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Degradation Based Framework for Long-term Optimization of Energy Conversion Systems. Case Studies: Solid Oxide Fuel Cell Gas Turbine

Parhizkar, Tarannom | 2016

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 49188 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Roshandel, Ramin
  7. Abstract:
  8. The energy systems efficiency is a great issue confront power plants professionals. Besides using high-tech components in power plants, plant operation optimization can significantly improve energy efficiency and economic performance, as efficiency of plant components generally depends on operating conditions. In addition, system preventive maintenance can reduce plant operation and failure costs, however it is also costly when done frequently. Therefore, optimizing operating conditions and preventive maintenance intervals can minimize the expected total cost of plant due to operation, failures and preventive maintenances. In recent years, the use of optimization models to determine plant optimal schedule has earned popularity. Scheduling is widely used to maintain and establish operating conditions and maintenance intervals of a plant over time. It should be noted that power plant components are degraded through long term operation. Therefore, components performance profile over time varies. To have a reliable and accurate scheduling optimization results it is necessary to consider components ageing models in the optimization procedure. The combination of plant optimal scheduling and ageing models is an extended approach that is presented in our study and a framework is developed. The proposed framework optimizes plant schedule over long term horizon considering components ageing. Modeling of components aging increases system simulation accuracy in long term operation and the optimum decision variables would be more reliable and realistic. Moreover, the developed aging based optimal scheduling framework considers aging cost in the objective function as well as components aging in the optimization procedure. Aging cost is defined as the hourly preventive and corrective maintenance costs. As a result, optimal hourly schedule is affected by not only the income of selling electricity and operation cost but also the maintenance cost. Therefore, plant hourly profit is more realistic and the optimal schedule has a higher utility in comparison with other scheduling methodologies such as day-ahead method. The framework outputs determine the plant startup time, production level and maintenance intervals. This framework can be used in the sensitivity analysis of energy price and ambient conditions as well. Validity and usefulness of the proposed methodology are demonstrated by optimizing the operating conditions and maintenance intervals of a gas turbine power plant and a solid oxide fuel cell power plant. The case studies result effectively meet all the positive expectation that are placed on the proposed aging based optimal scheduling framework. Results show that optimal operation schedule depends on the maintenance intervals. Therefore, operating conditions and maintenance intervals should be optimized simultaneously. The derived optimal schedule for gas turbine power plant increases system long term profit annually around 4% in comparison with design point operating schedule. In addition, in the anode supported- solid oxide fuel cell case study, the system lifetime profit is increased up to 10.45%
  9. Keywords:
  10. Solid Oxide Fuel Cell (SOFC) ; Operating Condition ; Optimal Operation ; Gas Turbines ; Energy Conversion ; Degradation Based Optimization Framework

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