Energy Singularity Challenge 2020: Testing Novel Grid Fee Models and Intelligent Peer-to-Peer Trading Strategies

The Context

The Process

“I’m very impressed by what has been set up and quite grateful to be able to address a very important issue for Engie which is how to organise value sharing within an energy community.”

The Teams

The Challenges

“It’s called a Canary Network because if the canary dies while you’re working in a coal mine, you have to run. It’s a proof of concept and we will experiment simulated scenarios on a live running exchange with real data.” No canaries were harmed in the development of Grid Singularity’s Canary Network.

Figures 1a and 1b. Architecture of Grid Singularity energy exchanges
Figure 2. OLI captain Felix Förster connecting a prosumer community in Germany to the GSy Canary Test Network
Figure 3. Energy storage demonstration to show charge and discharge via terminal and visual indicator (red polar bear for discharge, green for charge)

Paul Bierling from Stedin explained the underlying interest of grid operators: “Our goal is to try to manage the chaos by putting incentives in the system for the teams to use their flexibility, not only in a way that’s good for them, but for the system as a whole, so that we can keep the system cost low. One of the ways to do this is to apply a grid fee. The GSy platform makes it easy to conduct numerous experiments in just a few hours, and iterate towards a new tariff system that allows for effective grid management.”

Figure 4. Energy Singularity 2020 Experiments Setup: DSOs managed the Grid and Community market fees using the GSy Grid Operator API. The trading strategies of 68 assets in the community were managed by competing teams through the Asset API.
Figure 5. Different grid tariff models applied in Energy Singularity Challenge 2020 (Time of use in red and Aziiz model in orange) induce various behaviours in the use of flexibility, impacting the import and export of the community (energy imported and exported every 15 minutes under the Time of Use in brown and Aziiz model in green).

“It was interesting to see how teams reacted with the different strategies and that in each of the cases, the ability of using smart agents to reduce peaks was shown, and the different approaches of the teams led to different outcomes. However, lower peak doesn’t necessarily imply a lower grid fee.”

“We have a right set of questions that we can take out of this hackathon and continue to answer in order to come up with specific requirements to build a real-life product to serve real-life people, with real-life devices and real-life apps.”

Figure 7. During the Energy Singularity Challenge 2020, the Rebase team was able to reduce export peaks to 68.6% of the baseline, although the import peak remained at 100% of the baseline.
Figure 8. The Energy Singularity Challenge 2020 Miro board where teams laid out how they approached designing their agents, market design, and future business models.

“Why is self-consumption of local PV an objective? Is it monetisable? From a power system perspective, self-consumption isn’t that valuable except for avoiding some losses. The timing of in-feed and export is more important for improving grid flexibility, and this can be influenced by different grid fee models. Self-sufficiency is different and can be valuable in reaching higher grid resiliency.” He proceeded to suggest a research paper with further insight on the tradeoff of decentralised and centralised resources.

“The grid fee models we tested in the framework of the Energy Singularity Challenge are valuable input for the future energy system design. The grid fee experiments showed both the workings and the value of the digital twin environment for innovating grid management tools. In the coming years we will see the first new grid tariff designs come to reality, and these may not be as advanced as those tested during this hackathon. We look forward to further development of the simulation model to enable realistic testing of current and future grid fee models, accelerating the implementation of innovative grid management. ”

The Finale



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Grid Singularity

Engineering open source software that simulates and operates grid-aware energy exchanges, creating local marketplaces that interconnect to form a smart grid