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To better understand the thermal hydraulic characteristics of the parabolic trough solar field (PTSF), a comprehensive thermal hydraulic model (CTHM) based on a pilot plant is developed in this paper. All of the main components and thermal and hydraulic transients are considered in the CTHM, and the input parameters of the model are no longer depen...
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... Badaling parabolic trough solar power pilot plant, which is the first operational PTC solar thermal plant on the MW scale in China, is situated in Yanqing at a latitude of 40.5 °N and a longitude of 115.94 °E. A view of the pilot plant is shown in Figure 1. As illustrated in Figure 2, two east-west layout loops (Loop 1 and Loop 3) with 4150 m long solar collector assemblies (SCAs) and one south-north layout loop (Loop 2) with 6100 m long SCAs together form the principal part of the PTSF. ...
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... the total flow rate in the header, simulating the flow distribution in loops is another essential function for the CTHM that needs to be calculated and validated. Good agreement between the calculated results and measured data is shown in Figure 10a-c; the RMSEs for the outlet flowrate of loops 1 to 3 are 1.75 m 3 /h, 0.86 m 3 /h, and 0.97 m 3 /h, respectively. The uncertainty of the electric control valve actuators with a value of 5% (shown in Table 2) is considered as the primary error sources. ...
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... uncertainty of the electric control valve actuators with a value of 5% (shown in Table 2) is considered as the primary error sources. Besides, Figure 10 shows that the discrepancy is changing with the flow rate; this is due to Equation (16), which can cause different errors in the different flow rates and Re values [27]. The relative errors shown in Figure 10d can make this clear: when the valve opening varied, the relative errors present almost simultaneous fluctuations. ...
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... Figure 10 shows that the discrepancy is changing with the flow rate; this is due to Equation (16), which can cause different errors in the different flow rates and Re values [27]. The relative errors shown in Figure 10d can make this clear: when the valve opening varied, the relative errors present almost simultaneous fluctuations. Another noticeable feature presented in Figure 10b is that the outlet flow rate of Loop 2 has a lower value and opposite trend compared to the other two loops. ...
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... relative errors shown in Figure 10d can make this clear: when the valve opening varied, the relative errors present almost simultaneous fluctuations. Another noticeable feature presented in Figure 10b is that the outlet flow rate of Loop 2 has a lower value and opposite trend compared to the other two loops. The lower value is caused by an extra loop in Loop 2 that can increase the flow resistance of Loop 2 and diminish the flow rate. ...
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... hot test was conducted from 09:50 to 11:30 on 23 August 2018. As shown in Figure 11a, the opening of LCVs varies similarly to the cold test in addition to an intermission at about 10:50; this is because the operator should open the heat exchange bypass in case of overheating the HTF at this moment, and this operation also leads to the decreasing of the inlet temperature of the header, as shown in Figure 11b. 09 Compared with the cold test, the parameters refering to the solar irradiance absorption must be measured firstly in the hot test. ...
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... hot test was conducted from 09:50 to 11:30 on 23 August 2018. As shown in Figure 11a, the opening of LCVs varies similarly to the cold test in addition to an intermission at about 10:50; this is because the operator should open the heat exchange bypass in case of overheating the HTF at this moment, and this operation also leads to the decreasing of the inlet temperature of the header, as shown in Figure 11b. 09 Compared with the cold test, the parameters refering to the solar irradiance absorption must be measured firstly in the hot test. ...
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... Compared with the cold test, the parameters refering to the solar irradiance absorption must be measured firstly in the hot test. The measured DNI is shown in Figure 11b, and the defocused factor of the SCAs in Loop 1 and Loop 3, which are measured by the inclinometer and calculated according to Figure 3c, are presented in Figure 12. ...
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... Compared with the cold test, the parameters refering to the solar irradiance absorption must be measured firstly in the hot test. The measured DNI is shown in Figure 11b, and the defocused factor of the SCAs in Loop 1 and Loop 3, which are measured by the inclinometer and calculated according to Figure 3c, are presented in Figure 12. ...
