Comparison of control strategies for a solar heating system with underground pit seasonal storage in the non-heating season

Journal of Energy Storage 26 (2019) 100963

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Comparison of control strategies for a solar heating system with underground pit seasonal storage in the non-heating season

T

Xiaoxia Lia,b,d, Zhifeng Wanga,b,c, , Jinping Lia,d, Ming Yangb,c, Guofeng Yuanb,c, Yakai Baib,c, Longfei Chena,b,d, Tao Xue, Alina Gilmanovab,c ⁎

a

School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Solar Thermal Energy and Photovoltaic System, Institute of Electrical Engineering, Chinese Academy of Sciences, No. 6 Beiertiao, Zhongguancun, Beijing 100190, China c Beijing Engineering Research Center of Solar Thermal Power, China d Western China Energy and Environment Research Center, Lanzhou University of Technology, Lanzhou 730050, China e Academy of Building Energy Efficiency, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China b

ARTICLE INFO

ABSTRACT

Keywords: Seasonal thermal energy storage Solar heating system Control strategy TRNSYS Underground water pit seasonal storage

The solar heating system coupled with seasonal thermal storage (SHSSTS) is a promising solution to solve the seasonal mismatch between the solar energy supply and heating demand. The performance of SHSSTS in the non-heating season has a vital impact on the discharging process during the heating season, not only the quantity of energy, but also the level of energy. The aim of this work is to analyze the impact of different control strategies on the performance of the system during the non-heating season by means of both experiments and simulation methods. A pilot solar heating system integrating with a 3000 m3 underground water pit seasonal storage (UWPS) was built in Hebei, China. A calibrated system model was established in TRNSYS. Good agreement between measurement and simulation was obtained. The solar collection efficiency, stratification of the UWPS, energy storage efficiency and exergy storage efficiency were demonstrated for evaluating the system performance. Results showed that the control strategies were significant for improving the heat collection performance of solar receiver and the exergy efficiency of the UWPS. The stratification of the seasonal storage has an impact on the collection efficiency of the receiver, especially at the end of the non-heating season. In addition, at the end of the non-heating season in typical year, the solar collection efficiency could be increased by 10% in variable flow control compared to the temperature difference control.

1. Introduction Space heating energy consumption accounts for a large proportion of building energy consumption in China, especially in the north. Coal, natural gas and electricity are still the main forms of energy used in space heating. However, in the north of China, solar energy is abundant, and about 70% of the region belong to rich or very rich solar energy area [1]. Including the characteristic of low density, intermittence, diurnal and seasonal variability of solar energy, the main obstacle towards the further application of solar heating system is the seasonal mismatch between energy supply and demand, which can be solved by seasonal thermal energy storage (STES) [2]. In this way, great amounts of energy are stored during peak months and used during periods of high energy demand to improve the fraction of solar energy. There are several STES technologies available since the 1970s,

⁎

including Aquifer thermal energy storage (ATES), Cavern thermal energy storage(CTES), Borehole thermal energy storage (BTES), Hot water tank storage (HWTS) and Underground water or Water-gravel pit storage (UWPS or WGPS) [3,4]. UWPS is a promising seasonal storage method because of its high specific heat of water and the high capacity rates for charge and discharge [5]. Since then, the STES has been extensively explored consisting of solar residential projects and simulations. Many of these projects are mostly built in Denmark (Marstal, Dronninglund, Vojens, Brædstrup, Gram, etc.) [6,7], Germany (Friedrichshafen, Crailsheim, Eggenstein, Neckarsulm, Hannover, etc.) [8,9], Sweden (Augsburg, etc.) [10] and China (Hebei [11], Chifeng [12], Shanghai [13], etc.), Canada (Alberta) [14,15], USA (California) [16]. Among those, some researchers have established TRNSY model according to the actual project, which was used to analyze the performance of STES system. Zhang et al.

Corresponding author. E-mail address: [email protected] (Z. Wang).

https://doi.org/10.1016/j.est.2019.100963 Received 10 June 2019; Received in revised form 17 September 2019; Accepted 17 September 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved.

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Nomenclature A Aside, i cp Exc Exst F G Gd h hnc hfc Hb, n IDNI l M m mmin mc Nt N Nuw Pp Pm Pr Qcol Qsolar Qrec Qst Qin Qout Qloss Qconv Qrad Qc Re w Tin Tout, set Tout Tout, s Tin, s T¯f T¯i Tw Ta Ttop, s Ts, i T0

