scholarly journals Economic Optimization of a Concentrating Solar Power Plant With Molten-Salt Thermocline Storage

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Scott M. Flueckiger ◽  
Brian D. Iverson ◽  
Suresh V. Garimella
Keyword(s):  
Power Plant ◽  
Molten Salt ◽  
Fossil Fuels ◽  
Minimum Cost ◽  
Plant Performance ◽  
System Level ◽  
Weather Data ◽  
Economic Optimization ◽  
Levelized Cost Of Electricity ◽  
System Level Simulation

System-level simulation of a molten-salt thermocline tank is undertaken in response to year-long historical weather data and corresponding plant control. Such a simulation is enabled by combining a finite-volume model of the tank that includes a sufficiently faithful representation at low computation cost with a system-level power tower plant model. Annual plant performance of a 100 MWe molten-salt power tower plant is optimized as a function of the thermocline tank size and the plant solar multiple (SM). The effectiveness of the thermocline tank in storing and supplying hot molten salt to the power plant is found to exceed 99% over a year of operation, independent of tank size. The electrical output of the plant is characterized by its capacity factor (CF) over the year, which increases with solar multiple and thermocline tank size albeit with diminishing returns. The economic performance of the plant is characterized with a levelized cost of electricity (LCOE) metric. A previous study conducted by the authors applied a simplified cost metric for plant performance. The current study applies a more comprehensive financial approach and observes a minimum cost of 12.2 ¢/kWhe with a solar multiple of 3 and a thermocline tank storage capacity of 16 h. While the thermocline tank concept is viable and economically feasible, additional plant improvements beyond those pertaining to storage are necessary to achieve grid parity with fossil fuels.

Simulation of a Concentrating Solar Power Plant With Molten-Salt Thermocline Storage for Optimized Annual Performance

2013 ◽  
Author(s):  
Scott M. Flueckiger ◽  
Brian D. Iverson ◽  
Suresh V. Garimella
Keyword(s):  
Molten Salt ◽  
Storage Capacity ◽  
Computational Cost ◽  
Minimum Cost ◽  
Plant Performance ◽  
System Level ◽  
Weather Data ◽  
Annual Plant ◽  
Steam Generation ◽  
Levelized Cost Of Electricity

A finite-volume-based model of a molten-salt thermocline tank is developed to achieve simulation at a sufficient level of detail but at low computational cost. Combination of this storage model with a system-level power tower plant model enables yearlong thermocline tank simulation in response to historical weather data and corresponding plant control. The current study simulates a 100 MWe molten-salt power tower plant to optimize annual plant performance as a function of the thermocline tank size and the plant solar multiple. Thermocline storage performance is characterized by the effectiveness of the tank in storing and delivering utilizable heat for steam generation and power production. Additional system-level metrics include thermal energy discard due to saturation of storage capacity and annual plant capacity factor. Economic assessment of the power output is characterized with a simple levelized cost of electricity. Minimum cost is observed with a solar multiple of 3 and a thermocline tank storage capacity of 16 hours.

Techno-Economic Optimization of Molten Salt Concentrating Solar Power Parabolic Trough Plants With Packed-Bed Thermocline Tanks

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Freerk Klasing ◽  
Tobias Hirsch ◽  
Christian Odenthal ◽  
Thomas Bauer
Keyword(s):  
Molten Salt ◽  
Power Plants ◽  
Thermal Power ◽  
Particle Diameter ◽  
Packed Bed ◽  
System Level ◽  
Economic Optimization ◽  
System Behavior ◽  
Parabolic Trough ◽  
Levelized Cost Of Electricity