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... shown in Figure 13, errors regarding the compared inlet flow rate and pressure drop present obvious differences before and after the opening of the exchange bypass; this is due to the misestimate of the flow resistance in the exchange bypass pipe. Compared with the cold test (Figure 9), the hot test has a greater inlet flow rate and lower pressure drop, which is mainly because the HTF has a lower viscosity at a higher temperature. ...
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... with the cold test (Figure 9), the hot test has a greater inlet flow rate and lower pressure drop, which is mainly because the HTF has a lower viscosity at a higher temperature. As shown in Figure 14, the calculated flow rate distribution maintains good consistency with the measured data in the hot test, the RMSEs for the outlet flow rate of loops 1 to 3 are 1.82 m 3 /h, 1.01 m 3 /h, and 1.50 m 3 /h, respectively. Compared with the cold test, there are two extra sources that cause the error. ...
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... one is that the larger outlet flow rate of the header will magnify the error caused by the uncertainty of electric control valve actuators; another is that the error of the calculated temperature will influence the flow resistance distribution of the PTSF, and then increase the error of the flow rate. Figure 15 shows that the RMSEs for the calculated outlet temperatures of three loops were 8.54 °C, 6.30 °C, and 8.14 °C, respectively. This good agreement mainly benefited from the precisely calculated flow rate distribution and the accuracy of the thermal part in the CTHM. ...
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... 1, which lasts for 1.3 h, is implemented for simulating the impacts of DNI saltation on the total flow rate and pump pressurizing of the PTSF. As shown in Figure 16a, the DNI changes from 0 to 800 W/m 2 at 0.4 h and turns back into 0 at 0.9 h, and these two disturbances cause a drastic fluctuation in the header flow rate, as shown in Figure 16b. At 0.4 h, as shown in Figure 16b, the inlet and outlet header flow rate present two different trends. ...
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... 1, which lasts for 1.3 h, is implemented for simulating the impacts of DNI saltation on the total flow rate and pump pressurizing of the PTSF. As shown in Figure 16a, the DNI changes from 0 to 800 W/m 2 at 0.4 h and turns back into 0 at 0.9 h, and these two disturbances cause a drastic fluctuation in the header flow rate, as shown in Figure 16b. At 0.4 h, as shown in Figure 16b, the inlet and outlet header flow rate present two different trends. ...
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... shown in Figure 16a, the DNI changes from 0 to 800 W/m 2 at 0.4 h and turns back into 0 at 0.9 h, and these two disturbances cause a drastic fluctuation in the header flow rate, as shown in Figure 16b. At 0.4 h, as shown in Figure 16b, the inlet and outlet header flow rate present two different trends. The peaking of heat gain causes an expansion of the HTF and further leads to the increasing velocity; this will result in an increase of the pump pressurizing (as shown in Figure 16c) and a reduction of pump capacity (inlet header flow rate). ...
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... 0.4 h, as shown in Figure 16b, the inlet and outlet header flow rate present two different trends. The peaking of heat gain causes an expansion of the HTF and further leads to the increasing velocity; this will result in an increase of the pump pressurizing (as shown in Figure 16c) and a reduction of pump capacity (inlet header flow rate). Meanwhile, the viscosity of the HTF decreases with the temperature rise (as shown in Figure 16d). ...
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... peaking of heat gain causes an expansion of the HTF and further leads to the increasing velocity; this will result in an increase of the pump pressurizing (as shown in Figure 16c) and a reduction of pump capacity (inlet header flow rate). Meanwhile, the viscosity of the HTF decreases with the temperature rise (as shown in Figure 16d). Together with the density and viscosity, the total flow resistance and pump pressurizing will reach a local maximum and begin to decrease; when the effect of expansion surpasses the effect of viscosity reduction, the total pressure drop increases again, and the pump capacity decreases until it reaches a steady state. ...