Ui

area of heliostat (m2) side area of tank at node i (m2) specific heat capacity (J/kg · K) exergy input to the UWPS (kJ) internal exergy change of the UWPS (kJ) design flowrate of pump (m3/h) total irradiance on horizontal plane (W/m2) diffuse irradiance (W/m2) total convective coefficient (W/m2k) natural convective coefficient (W/m2k) forced convective coefficient (W/m2k) monthly solar irradiation of beam radiation (MJ/m2) incident direct normal irradiance (W/m2) height of receiver (m) mass of storing water in UWPS (kg) flowrate of the circulation pump (kg/s) minimum flowrate of the pump (kg/s) transported mass over the charging process(kg) total numbers of time-steps numbers of heliostat Nusselt number input power of pump (W) input power of motor (W) Prandtl number collecting energy of the receiver (W) total energy input of the system (W) incident energy on the receiver (W) energy accumulation in UWPS (W) energy input of UWPS (W) energy output of UWPS (W) energy loss (W) convection loss via outer surface of receiver (W) radiation loss of receiver (W) charging energy to UWPS (W) Reynolds number inlet temperature of receiver (K) desired outlet temperature of receiver (K) outlet temperature of receiver (K) outlet temperature of UWPS (K) inlet temperature of UWPS (K) finally average temperature of UWPS (K) initially average temperature of UWPS (K) temperature of outer wall of receiver (K) ambient temperature (K) fluid temperature of the UWPS at the top (K) fluid temperature of the UWPS at i node(K) reference temperature for exergy(K)

V

effective heat transfer coefficient of tank at node i (W/ m2k) volume of UWPS (m3)

Greek α θz ϕ δ ω n ρ λ σ ɛ ηh ηcos ηs ηu sh & b

ηrefl ηatten ηspill ηr ηp ηm τ ηs ηx, s

absorptivity of the receiver zenith angle (∘) latitude (∘) declination (∘) hour angle (∘) day of year density (kg/m³) conductive coefficient (W/m k) Stefan–Boltzmann constant, (W/m2 k4) emissivity instant efficiency of the heliostat field cosine efficiency of the heliostat field total energy efficiency of system energy storage efficiency of UWPS shading and blocking efficiency mirror reflective efficiency atmospheric attenuation efficiency spillage efficiency efficiency of receiver efficiency of pump efficiency of motor time (s) energy efficiency of system exergy efficiency of tank

Abbreviations ATES AVG BTES CTES CON DNI HWTS MAX RMSD STES SDHS STEC Str SHSSTS UWPS WGPS

established a TRNSYS model of seasonal soil heat storage system used for greenhouse heating which was calibrated by the actual data and concentrated on the parameters optimization of the system [17]. Raab et al. employed the XST-model for calculating the underground seasonal hot water storage of a solar assisted district heating in Hannover. In addition, the model was validated by the measured data in 2002 and integrated in a TRNSYS system model for system validation. The deviation between measured and simulated did not exceed 5% [18]. Sibbitt et al. utilized five years measured data of the solar district heating system (SDHS) with STES of the Drake Landing Solar Community (DLSC) in Okotoks, Alberta, Canada to characterize system performance and improve the TRNSYS model. The monitored data showed that the system solar fraction increased from 55% to 97% in five years operation [19]. Flynn et al. also established a TRNYS model

aquifer thermal energy storage average borehole thermal energy storage cavern thermal energy storage control strategy direct normal irradiance hot water tank storage maximum root mean square deviation seasonal thermal energy storage solar district heating system solar thermal electric components stratification number solar heating system with seasonal thermal storage underground water pit storage water-gravel pit storage

based on the monitored data of DLSC and analysed the impact of location changes on the DLSC [20]. The design and structural parameters optimization of the STES system have drawn a lot of attention during the past decade [21,22]. However, the STES system cannot be completed without reasonable control to coordinates the operation and interaction of system components. Therefore, control strategy is also the key to improving system efficiency and operation stability and reliability. It has been proven that poor control strategies could result in a 10–15% decline in system performance [23]. Quintana presented a calibrated model in TRNSYS including the control strategies based on the practical application of Model Predictive Control (MPC) theory. Four control alternatives were conducted and evaluated for different parts of the current control strategies: winter-mode BTES charge enable, winter-mode BTES 2

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discharge enable, solar loop and BTES loop. Furthermore, the results showed that when the perfect forecast of weather and load demand were used as input of the model, the energy efficiency of these control strategies could be improved by about 5% [14]. Camacho et al. presented a survey of the diverse automatic control techniques applied to control the outlet temperature of solar plants [24]. Rehman et al. investigated various simple control strategies through TRNSYS model with an integrated ground source heat pump and seasonal borehole storage. The solar collector output temperature was controlled by adjusting the flow rate of the pump, including temperature tracking control and temperature difference control. Yearly renewable energy fraction and electricity demand of the system were the evaluating indicators [25,26]. Admittedly many studies have been carried out on the influence of the control strategies on the performance of STES system. There has been limited work focused on optimizing control the SHSSTS during the non-heating season and analysing their influences on the solar collection and stratification of the seasonal storage. Characteristics of SHSSTS in the non-heating season are different from those in the heating season, such as the gradual increase in the inlet temperature of the receiver. Furthermore, the stratification of the seasonal storage is crucial to the heating season. To that end, in this study, a new demonstration project of solar heating system is introduced. The project aims to develop a novel coupling way for a concentrated solar tower heating system with seasonal thermal energy storage. Meanwhile, a model calibrated by the experimental data was established in TRNSYS. Through comparison of three operational control strategies on the performance of the system at different time and space dimensions, the primary purpose of this work is to provide the technical and instructive support for the solar heating system with underground pit seasonal storage in the non-heating season.

