This deliverable presents the latest developments carried out within the OPENTUNITY to ensure a proper long-term management of the operations and assets under the control of DSOs and TSOs. A suite of modules has been developed to provide advanced capabilities for asset management and distribution system planning, aiming to improve the reliability of the equipment and the overall resilience of the system. These modules extend the functionality of existing SCADA and AMI systems, taking advantage of the great amount of data that are available from control centers and field devices. The long-term asset management module utilizes statistical reliability models and machine learning (e.g., XGBoost) to predict the end-of-life and failure probability of smart meters, reducing inspection costs and enabling data-driven replacement strategies. The short-term asset management module focuses on real-time monitoring and anomaly detection, to assess the health status and issue timely alerts for critical grid assets, such as transformers. The module supports a prediction of top-oil temperature that can facilitate system operators in identifying critical operating conditions that could lead to equipment failure. Both modules utilize data from SCADA, AMI, as well as historical logs to enhance the system’s reliability through early-stage malfunction detection. Additionally, a non-technical losses detection module has been developed to identify energy theft and unregistered consumption. This module uses hybrid machine learning and network analysis techniques, enabling the accurate detection of anomalies and illegal connections. The network planning tool offers Distribution System Operators (DSOs) an intelligent, flexible platform for long-term planning of electrical distribution networks. It addresses multi-year planning problems using advanced optimization techniques (Mixed-Integer Linear Programming ‘MILP’/ Mixed-Integer Second-Order Cone Programming ‘MISOCP’) solved with GUROBI, enabling DSOs to simulate alternative grid strategies. The DSO user, via the developed UI, provides information to the network planning tool, such as the system topology, load curves, and equipment data that should be considered in future upgrades. The user also defines specific settings for the analysis that the tool performs, such as the location and capacity of future PV installations, the future horizon in years to run the analysis, load growth rate, etc. The optimization problem also considers constraints related to the flexibility offered by the demand and Renewable Energy Sources (RES). The formulation of the optimization problem (along with the relevant objectives/constraints), the identification of data availability across pilot sites, the development of supporting functions and the creation of the underlying database have been described in D5.3. This deliverable focuses on describing the tool’s user interface, which allows scenario testing based on user-defined goals such as investment deferral, cost minimization, and RES maximization. This deliverable emphasizes the presentation of the latest version of the User Interfaces that have been developed for the WP5 modules and tools. Since the design and implementation aspects of each module were documented in D5.3, the present document (D5.4) highlights the functional features of each module and provides a detailed User Manual to guide the tools/modules’ users in deploying and operating them effectively.
Lagos et al. (Mon,) studied this question.