Kwantis is conducting a Qualitative & Quantitative Risk Assessment for the upcoming technological upgrade of a thermoelectric power plant. The objective is not only to identify technical and operational risks, but also to establish a traceable assessment process that captures uncertainties associated with each risk in order to support cost and schedule evaluations across different confidence levels.
Project Overview
Kwantis is conducting a Qualitative and Quantitative Risk Assessment for the upcoming technological upgrade of a thermoelectric power plant located in Europe. The modernization project involves the construction of a new, latest-generation combined cycle gas turbine (CCGT) unit. The updated facility aims to improve regional energy efficiency while supporting grid flexibility and the integration of intermittent renewable energy sources.
Risk Management Framework and Implementation
To manage the complexities associated with this 40-month construction timeline and the site’s long-term operations, Kwantis is leading a structured risk assessment framework based on ISO 31000 and PMBOK standards. Utilizing specific Risk Breakdown Structures, Kwantis’ risk management team is conducting collaborative workshops and qualitative evaluations with project engineers. The framework systematically addresses technical and operational risks across key areas, including thermodynamic train components, electrical control systems, brownfield integration challenges, permitting conditions prior to the Final Investment Decision (FID), and final asset handover. During the qualitative assessment phase, mitigation strategies are also identified and proposed where applicable to reduce risk exposure and improve project resilience.
Once the qualitative assessment phase is completed and project risks have been identified and categorized, a quantitative assessment is performed in which probabilities and potential impacts are assigned to each risk. This enables the development of cost and schedule estimates across different confidence levels through Monte Carlo simulation methods, providing a probabilistic understanding of overall project uncertainty and exposure.