energy resources. The SHSSTS formally started working in April 2018; the main components of the system consist of heliostat field, tower receiver, UWPS, buffer tank and so on. The aperture area of the heliostat field is 739.2 m2. Underground pit seasonal energy storage with a volume of 3000 m3 is located close to the heliostat field. A dedicated machine room is located next to the storage body containing all the mechanical equipment, i.e. heat exchangers, pumps, valves, control system and auxiliary components. The project has completed the construction phase and is currently under operation. 2.1. System configuration As schematically depicted in Fig. 2, the entire solar heating system is divided into three subsystems: the solar collection subsystem, the UWPS subsystem and the heating subsystem. The system works in two periods during one cycle in a year. In the non-heating seasons (Apr.–Oct.), sunlight is reflected from the field of tracking heliostats and concentrated onto the receiver, which heats up the water from the bottom of seasonal storage. Then the hot water is charged into the top of the seasonal storage with the circulation pump, which is controlled by the control system. While in the heating seasons (Nov.–Mar.), the heat transferred by the solar energy via collection subsystem is charged into a buffer tank through the heat exchanger. And then the buffer tank supplies the heat for the hotel. When the temperature of the buffer tank or the heating temperature is lower than the set value, the stored heat in the UWPS will be retrieved to meet the heating loads; this reverse process is also called the discharging process. Therefore, solar energy in the nonheating seasons compensates for the problem of the insufficient heating capacity in the low-temperature seasons. 2.2. Control strategy Three control strategies for the non-heating season are investigated:

2. System description

Control I: Constant flow control. The condition for the circulation pump of the solar collector system to switch on is that either the Direct Normal Irradiance (DNI) is higher than a minimum starting threshold or the monitored outlet fluid temperatures in the collector is higher than a pre-set value during the daytime. The turn-off

The demonstration project of SHSSTS is located in the south of Zhangjiakou City, Hebei, China (40∘13′18″N, 115∘26′6″E). The photographic view of the system is illustrated in Fig. 1. The hot spring hotel which are mainly served by the demonstration projects, used to rely on electric and gas boilers for heating, without using abundant solar

Fig. 1. Photographic view of the solar heating system with UWPS. 3

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Fig. 2. Schematic diagram of SHSSTS.

condition is the negation of the turn-on condition. The flowrate of the circulating pump was fixed during the operation procedure. Control II: Temperature difference control. Different from the conventional on-off temperature difference control which was proved much electricity consumption needed [26]. In this case, advanced temperature difference control is proposed. When the DNI is higher than the minimum starting threshold or the monitored outlet fluid temperatures in the receiver is above the upper limit, the circulation pump is turned on. The pump works at the minimum flowrate until the difference between the monitored outlet fluid temperatures of the receiver and the temperature at the top of the UWPS is above a pre-set value. The flowrate then turns to the rated value. When the difference is lower than another pre-set temperature, the flowrate is back to the minimum value. Compared with the traditional temperature difference control, the advanced temperature difference control can prevent the pump from starting and stopping frequently. Control III: Variable flow control. The condition of the circulating pump to start to work is just the same as another two control strategies. Then the flowrate of the circulating pump is regulated by a theoretical relation in Eq. (1), where Tin denotes the measured inlet temperature of receiver and Tout,set is the desired outlet temperature. Normally, Tout,set is set at 10 °C higher than the water temperature at the top of the tank. The maximum flow rate is limited to 1.1 times rated flow, taking into account pipeline resistance.

m = max[m min , Qcol /(cp·(Tout , set

Tin ))]

where ϕ means latitude, δ is declination, ω is hour angle, n represents the day of year in which the test was conducted. 3. Methodology To analyze the impact of control strategies on the system performance during the non-heating season, a SHSSTS model was established and simulated in TRNSYS. The mathematical model and simulation approaches for the main components, including the heliostat field, receiver, circulation pump, UWPS are described below. 3.1. Mathematical model Based on the first law of thermodynamics, the energy balance of the system could be expressed as Eq. (5):

Qsolar

3.1.1. Modeling of heliostat field The heliostat field is modelled by a single element model from TRNSYS (Type 394), which contains information including the field area, heliostats number, field efficiency matrix and other operational limits (wind velocity). The efficiency of the overall heliostat field is calculated by the ray tracing and mathematical simulation techniques, which was presented by Wei et al. [28,29]:

(1)

h

z

= cos cos cos

= 23.45 sin 360

+ sin sin

284 + n 365

=

cos · sh & b · refl · atten · spill

(6)

where ηh represents the instant efficiency of heliostat field, ηcos means cosine efficiency, sh &b is shading and blocking efficiency, ηrefl is the mirror reflective efficiency, ηatten is atmospheric attenuation of reflected sunlight, ηspill is spillage efficiency. The field efficiency matrix is imported as an external file, which is varied with the solar azimuth and zenith angle and shown in Fig. 3. The power reflected by the heliostat field into the receiver aperture area is evaluated by Eq. (7):

(2)

where G and Gd are total irradiance and diffuse irradiance on a horizontal plane, respectively, which are measured by the pyranometers. Zenith angle θz is calculated as [27]:

cos

(5)

where Qsolar represents the total solar input of the system, Qst denotes energy accumulation in UWPS during charging and storing, Qloss means the heat loss of the main devices of the system. Mathematical models of the main components are described as follows:

where Qcol represents the collecting energy of receiver. The turn-off condition is that the DNI is lower than a minimum starting threshold. The DNI is obtained by the Eq. (2):

