Modeling, transient simulations and parametric studies of parabolic trough collectors with thermal energy storage

Solar Energy 199 (2020) 497–509

Contents lists available at ScienceDirect

Solar Energy journal homepage: www.elsevier.com/locate/solener

Modeling, transient simulations and parametric studies of parabolic trough collectors with thermal energy storage

T

Tufan Akbaa,b, , Derek Bakerc,d, Almıla Güvenç Yazıcıoğluc ⁎

a

Department of Mechanical Engineering, Özyeğin University, Çekmeköy, 34794 İstanbul, Turkey Center for Energy, Environment and Economy (CEEE), Özyeğin University, Çekmeköy, 34794 İstanbul, Turkey c Department of Mechanical Engineering, Middle East Technical University, Çankaya, 06800 Ankara, Turkey d Center for Solar Energy Research and Application (GUNAM), Middle East Technical University, Çankaya, 06800 Ankara, Turkey b

ARTICLE INFO

ABSTRACT

Keywords: Concentrating solar power Parabolic trough collector Thermal energy storage Solar thermal energy Two-tank storage

For investigating the system response of parabolic trough collector heat generating system, a plant with parabolic trough collector field and two-tank molten salt thermal energy storage model with component-level control algorithm is developed for managing various working conditions. The model is transient inside the components and responds with hourly weather and demand data. The main purpose of this work is providing an alternative design methodology that focuses on the collector field, and storage size by investment, location, and load type. Using a simple economic model, the plant parameters are calculated, which contains only initial investment costs of the parabolic trough collector field and thermal energy storage costs. Depending on the economic model, various sizes of collector field and storage combinations are created at fixed initial investment costs in the mathematical model. A parametric study is performed by using the economic model simulating at several initial investment costs, two different locations in Turkey, and four different load profiles. As a result of the parametric study, maximum solar fraction cases are selected and the generalized trend is observed. The effect of thermal energy storage on the solar fraction is discussed and the change in thermal energy storage with optimum plant size is investigated. After the optimum investment, the linear increment trend of dispatchability is disappearing and increases asymptotically by increasing the plant and/or storage size. Later in this work, the significance of the load profile is emphasized, which should be one of the major design parameters for solar-powered energy systems.

1. Introduction Due to high electricity cost, energy independence and increased CO2 in the atmosphere, solar power became an emerging technology in the last decade. Today, after wind energy, it is the second-largest power source for electricity generation (IEA, 2014). Both photovoltaic (PV) and concentrating solar thermal (CST) systems are getting a feasible solution more for small and large-scale applications. The installed capacity of PV panels is increased from 23 GW to 135 GW from 2009 to 2013, respectively. According to the International Energy Agency (IEA), in a projected high-renewable scenario, installed capacity will be 1721 GW in 2030 and 4675 GW and 2050. With the increased use of utilityscale PV systems, it is expected before 2020, installed capacity cost of PV, will be less than 2000 USD/kW (IEA, 2014). For CST applications, installed capacity increased relatively slower for electricity generation. Today, worldwide installed capacity is over 4.8 GW when compared to

⁎

over 300 GW PV capacity (IEA, 2019b). In 2016, cumulative electricity generation for PV applications exceeded 325 GWh and CST applications exceeded 10 GWh (IEA, 2019a). In CST, direct solar radiation is concentrated on the smaller selective surface for heating heat transfer fluid (HTF). Today, large-scale solutions (for parabolic trough collector (PTC), over 350 °C HTF outlet temperature and approximately 100 °C temperature difference, and for central receiver system (CRS), over 560 °C HTF outlet temperature and approximately 320 °C temperature difference) are feasible option for electricity generation and small-scale solutions (about 100 °C to 250 °C HTF outlet temperature for PTC) are feasible for industrial solar heating and cooling applications. Four different collector systems are commercially available for CST systems. These are linear Fresnal reflector (LFR), CRS, PTC and parabolic dishes (PD) separated as their focus (tracking) or receiver type as shown in Table 1. Solar tracking can be two-axis, it requires more moving parts but decreases the incidence

Corresponding author at: Department of Mechanical Engineering, Özyeğin University, Çekmeköy, 34794 İstanbul, Turkey. E-mail address: [email protected] (T. Akba).

https://doi.org/10.1016/j.solener.2020.01.079 Received 15 June 2019; Received in revised form 12 January 2020; Accepted 28 January 2020 0038-092X/ © 2020 Published by Elsevier Ltd on behalf of International Solar Energy Society.

Solar Energy 199 (2020) 497–509

T. Akba, et al.

List of Symbols A c f Gbeam h m QTES T t u UL U V Waper

CSP CRS CST des DIL DNI HEX HCE HTF IAM IEA IL LFR PD PL PTC PV SAM SEGS TES TMY2

collector aperture area [m2 ] constant specific heat [kJ/kg K ] loss coefficient incident beam solar radiation [W/m2 ] enthalpy [kJ/kg ] mass [kg] heat capacity of TES [kWht ] temperature [K] time [s] internal energy [kJ/kg ] heat loss coefficient [kW/m2 K ] linearized heat loss coefficient [kW/m ] volume [m3 ] aperture width [m] absorptance transmittance incident angle [rad] density [kg/m3]

