Failure localization in time critical market applications

TL;DR

This paper introduces a regularization method to help operators identify and correct failures in algorithms used for time-critical market applications, reducing financial risks and improving reliability.

Contribution

The paper presents a novel regularization procedure that aids in failure localization and diagnosis in algorithms for time-critical market operations.

Findings

Effective in the Russian Balancing Market operations

Helps operators identify failure points quickly

Reduces financial and operational risks

Abstract

In time critical market applications such as for example scheduling and price computation for the balancing market, failure of the algorithm in finding a solution would result in cancelation of the session and respective financial consequences for the market participants. In the paper we propose a regularization procedure that could help an operator to spot the problematic location and find out the reasons that caused the algorithm to breakdown and make necessary corrections. The approach has proved to be useful in the operation of the Balancing Market in the Russian Federation.