G Gd DNI = cos z

Qloss = Qst

(3) (4)

Qrec = Qsolar · 4

h

= N · A·IDNI ·

h

(7)

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Ar (Tw4

Qrad =

(14)

Ta4 )

where ɛ is the receiver surface emissivity, which is set to 0.87 [34]. σ is the Stefan-Boltzmann constant, which is 5.67 × 10 8W /(m2 · K 4 ) . The calculation of the outlet temperature of the receiver is shown in Eq. (15):

Tout = Tin +

Qcol (mcp)

(15)

3.1.3. Modeling of pump In this study, there are two kinds of operation modes of the circulation pump: constant speed mode and variable speed mode. The performance curve of the pump is provided by the manufacture as follows: The input power of the circulation pump for pumping process:

Pp = 0.0051F 2 + 0.052F + 0.8416 The efficiency curve of the pump:

Fig. 3. The efficiency of the heliostat field. p

where N and A denote the number of the heliostat and the mirror area of one heliostat, respectively. IDNI is the transient direct normal irradiance.

=

0.6745F 2 + 10.64F

(17)

The input power of the motor can be calculated as follows:

Pm =

Pp (18)

m

3.1.2. Modeling of receiver In this project, the receiver is a non-pressurized receiver. Based on the energy balance, Qcol is evaluated as follows, neglecting of conductive losses [30]:

Qcol = Qrec

Qconv

where Pp means shaft power of the pump for pumping process, W; Pm represents the input power of the motor including the motor loss, W. 3.1.4. Modeling of seasonal storage tank The seasonal storage tank is modelled by a stratified tank with fixed positions of entering fluid and load flow. This is a one-dimensional multi-node model that assumed a vertical cylindrical tank, divided into N number of unequal sized nodes. The rate of the internal energy change of the node can be expressed by Eq. (19) and the discretization scheme is shown in Fig. 4.

(8)

Qrad

where α is the absorptivity of the receiver, which is assumed to be 0.90. Convection loss Qconv could be determined as follows [31]:

Qconv = hAr (Tw

(9)

Ta) 2

where Ar denotes the surface area of receiver and is equal to 14.9 m , Tw is the mean temperature of the outer wall of receiver, h means the total convective heat transfer from the receiver to the environment including forced and natural convection heat transfer coefficients (hfc and hnc), which could be approached as follows [32]:

h = (hfc a + hnc a)1/ a hnc = 0.81(T w

Ta) 0.426 0.8

mi c p

hfc = Nu w

(13)

Qout ( )

Qloss ( ) = (Ui) Aside, i (Ts, i

Qloss ( )

(19)

Tsoil, i )

(20)

where Ts, i is the temperature of the node i; control function determines which node obtains water from the receiver, and the returning water from the load can be controlled with FiL [27]. In the non-heating season, Qout(τ) and FiL are set to 0. Ui is the effective heat transfer coefficient, which is listed in Table 1. And only the influence of the thermal conductivity of the insulation is considered. The assumption of constant and temperature independent thermal conductivities of thermal insulation and soil is made [35]. In the water tank, as the charging velocity is small (lower than 5 × 10 6m / s ). The forced convection is assumed to have negligible effect compared to natural convection. The form of heat transfer in unsaturated soils is very complex

Fic

(11) (12)

Ts, i = Qin ( ) d

The heat loss of the storage tank could be calculated as Eq. (20)

(10)

Nu w = 0.0287Re w Pr1/3 l

(16)

where a is an empirical parameter related to receiver type and equals to 1 in cavity receiver. λ represents the heat conductivity coefficient of air, characteristic length l is set as the height of receiver. Radiation loss Qrad could be calculated by Eq. (14) for simplifying the calculation [33].

Fig. 4. Energy balance of node i. 5

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Table 1 Main component and settings. Name

Component type

Main parameters

Descriptions

Weather data

Type 15–3

Typical Meteorological Year of the Huailai, Zhangjiakou

Test conditions Heliostat field

Type 99 Type 394

Tower receiver

Type 495

Output pipe of receiver

Type 394 Type 709

Pump

Type 750

UWPS

Type 534-NoHX Type 957

Control models

Type 2b Equation block Type 65d Type 25c

Input measured meteorological conditions Input the field efficiency matrix [36] Number of concentrator units: 66 Mirror surface area:11.2 m2 The surface area of receiver: 14.9 m2 The height of receiver: 1.7 m Receiver capacitance: 668.8 kJ/K The diameter: 50 mm (inner), 60 mm (outer); The pipe thermal conductivity: 50 W/(m K) The rated flowrate: 2.22 kg/s The motor efficiency: 0.85 The volume of tank: 3000 m3 The effective heat transfer coefficient: 0.14 W/(m2 K) The thermal conductivity of soil: 1.3 W/(m K) Soil density: 900 kg/m3 The thermal conductivity of the insulation: 0.042 W/(m K) Temperature difference is 10 °C

Get from EnergyPlus website, used for the non-heating season simulation Used for validation of the model Belong to Solar Thermal Electric Components (STEC) [30]

Output

The mathematic model is described in Section 3.1.2 Consideration of the heat capacity Performance curve of pump is imported as external file