Acronyms BL

baseload

angle and higher concentration can be achieved. Besides, two-axis tracking focuses on a point that provides a smaller receiver area. Therefore, a higher concentration can be achieved in the same collector area. In mobile receivers, a receiver located at the focal point of the curved reflecting surface. In this type of design, a receiver located at the focus of the reflecting surface and fixed to the frame. The frame design is relatively more complex comparing a fixed receiver. But in a fixed receiver, reflecting surface slightly deviated from the Sun for concentrating incident radiation to the receiver which decreases the active aperture area to the cosine of the incidence angle, resulting in additional loss factor with comparing with mobile receivers. PTC and CRS technologies are dominant in the market. Currently, the majority of the operating plants are PTC power plants. On the other hand, CRS power plants are emerging because of reaching a higher temperature comparing to PTC. Also, they can be integrated better with thermal storage which allows to shift the production. There are several concentrating solar power (CSP) plant modeling and validation studies in the literature. They are focused on real plant data and validation (Patnode, 2006), heat loss modeling (Forristall, 2003), optimization (McMahan, 2006), thermal storage implementation and performance modeling (Llorente García et al., 2011; Biencinto et al., 2014). The term “performance modeling” starts with current plant modeling, derives new options for increasing efficiency and maximizes the desired output. There are different applications as PV hybridization (Starke et al., 2018), cogeneration (Valenzuela et al., 2017), desalination, combined cooling require performance model which consist different control and optimization methods. In Patnode’s study, TRNSYS model is developed and validated with real plant data, this work includes heat loss effect by broken glass

annulus, vacuum loss and very low hydrogen permeation between glass to absorber surface. It is concluded that very low hydrogen permeation (1 torr) increases heat loss from 400 W/m to 1200 W/m in heat collecting element (HCE) from perfect vacuum annulus. For broken glass heat loss is at 600 W/m at 400 °C HTF temperature (Patnode, 2006). Forristall focused on 1D resistance model of PTC field and operation parameters affecting the collector field performance. Also, this study states longitudinal effects are significant for long HCE beyond 80 m and adds axial heat loss mechanisms and proposes 2D model (Forristall, 2003). Yılmaz reviewed different collector field thermodynamic performance models extensively including optical methods and two-phase flows (Yilmaz and Mwesigye, 2018). Regarding the PTC field, thermal energy storage (TES) and performance simulation, plant decision strategy takes important role due to daily solar input variation. One of the most detailed control flow diagram is demonstrated in NREL’s System Advisor Model (SAM) technical manual (Wagner and Gilman, 2011). This flow diagram includes different TES options and plant configurations depending on the plant design. In the previous references, two-tank (Llorente García et al., 2011) and single-tank (thermocline) (Biencinto et al., 2014) TES plant decision strategies are explained. Using proposed strategies above, required load can be dispatched in maximum heat rate with different constraints (constant mass flow rate or minimum temperature limit). In last decade, CST applications become widespread and for decreasing initial investment, same plant design applied in different locations. These locations may require different constraints (i.e. low water consumption (Duvenhage et al., 2019) or fossil aggregated CST or additional desalination facility). Therefore, various plants are simulated for in different location with/without any change. Such as, Andasol plants simulated in Libya (Belgasim et al., 2018) and generates almost same power output comparing with existing location of the plant (Granada, Spain) or several predefined plant designs tested in different locations in India for determining the best CST installation locations (Purohit et al., 2017), this study shows that for 142 of 591 districts/ locations levelized cost of electricity is less than India Central Electricity Regulatory Commission’s levelized total tariff price for fiscal year 2016/17. These works are important for renewable penetration to emerging markets and decreasing the fossil fuel use. More importantly, most of the undeveloped countries have high solar resource and small

Table 1 The four CST technology families (IEA, 2014).

Fixed Receiver Point Receiver

concentrating solar power central receiver system concentrating solar thermal desired delayed intermediate load direct normal irradiation heat exchanger heat collecting element heat transfer fluid incident angle modifier International Energy Agency intermediate load linear Fresnel reflector parabolic dishes peak load parabolic trough collector photovoltaic System Advisor Model Solar Energy Generating Systems thermal energy storage Typical Meteorological Data

Line Focus (1-Axis Tracking)

Point Focus (2-Axis Tracking)

Linear Fresnel Reflectors (LFR) Parabolic Trough Collectors (PTC)

Central Receiver Systems (CRS) Parabolic Dishes (PD)

498

Solar Energy 199 (2020) 497–509

T. Akba, et al.

plants allow electricity access in remote locations and provides local work opportunities. This paper focuses only on the PTC field with a two-tank TES system. The solar part of the plant is modeled for observing utilization characteristics by changing the TES and PTC capacities. The rest of the plant can be any of the application described above, therefore the load model is only modeled as a function of time and required load as temperature and mass flow rate. Several parameters (location, weather, load profile, initial investment) changed during the parametric study for investigating the utilization behavior of the plant. In the model, actual PTC parameters used, which are adopted from LS-3 collector installed in Solar Energy Generating Systems (SEGS) plants in Mohave Desert, California (Llorente García et al., 2011). Modeling details of the system, component and component-based control algorithm explained in “Plant Model” section. The whole plant model is calculated as a combination of quasi-steady models (Klein and Beckman, 2007), including start-up/shut-down decisions of the plant and charging/discharging modes of the TES. The purpose of this study is to demonstrate the sole plant behavior in a wide range of TES capacities, PTC field sizes, loads and solar inputs. Current work aims to show the reader how PTC plant reacts in different cases not only in optimum design rather it is preferred to observe the plant behavior in a larger spectrum. The organization of the article continues with description of the plant, PTC and TES models, plant inputs and variables (Section 2). This section also includes the valve control algorithms with flow charts. In the next section, simulation input (initial and run-time) data, constraints and predefined constants are explained (Section 3). For each simulation, collector field initial values (Section 2.2) and weather data (Section 3.1) are used as input data. Using a cost model (Section 3.2), PTC field and TES capacity is sized. In the last part of Section 3, plant sizing (Section 3.3) is explained for operating the collector field in desired outlet temperature range. Plant response and the results are represented in simulations section (Section 4). Baseline methodology of the all analyses performed in this article is explained in “base analysis” (Section 4.1). After, different initial investment costs are analyzed in “initial investment” (Section 4.2) and “high initial investment” (Section 4.3) subsections. In the second part of the simulations, base analysis is replicated for a different location in Turkey (Konya) (Section 4.4) and same location (Muğla) is replicated four different load profiles (Section 4.5). In the final part, findings and overall methodology is evaluated

