Github repo: https://github.com/OpenEnergyData/Cheapest-Charging-Around

(English below)

Günstigste Lademöglichkeit in der Umgebung (#4)

Ziel

Weiterentwicklung der GIS-Plattform vom Bundesamt für Energie (BFE): Preisinformationen integrieren und die günstigste Lademöglichkeit in der Nähe für Elektroautofahrer finden.

Idee:

Ein Charge Point Operator (CPO) ist eine Firma, die einen Ladestationen-Pool betreibt. Der CPO stellt smarte Ladestationen den E-Mobility Service Providern zur Verfügung (EMPs). Ein EMP ist eine Firma, die Ladeleistungen den Elektroautofahrern anbietet, z. B. mit einer Zugangskarte zur Identifizierung und Aktivierung des Ladenvorgangs und entsprechenden Ladeabonnements. Der EMP ermöglicht dem User den Zugang zu einer Auswahl von Ladestationen in einer bestimmten Region. Eine Firma kann beides CPO und EMP sein. Unterschiedliche EMPs haben unterschiedliche Preise für das Laden an der gleichen Ladestation. Die Idee ist, die günstigste Lademöglichkeit für den Kunden eines bestimmten oder mehrerer EMPs zu finden.

Warum:

Grosse Preisunterschiede existieren heute zwischen den unterschiedlichen EMP Preisen für das Laden an der gleichen Ladestation. Diese Lösung soll die Kunden besser informieren und ihnen helfen, die beste Preisoption auszuwählen. Die Transparenz ist erhöht und die Market Performance ist verbessert.

Data

• Ich-tanke-strom.ch auslesen
• Preisinformationen von EMP/CPO Webseiten auslesen
• Statistical and availability information available in .json format: https://opendata.swiss/dataset/ladestationen-fuer-elektroautos.

Cheapest charging around

E-Mobility - further development of ich-tanke-strom.ch

Goal:

Further development of the GIS platform of the Swiss Federal Office of Energy (SFOE): Add price information to the charging stations and find the cheapest option around for electric car drivers.

Idea:

A Charge Point Operator (CPO) is a company operating a pool of charging points. A CPO provides value by connecting smart charging devices to E-Mobility Service Providers (EMPs). An EMP is a company offering an EV charging service to EV drivers. EMPs provide value by enabling access to a variety of charging points around a geographic area. One company can be CPO and EMP at the same time.

As a result, at each charging station of a certain CPO pool, different EMPs have different prices. The idea is to find the cheapest charging station around a user according to their EMP subscription(s).

Big picture:

Big differences in EMP prices exist at certain charging stations. This solution should help users to choose the best pricing options. Transparency and market performance is increased and the user/consumer is better informed.

Data:

• Get information from Ich-tanke-strom.ch
• Get information from EMP/CPO websites
• Statistical and availability information available in .json format: https://opendata.swiss/dataset/ladestationen-fuer-elektroautos.

Update: Removed old not continued documentation: https://md.schoolofdata.ch/7wV5czYKSb2R6_zJiD8PyA#

28.08.2020 19:11

Slides Notebook ►Pitch

Challenge

Kundensegmentierung/-Analyse auf Basis offener Daten vs Daten eines Energieversorgungsunternehmens. Beispiele haben bereits gezeigt, dass eine Kundenanalyse/Kundensegmentierung auf Basis offener Daten bereits möglich ist und es keine Kundendaten mehr benötigt. Ist dem so und wenn ja, wie sieht es aus?

(Challenge #13)

This task will create a decision support tool for asset managers. The utility asset manager will be able to put results of transformer inspections into the tool and receive:

1. A health value for the transformer ("condition-based rating")
2. A comparison of the transformer's health with other transformers from all over the world
3. Possible faults, based in particular on gas levels
4. A prediction on how the transformer's health will degrade in the next 1-2 years, based on how similar transformers have behaved
5. Advice on what gas levels to monitor, and how to watch them

A central part of the task is to conduct the analysis without a centralised dataset, and without being able to see the asset information of other transformers. The analyst is working blind to protect the privacy of the transformer owners! This is achieved using TAC, a system provided by the challenge sponsor, VIA.

