GridQube CORE has been designed not only to perform Distribution System State Estimation (GridQube DSSE), but also to support the implantation of secondary assessment and analysis tasks, called Post Estimation Applications (GridQube PEA)
Post Estimation Applications
In GridQube CORE three different levels of Post Estimation Applications are defined.
Level 1 Post Estimation Applications process the estimation results directly. GridQube CORE offers the software framework for their integration and automated execution.
Potential examples include performance assessments, statistical analysis and report generation.
Level 2 Post Estimation Applications use functionality offered by GridQube CORE to perform ‘what-if’ scenario based automated network analysis using GridQube CORE as a simulation engine. GridQube CORE supports the rapid implementation of these functions as well as consistency between the underlying network model and assumed base-case system state by instantiating the network model and initialising it with estimation results of the selected estimation run. A developer can then focus on only making the changes to the network model and assumed consumer/generator behaviour that differ from the base-case. Model generation, initialisation and scenario execution is then handles by GridQube CORE.
Possible examples are connection assessments, tap position optimisation, network augmentation assessment, etc.
Level 3 Post Estimation Applications rely on a full network abstraction by GridQube CORE. This means that the actual network no longer has to be considered by the developer, but that relevant constraints and performance indicators are mapped directly to the decision variables used by the developer. This greatly reduces the complexity in high-value optimisation functions and reduces the risk of inconsistencies.
Possible examples are generally optimisation problems, such as constraint mapping for network capacity constrained capacity allocation to network users, loss minimisation, etc.