(Section 5). It is shown that using the proposed design methodology, the solar and storage side of the plant can be designed and the control algorithm can overcome various conditions. Also, using the proposed optimization methodology, existing plant designs can be easily optimized for different investment, size, location, and load conditions. 2. Plant model The plant model built using TRNSYS 17, which consists of an HTF tank (variable volume tank, Type 39) for compensating the thermal expansion, an HTF pump (main pump, Type 3), a PTC field (Type 1257), a TES subsystem and a load model. The load model consumes the energy utilized from solar power. Also, flow is controlled by six diverter valves and six mixing chambers as shown in Fig. 1. Dashed lines are bypass lines that direct inlet fluid to the HTF tank. Diverter valves, HTF pump, and TES controller are controlled by a plant controller code written in MatLab. The plant model does not include predefined system operation modes. Rather, the plant modes are a combination of the valve and pump modes. The provided control algorithm creates various systemlevel modes in combination. The controller collects weather data and plant states (TES tanks’ fluid volume and temperature, PTC output fluid temperature and mass flow rate and demand) then controls the valves and pump in the model. Each valve has a specific task and depending on the valves’ states, the pump signal is calculated at the end of the algorithm. The control scheme of the valves and pump are explained next parts. 2.1. Parabolic trough collector model A built-in PTC field TRNSYS model (Type 1257) used which is divided into a series of fixed length nodes and each node is modeled in a fixed time step defined by the user. It is assumed that an incompressible HTF passes through a constant volume heat collecting element. Thermodynamic properties (density, enthalpy, and internal energy) are dependent only on temperature result of the incompressible fluid assumption and the effect of temperature on the properties are modeled as a quadratic function. For a single node, the time rate of temperature change is defined as,

Fig. 1. Plant Layout (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 499

Solar Energy 199 (2020) 497–509

T. Akba, et al.

(cloudy day), models respond differently. Even, fluctuating DNI is observed in other days, all of them above a threshold limit (2500 kJ/hm2 ) and it is not impacting the outlet temperature. The main difference between SAM and TRNSYS model is low DNI heat input. But below the threshold limit, while the SAM model dispatches the load, the TRNSYS model fluctuates. The HTF outlet temperature difference between TRNSYS and SAM models is less than 3%. For the non-zero mass flow rate period, the difference increase to 5.6%.

A·Gbeam· cos · IAM· glass· coat · floss dT = dt m (u1 + 2u2· T ) + V ( 1 + 2 2 · T )(u h) +

m (hin h) UL·A (T Tamb) m (u1 + 2u2·T ) + V ( 1 + 2 2 ·T )(u

h)

(1)

Derivation of the equation is referenced in the mathematical model reference of TRNSYS, TESS libraries (TESS, 2010). TRNSYS solves Eq. (1) numerically using a second-order Runge–Kutta method for each time step for all nodes. In the equation, the variables are the temperature (T) of the HTF in specific node, at time (t), collector aperture area (A) of the node, the incident beam solar radiation Gbeam including the collector row shading, the incident angle ( ) of the beam solar radiation, and the incident angle modifier (IAM) of selected collector. glass, coat are transmittance of the glass cover and absorptance of the absorber tube, respectively. floss are the multiplication of loss coefficients in the collector model (in this model those are end losses, dust, bellows and miscellaneous losses which are taken as 1 because of new collector assumptions, i.e. no dust). m is the mass of HTF in calculated in the node of volume (V) of HCE and m is the mass flow rate of HTF passing in HCE. For modeling of thermal and physical properties of HTF (density ( ), enthalpy (h) and internal energy (u)) quadratic polynomial fit is done provided by the HTF manufacturer as described in following form: 2 2T

(2)

h (T ) = h 0 + h1 T + h2 T 2

(3)

u (T ) = u 0 + u1 T + u2 T 2

(4)

(T ) =

0

+

1T +

2.3. Load model The load model is a basic heat transfer equation as shown in Eq. (7) for modeling the hot side of the heat exchanger. At design condition, 34.5 MWt energy is rejected from the system for meeting the load. But the nature of the solar plants, daily variations, storage only operation and rapid variations on DNI, the inlet temperature of the HTF fluctuates. For meeting the demand, the minimum inlet temperature is defined for meeting the demand set at 370 °C. And for providing the same power output in simulation time, the temperature difference is set to 100 °C.

Pload = mload · cp, load (THTF , in

At the design condition, Eq. (7) has a numerical value of Eq. (8). If the temperature or mass flow rate is less than the design condition, the load is not supplied and flow is routed by the control algorithm written in Matlab using the valves as shown in Fig. 1.