The steps we are taking in the hackathon are:

1. To identify typical condition curves
2. To match these curves to the test transformer
3. To map the changes in health over time for the matching transformer, in particular looking at gas levels
4. To predict how the test transformer will change with time by generating a heatmap and plotting the test transformer to the heatmap
5. Visualisation and business value!

We have four datasets that we can use:

1. A global repository of transformers that we access using VIA's privacy-preserving TAC system (including fault information)
2. An anonoymised database of Swiss transformers
3. A list of faulty transformer readings, from the IEC60590 standard
4. A list of normal transformer readings, from the IEC60950 standard

The decision support tool will be combined with online and offline asset management approaches. It will be combined with load and geospatial data to create an exploitable tool.

31.08.2020 12:37

Prototyping has been completed and now the project will be taken forward for funding

31.08.2020 12:38

(English below)

Analyse E-Mobilitätsverhalten (#5)

Ziel:

Antworten auf Fragen bezüglich Mobilitäts- und Ladeverhalten in der neuen Ära der Elektromobilität.

Idee:

Öffentliche und historische Daten analysieren, um Klarheit bezüglich des Themas Elektroautos zu schaffen und Fragen beantworten, wie: - Wie und wann bewegen sich Menschen heute? - Wie, wann und wo laden Elektroautofahrer ihre Autos? - Wie vergleicht sich eine öffentliche mit einer privaten Ladestation bezüglich Nutzung, Nutzungszeit, Nutzungsdauer usw.? - …?

Warum?

Die heutige Situation der Elektromobilität verstehen, so dass man sich die Zukunft vorstellen kann und den benötigten Infrastrukturausbau (Verteilnetz, private Ladeinfrastruktur zu Hause) planen kann.

Daten:

• Anonymisierte Profile von privaten Ladestationen in Mehrfamilienhäusern
• Historische Daten von öffentlichen Ladestationen aus ich-tanke-strom.ch (10GB Daten Feb. - Aug. 2020)
• Öffentliche Daten aus anderen Quellen

E-Mobility behavior analysis

Goal:

Find answers to questions regarding mobility and charging behavior in the new era of electric mobility.

Idea:

Analyze open and historical data to get a clear understanding of the situation regarding electric cars by answering questions like the following: - How and when do people move? - How, when and where do people charge their electric cars? - How does a public charging station compare to a private one in terms of utilization, time of use etc.? - …?

Big Picture:

Understand the present situation regarding electric mobility in order to be able to imagine how the future will look like and plan the infrastructure development needed (electrical distribution grid, charging infrastructure at home) accordingly.

Data:

• Anonymized profiles of private charging stations in multi-family homes
• Historical data of public charging stations from ich-tanke-strom.ch (10GB of data from Feb. to Aug. 2020)
• Open data from other sources

28.08.2020 13:24

28.08.2020 17:34 mockup for our emobility behaviour dashboard done.

29.08.2020 13:11 presentation & live demo of our amazing dashboard

(Challenge #7)

Novel energy certificate assesses where and how strongly building / user behaviour causes deviation from theoretical/optimum behaviour.

💭 What problem are you solving?

Empowering people to understand what they can do to take action against climate change.

💡 How are you solving that problem?

Monitoring of people’s energy consumption, with the implementation of a rating system to understand how good they and the building they are living in are. Personalized information about what they can do to improve themselves is then presented. For example: for additional x Fr./month, get 100% electricity from renewable sources and move to category A.

🗽 What have you achieved during the Hackdays?

A new business concept for smart meter data.

📮 Include links to source code or prototypes.

https://www.figma.com/proto/b0tYMzO51sRp4McNm7A8qF/Open-Energy-Hackdays?node-id=16%3A46&scaling=min-zoom

📎 Indicate any relevant terms or licenses on your work.

Live or frequently updated and historic smart meter data.

🎢 What issues have you encountered, for example in regards to the data you wanted to use?

Choosing the most impactful customer, addressing the exact issue which is tackled by our solution. Data will most probably not come from one unique source, but from a variety of sources. The users should authorize data hosters to use their data.

💹 Which data have you used? Format…? Permissions?