Some simple equation could be written in equation block

Display of results Output of results

including conduction, convection caused by pore water flow in liquid and vapor under thermal and hydraulic gradients, and the latent heat transfer [16]. Regarding the computation time of the entire system, only heat conduction was considered and the soil was assumed to be saturated for simplicity [15]. And the buried depth of water pit is 7 m assuming no background groundwater flow. The heat transfer coefficient between the ground surface and the ambient air was assumed to be infinite [18]. Thus, the effect of ambient air variation to the ground surface is only through conduction. The seasonal storage tank is constructed of the reinforced concrete. A polystyrene board insulation coating is placed at the top and the surrounding of the storage tank, which is buried in the soil. The thermal conductivity of the insulation is 0.042 W/(mK). The specific heat capacity and the thermal conductivity of the soil are 2.5 kJ/(kgK) and 1.3 W/(mK), respectively. The dimensions of the UWPS are illustrated in Fig. 5.

includes weather data processor model, solar collection subsystem, seasonal thermal energy storage, control model and so on. A detail of the overall TRNSYS model is shown in Fig. 6. The time interval of the simulation was the same with the measured data recorders which was 60 s. The main system components and simulation approaches are given in Table 1. 3.3. Method For comparing the impact of the control strategies on the performance of the UWPS system, some indicators are depicted as follows. The collection efficiency of the receiver is defined as the ratio of energy collected by the receiver to the total amount of solar energy incident on the receiver and can be expressed as: r

3.2. TRNSYS system model

=

Qc ol Qrec

(21)

where Qrec represents collecting energy of the receiver and the reflected solar radiation incident on the receiver during the test time, respectively. Qcol and Qrec can be written as:

Based on the established mathematical model of components, a system model is built-in TRNSYS 17. The TRNSYS system model

Fig. 5. The geometry and dimension of the UWPS. 6

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Fig. 6. TRNSYS model of the simulation system.

Qcol =

Qrec =

0

0

cp·m ·(Tout

N ·A· IDNI ·

Tin ) d hd

Ex c = mc ·cp· (Tin, s

(23)

where T0 and mc mean the average ambient temperature and transported mass over the charging process, τ(s). mc can be expressed as:

The energy efficiency of the UWPS in the non-heating season can be defined as: u

Qst Qin

=

mc =

Qin =

0

cp·m ·(Tin, s

(26)

where Tout, s and Tin, s are the fluid temperatures at the outlet and inlet of UWPS, respectively, K. The total energy efficiency of the whole system in the non-heating season can be defined as:

=

Qst Qsolar

(29)

ui )

T0·(sf

x,s

=

Ex st Ex c

(27)

4. Results and discussion

Both of energy and exergy are important parameters for evaluating heat storage systems. Moreover, Exergy reflects the temperature of a heat transfer and the degradation of heat level due to irreversibilities in any process. Exergy analysis is a supplement to energy analysis. And by providing more reasonable evaluation and comparison basis, many difficulties of traditional energy-based heat storage system methods are avoided. Among them, exergy efficiencies are rational indicator for evaluating the approach to ideal heat storage system performance [38]. The total exergy input to the UWPS during charging period is given by [38]:

4.1. System model validation

s

(28)

si )]

(30)

where uf and sf represent the specific internal energy and the specific entropy at the final condition, ui and si mean the specific internal energy and the specific entropy at the initial condition, respectively. The dead state temperature T0 is the average ambient temperature during the non-heating season, which is 293.17 K. The total exergy efficiency of the UWPS in the non-heating season can be evaluated as the ratio of the internal exergy change (Exst) to the total exergy input to the UWPS (Exc). That is:

(25)

Tout , s ) d

T 0·ln

md

Exst = M·[(uf

where Qst represents energy accumulation in UWPS during charging and storing, and Qin denotes input thermal energy to the UWPS during charging [37]. Qst and Qin can be written as:

T¯i )

0

Tout , s )

The internal exergy change of the UWPS in the non-heating season can be written by [39]:

(24)

Qst = ·cp·V ·(T¯f

Tin, s Tout , s

(22)

(31)

The dynamic comparisons of measured and simulated system performance were carried out in this section, in temperature difference control operation and constant flow control respectively. The weather conditions, the flowrate of the circulation pump, the number of the heliostats and the initial condition of the seasonal storage in simulation were setting consistently with the experimental test data of the system. Meteorological conditions of measurement were invoked as the input meteorological conditions for the model validation. 7

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heating season (which was from April 1st to October 1st of the calendar year). And the other conditions were the same, including meteorological condition, initial stratification and temperature of UWPS, rated flowrate and so on. Three different control strategies were evaluated: (i) constant flow control. The pump is turned on when a certain minimum DNI is available, in this analysis which is set to 200 W/m2; (ii) temperature difference control operation. Considering the reduction of frequent adjustment of the circulation pump, in this section the condition of 10 °C temperature difference will be discussed, that is, once the temperature difference between Tout and the UWPS's top temperature is greater than 10 °C, the pump will work in rated flow rate, until the difference is below 0 °C, the minimum flow will be set. The minimum flow rate of the pump is 0.3 times of the rated flowrate; (iii) variable flow control, the Tout, set is set 10 °C higher than the top temperature of the UWPS, and the flow rate of the circulation pump is adjusted according to the Section 2.2. Analysis is performed from the primary device to the entire system in different time dimensions for investigating the influences of different control strategies on the performance of SHSSTS.