34.5 MWt = 150 kg/ s× 2.3 kJ/kg· K× (370 ° C

UL is the heat transfer coefficient calculated experimentally as, UL =

U Waper (T

25)

(8)

270 °C)

2.4. TES model and TES control algorithm (5)

Two-tank indirect molten salt TES contains 2 variable volume tanks (type 39), a heat exchanger (type 5) between HTF and molten salt and a pump (type 3) for charging and discharging of TES. The model does not contain auxiliary heater for the molten salt freeze. For faster analysis and simpler control in TRNSYS, valves did not use in the TES model. Charging and discharging modeled separately as shown in Fig. 3. In real life, the heat exchangers and the pumps are one rather than two, and reverse operations (charging and discharging) can be designed with valves easily. In each time step, the mass flow rate (mHTF ) and the inlet temperature (THTF ) of HTF, the level of the tanks and temperature data are sent to TES controller written in MatLab. Then, four control processes are performed for allowing the charging/discharging operation. First, the mass flow rate of HTF (mHTF ) is checked, second the inlet temperature of HTF (THTF ) is checked with reference value (THTF , des ) for charging/discharging with the hot fluid. For possibility of excessive charging/discharging, volume of hot and cold tanks are checked. Last, tank temperatures are checked for preventing unexpected cooled storage medium (Tcold tank ). If the conditions do not meet, charging and discharging is cancelled and HTF flows through the heat exchanger, without any TES fluid flow (mpump = 0 ). Absence of heat transfer between HTF and TES fluid assumed as bypassing TES. After checks are

And Waper is the aperture width of the collector mirror and U is linearized heat loss coefficient from the HCE to the environment per unit length of collector. It is experimentally calculated with temperature and DNI dependent as

U = a0 + a1 T + a2 T 2 + a3 T 3 + DNI (a4 + a5 T )

(7)

THTF , out )

(6)

2.2. Collector input data and solar field validation For running the HTF in desired temperature range, as an initial step, the length of a single PTC loop is optimized depending on the location and TMY2 data. The main reason for optimizing the loop length is adapting the plant to different places and meteorological conditions. The calculation of the optimum number of HCE depends on location, direct normal irradiation (DNI), maximum PTC field outlet temperature and HTF mass flow rate when the same collector is used. Optimization is done for the LS-3 collector and the optimized number of HCE is used for all simulations. Selected collector (LS-3) is designed by Luz Int. Ltd and used in SEGS 7, 8, and 9 (Llorente García et al., 2011). The main characteristics of the LS-3 collector are shown in Table 2. For validating the PTC field model, the TRNSYS model is compared with SAM (Blair et al., 2018). In SAM, a “Process Heat Parabolic Trough” model is created without TES using location #1 weather data (Muğla data in Fig. 9) and selected collector parameters (in Table 2). Validation is performed in two stages. In the first stage, the SAM model ran and collector inlet and outlet temperatures and the mass flow rate of HTF are logged. In the second step, the TRNSYS model ran with the same inlet temperature and mass flow rate of HTF. The difference between HTF outlet temperature in TRNSYS and SAM models is compared for PTC field validation. In Fig. 2, outlet temperatures of both PTC field models are shown. Both models show similar responses in high DNI (sunny day). SAM model reaches maximum temperature faster TRNSYS model. In low DNI

Table 2 Main characteristics of LS-3 collector (Llorente García et al., 2011). Max. operating temperature [°C] Aperture area [m2 ] Aperture width [m] Length [m] Focal length [m] Absorber tube diameter [mm] Reflectance Transmittance Absorptance Emittance (at temp.[°C])

500

390 507.2

5.76 99 1.71 70 0.94 0.96 0.96 0.15 (350)

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 2. Outlet temperatures of TRNSYS and SAM models (Solid lines are HTF mass flow rate and inlet temperature used as base condition for both simulations).

completed mass flow rate of storage fluid (mpump ) is calculated. Then, the TES controller calculates the required mass flow rate of storage medium that should be pumped (mpump, req ). Finally, after comparing required mass flow rate with pump capacity (mpump, max ), the controller evaluates the pump controller signal whether charging or discharging or bypassing TES. Algorithm works in simulation run-time in MatLab for controlling TES is shown in Fig. 4.

2.5. Flow control valves Rather than a plant control, a component-based (valves, pumps) controller algorithm is built for regulating the valves and the TES as showed in Fig. 1. Main bypass valve (V1) works in two modes; either it routes all the HTF to the TES and load or bypasses by directing the HTF back to the PTC field passing through the HTF Tank for preheating. Decision algorithm of the main bypass valve is controlled comparing the outlet

Fig. 3. TES Layout (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 501

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 4. TES control algorithm flow chart (mpump is the algorithm result).

temperature (TPTC ) and mass flow rate (mPTC ) of the PTC field and predetermined set values (TPTC, des and Tdischarge, des ), and the state of the TES. If the TES is empty, PTC field outlet temperature is compared with

the required load value (TPTC, des ) for dispatching the load. Otherwise, HTF is bypasses to PTC field for warming up. If the TES is not empty, discharging the TES fluid and heating up the HTF at outlet of PTC field is the worst case for control scheme. Outlet of

Fig. 5. Flow diagram of main bypass valve (V1) (Left) and PTC – TES valve (V2) (Right). 502