Finding business partners, implementing the new certificate, and build a solid user base.- Federal office of energy/statistics (public data),

• VSE Homepage (public data)
• Customer segmentation data from City Energy Provider of St. Gallen (confidential)
• Smart meters data (from public utilities)
• Building characteristics (from Building and Dwelling register)
• Weather data

🗻 What are the next steps?

Finding business partners, implementing the new certificate, and build a solid user base.

💁 Who are you and your team mates, and how can you best be contacted?

Clara Esteve, Jeremias Rehn, Philipp Schütz, Wim Ton, Holger Wache

29.08.2020 12:36 Initial project release

Big picture: (Challenge #6)

Integrated Energy System models produce a large amount of results, with a wealth of information. There need to be interactive tools to explore this data by drilling down the data sets.

Idea:

Based on databases containing the results, a concept for the visualisation (including data plots/charts) as well as the actual implementation with modern and open-source web publishing and programming tools has to be implemented. It might also be used to display input parameters.

We provide:

• Our knowledge and help
• Flexibility to choose a focus
• Clearly defined questions
• Cleaned and structured data
• Established Python-based framework
• Opportunity to see your idea in production soon ## We expect: Your creativity!

Current framework:

(not restrictive): - Python - Plotly - CSV

29.08.2020 09:07 New landing Page with Scenario overview UI Improvements: - Dynamic triggered graphs. - Immediate trigger of Graph plot on change of parameters or scenario - Moving Scenario Tabs to center page

29.08.2020 12:42 Prototype finished - ready for presentation.

Slides Demo ►Pitch

Note: demo works however only for the Germany Nord Rhein Westfallia (Cologne, Dusseldorf, Munster and so on)

Challenge

Kundensegmentierung/-Analyse auf Basis offener Daten vs Daten eines Energieversorgungsunternehmens. Beispiele haben bereits gezeigt, dass eine Kundenanalyse/Kundensegmentierung auf Basis offener Daten bereits möglich ist und es keine Kundendaten mehr benötigt. Ist dem so und wenn ja, wie sieht es aus?

Recording Slides In English

Ziel: (Challenge #8)

• Entwicklung von Machine Learning-Algorithmen (oder Tools/Apps) für verbesserte standortspezifische Leistungsprognosen von Windenergieanlagen.
• Entwicklung von alternativen Algorithmen z.B. Artificial Neural Networks

Idee:

Die genaue Vorhersage der Stromproduktion einer Windkraftanlage an einem bestimmten Standort ist sowohl in der Planungs- als auch in der Betriebsphase wichtig, aber die standardmäßige Methode zur Speicherung von Leistungskurven ist nicht spezifisch für die atmosphärischen Bedingungen am Standort und kann daher ungenau sein. Das Ziel dieser Herausforderung ist die Entwicklung eines Algorithmus für maschinelles Lernen, um die Genauigkeit der standortspezifischen Leistungskurvenvorhersage zu verbessern. Dazu wird Ihnen ein Datensatz von 8'000 simulierten Leistungen der NREL 5MW-Referenzwindturbine aus dem Simulationswerkzeug ASHES bei verschiedenen atmosphärischen Bedingungen zur Verfügung gestellt. Dieser neue Algorithmus könnte in einem zukünftigen Innovationsprojekt zu einem Werkzeug für Windparkbetreiber entwickelt werden.

Daten:

• Ein Datensatz von 8'000 simulierten Leistungen der NREL 5MW-Referenzwindturbine aus dem Simulationswerkzeug ASHES bei verschiedenen atmosphärischen Bedingungen wird zur Verfügung gestellt.

Machine Learning Wind Turbine Power Curve Prediction

Goal:

• Development of machine learning algorithms (or tools/aps) for improved site-specific performance prediction of wind turbines.
• Development of alternative algorithms e.g. Artificial Neural Networks

Idea:

The accurate prediction of the power production of a wind turbine at a particular site is important in both the planning and operation phases, but the standard power curve binning method is not specific to the atmospheric conditions at the site and can therefore be inaccurate. The goal of this challenge is to develop a machine learning algorithm in order to improve site-specific power curve prediction accuracy.

This new algorithm could be developed into a tool for wind farm operators in a future innovation project.

Data

For this challenge, you will be provided with a dataset of 8'000 simulated powers of the NREL 5MW reference wind turbine from the simulation tool ASHES at a range of different atmospheric conditions.

