Load Flow Optimization (OL)
PSS®SINCAL Load Flow Optimization (OL) is a module for evaluating and enhancing network structures and loads. It is used for network operating maintenance and in network planning. The method alters network variables within a defined control range to minimize active power losses. This lets the user evaluate the networks and work out network variations to ensure cost-effective network planning.
The module determines network conditions with the fewest possible transmission losses and the smallest number of violated technical limits. This reduces the voltage variance at the network nodes.
The system variables in this case are generator voltages, generator reactive powers and the transformation ratios of the transformers. Observed limits are the loading of plant and equipment, the voltage range and the P/Q diagram allowed for the generators.
Two different algorithms are offered to solve the problem:
Indirect gradient method
Generic method
Extended Analysis Modules
PSS®SINCAL Extended Modules for Analysis
Extend the core calculation modules into deeper analysis with these modules related to the model enhancement and future grid development paths, from Optimal Power Flow, to Time-Series Calculation, Network Development, and more.
PSS®SINCAL supports your entire engineering workflow
For over 20 years, PSS®SINCAL has enabled engineers to tackle different challenges of the changing distribution, transmission, and industrial power systems, including maintaining high reliability of supply and efficiently integrating Distributed Energy Resources. With the modular platform of PSS®SINCAL, power system planning and operation engineers are supported with their entire workflow from the initial data import and network modeling (considering past, current and future conditions), to basic and extended calculations, all the way to extensive protection simulations and analysis as well as other methods in time and frequency domains.
To explore the comprehensive range of Siemens PSS SINCAL SIMULATION SOFTWARE click here!
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