Fig. 7. Comparison of the calculation DNI and measurement.

4.2.1. Impact on the solar receiver Comparison of operational state and hourly collecting energy of the receiver under the different control strategies are illustrated in Figs. 10 and 11. A typical measured overcast weather data in July were selected for analysis. And Ttop, s and Tin were set at 80 °C and 60 °C, respectively. It can be observed that in CON III, the outlet temperature of the receiver is

Fig. 7 shows the discrepancy between the calculated value of DNI by the measured G and Gd, which are shown in Eq. (2), and measured by the pyrheliometer in a clear sky day. Good agreement between measurements and calculation can be obtained. Hence, the calculated value of DNI was imported as the irradiance data to the model. An overall system TRNSYS model was built and consistent with the real project both in components and running state. Fig. 8 shows outlet and inlet fluid temperatures of the receiver's measured and simulated data for two different types of weather conditions. Table 2 presents an overview of the comparison between model and measurements in terms of fluid temperatures and the gross energy output from the solar collection system for two different types of weather conditions. It can be seen that a good agreement of outlet temperatures of the receiver between measurements and model could be appreciated in July 3rd with clear sky weather. While in July 1st with cloudy weather, the flowrate of the circulation pump varied intensely, along with the fluctuation of DNI, which lead to higher deviations of outlet temperatures between measurement and model. The main reason for this was that the response of the pump lagged behind the fluctuations of the solar radiation and the solar receiver reacted slowly than the pump due to the capacity of the pipe. This phenomenon was more evident in the case of the higher frequency change in solar radiation. However, the whole trend was close; the relative errors of the accumulated heat energy, the outlet temperature and the inlet temperature for the solar receiver were within 14%. And the root mean square deviations (RMSD) of the outlet temperature were 3.79 K and 1.34 K in cloudy and sunny respectively, which could be acceptable in real engineering. For the reason of the outlet pipe of the seasonal storage to the heat source was not completely at the bottom of UWPS in the actual project, the inlet temperature measurement of the solar receiver was slightly higher than the simulation. Fig. 9 shows a clear comparison of measured and simulated values of temperature at different locations and average temperature in a seasonal tank. The RMSD and the average relative error of the temperature are lower than 0.82 K and 2.0%, respectively. And the deviation of the accumulated heat energy of the seasonal storage between the measurements and simulations is 2.2%. The simulation results show that the calculation error could be accepted in real engineering, which suggests that the system simulated model is accurate and reliable for the following analysis. 4.2. Performance analysis of the control strategy

Fig. 8. Comparison of outlet and inlet fluid temperature of receiver between the simulation (S) and measurement (M) under cloudy and sunny weather.

Based on above system simulation model, dynamic simulations of system under different control strategies were conducted for the non8

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Table 2 Comparison of measurement and simulation in solar receiver. Outlet temperature

RMSD Relative Error (MAX) Relative Error (AVG) Deviation in energy

K % % %

Inlet temperature

The useful energy gain

Sunny

Cloudy

Sunny

Cloudy

Sunny

Cloudy

1.34 7.1% 1% —

3.79 13.6% 1% —

0.89 2.5% 0.8% —

0.89 2.6% 0.8% —

— — — 0.67%

— — — 0.44%

initial mean temperature gradient for the charging period, which can be expressed as follows [41]:

Str =

( T / z)t ( T / z )t = 0

(32)

For the analysis of the influence of the control strategies for the UWPS, and clearly reflecting the influence of outlet temperature of the receiver to the performance of UWPS, continuously one week charging process is conducted in this section. Due to the small difference and short time, the impact of heat collection and storage efficiency can be neglected within one week. Figs. 12 and 13 show the temperature profiles of the storage as a function of storage height and the temperature rise at the top and bottom positions of storage. It can be seen that the temperature at different heights has increased in most of time. The discrepancies of different control strategies are obvious after 168 h. The temperature rise at the top of the UWPS is obviously higher than the bottom in CON III, followed by CON II. While in CON I the temperature rise at top of the tank is much closer to that at the bottom. In June 26th, during the charging process, the temperature rise at the top dropped sharply, while the temperature rise at the bottom went up on the contrary. As described in the previous section, in CON I the circulation pump operates at constant flow throughout the daytime, except for the case when the DNI is lower than 200 W/m2, which results in the outlet temperature of the receiver being lower than the top temperature of UWPS in some cases. Thus, lower temperature hot water reduces the temperature at the top of tank, causing the mix of the stored fluid with the charging fluid, which translates into destruction of availability, and destratification of the UWPS, as shown in Fig. 14. The stratification number as a function of time in the different control strategies are illustrated in Fig. 14. Meanwhile, the intense mixing enhances the magnitude of buoyancy in the inversion layer and the thermal diffusion between the stratification, indicating that the temperature at the bottom rises faster in CON I. Based on above comprehensive discussions, it can be concluded that CON III is preferred for operation, taking into account the excellent performance of receiver and better stratification of the storage.