Download data here: https://github.com/sarah-barber/powercurve/tree/master

29.08.2020 10:54 Project updated

Challenge #1

Notebook ►Pitch

Challenge #10

(english below)

Warum:

Die Verbreitung der Solar-Photovoltaik in der Schweiz ist nur in grossem Umfang möglich, wenn sie wirtschaftlich sinnvoll ist und gleichzeitig die Treibhausgasemissionen reduziert.

Idee:

Diese beiden Faktoren können durch einen Vergleich der Lebenszykluskosten (LCC) und der Lebenszyklustreibhausgasemissionen (LCE) mit ihrem lokalen Stromtarif und dem LCE des Strom-mixes der Versorgungsunternehmen bewertet werden. Die LCC und die LCE variieren für jedes einzelne PV-System, abhängig von ihrer Grösse, den erneuerbaren Ressourcen und anderen technischen Faktoren.

Angesichts der bisherigen Erfahrungen des PSI über das PV-Erzeugungs-potenzial und -kosten kann diese Analyse durchgeführt werden, um mehr Erkenntnisse darüber zu gewinnen, wo der Einsatz von Solarphotovoltaik in der Schweiz am sinnvollsten ist.

Where solar photovoltaics make the most sense

The big picture:

The rollout of solar photovoltaics to a great extent in Switzerland is only possible when they make economic sense and at the same time reduce greenhouse gas emissions.

Idea:

These two factors can be assessed by comparing the Life Cycle Cost (LCC) and Life Cycle greenhouse gas Emissions (LCE), with their local electricity tariff and the LCE of electricity mix supplied by utility providers. The LCC and LCE varies for each individual PV system, depending on their size, renewable resources, and other technical factors.

Given the previous experience on PV costs and generation potential, this analysis can be undertaken to give more insights on where it makes the most sense to deploy solar photovoltaics in Switzerland.

Call for expertise

1. GIS expert
2. Application/UI developer
3. Energy consultant/scientist
4. Expert from government/utilities working on PV system rollout/implementation

Questions to answer in two perspectives:

1. Country-wide (mainly analysis): how much potential do we have for Switzerland considering LCC and LCE in a realistic sense

2. Individual resident (requires UI):

   • Does it make sense to install PV on top of my roof
   • How can I find residents around me with similar conditions/interest to reduce system investment cost (economy of scale)

Data

1. levelized cost of electricity for each roof in Switzerland from this analysis by Technology Assessment Group, Laboratory for Energy Systems Anlaysis, PSI
2. local electricity mix by electricity generation technology for municipalities and entities in 2018, obtained from Stromkennzeichnung
3. 2 sources for PV potential in Switzerland
   • data from Sonnendach
   • data from Walch, A. 2020
4. life cycle greenhouse gas emissions (LCE) per kWh for different electricity generation technologies based on ecoinvent version 3.6
5. electricity tariffs in Switzerland from ElCom
6. simulating the performance of photovoltaic energy systems (pvlib-python)
   • is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. It was originally ported from the PVLIB MATLAB toolbox developed at Sandia National Laboratories.
   • see more on project gibhub page https://github.com/pvlib/pvlib-python
7. remuneration from electricity providers
   • an official portal providing numbers on compensating the electricity production using PV in households - pvtarif.ch
   • https://www.vese.ch/wp-content/uploads/pvtarif/pvtarif2/appPvMapExpert/pvtarif-map-expert-de.html
   • there is an API but the data license forbids any use of this data in digital form in research

Thinking process

In this project we did additional study and compiled research notes, sample data and suggestions, expanding on the original challenge

1. Limitations of the solardach.ch calculator

   • 1.1 Number of factors: it considers factors like solar irradiance, angle of roof area, shading. The new study also considers additional factors like temperature (which affects the module efficiency), obstacle structure on roof (based on machine learning) = more realistic estimate --> use data from new study
   • 1.2 Calculation not based on current technology/efficiency factor: Someone planning to build a new PV installation would (reasonably) use the latest technology, while most calculations by tools are based on averages (which consist of a mix of old and new technology). Estimates for kWh potential might be 1/3 to 1/2 too pessimistic. --> use better data based on new technologies?
   • 1.3 Grid network restrictions (maximum power which can be fed into the network): depending on location (more rural = usually worse), maximum capacity which can be fed into the network is much lower, dependent on three factors: connecting cable ampacities, voltage range limitation, and transformer power rating. Data on these three factors is proprietary information of the electricity companies. Might be in a range between 0 and 90% restriction on potential --> data is not openly available. Is there any way we can find a proxy? (especially for CH-wide potential estimate) --> for individuals data might be retrievable from their energy provider for inclusion in calculators (e.g BKW online portal offers the information for individuals for their own place)

