An excellent use case for the proposed bridge is the balancing of power, according to natural flows of electricity. You need to consume power the moment it is generated, therefore the energy grid is architected around energy planning partners. They have the responsibility for balancing consumption and production of power at each hour of the day. Renewable energy sources are quite challenging here because of the intermittency of power generation. To aim for decarbonization we need a near-realtime power production forecast. Needless to say, the availability of reliable (metered) data is key to this.
For this purpose we are going to use the trust network of EnergyWeb to identify renewable energy producing devices and leverage the data infrastructure of Ocean to curate and verify production data of these devices. A simulation model shows the effects of this integration.
About Tokenized Power Balancing
The solution is a 3-step process:
ad 1.) we cloned the EW-DOS origin repo and managed to get a dashboard with devices. Unfortunately to register a device ourselves, you need to have undergone some formal approval process so that’s too inconvenient for this submission. However, we are able to simulate having a device registered and attach metered data to it, therefore we used the data.csv file of the solar-simulator package as an example.
ad 2.) Building upon the solar-simulator package, we used the i-rec registered devices to add a dropdown field in the Ocean Market front-end populated with these devices in order to have an EnergyWeb registered device use their metered data as a proxy for the datatoken pool. Selecting a device will populate all meta-data fields of the Publishing form, having a basic integration between EnergyWeb and Ocean Market.
ad 3.) This is where the actual Tokenized Power Balancing job is done. Once we have a datatoken pool with metered data as an underlying dataset, stakeholders are going to use it to add value. Stakers signal curation by investigating the behavior of the power producing device as inferred by EnergyWeb EACs (and the claims thereof), but also by relying on other stakeholders like Auditors verifying metered data with sell orders in EnergyWeb Exchange and Optimizers making power production forecasts based on this metered data and possibly other datatoken pools having weather history and forecast datasets. For a detailed interplay between these stakeholders, see our documentation.
The output of 3. is accomplished by using an energyweb branch of the tokenspice2 simulation model looking primarily at the effects of the Optimizer.
Token engineering | Ocean Market | Python | Tokenspice