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... 2, which lasted for 1.7 h, was conducted for simulating how the thermal effect influences the flow distribution of the PTSF; in this case, the DNI is maintained at 800 W/m 2 throughout the whole process. As shown in Figure 17a, the SCAs in Loop 1 will be defocused in a positive (from one to four) and a negative (from four to one) sequence. The reasons for the fluctuation of flow rate and pump pressurizing at the time of defocus are demonstrated in Case 1. ...
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... reasons for the fluctuation of flow rate and pump pressurizing at the time of defocus are demonstrated in Case 1. As shown in Figure 17b,c, both defocus sequences cause an increase of the header flow rate and a reduction of pump pressurizing. This is because the HTF will stop expanding and has a lower velocity in the defocused SCAs compared with a focused one; this further leads to a lower pump pressure and higher pump capacity. ...
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... is because the HTF will stop expanding and has a lower velocity in the defocused SCAs compared with a focused one; this further leads to a lower pump pressure and higher pump capacity. This effect is presented in a more obvious way in the comparison of flow rate among the three loops shown in Figure 17b; the flow rate in the defocused Loop 1 increases step-by-step compared with the other two focused loops due to its lower pressure drop. Besides, compared with the negative defocused sequence, the cold HTF will flow over longer distances when the SCAs are defocused in a positive sequence. ...
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... a result, the higher flow rate and lower pump pressure exist in the positive defocused sequence due to the lower flow resistance. Finally, the HTF in the SCAs, which is closer to the inlet, has greater thermal inertia than the SCAs near the outlet; this causes the difference of outlet temperature between the two opposite sequences of defocus shown in Figure 17d. The density, specific heat capacity, and the viscosity are the most relevant properties to the thermal hydraulic characteristics of the PTSF. ...
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... first strategy is studied under a uniform solar irradiance; as shown in Figure 18a, the DNI of the whole PTSF varies with a cosine disturbance from 800 W/m 2 to 400 W/m 2 , of which the period is 0.2 h. Due to the uniformity of the DNI, a balanced flow distribution must be maintained for the same outlet temperature of the three loops, so the openings of the three LCVs are kept constant. ...
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... to the uniformity of the DNI, a balanced flow distribution must be maintained for the same outlet temperature of the three loops, so the openings of the three LCVs are kept constant. The ideal flow rate under different DNI values can be calculated according to the thermal part of the CTHM, while the opening of the HCV and pump pressurizing can be solved according to the hydraulic part of the CTHM and the calculated ideal flow rate; these results are shown in Figure 18b. The control results of the outlet temperature are shown in Figure 18c, the outlet temperatures of the three loops and header are all very close to 390 °C, and the small-range fluctuations of the outlet temperature in the three loops and headers are caused by the heat loss in the header, which varies with the flow rate. ...
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... ideal flow rate under different DNI values can be calculated according to the thermal part of the CTHM, while the opening of the HCV and pump pressurizing can be solved according to the hydraulic part of the CTHM and the calculated ideal flow rate; these results are shown in Figure 18b. The control results of the outlet temperature are shown in Figure 18c, the outlet temperatures of the three loops and header are all very close to 390 °C, and the small-range fluctuations of the outlet temperature in the three loops and headers are caused by the heat loss in the header, which varies with the flow rate. The second strategy is researched under a nonuniform solar irradiance. ...
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... second strategy is researched under a nonuniform solar irradiance. As shown in Figure 19a, the DNI values in Loop 1 and Loop 3 are kept at 800 W/m 2 , while for Loop 2, the DNI varied in a similar way as shown in Figure 18a. The balance of flow distribution must be thrown off due to the nonuniformity of DNI, so the opening of three LCVs should be recalculated to meet the varied DNI. ...
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... second strategy is researched under a nonuniform solar irradiance. As shown in Figure 19a, the DNI values in Loop 1 and Loop 3 are kept at 800 W/m 2 , while for Loop 2, the DNI varied in a similar way as shown in Figure 18a. The balance of flow distribution must be thrown off due to the nonuniformity of DNI, so the opening of three LCVs should be recalculated to meet the varied DNI. ...