2. Calculate potential (economical)

   • 2.1 Consider energy usage in network: network limitations might be less extreme because numbers given are based on no consumption --> unclear how this can be estimated
   • 2.2 effects of everyone having PV installed: excess energy supply at peak times in an area = limitation of the network

3. Calculate potential (environmental)

   • 3.1 data on energy mix by city is available

Link to Drawing of data structure

Network Limitations

The past generation potential estimates don't seem to take into account the limitations of the network infrastructure. There are three limiting factors related to adding PV to the existing network: connecting cable ampacities, voltage range limitation, and transformer power rating:

• the voltage increase due to feeding in from a new PV cannot exceed +3% threshold allowed by law
• the current increase due to feeding in from a new PV cannot cause the current to exceed a cable rating (on the entire chain from house to transformer)
• the power fed in by a new PV cannot exceed the transformer rating; the ones connected to the medium voltage grid or others connecting any intermediate subtransmission (1000V intermediate voltage system) network

The available public data does not include any of this (cable ratings, network topology, transformer ratings) since it is all proprietary to the power company (Elektrizitätsversorgungsunternehmen or EVU).

Using proprietary data and comparing the Sonnendach available roof area capacity (sum of all roof sections identified, i.e. good, marginal and bad, using the maximum area) with the calculated maximum feed in values for the electrical connection of the utility, shows a large overestimate of the energy capacity for rural and industrial sites.

The network maximum is calculated under the worst case assumption that all PV (proposed and installed) are generating at the maximum (peak) value and no consumption is occuring. By and large, the majority of these limitations are based on an over-voltage situation (>3% above nominal). This engineering limit would not normally apply when the PV owner self-consumes or the energy is shared with neighboring consumers.

These network maximums are also calculated when only one additional PV is installed (at the indicated site). So this brings up the additional issue that, should everyone in an transformer service area have a PV installation, these maximum values would be replaced by other limitations, for example overloading of shared cables, overloading of supplying transformers or general over-voltage conditions from multiple injection points. A more detailed analysis is needed on a per transformer service area basis with perhaps PV installations at only the most opportune locations (large area, south facing roofs).

Thus, the “Life Cycle Cost”, LCC, would need to include upgrades to the network infrastructure with the addition of large amounts of PV, or the maximum potential for PV installation would need to be adjusted downwards under the assumption of no network upgrades.

Regarding the proprietary data needed for the engineering analysis:

• the data is owned separately by each of the over two hundred EVU in Switzerland
• these EVU are very unlikely to be able to provide this network data due to privacy restrictions and competitive considerations
• only a small fraction of the German speaking EVU are doing this maximum feed in analysis on a broad scale
• for these few, the best approach may be to provide a list of candidate sites with kW suggested and ask if their network can support this size of installation at those sites

Help meet the Paris Convention goals to achieve net 0 by 2050, less than 2 tonnes CO^2 per person! We want to raise awareness around energy use and consumption by putting Switzerland on the map at electricitymap.org and put its open data API to use. (Challenge #11)

28.08.2020 15:04 First solution ideas have been explored and discussed. Beer has been deployed. 28.08.2020 23:00 Despite some hurdles we got everything ready for our idea but realised that it won't be accepted by electricity maps. 29.08.2020 11:00 Complete restart to a much simpler model that will hopefully be accepted by electricity maps and still be better a gray spot on tha map.

29.08.2020 13:40 Ideas are tested and documented. However, it's not running live.

With the AEW data of a 60 kWp PV installation, we optimized the solar energy self-consumption by a battery system. For this, we calculated different parameters such as battery load, new self-consumption and new grid supply and estimated the optimal battery capacity with further values such as energy and battery prices, life span, contract duration, minimal charge, maximum load power in Google sheet and as well with Python.