Solar Energy 199 (2020) 497–509

T. Akba, et al.

the PTC field is check with limiting discharge temperature (Tdischarge, des ) sufficient for discharging the TES and mass flow (mload ) should be supplied the load. Otherwise, HTF is bypasses to PTC field for warming up. The PTC - TES valve (V2) checks the day or night condition (by controlling DNI), TES states, and bypass valve state as explained above. Since DNI only solar resources can be utilized by concentrating collectors, for PTC, a day without DNI is equivalent to night condition. The model also classifies the “day - night” condition under which the collectors are completely shaded due to row shadowing as night. Unlike the main bypass valve, the PTC - TES valve can regulate flow partially to the TES and PTC at the same time. At night time this valve routes all flow to the TES for discharging. During the daytime, first, the controller checks the bypass valve to determine the HTF is being warmed up, then checks for charging possibility by checking TES state as hot tank volume and temperature. If any of those conditions are not satisfied, all the HTF is routed to the PTC field. The last condition is charging possible and warming up is necessary. In this condition, priority is given to dispatching the load and depending on the capacity of pump flow is routed to load. When the pump is at its maximum flow rate (mpump, max ), the mass flow rate required for the load (mload ) is directed to the PTC field and the rest of the fluid is diverted to TES. This case occurred in peak demand analyses when DNI dropped from a high value (cloudy summer noon). A flow diagram of the control algorithm of the PTC - TES valve is shown in Fig. 5. The charging valve (V3) checks the mass flow rate of the load (mload ), PTC field outlet temperature (TPTC ) and mass flow rate (mPTC ) and status of TES. Like the PTC - TES valve, the charging valve regulates the flow partially in both outlets, preferably for dispatching the load, first. If the temperature is higher than the limiting charging temperature (Tchar , des ) and the outlet mass flow rate of the PTC (mPTC ) exceeds the load, the charging valve routes the excess fluid to the TES for charging. A flow diagram of the control algorithm of the charging valve is shown in Fig. 6. The discharging valve (V4) checks the states of the TES, the outlet temperature (TPTC ), and mass flow rate (mPTC ) of the PTC and the required load temperature (Tload ). This valve works on–off mode as the main bypass valve, and it routes the fluid completely to the TES for discharging or to the load for dispatching. The controller for the discharge valve checks the TES level; if TES is empty, the valve does not divert to the TES. If the TES is not empty and the temperature of the PTC outlet is sufficiently hot for discharging (Tdischarge, des ), the valve controls the inlet mass flow rate and load temperature. At the last step, the controller checks the hot tank temperature; if it is too cold the valve

does not allow discharging. When all conditions are satisfied, the discharge valve directs all the fluid to discharging the TES. The flow diagram of charging and discharging valves are presented in Fig. 6. In Fig. 7, the flow diagram for the discharge bypass valve (V5) (left) and load bypass valve (V6) (right) are shown. The controller for the discharge valve checks the mass flow rate (mdischarge ) of the TES inlet and the mass flow rate of excess HTF bypasses when the inlet flow to this valve is more than the maximum limit for discharging. Similarly, for the load, more than the required mass flow rate of inlet load (mload, in ) bypasses the load component. The maximum discharge capacity (mdischarge, max ) and load (mload ) profiles are defined before the simulation is started according to the design point of the plant. 2.6. Main pump and control algorithm For controlling the mass flow rate of the pump (mpump ), DNI, TES state and PTC field outlet temperature and mass flow rate are evaluated as controller input data. Flow diagram of main pump is presented in Fig. 8, one of four operation mode is selected by the algorithm as defined below:

• Zero mass flow rate (plant closed), • PTC mass flow rate (mass flow rate calculated by DNI at initial time step), • Dispatch mass flow rate (mass flow required for sustaining the load, calculated at the initial time step), • PTC and dispatch mass flow rate. 3. Parameter optimization and plant sizing For optimum plant size, the solar fraction is selected as optimization criteria defined as maximizing the utilized DNI by the load model, the main variables are TES tank volume and PTC area by fixing the initial investment other constraints are keeping preserving the temperature limits in physical limits. Several plant models created for predefined initial cost, location and weather data and analysis done for a general trend is obtained. After explaining parameters, inputs and constraints in this chapter, in Section 4 analysis methods will be explained. For a fixed investment, the analysis of the maximum solar fraction calculation will be referred as “base analysis” then four different optimization scenarios generated by simulating different base analyses in different conditions as “initial investment”, “high initial investment”, “comparison between different locations” and “demand analyses”.

Fig. 6. Flow diagram of charging valve (V3) (Left) and discharging valve (V4) (Right). 503

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 7. Flow diagram of discharge valve (V5) (Left) and load bypass valve (V6) (Right).

Fig. 8. Flow diagram of main pump.

3.1. Weather data

3.2. Cost model

All the simulations are performed in weekly periods (168 h) and hourly weather data resolution in Typical Meteorological Data (TMY2) format (Klein and Beckman, 2007). TMY2 data of two different locations in Turkey (Muğla and Konya) are used, which are supplied from the TRNSYS meteorological database. In all the simulations, these two weather data is used. Weekly simulations performed from the 195th day (July 15th ) to the end of the 21st day (July 21st ). The model uses ambient temperature and DNI provided from the TMY2 data. The sample data of Muğla and Konya are shown in Fig. 9. Average DNI for Muğla and Konya are 1226.1 (340.58) and 1035.2 (287.56) kJ /h ·m2 (W / m2) (15.6% less than Muğla) respectively and average ambient temperatures are 25.7 °C and 22.9 °C, respectively.

The cost model built for initial investment rather than life cycle of the plant. For the initial investment calculation, costs projected for 2010 and 2020 are used from the S&L report (NREL, 2003) and are linear interpolated to 2014 values. In Table 3, the interpolated initial investment costs are shown. For the sample plant calculation, the TES capacity is calculated as follows, where a higher heat capacity can be obtained by changing the fluid and reservoir temperatures.

QTES =

TES

×

TES

× cTES × (THotTank

TColdTank )

(9)

QTES is the heat capacity of the TES. Recall that a 2-tank TES design is assumed with a same size cold and hot tank, and the TES fluid is

504

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 9. Weekly DNI and temperature versus time data for Muğla and Konya (July 15th - 21st ) (Extracted from TRNSYS TMY2 Database). Table 3 Interpolated initial investment values for 2014 (NREL, 2003). Solar Field [USD/m2 ]

HTF System [USD/m2 ] TES System [USD/kWht ]

Table 4 Results of base analysis (fixed initial investment cost at 50 M USD for Mugla).