Fig. 9. Comparison of fluid temperature of seasonal storage between the simulation (S) and measurement (M).

stabilized at 90 °C in most of the time during the operation, which is 10 °C higher than the temperature at the top of the UWPS. Similarly, in CON II, the outlet temperature fluctuates between 80 °C and 90 °C. In CON I the outlet temperatures of the receiver change with the variation of solar radiation, which is much lower than that of the other two modes. Sometimes it is lower than the top temperature of the UWPS. The collection efficiency in the whole day are 73.8%, 69.4% and 73.5% in CON I, CON II and CON III, respectively. The reason why the lower collection efficiency occurred in CONII is that it is insufficient to meet the criteria of turning to the rated flowrate when the solar irradiance is low, so that the receiver operates inefficiently at the minimum flow rate, resulting in an increase in heat loss. Therefore, when solar radiation is low and flow oscillation occurs, there are obvious differences in hourly energy collection between different control strategies, as shown in Fig. 11. Meanwhile, when the output temperature of the receiver is slightly higher or lower than the temperature difference, the flowrate of the pump is prone to oscillate between the rated flow and minimum flow. CON III minimizes oscillation of the flowrate and shows to provide reasonably stable operation of the system. CON I and CON III show outstanding performances of the receiver contrasting with CON II, which is not introduced for operation considering the collection efficiency.

4.2.3. Long term performance analysis The collection efficiency of the receiver and the accumulated heat energy of the UWPS in the non-heating season were simulated for various control strategies based on the typical yearly meteorological weather data and listed in Table 3, which were used as the evaluating indicators for comparison. The energy and exergy performance analysis of the UWPS for a long-term were also conducted. It can be seen that the performance of the system was better in solar collecting and UWPS, the average collection efficiency of solar receiver and energy efficiency of UWPS during the non-heating season were about 82% and 66%, respectively. From April to October, in CON III a total amount of 1263.01GJ heat was collected from the solar collection subsystem and 828.81 GJ was stored in UWPS. The energy loss of the UWPS was 34.38%, including the system loss and the energy stored in soil, a part of which also can be utilized during the discharging period. Moreover, it could be found that CON III strategy can further improve the performance of both solar receiver and UWPS. What's more, exergy efficiency of UWPS in CON III was 4.8% larger than that in CON I. The monthly average solar collection efficiency and solar energy

4.2.2. Impact on the UWPS Not only is the storage efficiency an important criterion for assessing the performance of storage, but the stratification is also crucial for storage considering the discharging. Good stratification means that high temperature differences and gradients leading to a high exergy level are realized in the storage [40]. The discharge process is definitively influenced by the stratification of the storage tank after the charging period. To better analyze the impact of control strategy to the UWPS, the stratification number is defined as the ratio of the mean temperature gradient along the tank height at any time interval to the 9

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Fig. 11. Comparison of hourly collecting energy of receiver in different control strategies.

Fig. 12. Temperature profiles of the storage as a function of storage height.

Fig. 10. Operation state of the receiver in different control strategies.

irradiation of beam radiation on a plane normal to the direction of propagation (Hb, n) are shown in Fig. 15. In CON III the discrepancies of the solar collection efficiency in different month were not particular obvious. Conversely, in CON II solar collection efficiency notably decreased with months. The main reason for the decrease of solar collection efficiency in CON II was analyzed in Section 4.2.1. With the increase of the operating temperature of the receiver and the decrease of the solar energy irradiation in the non-heating season, the difference of solar collection efficiency between CON III and CON II increased.

Fig. 13. Temperature rise at top and bottom position of storage.

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Fig. 15. Comparison of monthly average solar collection efficiency in different control strategies.

Fig. 14. The stratification number as a function of date in different control strategy.

And in September, solar collection efficiency was improved by 10% in CON III comparing with that of CON II. Meanwhile the collection efficiency of receiver in CON I also decreased at the end of the non-heating season. Fig. 16 shows the temperatures profiles of the UWPS in different control strategies during the non-heating season. It can be seen that the temperature difference between the top and the bottom of the UWPS was smallest in CON I. That is to say, the worst stratification resulted in higher inlet temperature of the receiver, as shown in Fig. 17. Since the end of August, the outlet temperature of the receiver in CON I was close to the boiling temperature of water at local atmospheric pressure, which led to an increase in heat loss. However, the outlet temperatures of the other two modes were lower. In the flow oscillation process, there was a certain increase in heat loss in CON II. Therefore, the stratification of seasonal storage had an impact on the collection efficiency of the receiver, especially at the end of the nonheating season. The simulations results indicated that Con III not only has excellent solar heat collecting performance, but also has good stratification and exergy efficiency for seasonal storage.

Fig. 16. Comparison of temperatures of the UWPS in different control strategies.