Finally, our customer can select, whether he wants the most economical battery solution or maximise his autarky. Our tools calculate the maximized economic benefit over lifetime.

Google Sheet Prototyp

Create a copy

Github

https://github.com/ehackdays/PV-Optimisation Contains initial Python implementation and some exploratory analysis, see also README below.

29.08.2020 12:02

(English below)

Eigenen Smart Meter auslesen (#3)

Ziel

Eigenen Smart Meter durch die lokale Customer Information Interface (CII) Schnittstelle auslesen und Stromverbrauch visualisieren. Dashboard mit den wichtigsten Informationen gestalten.

Idee

Zwei Smart Meter werden vor Ort installiert und werden den Verbrauch von unterschiedlichen Geräten messen. Der Live-Verbrauch wird auf einem web-basierten Dashboard visualisiert. Live-Werte werden mit historischen Daten kombiniert. Am Ende kann das Dashboard einer Person klare Auskunft über die wichtigsten Informationen bezüglich des Stromverbrauchs geben.

Warum:

In der Schweiz ist es gesetzlich vorgeschrieben, dass alle Smart Meter, die von Energieversorgern installiert werden, eine lokale Schnittstelle (CII) haben, so dass die Kunden Zugriff auf die eigenen Daten haben können. Das schafft mehr Transparenz, da die Kunden ihre eigenen Daten managen können. Innovation wird ermöglicht, weil genaue Daten neu gratis in Echtzeit verfügbar sind.

Data:

• Live-Messungen mit Hilfe von Smart Metern
• Historische Daten Testanlage EKZ
• Eine kurze Einführung zu den Kommunikationsprotokollen der Smart Meters findet man hier: https://icube.ch/DLMSSurvivalKit/dsk1.html

Read your own Smart Meter

Goal:

Read your Smart Meter through the local Customer Information Interface (CII) and visualize your consumption. Design a dashboard with the most useful information.

Idea:

Two Smart Meters will be installed on-site and will be measuring the consumption of different devices. The live consumption is to be displayed on a web-based dashboard. Live measurements are to be combined with historical data. At the end, the dashboard will be able to display the most important information to an individual about their electricity consumption.

Why:

In Switzerland, it is prescribed by law that all electricity Smart Meters installed by utilities must have a local interface (CII), so that customers can have access to their own data. Transparency is increased as individuals can manage their own data. Innovation is promoted, as precise data is available for free in real time.

See also Challenge 582: "Unleashing the Swiss Smartmeter's CII" https://hack.opendata.ch/project/582

Data:

• Live measurements from Smart Meters
• Historical data of an EKZ test site
• Short introduction to Smart Meter communication protocols can be found here: https://icube.ch/DLMSSurvivalKit/dsk1.html

Members of the team: Moritz Bolli Peter Zbinden Derrick Oswald Hubert Kirrmann Hermann Hueni Angelos Selviaridis Christos Konstantinopoulos

28.08.2020 14:34

The team will focus on moving data from the metering system and visualizing the data graphically. It will not rewrite the existing proprietary components.

28.08.2020 14:38

A quick prototype system has been constructed. There is a MQTT broker installed on the Raspberry Pi. It is fed by a bash script that monitors the log file of the proprietary software and publishes on four topics at roughly 5 second intervals: - smartmeterevents/raw sends messages containing the text of each new line in the proprietary software CSV file - smartmeterevents/voltage sends a time stamp and the voltage - smartmeterevents/pf sends a time stamp and the power factor - smartmeterevents/power sends a time stamp and the power MQTT clients, for example on members laptops, subscribe to the above topics and reports the messages as they are received. Graphing software has been prototypes to display the time series (sample data).

28.08.2020 14:42

OpenHUB 2 has been installed, and still being worked on, but as yet not successful.

28.08.2020 14:53

Initial success in reading the data and transferring it to a PC has been demonstrated.

28.08.2020 14:55

Smart meter readings are saved to the InfluxDB database by a MQTT <=> InfluxDB bridge python script.

28.08.2020 17:35

The first successful dashboard has been created with Grafana.

28.08.2020 18:18

Presented at Open Energy Data Hack Days - Saturday, 29 August, 2020

01.09.2020 05:58

Source code is available, per the Source link.

