Abstract
The paper presents the development of a model-based Smart Power Plant Supervisor, a digital tool targeting the optimization of the operation and maintenance of hydroelectric units to improve ancillary services provision to the power system. The paper focuses on a control-oriented modelling methodology which allows integrating the operational parameters of the hydroelectric unit in an optimization algorithm steering the advanced control of the units. The technique to develop analytical functions representing the behaviour of the hydropower plant is presented and validated by comparing numerical simulations with measurements of the real time operation of the run-of-river hydropower plant Vogelgrun. The results show a good performance of the modelling technique able to correctly predict the power generation of the power plant over one month of operation.
Highlights
The European Energy Strategy 2050 is prescribing a very ambitious rise in annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources
The paper presents the development of a model-based Smart Power Plant Supervisor, a digital tool targeting the optimization of the operation and maintenance of hydroelectric units to improve ancillary services provision to the power system
In this paper, a control-oriented modelling methodology is proposed within the development of the Smart Power Plant Supervisor, a digital tool targeting the optimization of the operation and maintenance of hydroelectric units to improve ancillary services provision to the power system
Summary
The European Energy Strategy 2050 is prescribing a very ambitious rise in annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. The SPPS contributes to the digitalization of the hydropower generation, and in particular to the Maintenance 4.0 asset, as it integrates the conversion of the deep knowledge of the hydroelectric units behaviour and operational parameters into meta-models which can be read and leveraged by an advanced automatic control, which can improve both operation and maintenance strategies. This can provide relevant benefits for by the power plant operators by maximizing the availability of the unit and the efficient power production.
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