Net Schedule Management as a Route to Dynamic Optimisation
- Balazs Rosta (FGSZ) | Marko Haulis (SIMONE Research Group) | Ludek Reinstein (SIMONE Research Group)
- Document ID
- Pipeline Simulation Interest Group
- PSIG Annual Meeting, 14-17 May, London, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. PSIG, Inc.
- 1 in the last 30 days
- 18 since 2007
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As the gas industry landscape gets ever more complicated with thousands of new players entering the gas business, the challenges for efficient management of day-to-day operations are mounting. Situation on the gas market with ever changing nominations for transport pose significant problems for TSO’s to change their network mode (compressor stations, regulators, valves) to match the today and tomorrow’s demand.
There is a need to dynamically manage network switching and network elements to match the situation when nominations can dramatically change every hour. Gas companies are looking for a tool that can optimise the current situation and nearest future with as low as possible transport cost while fulfilling the contract constraints.
We have found that after talking to a number of TSO’s that the actual question posed by dynamic optimisation varies from one company to another. Therefore, it is impossible to create one universal algorithm that would fulfill the needs of everyone (apart from brute force, but that is not a task for today’s computers). We have devised a way how to supplement this lack of raw computing power with human interaction and knowledge of individual network.
We will talk about our experience of creating a tool that assists control room dispatchers in quick reaction to changing transport situation. This tool we call Net Schedule Management (or NSM in short) and it consists of two modules – one that archives a gas day simulation and cleans bad SCADA data from it and one that actually optimises the network mode.
To dynamically optimise network, operator can pull a historical day that most matches the current situation and predicted consumptions and transport, simulate different scenarios and try to improve the criteria of fuel gas while observing the pressure limits. Steady state optimisation can help him as well as a library of network switching, but ultimately it is the human experience and knowledge of the network that does the trick. We will present a case study that confirms 4% - 23% daily savings of fuel gas on a medium complexity gas network using the NSM tool.
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