245 90 80

charging or discharging as it flows in heat exchanger model while transporting between the two tanks. The reservoir temperatures for the hot (THotTank ) and the cold (TColdTank ) TES tanks are 380 °C and 290 °C, respectively. Other TES parameters are volume ( TES ), density ( TES ) and constant specific heat (cp, TES ) of the storage medium. After calculating the heat capacity of the TES, the initial cost is calculated as, Ini. Cost = (QTES × TES Sys. ) + (Coll . Area × (Sol . Field + HTF Sys. ))

(10)

Tank Radius [m]

Tank Volume [m3 ]

TES Heat Capacity [kWht ]

TES Cost [M"$" ]

Coll. Area

[m2 ]

PTC Cost [M"$" ]

Solar Fraction %

0 2 4 6 7 8 9 10 11 12

0 25 201 679 1,078 1,608 2,290 3,142 4,181 5,429

0 999 7,992 26,974 42,833 63,938 91,036 124,878 166,213 215,790

0.0 0.1 0.6 2.2 3.4 5.1 7.3 10.0 13.3 17.3

149,254 149,015 147,345 142,812 139,025 133,985 127,514 119,432 109,561 97,722

50.0 49.9 49.4 47.8 46.6 44.9 42.7 40.0 36.7 32.7

33.9% 34.5% 35.3% 37.3% 38.8% 40.9% 42.1% 41.5% 38.5% 32.9%

4. Simulations

3.3. Plant sizing

4.1. Base analysis

Before the parametric studies, the PTC field resized by changing the number of HCE in a single loop for adopting a new location. The new loop length allows reaching the same optimum fluid temperature (390 °C) for different incidence angle values due to location and average DNI. In “Plant Sizing” part, the number of HCE recalculated for each location using LS-3 collector installed in SEGS 7,8 and 9 plants. The main characteristics of the LS-3 collector are shown in Table 2. In new plants, number of HCE in a loop is calculated for the same inlet and outlet temperatures as SEGS plants as 290 °C and 391 °C respectively. Result of the calculation, location #1 (Muğla) has an average daily cumulative DNI of 5.313 kWh/m2 and approximately 12 h of solar radiation can be utilized in a day (average DNI 442.7 W/m2 ). Reference value, the SEGS plants (Daggett, CA), have an average DNI of 637.2 W/m2 , which is 43.9% more than Muğla. In the SEGS plants, each HCE string has 16 HCE modules in a row. For Muğla, the number of HCE modules are increased to 18 and the desired inlet and outlet temperature achieved (12.5% longer), for dispatching the load without TES.

In the base analysis, the main purpose is demonstrating a sample of the parametric study for 50 M$ initial investment cost and constant load profile with 34.5 MWt demand. Analysis purpose is finding the maximum solar fraction, the change with TES with the solar fraction is shown in Table 4. Although the sensitivity of the plant is important for the performance assessment, for further assessments, the highest solar fraction case will be the solution for base analysis. The maximum solar fraction (42.1%) is observed at 2,290 m3 TES volume and a 127,514 m2 PTC field area. The results in Table 4 show that the solar fraction is increasing with increasing TES up to 91 MWht heat capacity. For larger TES, the collector area decreases and cannot dispatch the load or charge the TES efficiently. The change of the utilization with TES tank volume is shown in Fig. 10. Analysis results showed that with the optimum TES size, over 40% of continuous load can be supplied from solar energy.

505

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 10. Change in solar fraction with volume of TES tank (fixed initial investment cost at 50 M USD for Mugla. Markers Represent Simulation Results).

4.2. Initial investment analysis

transient and plant can be very insufficient for low or fluctuating solar input. For this reason, less than 30 M"$" initial investment is not analyzed. For small plants, HTF temperature is fluctuating around the set temperature which requires preheating during the day when the load is dispatched. For the first drop around 400 m3 , the plant requires one preheating at the sunset and during the day TES supplies the plant when DNI drops but TES is insufficient for self-supply after sunset. Before the rapid drop around 1600 m3 TES volume, TES is not charged, and demand fluctuates several times before noon but after sunset, TES can self-supply for a short period. Above 1600 m3 , the PTC field relatively small and cannot charge the TES completely and the plant only supplies load when it reaches set temperature.

In the base analysis, the highest solar fraction is calculated for fixed initial investment. For observing optimum base analysis points with different initial investment values, base analysis repeated, and the change of initial investment is investigated. Initial investments are investigated from 30 M USD to 65 M USD to observe the change in solar fraction. In Fig. 11, the change in solar fraction with the volume of TES tanks is shown. The trend of the curve of the different initial investment costs is expected as base analysis. But, the “30 M$” case shows a different trend (shown in Fig. 11), it decreases then keeps constant with high drop increased TES capacity. Very low investment makes the plant highly

Fig. 11. Change of solar fraction with volume of TES tank (fixed initial investment cost at 50 M USD for location #1 (Mugla). Markers Represent Simulation Results). 506

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 12. Change in PTC and TES cost and supply percentage with increasing initial investment cost (30 – 65 M USD) for location #1 (Mugla) (PTC Cost is Summation of Solar Field and HTF Costs).