Citations
... The coverings, as a linguistic variable, carry with them the concept of quali ers and forms of fuzzy sets. Three adverbs of fuzzy subsets are chosen as low, medium and high, which allows to establish precise rules without making the system extremely complex [27]. ...
This chapter presents an hourly energy management system (EMS). The hybrid renewable energy system (HRES) is composed of a hybrid ae-generator system (WT), solar photovoltaic (PV) panels, a diesel generator (DG) and a distributed collector system (DCS) as the primary energy source. The distributed collector system (DCS) consists of a field of parabolic trough collectors that carry solar thermal energy to a power conversion unit to produce electricity, an energy storage system (ESS), the wind turbine, the PV panels and the distributed control system (DCS) are made to work at the maximum power point, while the battery acts as storage. The energy management system (EMS) uses intelligent rule-based controllers to meet the energy demanded by the load and maintain the state of charge (SOC) of the battery between certain target ranges. The controller implemented for this study is a fuzzy logic controller that mimics human understanding of various phenomena that may occur during microgrid operation. The simulation results show that the fuzzy logic-based control meets the objectives established for the energy management system (EMS) of the hybrid renewable energy system (HRES).
... Nevertheless, when the plant is very large and the pipes are very long, as happens with existing commercial plants, the energy losses in the pipes play a significant role in calculating the flow distribution. In [24] a mathematical hydraulic model is developed for a 1 MW solar trough plant in China. The paper showed the importance of considering the hydraulic model in these types of plants by implementing two feedforward control approaches tested on a simulation environment. ...
... It is shown that when the pipe length increases, the effect of the energy losses becomes perceptible, and the thermal balance worsens when the hydraulic model is not considered. The differences between the algorithm proposed here and other control strategies proposed in [18,21,24] are pointed out below. ...
... Minimizing the number of defocus actions reduces not only the energy losses but the degradation of the actuators [20]. The hydraulic model used is explained in section 3 and uses equations similar to those proposed in [24]. 3. ...
The size of existing commercial solar trough plants poses new challenges in applying advanced control strategies to optimize operation. One of these challenges is to obtain a better thermal balance of the loops’ temperature. Since plants are made up of many loops, the efficiency of the loops can vary substantially if a group has been cleaned or affected by dust. This leads to the need to defocus the collectors of the most efficient loops to avoid overheating problems, thus producing energy losses. In order to minimize these energy losses, the input valves have to be manipulated to reduce the temperature difference of the loops. However, when the pipes connecting the loops are very long, the pressure drop and energy losses in those pipes become notorious, affecting the flow distribution. The need to consider the hydraulic model becomes very important. In this paper, a non-linear model predictive algorithm is presented that uses a hydraulic model of the solar field to compute the aperture of the input valves. The results show that when the length of the pipes is increased, the algorithm proposed in this paper obtains better results than other algorithms proposed in the literature.
... Salazal et al. [14] developed and valuated an analytic modelling of the energy flows in parabolic trough solar thermal power plants that allows for evaluation of energy savings in a case of potential modifications in components, system design and location. Whereas Ma at al. [15] presented a thermal hydraulic model solved by a novel numerical approach based on graph theory and the Newton-Raphson method. Rogada et al. [16] focused on a heat transfer fluid (HTF) used to transfer the thermal energy of solar radiation through parabolic collectors to a water vapour Rankine cycle, and proposed a model to optimize the temperature of the fluid. ...
