Models are often used in engineering and the sciences to be able understand, predict, and control very complex systems. Generation and transmission control and the optimization of industrial control processes are two very good examples where modeling is used in operations. Modeling is starting to be used more often in road traffic control. There are a high number variables in traffic control, some of which are analogous to distribution system control. The amount of traffic can be compared to load; construction projects can be thought of as planned switching; and road accidents can be thought of outages, to some extent. The control variables in traffic can be the traffic signal timing, change in the traffic flow direction in certain lanes, and communication to drivers of traffic conditions on particular routes, via electronic signs, mobile devices, or radio, so they can change their routes.
Another very interesting area of complex system modeling is weather prediction. For example, forecasters rely upon a number of different models to predict the track and intensity of tropical storms and hurricanes. This prediction allows emergency response personnel, government officials, and the public to prepare adequately for the upcoming conditions. (Weather prediction is an open-loop control system, since there are no control variables to influence the storm. You can’t do anything about the weather!)
As distribution system operation gets more complex with distributed energy resources, heavier loading, and the need to economically optimize the system, models that can be used for operations increases in value.
Distribution system models weren’t traditionally used for operational purposes until about twenty years ago. That was when connectivity models began to be used in outage management systems (OMS). Models have been used for a longer period of time in distribution planning and engineering, for tasks like load allocation and load flow analysis, short-circuit analysis, and capacitor placement. So the knowledge of how to model the distribution system is well-known. The practicality of using that knowledge in operations was not always there.
Now with model-based distribution management systems (DMS), models are increasing being used not only for outage management purposes, but also for switching management and for electrical analysis and optimization applications.
With the growing complexity of distribution operations, there are three technology drivers that are leading to the increasing use and sophistication of distribution models, and their use in the operations environment:
1. More distribution organizations have good models in their geographic information systems (GIS).
2. The capabilities of the IT infrastructure for modeling and distribution system, and for performing system analysis, continues to increase relative to cost. This includes memory and CPU, which are important for handling the large-scale model in distribution
3. More IED’s (intelligent electronic devices) are being installed on distribution systems, that can provide the data to the operational model to keep it up to date with the state and conditions of the actual system.
Using a model of the distribution system has particular advantages:
- Increased awareness of system state / conditions. Most distribution system are highly unobservable. While the number of IED’s is increasing, the large percentage of a distribution system is still unmonitored. A computer model provides the opportunity to use analysis such as load flow and state estimation, in order to calculate the conditions on the unobservable part of the system, and provide operators with improved situational awareness.
- Mathematical optimization of controllable parameters – Methods such as mixed integer non-linear programming can be used in conjunction with the model and the engineering analysis, such as load flow, to estimate the optimal state of control variables on the system, including load tap changers (LTC’s), voltage regulator taps, shunt capacitor switch positions, in-line switch positions, var contribution from large distributed energy resources, and optimal levels of demand response.
- Deal with frequent change on the system – A distribution system is highly dynamic, with large amounts of change occurring on it every day. This includes planned load transfers, distributed generation contributions, maintenance switching, and outages. During outages, switching occurs, and frequently line cuts, jumpers, and temporary generators are used. A model permits explicit representation of these changes on the system, so that a true as-operated model is always available to the operators and to the analysis and optimization applications.
- “What-If” Scenarios – A model permits the performance of “what if” scenarios by operators and support engineers. These can include prediction of future system conditions, and other off-line simulations / studies. Scenario analyses can be used for anticipated loading changes, switch plans, contingency analysis, and changes in renewable generation production.
Models allow for improved observability, control, and prediction of distribution system behavior under a large number of operating conditions.
