Energy system planning

Fluvius is the primary operator of electricity and gas distribution networks across Flanders in Belgium. Fluvius was created following the merger of an amalgamation of distribution system operators in 2018. The various legacy data systems of the merged entities created challenges with respect to data integrity and fragmentation. Fluvius also faced challenges related to aging infrastructure, decreasing reliability, rapid electrification of transportation and heating, growth of distributed energy resources (e.g., rooftop solar panels) and increasing pressure on tariffs. They also faced new regulation in Belgium that restricts multi-utilities from conducting capital works on the same location within five years. 

Deloitte Belgium delivered a broad set of data-driven asset management solutions to help support the transformation of the Flemish energy network. One of the foundational achievements was the consolidation of grid data from legacy  distribution system operators into a unified model, enabling consistent scenario modelling and advanced analytics across the network.

To support reliable system analysis and planning, the team developed broad models that accurately represented the connectivity and operational rules of the amalgamated grid. This enabled the subsequent development of a scalable, cloud-based platform to perform advanced load flow and scenario analysis. Capable of processing millions of scenarios in parallel, it provided insights into transformer and cable loading, voltage variations, and network losses, with robust sensitivity and uncertainty analysis. The impact of these scenarios was visualized in a unified ElectrifiedGrid^TM digital twin representing the Fluvius network (i.e., both the electricity and the gas networks). This digital tool enabled spatial analysis of asset risk, maintenance prioritization, and integration of demographic and environmental data. 

Driving excellence in asset management, Deloitte Belgium implemented end-to-end asset management solutions, including interactive dashboards, operational maps, export functionalities, and executive reporting, all  designed to enable transparent, data-driven decision-making and facilitate regulatory engagement.  

To help strengthen investment planning, predictive models were developed for asset risk assessment and investment optimization, including financial impact modelling for capacity constraint mitigation. These investment planning tools harmonized risk, performance, and budget considerations across the multi-utility context, minimizing disruption and maximizing synergies.

Finally, the platform enabled transparent data sharing with regulators and other key stakeholders regarding scenario inputs, assumptions, and anticipated grid impacts. This began with Fluvius gathering future belief inputs from stakeholders (e.g., regulators, municipalities, and transmission system operators), including from other utilities. Based on these inputs, Fluvius defined 150+ different scenarios for modelling grid impacts  and shared the output analyses with the regulator. This transparency helped Fluvius get alignment on the required capital expenditures to meet future grid needs, helping leaders to secure regulatory approval for their rate case application.

• Deloitte Belgium developed a transparent, automated and reproducible risk-based and data-driven investment plan, which supported a ~US$4.7 billion (€4 billion ) rate case application increase.  
• By enabling the identification of investments in the grid network that could be avoided or deferred, this helped to reduce capital expenditures (CAPEX) by ~US$11.8 million  (€10 million ) relative to business-as-usual spending over 10 years. Furthermore, it improved the efficiency of analyzing network extensions, reducing operating expenditures (OPEX) by ~50%.
• Finally, the quality of Fluvius’ grid data model increased from 60% to >90% of correctly connected circuits, supported by ElectrifiedGrid’s capabilities in detecting and rectifying incorrect switching states, connecting gaps, and geographic information systems (GIS) errors.