The variation of maximum solar fraction in each initial investment costs is shown in Fig. 12. In this range of initial investment cost and maximum solar fraction increases linearly with initial investment cost. In the same figure, the ratio of TES cost to total initial investment and change of solar fraction with initial investment costs are shown. It shows that in optimum plant design, percentage of investment costs TES cost increases with increasing investment and plant becomes more storage dominant. If electricity price and power generation costs are included in cost model, plant lifetime required initial investment can be compared and the desired plant size can be calculated for the required cost. Since the scope of this work is providing an alternative sizing methodology of optimum PTC field and TES, the power generation and electricity market model is not included in the cost model.

the values are extremely high for real plants, the purpose is observing the plant response and maximum achievable solar fraction. At a very high solar fraction, preventing overheating of HTF, PTC field slightly defocuses and reduces the efficiency of PTC. It is a safety restriction and HTF allowable maximum temperature limit is set to 400 °C which is the maximum allowable HTF temperature limit in SEGS plants. With increasing initial investment cost, solar fraction asymptotically approaches as shown in Fig. 13. Upper limit of the analysis is determined as the change of solar fraction is less than 1%. For demonstrating the total range and response of the plant, the overall solar fraction is shown from 30 M USD to 275 M USD in Fig. 13.

4.3. High initial investment analysis

In this part, applicability of the plant is tested by comparing two different cities in Turkey. As shown in Fig. 9, Konya is selected as location #2 which is almost at the same latitude and a few hundred kilometer east of location #1. Initial investment analysis is done for

4.4. Comparison between two different location

The limits of the initial investment analysis are extended in order to observe the solar fraction change in high dispatchability range. Even

Fig. 13. Change in maximum solar fraction with all initial investment costs (30 – 275 M USD) for location #1 (Mugla). 507

Solar Energy 199 (2020) 497–509

T. Akba, et al.

Fig. 14. Change of supplied load with increasing initial investment costs (35 – 65 M USD) for location #1 and #2 (Mugla and Konya, respectively).

DNI than the installed location. In order to collectors operate in design conditions, HCE string length is increased 15% and analysis results showed that up to 50% of the load can be supplied with an optimum size of collector and storage for Turkey during the summer. In simulations, it is realized that PTC plants are very sensitive to DNI variations and small storage becomes mandatory for short solar input interruptions such as low cloudy days which provides high cumulative solar input in a day, therefore analyzing systems always in the transient state provides more insight for sizing the TES and controlling the whole plant. The proposed control scheme focused on component level (PTC, TES, and load) valve control and for given input and load all conditions handled by the algorithm easily. Simulations have done for two different locations (Muğla and Konya) in Turkey, which have a 15% difference in cumulative weekly DNI in summer. Also, the analyses performed in different load profiles and initial investment values for investigating plant behavior for different design scenarios. For a fixed investment cost (50MUSD ) and constant demand profile (baseload) condition, it is observed that the solar fraction increases 10% with using TES. In a range of initial investment, the need for TES to PTC ratio changes for the increasing solar fraction. Polynomial correlations are created for TES to PTC ratio and optimum plant size with initial investment cost for future feasibility analyses. It is shown that for a wide cost range, there is a linear relation with solar fraction and initial investment cost. Once solar fraction approaches one, the relation becomes asymptotic with the initial investment. For comparing different locations (Konya), the most important parameter is DNI, which is 15% less solar input then the reference city

location#2 and results are shown together in Fig. 14. In Fig. 14, it can be inferred that in the range of initial investment, the change in solar fraction with initial investment is linear for both locations. For same initial investment, the difference in the supplied load between locations is 5% and increases to 10% with increasing investment. As stated in the weather model, average DNI for location #2 is 15.6% less than location #1. From the trend lines in Fig. 14, in order to supply the same load, approximately 28% more investment is required for location #2 relative to location #1. 4.5. Demand analysis Today, most of CSP plants are designed for sustaining peak load (peak shaving) at noontime rather than meeting the baseload. In this part, different demand profiles are simulated for location #1 at a constant 50 M USD initial investment. Four different demand profiles are used in these simulations, which are the major profiles provided by IEA in “CSP Roadmap” (IEA, 2010). For all demand profiles, the total daily generated energy is equal by adjusting the mass flow rate of the load. Analyzed demand profiles are baseload (BL), intermadiate load (IL), delayed intermdiate load (DIL), and peak load (PL). In Table 5, load profiles and load periods are stated, and maximum solar fraction calculated for new load for each load profile. The intermediate load has the highest solar fraction with the smallest TES. This result is expected, since demand profile is coinciding with solar input. Thermal loses are also less than the other profiles because of the small difference between charging and discharging times. Even for the same plant parameters, the peak load profile supplies 4.5% less than the base load profile. For the delayed intermediate load profile, even when the plant has larger size TES, it cannot sustain the demand properly when compared with other load profiles.

Table 5 Load profiles and results of demand analyses.

5. Conclusion This paper focuses on PTC plant design optimization by sizing TES and PTC field comparing dispatchability trends and important factors affecting the plant parameters. A current plant design (SEGS) selected as initial design and moved to a different location which has 40% less 508

Load Type

Load [kg/s]

Load Time [h]

Solar Fraction

TES [m3 ]

Vol. of

Capacity of TES [kWht ]

IL BL PL DIL

327 150 900 327

7–18 0–23 10–14 11–22

55.2% 41.5% 37.0% 36.9%

679 3,142 3,142 4,181

269.9 1,249 1,249 1,662

Solar Energy 199 (2020) 497–509

T. Akba, et al.

(Muğla). Konya has 10% less solar fraction than Muğla with optimized TES size. Simulating with different demand profiles are showed that supply ratio changes up to 18%. Simulations showed that different input conditions can be optimized with scaling TES and PTC properly. The main conclusion of this paper is the trend and response of CSP plants with TES, especially a new design required or current design that needs to be adopted for different cases. In current work, those cases are the initial investment, location, and load profile. This methodology can provide an idea about the optimum plant sizing and the output which reduces the significant work for preliminary design phases.

