Auto Fast Balancing

The changing grid operates on a much faster time scale. Before, disturbances on the grid happened on a scale measured in tens of minutes which was slow enough for human intervention to be effective. The time scale of the changing grid is on the order of 4 to 60 seconds and requires computers to take corrective actions.

Currently, grid managers rely on an Energy Management System to identify deviations from the scheduled generation, after which operators make adjustments manually and wait for the adjustment to take effect. All this can take 10 to 30 minutes by which time the disruption caused by the volatility of wind and solar has most likely disappeared or changed. This lengthy control process is referred to by control engineers as a long loop process. Without an ability to correct the swing in power within the 10 minute time frame used by NERC grid managers run the risk of violating reliability standards such as CPS-1 (criteria to maintain system frequency) and CPS-2 (criteria to limit the unscheduled import of MWs from adjacent Balancing Authorities).

The innovative nature of Onset’s AFB solution, the UniGen control system, stems from it being a short loop system that directly couples renewable resources and any firming resource such as gas-fired power plant or battery. This is accomplished by slaving the output of the firming resource to the output of the Variable Energy Resources using proprietary algorithms and software. This is done in real time so that the combined output never deviates (except within a very small range) from the committed schedule. The problem is solved at the project or distributed level instead of allowing the problem to make its way into the grid manager’s Energy Management System.

UniGen Simulations

Onset has developed a simulation version of UniGen modeled that models the dynamic interactions of wind and/or solar projects, a gas plant, and energy storage systems all working in combination to maintain the schedule. Time frames from a single day to an entire year have been simulated. The model calculates how power plant output, heat rate, fuel costs and air emissions change as the plant moves through its operating range (mostly at part load) as well as the energy levels in storage as it is used to help maintain the schedule, especially if it means not starting or stopping a gas plant.

Data provided by the California ISO and others are used for forecasts and actual renewable generation. Realistic limits such as ramp rates and startup times for gas plants, and charge and discharge capabilities of energy storage systems are taken into account. Designs of most wind turbines, solar PV plants, natural gas plant, or battery energy storage systems have been modeled.

UniGen-Predictive Analytics

A more advanced version of UniGen that incorporates the use of predictive analytics is being developed. UniGen-PA will use relevant sources of information to spot trends and make predictions to further optimize the mix of resources being managed by UniGen. An example would be an algorithm that detects a trend in schedule misses and positions the power plant and storage system to be available to meet the need. One of the exciting possibilities is to use predictive analytics to create hourly schedules that optimizes the use of the various resources, in other words, use the schedule as a design variable dependent upon predicted conditions on the grid and in the market.