01.09.2020 06:00

Demo: https://meteros.staging.akenza.io/

28.08.2020 12:54 Project scope has been defined and work has been started

28.08.2020 19:54 We have deployed a very first version to a k8s cluster which can be viewed here: https://meteros.staging.akenza.io/

The backend still needs some work though, the frontend does not query the data from the backend yet

28.08.2020 23:25 We drafted definitions of anomalies (human definitions) and meter data is sent to the cloud where it is processed and stored along with various improvements to the UI. The current state can be checked at the project link.

The API and online processing of the data (live detection of anomalies) is still to be implemented.

29.08.2020 12:38 The last finishing touches are being applied. Forecasts have been removed from scope due to time restrictions.

Introduction

Swiss law (StromVV Art 8a) requires all installed smartmeters to offer a local "consumer information interface" (CII) to provide all measured data at the moment of its recording. Despite 2+ years since this has become mandatory by the end of 2017 it is still very hard to obtain sufficient information from the DSO (distribution system operator) on the particular CII put in place on the installed smartmeter.

Different technical interfaces (CII) are in use on various smartmeters in Switzerland which inhibits interoperability for innovative solutions. It is almost impossible to find publicly available information on what kind of CII is offered by the 600+ different DSO of Switzerland.

Where as in the netherlands, the energy sector organisation (netbeheernederland.nl similar to VSE in Switzerland) has required all smartmeters installed during the past years already to offer the same well designed CII called DSMR P1 (also adopted by Belgium and Luxembourg), the swiss authorities or the market participants have regrettably not decided on a mandatory standard to be required. This unfortunate situation has inhibited the market in Switzerland for innvovative applications. And a large amount of money and ressources is currently spent (and are finally wasted) by installing additional private energy meters behind the DSO's smartmeter.

See also [D]: https://forume.ch/t/kundenschnittstelle-der-intelligenten-messsysteme/938/4

Let's try to improve on that.

Motivation

Photovoltaic systems (PVS) with an output of 5-15 kW are increasingly being installed on roofs of swiss houses. Often there is a 300 L electric boiler connected on 3 phases of 400 V with a power of 6 kW for the hot water demand, which is charged during the night via a ripple control of the DSO. Because Art 8c of the StromVV has obliged the DSOs since 2018 to remunerate the customer for such flexibility useful to the grid, some DSOs remove the ripple control when connecting a new PVS to the grid and at the same time installing a smartmeter.

If energy is permanently supplied to the electric boiler, around 30-50% of the annually required heating energy of approx. 3.8 MWh/a [1] can already be used from the PVS production. By means of an electronic control system, an additional max. of 40% of the grid supply could be substituted trough PVS production. With a tariff difference between the feed-in tariff and grid procurement of approx. 100 Fr/MWh, savings of only 150 Fr per year would result.

For an economic solution, the costs for the necessary power control (including installation) should not exceed a few 100 Fr. However, many products available today [2] quickly require investment costs of 1'000 - 3'000 Fr which is far beyond any efficient and economically justifiable solution.

[1] https://www.energie-lexikon.info/warmwasser.html
[2] Example: (a) Energy4me units 1'083 Fr [ control unit 377 Fr, switching power supply 39 Fr, AC meter 248 Fr, thyristor controller 280 Fr, mains filter 139 Fr ] Q:https://www.energy4me.ch/energiemanagement/ (b) Expenses for electricians for installations: 500-1'000 Fr.

Goals & tasks

Together we shall discuss and demonstrate how easy and cost-efficient a simple solution to this scenario can be put in place using DSMR-P1 with an ESP Microcontroller and a piece of open-source software coupled with a very affordable wifi-enabled powerswitch-actor. Attaching any of the various visualisation- or energymanagement-sytems (EMS) for home automation over MQTT might then be relatively straight-forward.

But the key challenge is how to deal with the diversity of CII such that all citizens may get easy and efficient access to their own owned energy data. Many additional questions still have to be worked on. We hope that we may tackle some of these together with the expertise of those supporting this challenge.

29.08.2020 07:19 We developed a concept and PoC roadmap to provide a "universal" adapter from smart meters to home IoT platforms, see "Source" for details.