S0960148118309832. Forristall, R., 2003. Heat transfer analysis and modeling of a parabolic trough solar receiver implemented in engineering equation solver. Technical Report, NREL. IEA, 2010. Technology Roadmap: Concentrating Solar Power. IEA. ISBN 9789264088139. https://doi.org/10.1787/9789264088139-en. https://www.iea.org/publications/ freepublications/publication/csp_roadmap.pdf. IEA, 2014. Technology Roadmap: Solar Photovoltaic Energy 2014 Edition. Technical report, International Energy Agency (IEA). http://www.iea.org/publications/ freepublications/publication/TechnologyRoadmapSolarPhotovoltaicEnergy_ 2014edition.pdf. IEA, 2019a. Statistics — World - Electricity generation by fuel (chart). https://www.iea. org/statistics/?country=WORLD&year=2016&category=Electricity&indicator= ElecGenByFuel&mode=chart&dataTable=ELECTRICITYANDHEAT. IEA, 2019b. Solar Energy. https://www.iea.org/topics/renewables/solar/. Klein, S., Beckman, W., 2007. TRNSYS 16: A Transient System Simulation program: Mathematical Reference. Llorente García, I., Álvarez, J.L., Blanco, D., 2011. Performance model for parabolic trough solar thermal power plants with thermal storage: Comparison to operating plant data. Sol. Energy 85 (10), 2443–2460. https://doi.org/10.1016/j.solener.2011. 07.002.. ISSN 0038092X. https://www.sciencedirect.com/science/article/pii/ S0038092X11002441. McMahan, A.C., 2006. Design and Optimization of Organic Rankine Cycle Solar-Thermal Powerplants. Master’s thesis, University of Wisconsin, Madison. NREL, 2003. Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts. Technical Report October, NREL. https://www.nrel.gov/docs/fy04osti/34440.pdf. Patnode, A., 2006. Simulation and Performance Evaluation of Parabolic Trough Solar Power Plants. Master’s thesis, University of Wisconsin, Madison. Purohit, I., Purohit, P., 2017. Technical and economic potential of concentrating solar thermal power generation in India. Renew. Sustainable Energy Rev. 78, 648–667. https://doi.org/10.1016/j.rser.2017.04.059.. ISSN 1364-0321. https://www. sciencedirect.com/science/article/pii/S1364032117305646. Starke, A.R., Cardemil, J.M., Escobar, R., Colle, S., 2018. Multi-objective optimization of hybrid csp+pv system using genetic algorithm. Energy 147, 490–503. https://doi. org/10.1016/j.energy.2017.12.116.. ISSN 0360-5442. http://www.sciencedirect. com/science/article/pii/S036054421732159X. TESS, 2010. TESSLibs 17: Component Libraries for the TRNSYS Simulation Environment. Valenzuela, C., Mata-Torres, C., Cardemil, J.M., Escobar, R.A., 2017. Csp+pv hybrid solar plants for power and water cogeneration in northern chile. Sol. Energy 157, 713–726. https://doi.org/10.1016/j.solener.2017.08.081.. ISSN 0038-092X. http:// www.sciencedirect.com/science/article/pii/S0038092X17307624. Wagner, M., Gilman, P., June 2011. Technical Manual for the SAM Physical Trough Model. Technical Report, National Renewable Energy Laboratory, https://www.nrel. gov/docs/fy11osti/51825.pdf. Yilmaz, H., Mwesigye, A., 2018. Modeling simulation and performance analysis of parabolic trough solar collectors: A comprehensive review. Appl. Energy 225, 135–174. https://doi.org/10.1016/j.apenergy.2018.05.014.. ISSN 0306-2619. http://www.sciencedirect.com/science/article/pii/S0306261918307074.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement We acknowledge “Center for Solar Energy Research and Application (GUNAM)” for providing us to use of TRNSYS 17 program under academic licence provided to Middle East Technical University. References Belgasim, B., Aldali, Y., Abdunnabi, M.J.R., Hashem, G., Hossin, K., 2018. The potential of concentrating solar power (csp) for electricity generation in Libya. Renew. Sustainable Energy Rev. 90, 1–15. https://doi.org/10.1016/j.rser.2018.03.045.. ISSN 1364-0321. https://www.sciencedirect.com/science/article/pii/ S1364032118301357. Biencinto, M., Bayón, R., Rojas, E., González, L., 2014. Simulation and assessment of operation strategies for solar thermal power plants with a thermocline storage tank. Sol. Energy 103, 456–472. https://doi.org/10.1016/j.solener.2014.02.037.. ISSN 0038-092X. http://www.sciencedirect.com/science/article/pii/ S0038092X1400125X. Blair, N., DiOrio, N., Freeman, J., Gilman, P., Janzou, S., Neises, T., Wagner, M., 2018. System Advisor Model (SAM) General Description (Version 2017.9.5). Technical report, National Renewable Energy Laboratory, https://www.nrel.gov/docs/fy18osti/ 70414.pdf. Duvenhage, D.F., Brent, A.C., Stafford, W.H.L., 2019. The need to strategically manage csp fleet development and water resources: A structured review and way forward. Renewable Energy 132, 813–825. https://doi.org/10.1016/j.renene.2018.08.033.. ISSN 0960-1481. https://www.sciencedirect.com/science/article/pii/

509