MPO Optimal batteries operation and inventory schedule
Forecasted wind and PV power production
Figure 4 Hybrid power plant EMS, wind and PV power production forecast, and MPO-based batteries charge/discharge and inventory optimal schedule
with an MPO-based application that helps drive the optimal real-time operation. Conclusions Energy transition activities introduce several challenges when trying to manage energy use in process plants effectively. When planning, monitoring or managing in real-time, either for a single site or a set of interconnected facilities, detailed knowledge of the sources and uses of energy is necessary. The coordination of information, forecasts, scheduling activities, regulations, reporting and control activities are necessary for consistent and optimal decision-making. This requires the appropriate set of software support tools. An ideal EMS should be based on a real-time, digital twin model of the energy system to constantly operate at the lowest economic cost and minimum GHGs emissions. This will allow users to monitor the past while optimising current operations, with the advantage of looking into the future.
like boilers, gas turbines or turbine/motor drivers for pumps and compressors. ➌ Wind power: to manage the inventory of batteries and/or hydrogen when it is produced to store energy, injected in a natural gas pipeline or further processed in a production plant. To be done as a function of the prediction of the wind speed and other restrictions. MPO could eventually be used to define the start/stop of the wind turbines. ➍ Biomass: MPO can be used to manage the biomass inventory used for the generation of steam or electricity as a function of the forecasted supply as well as constraints on the power and/or biomass storage capacity and alternative fuel costs. As a practical case of how the MPO is used in this context, we present an example of a hybrid power plant, where both PV and wind power production, as well as battery banks, are present. In this case, MPO can help optimally manage the batteries storage based on the solar and wind intensity forecasts, as well as the grid prices and demand. Figure 3 shows a screenshot of the EMS real-time optimiser and monitor web graphical user interface, while Figure 4 presents the results of the optimal batteries charge, discharge and inventory schedule, based on the forecasted variables. The optimal schedule was produced
Juan Ruiz Juan.Ruiz@kbc.global Carlos Ruiz email@example.com
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