainstorming results mind map and converting to a structured table

  

  Team 2 - Web Scraping

  Finding energy policy information on Swiss municipality websites.

  ## Step 1 - Manual Samples

  Manually retrieving text data from few sample municipalities

  

  Step 2 - Building a Scraper

  As some research revealed, there is no service or implementation which could be used out of the box, so it was decided to go for a simple prototype implementation:

  Semi automatic information gathering:

  Loop over 64 municipalities as test set

  For every municipality initiate a google search with municipality name and keywords «Energie Umwelt»

  Take top 3 google results

  Pick sentences where keywords from team 1 appear

  Run sentiment analysis over retrieved sentences

  

  Scraper result set

  

  Scraper result set - sentences and sentiment analysis detail

  

  Reconciliation and Validation

  Bringing the pieces together, checking if it makes sense...

  Results Visualization

  Visualize results on map ( interactive map )

  

  Showing municipalities by keywords on map (interactive)

  Energieberatung

  Subventionen

  Technologie

  

  Sample Municipality Aadorf

  Validating Sentences

  Sentences analysis

  

  Map Visualization

  Analyzing details ( interactive map )

  Thermal network - green

  High solar capacity (kW/cap) - red

  Energy city - blue

  

  Final Pitch

  By Jan-Philipp Sasse (with assistance of Thilo Weber)

  

  Challenge Data and Code

  See links below for Github repository (click on «Source» icon).

• Sector coupling – ETH/ESC Adriana Marcucci

  Can we visualize sector coupling and flexibility of future energy systems?

  Robert StantonStéphane BisingerBill ZollingerAdriana MarcucciLea RuefenachtTristan Gollmart
  Challenge description

  Sector coupling and flexibility are key characteristics of future energy systems. Sector coupling means integrating sectors, such as heating, transport, and power, e.g. using electric vehicles or heat pumps. Flexibility refers to the capability of the system to react to changes in renewable electricity production, through storage or imports, for example. Using existing scenario results, in this challenge you should visualize these indicators using the Nexus-e Viewer.

  The following project was developed based in part on this challenge:

  https://hack.opendata.ch/project/846

• Smart-Meter CII Interoperability with Smart-Home Systems – Gantrisch Energie AG and Partner

  Connect ten common smart meters to a data broker, establish a common information schema and develop apps and visualizations

  Hermann HüniRemo InderbitzinDFDavid FöllmiPGPaul GrafUAUrs AbplanalpSimon FurrerPAPhilippe AyerThomas StrösslinCBChristoph BrönnimannDerrick OswaldPeter Kreutzer
  Challenge description

  Challenge Presentation

  In last year's CII -challenge , we have demonstrated how 4 Swiss DSO smart-meter (SM) deliver their energy data through their local Customer Information Interface (CII, like M-BUS oder DSMR) by using specific IoT-Adapters that will publish the data towards a common interoperability layer, the MQTT-broker.

  This year, we shall extend the range of SM to include further SM like Ensor, Kamstrup, Sagemcom and explore others. We also intend to deploy some new SM-powered IoT-Adapters to allow DSO-customers easy and reasonably inexpensive local access to their energy data. And we shall report on the issues encountered with MBUS-HDLC-DLMS-COSEM, DSMR and other types of CII.

  The second part of the challenge shall investigate and show how the many EMS or smart-home systems (SHS like Loxone, KNX, Homeassistant, openHAB, ioBroker, and more) may obtain the energy-data by subscribing to the MQTT-broker.

  We also intend to explore how to integrate the hourly next-day price forecast from ENTSO-E over MQTT. EMS, SHS or even the IoT-Adapters may display this information or take sensible load-shifting decisions to flatten their power use.

  Because of much higher energy prices, PV system operatores may lose their previous economic incentive to use as much energy as possible from their own production for their local loads. Instead, local loads may be shifted to times of relativly lower energy prices which also nicely indicate less expected load.

• SolarDAO - The Digital Energy Cooperative – SolarDAO

  Accelerate the energy transition by making investment in renewables more flexible and inclusive.

  Vasileios PanagiotidisJLJonathan LehnerAurelien PelissierMLMou LinCLChang Li
  Challenge description

  Background

  e-swissolar

  We exist to work at the intersection between transformative technologies and renewable energies. We are passionate to work on the creation of innovative financing instruments, decentralised energy networks or energy marketplaces. We aim to create sustainable value across the whole energy value chain for our stakeholders and for the society.

  DAO

  A Decentralized Autonomous Organization (DAO) is an organization whose rules are in computer code. DAOs are performed according to computer-coded rules (smart contracts), for example on a blockchain, and are subject to the joint work of their employees and members of the organization [1] .
  On the 27. & 28 März 2022 during the [Open Legal Lab] (https://challenges.openlegallab.ch/project/47) we created a legal structure for a DAO that will facilitate direct investments in PV-Systems in Switzerland that is Blockchain compatible and legally compliant.

  Energy Communities

  e-swissolar AG believes that energy cooperatives are highly relevant for the energy transition. An energy cooperative is a non-profit organisation whose purpose is to locally produce and deliver renewable energy to its stakeholder or members. Members jointly own and democratically control the cooperative. Cooperatives around the world share the same seven principles, which are based on values such as democracy, equality and solidarity.

