The Target model moves beyond asset modelling to simulate network operations. Asset failures, protection operation, network reconfiguration and repairs are all modelled to accurately capture network behaviour and therefore performance.
The Target model moves beyond asset modelling to simulate network operations. Asset failures, protection operation, network reconfiguration and repairs are all modelled to accurately capture network behaviour and therefore performance. The model predicts network performance at each point on the network. Therefore, allowing the identification of areas with poor network performance due to either poor asset health or poor network design.
Target creates a detailed understanding of the consequences of fault for each asset on the network, predicting which customers will be affected by faults and for how long. This allows better targeting of network investment, knowing the consequence of failure for each asset on the network and the impact of network changes on risk. Assets with a high consequence of failure can be managed by changing open points, improving network sectionalisation or adding redundancy the impact of which can be calculated and valued in financial terms. Forecast network changes can be used to inform changes to asset replacement plans as certain assets become less critical. Analysis can identify customers likely to become “worst served” so operators can proactively address customer reliability before it becomes reportable. Alternatively, automated analysis can identify P2/6 violations, allowing for these issues to be addressed before they cause customer outages.
The Target model can also incorporate energy storage and real-time ratings calculating these technologies impact on average consequence of failure and network performance. These figures allow new technologies to be compared to traditional reinforcement options on a consistent financial basis.
Target is built around a distributed processing architecture, allowing studies to be simultaneously executed on tens or hundreds of computers simultaneously. This makes large scale studies practical and automated sweeps over entire network licenses a viable option.