{"id":4977,"date":"2017-06-12T13:39:00","date_gmt":"2017-06-12T13:39:00","guid":{"rendered":"https:\/\/ja.modelondev.com\/blog\/2017\/06\/12\/improving-power-plant-operation\/"},"modified":"2017-06-12T13:39:00","modified_gmt":"2017-06-12T13:39:00","slug":"improving-power-plant-operation","status":"publish","type":"post","link":"https:\/\/modelon.com\/ja\/blog\/improving-power-plant-operation\/","title":{"rendered":"\u767a\u96fb\u6240\u306b\u304a\u3051\u308b\u904b\u8ee2\u64cd\u4f5c\u306e\u6539\u5584"},"content":{"rendered":"

This blog piece reviews and summarizes our newest case study. Detailing how one of Germany\u2019s largest thermal power plants has improved their primary control reserves; resulting in an electric grid that can integrate a higher number of renewable energy sources, including wind and solar.<\/b><\/i><\/p>\n

Why is balancing an electrical grid\u2019s energy sources important?\u00a0 With multiple energy sources, an energy company can garner greater stability at reduced costs. To enable a system to integrate these multiple energy sources like wind and solar, new and existing plants need to be optimized in areas focused on load change rate, start-up time, minimum load and control reserves.<\/p>\n

In a recent research collaboration, Modelon, the University of Rostock<\/a>, and Vattenfall<\/a> (one of Europe’s leading generators of electricity and heat) set out to improve the flexibility of the German power plant referenced above.<\/p>\n

Modelon\u2019s Thermal Power Library<\/a> enabled researchers to develop a model consisting of different sub-sections, coupled through fluid-connections and a control signal bus. Nearly 11,000 differential-algebraic equations are used to describe the system which includes about 500 thermodynamic states.<\/p>\n

\"Improving
\nImplemented components of the reference power plant, numbers refer to measurement position for validation data.<\/em><\/p>\n

To ensure accuracy, model results were used to measure the deviation between measured and simulated values. In total, 47 reliable measurement points from the power plant were chosen to evaluate the accuracy of the model.<\/p>\n

\"Improving
\nSteady state validation showing relative deviation between measurement and simulation; numbers referring to measurement position in Figure 1.<\/em><\/p>\n

Next, utilizing the available detailed model in Modelon\u2019s Thermal Power Library<\/a>, accurate parameter identification could be performed. The results from the model could determine that the main block control oversteers the fuel flow too aggressively.\u00a0 By tuning the oversteering parameter in the block control, using the detailed dynamic power plant model, the control quality for the prequalification test increased significantly – reaching the desired bounds<\/p>\n

Fine-tuning of control parameters in field operation is rather complex and risky; however, with an optimized control system that has been verified in a virtual safe environment the next step is to proceed with updating the controller of the real plant.<\/p>\n

\"Improving
\nSimulated optimization results after parameter tuning in the block control to improve control quality during the double hump curve for primary control. <\/em><\/p>\n

With the new optimized controller, the large-scale plant has improved its primary control reserve fulfilling tougher grid code requirements and thereby improving the quality and robustness of the Germany electric energy system. With a more stable grid, it is possible to have a higher penetration of fluctuation energy sources such as wind and solar.<\/p>\n

This research demonstrates the necessity of model-based development where new optimized strategies can be developed and tested during various operating conditions in a safe-virtual environment. <\/b><\/p>\n

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Download a copy of the case study below.<\/h4>\n