Currently, intensive work is underway in Poland to increase the share of renewable energy sources in the overall energy balance. Therefore, this paper presents the possibilities of using concentrated solar power in zones with a temperate climate. A simplified model based on the energy balance in the solar collectors considering the main operating parameters of the typical solar power plant was developed. It should be noted here that the model does not take into account issues related to heat accumulation and electricity generation in a Solar Thermal Power Station. The simulation of forced convection inside the solar collector absorber was additionally included in the calculations to improve its accuracy. The model was verified using actual heat measurements at the outlet of the parabolic collector installation at a Solar Thermal Power Station located in the south of Spain. The heat generated by a similar solar collector system in a selected region with a temperate climate, the city of Bialystok (north-eastern Poland, geographic coordinates: 53°08′07″N 23°08′44″E) was determined by the developed simplified model for different months of the year. Based on the results of the analysis, it was found that the energy obtained from the same area of concentrated solar collectors located near Bialystok is eight times lower compared to the location in Cordoba depending on the variant of the power plant operation.
... Concentrating solar power (CSP) is renewable energy technology and offers important advantages as it has the ability of thermal storage. In CSP systems, parabolic trough technology is the most mature and widely used solar thermal power technologies worldwide [1,2]. Parabolic trough receiver tubes are the core components, which convert solar energy into thermal energy. ...
The loss of vacuum in the parabolic trough receivers is one of the most common problems in the parabolic trough solar power plants. The vacuum level and gas species in the annulus of the receiver determine the heat loss and have an important influence on the thermal efficient of the solar system. If hydrogen is inside the annulus, it can cause heat losses to be almost four times that of a receiver with good vacuum. However, it is hard to non-destructively measure the gas species and partial pressure in the annulus of the receiver. In this paper, a novel non-destructive method was presented to evaluate the vacuum performance by using combined dielectric barrier discharge and the spectral analysis technology. The discharge characteristics and spectrometric properties of four kinds of gases, which are the most likely gases to be found in the receivers, were studied in the experiments. The test results of the non-destructive vacuum evaluation method agree well with the results of the residual gas analysis. The feasibility and accuracy of the non-destructive test method was verified. The relationship between the vacuum performance of receiver and the spectral characteristics of dielectric barrier discharge were obtained by a series of experiments.
... In this paper, an optical structure of the solar furnace is described. The solar furnace with this design, chosen as the study case, was constructed in the solar thermal power plant in Yanqing, Beijing [21,22]. Four types of optical factors that impact on the concentrated heat flux distribution are analyzed by MCRT simulations: The tilt error of the heliostat, which originates from heliostat facets deviating from the ideal plane; the slope error of the concentrator, referring to the irregular deformation of reflecting curves of the concentrator facets by the surface adjustment approach; the layout error of the concentrator, caused by the same or similar shape of facets attached to the concentrator frame; and the tilt error of the concentrator, which arises from concentrator facets tilting. ...
... It indicates that even each error can be well controlled, for the reason of the optical system demanding extremely high precision, the comprehensive influence of factors can obviously decrease the facility's performance. For the solar furnace c in Figure 12a, the integral of the heat flux equals to the thermal power of 8.28 kW, which agrees with the value calculated by Equation (22). It can further verify the accuracy of the model. ...
... It can further verify the accuracy of the model. P = DNI · S H · N k N t · η 2 · η 3 · η 4 · η 5 = 8.29 kW (22) As shown in Figure 12a, the flux distribution in the simulation shows Gaussian shape, as the measured result presents. Their trend of gradient change and the shape and size of the spot are approximately similar. ...
In this paper, an optical structure design for a solar furnace is described. Based on this configuration, Monte Carlo ray tracing simulations are carried out to analyze the influences of four optical factors on the concentrated solar heat flux distribution. According to the practical mirror shape adjustment approach, the curved surface of concentrator facet is obtained by using the finite element method. Due to the faceted reflector structure, the gaps between the adjacent mirror arrays and the orientations of facets are also considered in the simulation model. It gives the allowable error ranges or restrictions corresponding to the optical factors which individually effect the system in Beijing: The tilt error of heliostat should be less than 4 mrad; the tilt error of the concentrator in the orthogonal directions should be both less than 2 mrad; the concentrator facets with the shape most approaching paraboloid would greatly resolve slope error and layout errors arising in the concentrator. Besides, by comparing the experimentally measured irradiance with the simulated results, the optical performance of the facility is evaluated to investigate their comprehensive influence. The results are useful to help constructors have a better understanding of the solar furnace’s optical behavior under conditions of multiple manufacture restrictions.