Abstract This study focuses on the techno-economic optimization of direct molten salt parabolic trough solar thermal power plants (STPPs) equipped with thermocline filler (TCF) thermal energy storage (TES). On one hand, this technology allows for cost reductions compared with state of the art two-tank (2T) TES. On the other hand, however, it leads to a performance decrease of the power block (PB) due to partial part load operation. To evaluate the dominating effect, annual simulations on a system level are performed for the TCF direct molten salt storage concept and, as a reference, for the two-tank direct molten salt storage concept. The levelized cost of electricity (LCOE) serves as a global measure to compare the two systems and to optimally size the TCF storage and the solar field (SF). The result of this study is that LCOE can theoretically be reduced by up to 8% by using a TCF instead of 2T storage system. The influence of temperature deviations from the nominal value at the end of charge or discharge, porosity and particle diameter of the TCF on LCOE, and system behavior is investigated in detail. This study further presents alternative operation strategies with improved system behavior and reveals determining factors for the integration of TCF storage into a system.

scholarly journals Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3798
Author(s):  
Hamid Iftikhar ◽  
Eduardo Sarquis ◽  
P. J. Costa Branco
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Field Data ◽  
Large Scale ◽  
Low Cost ◽  
Plant Performance ◽  
Energy Output ◽  
Weather Data ◽  
Real Field ◽  
Operation And Maintenance

Existing megawatt-scale photovoltaic (PV) power plant producers must understand that simple and low-cost Operation and Maintenance (O&M) practices, even executed by their own personal and supported by a comparison of field data with simulated ones, play a key role in improving the energy outputs of the plant. Based on a currently operating 18 MW PV plant located in an under-developing South-Asia country, we show in this paper that comparing real field data collected with simulated results allows a central vision concerning plant underperformance and valuable indications about the most important predictive maintenances actions for the plant in analysis. Simulations using the globally recognized software PVSyst were first performed to attest to the overall power plant performance. Then, its energy output was predicted using existing ground weather data located at the power plant. Compared with the actual plant’s annual energy output, it was found that it was underperforming by −4.13%, leading to a potential monetary loss of almost 175,000 (EUR)/year. Besides, an analysis of the O&M power plant reports was performed and compared to the best global practices. It was assessed that the tracker systems’ major issues are the forerunner of the most significant PV power plant underperformance. In addition, issues in inverters and combiner boxes were also reported, leading to internal shutdowns. In this case, predictive maintenance and automated plant diagnosis with a bottom-up approach using low-cost data acquisition and processing systems, starting from the strings level, were recommended.

scholarly journals ANALISIS TERMOEKONOMI SIKLUS KOMBINASI TURBIN GAS DAN UAP UNIT PLTGU GRATI

2021 ◽  
Author(s):  
Putri Sundari
Keyword(s):  
Power Plant ◽  
System Performance ◽  
Fossil Fuels ◽  
Exergy Analysis ◽  
Combined Cycle ◽  
Plant Performance ◽  
Combine Cycle Power Plant ◽  
Exergy Destruction ◽  
Combine Cycle ◽  
Research Analysis

The increasing of electricity needs and the crisis of fossil fuels have been requiring an improvement of power plant performance, including combined cycle power plant which has important role as a provider of national electricity nowadays. Thermoeconomic analysis is one of new concept that combine exergy analysis with cost approachment to improve a system performance. In this research, analysis applied in combine cycle power plant of PT. Indonesia Power Grati. The result shows that combustion chamber is the greatest irreversibility source with an exergy destruction was found 53,81%. Where as an economic analysis obtains a different result, LP steam turbine is the component which has a huge exergoeconomic loss was found Rp 33.655.386,46/hour. Based on this result, the efforts that we can do to get an optimal performance of combine cycle power plant are preheating a combustion air to reach a perfect combustion and cleaning all the components continually.

scholarly journals System-level simulation of molten salt small-scale CSP

2020 ◽  
Author(s):  
Armando Fontalvo ◽  
Ali Shirazi ◽  
John Pye
Keyword(s):  
Molten Salt ◽  
System Level ◽  
Small Scale ◽  
System Level Simulation

scholarly journals Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
Kshirasagar Naik ◽  
Mahesh D. Pandey ◽  
Anannya Panda ◽  
Abdurhman Albasir ◽  
Kunal Taneja
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Finite State Machine ◽  
State Machine ◽  
Plant Performance ◽  
Series Data ◽  
Cyberphysical Systems ◽  
Finite State ◽  
Machine Representation

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

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