Overview¶

RECreation (previously known as DigitalCER in legacy documentation) is a REST API platform for managing Renewable Energy Communities (RECs) under Portuguese regulation (Decreto-Lei 15/2022).

In these communities, a handful of operations can occur that you will set off via this API. To do so you will first need to configure the community structure and provide the platform with energy measurement data from the DSO or forecast data (if doing simulations or executing pre-delivery processes).

What is a REC?¶

A Renewable Energy Community is a legal entity formed by members — prosumers, consumers, and/or producers — who collectively share locally produced renewable energy. Locally generated energy (typically solar PV) is allocated among members, reducing grid dependence and lowering collective energy costs. These communities are registered with a DSO and receive monthly (sometimes daily) measurement data of consumption and generation.

Key Actors¶

graph TD
    SU[SUPER_USER] -->|creates & manages all| C[Communities]
    A[ADMIN] -->|creates & manages community| MEMBERS[Members, Meters, Resources]
    MB[MEMBER] -->|views own data + aggregated| D[Dashboard]

Role	Scope	Description
`SUPER_USER`	Platform	Manages every aspect of the platform. If you are integrating machine-to-machine scenarios or communicating through the API, you will likely have a `SUPER_USER` account.
`ADMIN`	Community	Role of community manager that has access to whole communities. Useful for interfacing with the platform through our frontend Web UI.
`MEMBER`	Own data	Access to the member dashboard (requires verified email). Can only see their own data and community-aggregated information. If using the API, members can be just generic representations of users from your own system and do not require emails or proper accounts.

Operation Modes¶

Mode	Description
Real Operation	A real licensed REC that receives data from the DSO (via API or SFTP depending on your needs). There are checks in place to guarantee correct data handling and thus bigger restrictions on what can be done.
Simulation	Testing and planning mode — RECreation emulates DSO allocation from provided generation/consumption profiles. You will likely not use this in API integrations, although it could be necessary for certain functionalities like sizing (scaling up optimization) simulations and rerunning historical data for comparison.

Note

Most of our initial testing used Simulation mode because DSO specifications are still evolving and DSO data may not yet be available for all deployments. Please be patient as we work in real operation with your integrations.

Platform Architecture¶

graph BT
    API[RECreation REST API] --> FE[Frontend Dashboard]
    DB[(Database)] --> API
    SIZ[Sizing Module] --> API
    EMS -->|generate setpoints| Resources
    Resources --> API
    TRX[Transactions Module] -->|market clearing| SETTLE[Settlement Module]
    SETTLE -->|billing outputs| EXPORT[Export through API, Excel or PDF]
    EXPORT --> API

Pre-delivery and Post-delivery¶

We have a concept of two stages regarding the platform. If you are working with Forecasts and most EMS, you are likely operating in the Pre-delivery phase. This likely means you are trying to optimize what your assets will do in the future.

If you have received DSO data, you are likely in the Post-delivery phase. This means you will create transactions and allocate the energy based on what the DSO sent you. If you are operating with Dynamic Coefficients, you will first generate coefficients a.k.a Bilateral Energy Allocations and send these to the DSO for validation. Otherwise, you run the market, then the settlement and you may use the results to help you bill your members.

In real life, you could work with both side by side. For example, first you generate forecasts based on historical data for tomorrow, then you feed different EMS modules (with different optimization strategies) your battery data and you receive Setpoints of battery behavior for tomorrow. You will apply this behavior and when the DSO sends you data in the future, their measurements will reflect the optimal behavior you predicted.