Enhancing network reliability
In response to the increasing challenges posed by aging assets within the energy distribution network, a Distribution System Operator (DSO) asked us to support them in a project focussed on enhancing the reliability of their networks through advanced statistical forecasting and strategic investment planning. As critical components of the energy infrastructure approach the end of their operational life or deteriorate faster due to more intense use, the DSO recognizes the urgency to minimize disruptions, optimize resource allocation, and strategically invest in corrective actions to maintain a reliable and resilient energy distribution network.
Within the context of deteriorating assets, the DSO faces a heightened risk of failures, which can result in prolonged downtimes, increased maintenance costs, and potential impacts on the reliability of the energy supply. The increased risk of component failures requires a proactive and strategic approach to ensure the continued functioning of the energy network. We addressed these challenges by integrating statistical forecasting models and investment planning strategies tailored to the unique demands of energy assets.
Estimating the propensity to fail
The project resulted in several key deliverables, each contributing to the overarching goal of mitigating the impact of failing assets on the energy network. We developed a robust statistical forecasting model capable of predicting failures in network components. Leveraging historical data, environmental considerations, and relevant parameters, this model aims to provide insights into the expected lifespan of critical assets and their propensity to fail. Following this, the potential impact of failures on the energy network was estimated. By considering factors such as outage duration, affected regions, and the criticality of the impacted components, we provided the DSO with the aims to gain a comprehensive understanding of the consequences of asset failures.
Next, a sophisticated investment planning model was designed and implemented. This decision support model was designed to incorporate statistical forecasts and impact assessments, aligning with budget constraints, resource availability, and regulatory requirements. It provided the DSO with the capability to make informed decisions on when and how to implement corrective actions.
Roadmap to reduce risk
With these models, the DSO can formulate of a long-term strategic roadmap for risk reduction. Recognizing the complexities of the reliability of the infrastructure, this roadmap outlined a phased plan for implementing corrective actions, considering the overall risk profile, budgetary constraints, and resource availability. As new data becomes available, using the decision support models provided, the DSO updates this roadmap every year to continuously improve the reliability and safety of the energy supply.