At the 2021 Modelica Conference in Linköping, Sweden, researchers from Norce, the Norwegian Research Centre, presented an approach* to reducing offshore gas turbine use with wind power and energy storage to ‘accelerate the shift towards lower greenhouse gases emissions’. The methodology accommodates variable wind resources and power demand and limited battery storage capacity. An embryonic Modelica library** has been developed to simulate the system with simplified components connected through a micro grid. Modelica is a meta-model programming environment that has recently been proposed*** as well-suited to digital twin applications. The environment has seen earlier oil and gas applications from Equinor, ENI and IFPen.
Offshore wind resources in Norway are put at some 12,000 ‘terawatt hours per year’ (1.5 terawatts to our way of thinking!), representing a significant opportunity to reduce greenhouse gas emissions from the gas turbines used to generate electricity. The researchers have developed a ‘simple to configure and fast to run Modelica model’ that quantifies GHG reduction under operational conditions over a long period. An autonomous control system balances the micro grid as wind resources and demand vary. The remaining issue is, what to optimize, GHG emissions, battery life cycle, gas turbine usage or what? Equinor provided the datasets and the work was financed from the Research Council of Norway’s ‘Electrification of Oil and Gas Installation by Offshore Wind’ project.
* An Approach for Reducing Gas Turbines
Usage by Wind Power and Energy Storage by Nejm Saadallah and Yngve
Heggelund in Proceedings of 14th Modelica Conference 2021.
** The ‘ELOGOW 2021’ model is available from the Norce Git repository.
*** See ‘Creating and Using Digital Twins within Simulation Environments’, Springer 2019.
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