RAO Business Logs

Introduction

These logs contain high-level business information about the search-tree algorithm. Activate them if you want an overview of the RAO steps, like the impact of topological actions and the optimization result of every perimeter. Most logs contain normal information, but some may contain errors (possible cases are listed below).

Package name:

com.powsybl.openrao.commons.logs.RaoBusinessLogs

Possible error cases

Module	Name	Label	Description	Consequence
ra-optimisation	Initial sensitivity failure	“Initial sensitivity analysis failed : {error message}”	The initial sensitivity computation (at the beginning of the RAO) has failed	The RAO exits and returns an empty result
ra-optimisation	Post-contingency failure	“Scenario post-contingency {contingency name} could not be optimized.”	The optimization of post-contingency scenario (during an auto or curative instant) has failed	The RAO skips this post-contingency scenario and moves on to the next contingency. No result for the failed contingency scenario will be produced. The RAO will fail when trying to merge preventive & post-contingency results.
ra-optimisation	LF failure	“Loopflow computation cannot be performed CRAC {crac ID} because it lacks a ReferenceProgram or a GlskProvider”	Self-explanatory	The RAO is interrupted from the beginning
data/crac-io-api	CRAC error	*	Any error during CRAC creation (for example trying to import a CSE CRAC with a non-UCTE network)	The import (thus the RAO) is interrupted
data/refprog	RefProg import error	“RefProg file is not valid for this date {date}” or “Cannot import RefProg file because its publication time interval is unknown”	The RefProg file could not be imported	The RAO is interrupted from the beginning
ra-optimisation	Single-state optimization failure	“Optimizing state {state ID} failed: {error message}”	One-state only optimization failed	RAO returns a failed RAO output
ra-optimisation	MIP failure	“Linear optimization failed at iteration {iteration number}”	The solver may return a status different from “optimal” and “feasible”. In this case OpenRAO consider that the MIP optimization has failed.	If a previous iteration of the MIP optimization succeeded, its results is used in the rest of the process. If it is the first iteration that failed, the RAO will fall back to the initial situation.