  Watch the video about the 7 cooperative principles:

  ReFi

  In 2021 in collaboration with the HSLU Institute of Innovation and Technology Management an Innocheque for e-swissolar’s innovative idea ReFi -Regenerative Finance for Smart Energy Communities (54557.1 INNO-EE) has been conducted. ReFi is an investment platform built on top of a blockchain network that has the potential to efficiently and cost-effectively mobilise and redirect funds to small-scale projects owned by private persons or energy communities not eligible for traditional funding sources. Fractional ownership-enabled tokenisation and decentalised finance has the potential to unlock investment at various levels from retail investors to institutional investors with less risk due to a liquidity lending pool. This peer-to-contract (many-to-many) borrowing/lending scheme with blockchain yields a faster financing process with no underwriting and due diligence costs. ReFi offers transparency, collaboration and fair governance for domestic energy communities, public and private investors, and DSOs as a first step; with endless possibilities that can be built on top later. In 2021 e-swissolar developed a [prototype] (https://refi-near-hackathon.vercel.app/) deployed on the AURORA blockchain testnet.

  Distributed Autonomous Cooperative (DisCoo)

  We are currently working on the development of the core architecture of the smart-contracts system and of the smart-contracts trying to build up a minimum prototype (backend) of a legally compliant decentralised autonomous cooperative.
  More information will follow soon...

  Pilot

  More information will follow soon...

  Problem

  Financing

  A big barrier slowing the development of both new and existing energy communities is the lack of accessible, targeted and consistent funding and financing. REScoop identified following barriers:

  Limited access to bank financing and financing tools

  Lack of financing tools for initiating energy communities

  Manage Stakeholders & Resources

  Energy cooperatives are relaying on the active participation of citizens and the involvement of multiple stakeholders (local authorities, local
  economic players, other cooperatives, etc.). Some citizens become members of the cooperatives, i.e. owners and users of the cooperative. Following challenges have been identified in this regard REScoop :

  Organisation of members

  Information and transperancy

  Solution

  Financing

  Create specific financing tools for energy communities

  Manage Stakeholders & Resources

  Innovative Tools for Governance

  Challenge

  In the context of the identified problems and proposed solutions, what are the design criteria and patterns (elements) that constitute the SolarDAO. We will use the DAO Canvas developed by Felipe Duarte. The canvas is to guide groups through the process of making the most vital decisions they face in the process of imagining their DAO. We created a miro-board where we will collectively work on the DAO canvas. Further data about energy cooperatives will be provided during the event.

  DAO Development Canvas

  [Click on picture to enlarge] (https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/public/QZQNRLDBNPQMPE6ULPS7PQ38.jpg )

  Final Presentation

  References

  [1] [Decentralized Autonomous Organizations (DAOs) as subjects of law - The recognition of DAOs in the Swiss legal order] (https://libra.unine.ch/export/DL/40516.pdf)

  [2] [Schmid, Benjamin; Seidl, Irmi (2022). Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse. EnviDat.] (https://doi.org/10.16904/envidat.349)

  Call for Action

  We are looking for energy & tech-enthusiasts, -students, energy cooperatives, municipalities or utilities who are interested to explore with us new ways of using transformative technologies for realising the energy strategy 2050. You will help us to design right solutions for energy cooperatives.

  Challenge slides

  Vasileios Panagiotidis|[e-swissolar AG] (http://www.e-swissolar.ch/index.php/en/) , Poststrasse 9, 6300 Zug

• eco-friendliness score
  Tobias GramlWolfram WilluhnSofiia KaminskaLucien ErdinMarc HolthuizenRMRemo MüllerFNFrederic NachbauerFabian WidmerChris MutelPeter Allenspach
  Challenge description

  The topic of energy has arrived at the center of society, and we are beginning to understand that we can change our behavior in a positive way.
  Data is of value to people because it enables them to develop a better understanding of themselves and the world around them. Data from fitness trackers, for example, help to benchmark one's own body/fitness and to derive motivation for behavioral changes from it.
  In order to be able to act and being incentivised, one needs a benchmark (score).
  For future flexible tarif models, an underlying metric is needed.
  In a decentralized, renewable energy system the customer plays an increasingly active role
  For exemple by using energy when renewable energy is abundantly available and by reducing one’s consumption when there is a scarcity
  To show customers their potential for improvement, we would like to introduce a simple score based on individual consumption patterns. This score ensures transparency and creates the basis for effective incentive measures.

  Challenge slides

Programme

Friday, 16 September

1. 08:00–09:00
   Registration, welcome coffee & croissants
2. 09:00
   Opening

   Peter MorfHightech Zentrum Aargau AG
3. Welcome and short introductions

   Darienne HunzikerOpendata.chMatthias EifertZukunftsregion Argovia, Moderation
4. Keynote

   Xiying LiuAncillary Services & Analytics, Swissgrid AG
5. Keynote: The need for data for a sustainable energy system

   Antonios PapaemmanouilLeiter Institut für Elektrotechnik, Hochschule Luzern
6. Keynote: Introduction EnergyLab

   Ludger FischerCo-Leiter CC Thermische Energiespeicher, Hochschule Luzern
7. 09:30–10:30
   Challenge pitching

   15 challenges, three minutes each.

8. 10:30–11:00
   Group building
9. 11:00
   Start hacking
10. 12:30
    Lunch
11. 13:30
    Continue hacking
12. 19:00
    Dinner, then hacking open end

Saturday, 17 September

1. 08:00
   Breakfast and hacking
2. 12:00
   Lunch
3. 15:30
   Presentation of results
4. 17:00
   Apéro and celebration

Organising team

• Matthias EifertGeschäftsführer, Zukunftsregion Argovia
• Peter MorfHightech Zentrum Aargau
• Bernhard IsenschmidTechnologie- und Innovationsexperte, Hightech Zentrum Aargau
• Gaston WeyOpendata.ch
• Darienne HunzikerOpendata.ch

Host

Platinum sponsor

Gold sponsor

Silver sponsor

Sponsor

Media partner

Research and university partner