Due to the growing interest in 4th and 5th generation district heating systems, characterized by lower working fluid temperatures compared to the past, poligenerative solar-assisted networks are experiencing increasing attention from the research community. In this framework, the adoption of large solar-based thermal systems will become more common for such applications. Therefore, optimising the design and operation of each solar field servicing a specific network will be crucial to improving its overall performance. In this context, the objective of the current research is to develop a method to maximise the performance of solar-assisted poligenerative district heating networks by optimising each associated solar system separately. To this end, a novel dynamic simulation tool for solar thermal field design and optimization has been developed and experimentally calibrated in the Simulink/Simscape environment. The tool is capable of investigating innovative control logics, and accurately assessing both the thermal and hydraulic behaviour of the systems. The latter is one of its novelty, as ignoring the hydronic behaviour of the system can lead to the adoption of flawed design or control logic. The resulting tool will enable accurate optimization of the design and management of each solar field servicing a given district heating network, thereby improving the efficiency of the system as a whole. In order to demonstrate the viability of the proposed method and the capabilities of the developed simulation tool, a proof-of-concept analysis is conducted on an existing solar thermal field serving the Geneva district heating network. Diverse control logics (including Rule-Based and Model Predictive Control) are evaluated for the selected case study. The performed analysis demonstrates that the proposed method has the potential to accomplish significant primary energy reductions (up to 10.3%) and CO2 emissions avoidance (up to 10.9 tCO2/year).
China General Nuclear Power Group (CGNPC) Delingha 50 MW parabolic trough solar thermal power plant is the first commercial trough solar plant in China, and its solar field consists of 190 parallel heat collecting loops. For large-scale trough solar plant, balancing the flow rate of heat absorbing medium in each loop and stabilizing the outlet temperature are the key technologies and difficult problems. Regarding the Delingha 50MW solar field as the research object, this paper mainly focusing on the dynamic characteristics of flow and heat transfer in the solar field. The hydrodynamic calculation model and the heat transfer dynamic model are established on the real-time dynamic simulation platform STAR-90. With the actual operation data, the built solar field model is validated by comparing the simulation results. On this basis, the disturbance simulations of direct normal irradiance (DNI), heat transfer oil’s mass flow and heat transfer oil’s inlet temperature are carried out. The dynamic response curves of disturbance and the thermal inertia time constant of the loops are obtained. The conclusions lay a theoretical foundation for the formulation of outlet medium’s temperature control strategy in the solar field of large-scale trough solar thermal power generation system.
In a commercial parabolic trough solar power plant (PTSPP), the solar field (SF) is large-scaled and consists of hundreds of parabolic trough collector (PTC) loops. The PTSPP performance is determined by an appropriate operation strategy of the SF, which is implemented by adjusting the outlet temperature, the flowrate, and the flow distribution through the valves and pump to make the PTSPP output cost-effective. Majority of the published literature study the behavior of the SF with an assumption that all the PTC loops have uniform perfect performance. Due to the lack of appropriate model and algorithm, although operating with nonuniform and degraded PTCs is inevitable for a realistic SF, there is scarcely any literature studies how to develop an optimal operation strategy under this situation. The present paper aims at solving this problem by establishing a PTSPP model and developing an improved operation strategy (IOS). The PTSPP model consists of an improved thermal hydraulic model of the SF and a simplified but effective model of the power block, and the IOS combines the features of both global and distributed operation strategy. Based on the model and the IOS, the mechanism of optimization is illustrated in a uniform case, and then according to the comparison and analysis, the most appropriate strategy for the nonuniform case is determined among three representative strategies. Compared with the traditional operation strategy, the chosen strategy can improve the net electric generation by 3.4% with the low computational cost and good engineering feasibility.