5. Conclusion In order to investigate the influences of different control strategies on the performance of SHSSTS in the non-heating season, a comprehensive experiment and simulation analysis were conducted. A dynamical simulation model of the system was established through TRNSYS, and validated by the experimental data. At the same time, the performance of SHSSTS was evaluated by a lot of indicators including the efficiency, the Str number and the amount of energy. In addition, the energy and exergy efficiency assessment were carried out for UWPS. The following conclusions could be obtained:

(2) Considering the available heat for the heating season, the stratification of UWPS was better in variable flow control. Contrarily, the constant flow control results in degrading the stratification of the UWPS in the cloudy weather. (3) Good control strategies can efficiently improve the exergy efficiency of the UWPS; exergy efficiency of UWPS in variable flow control was 4.7% larger than that in constant flow control. (4) The control strategies were significant for improving the heat collection performance of solar receiver, and also the stratification of the seasonal storage has an impact on the collection efficiency of the receiver, especially at the end of the non-heating season. Solar collection efficiency was increased by 10% in variable flow control

(1) The SHSSTS has prominent performance in the non-heating season. The average efficiency of solar receiver and storage efficiency were about 82% and 66%, respectively.

Table 3 Performance comparison of system in different control strategies throughout the non-heating season. Mode

Collection efficiency of receiver (%)

Accumulated heat energy in UWPS (GJ)

Energy efficiency of UWPS (%)

Exergy efficiency of UWPS (%)

Energy efficiency of system (%)

Str

CON I CON II CON III

81.45 79.59 81.78

809.60 798.48 828.81

64.37 64.96 65.62

53.20 56.31 58.02

34.81 34.33 35.63

7.25 9.88 10.98

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[12] L. Xu, J.I. Torrens, F. Guo, X. Yang, J.L.M. Hensen, Application of large underground seasonal thermal energy storage in district heating system: a model-based energy performance assessment of a pilot system in Chifeng, China, Appl. Therm. Eng. 137 (2018) 319–328, https://doi.org/10.1016/j.applthermaleng.2018.03.047. [13] J. Xu, Y. Li, R.Z. Wang, W. Liu, Performance investigation of a solar heating system with underground seasonal energy storage for greenhouse application, Energy 67 (2014) 63–73, https://doi.org/10.1016/j.energy.2014.01.049. [14] Quintana H., A practical approach to model predictive control (MPC) for solar communities. (2013). [15] N. Catolico, S. Ge, J.S. McCartney, Numerical modeling of a soil-borehole thermal energy storage system, Vadose Zo. J. 15 (2016) 0, https://doi.org/10.2136/ vzj2015.05.0078. [16] T. Başer, J.S. McCartney, Transient evaluation of a soil-borehole thermal energy storage system, Renew. Energy (2018), https://doi.org/10.1016/j.renene.2018.11. 012. [17] J. Shi, J. Mao, L. Zhang, Z. Li, X. Tang, P. Xu, A low cost seasonal solar soil heat storage system for greenhouse heating: design and pilot study, Appl. Energy 156 (2015) 213–222, https://doi.org/10.1016/j.apenergy.2015.07.036. [18] S. Raab, D. Mangold, H. Müller-Steinhagen, Validation of a computer model for solar assisted district heating systems with seasonal hot water heat store, Sol. Energy 79 (2005) 531–543, https://doi.org/10.1016/j.solener.2004.10.014. [19] B. Sibbitt, D. McClenahan, R. Djebbar, J. Thornton, B. Wong, J. Carriere, J. Kokko, The performance of a high solar fraction seasonal storage district heating system five years of operation, Energy Procedia 30 (2012) 856–865, https://doi.org/10. 1016/j.egypro.2012.11.097. [20] C. Flynn, K. Sirén, Influence of location and design on the performance of a solar district heating system equipped with borehole seasonal storage, Renew. Energy 81 (2015) 377–388, https://doi.org/10.1016/j.renene.2015.03.036. [21] C. Chang, Z. Wu, H. Navarro, C. Li, G. Leng, X. Li, M. Yang, Z. Wang, Y. Ding, Comparative study of the transient natural convection in an underground water pit thermal storage, Appl. Energy 208 (2017) 1162–1173, https://doi.org/10.1016/j. apenergy.2017.09.036. [22] Y. Bai, M. Yang, Z. Wang, X. Li, L. Chen, Thermal stratification in a cylindrical tank due to heat losses while in standby mode, Sol. Energy 185 (2019) 222–234, https:// doi.org/10.1016/j.solener.2018.12.063. [23] H. El Hasnaoui, Control Strategies and Performance Analyses of a Central Solar Heating Plant with Seasonal Storage, (1996). [24] E.F. Camacho, F.R. Rubio, M. Berenguel, L. Valenzuela, A survey on control schemes for distributed solar collector fields. Part II: advanced control approaches, Sol. Energy. 81 (2007) 1252–1272, https://doi.org/10.1016/j.solener.2007.01. 001. [25] H.U. Rehman, J. Hirvonen, K. Sirén, Design of a simple control strategy for a community-size solar heating system with a seasonal storage, Energy Procedia 91 (2016) 486–495, https://doi.org/10.1016/j.egypro.2016.06.183. [26] H. ur Rehman, J. Hirvonen, K. Sirén, Influence of technical failures on the performance of an optimized community-size solar heating system in Nordic conditions, J. Clean. Prod. 175 (2018) 624–640, https://doi.org/10.1016/j.jclepro.2017.12.088. [27] J. Duffie, W. Beckman, Solar Engineering of Thermal Processes, 1991, 2001, (2013). [28] X. Wei, Z. Lu, Z. Lin, H. Zhang, Z. Ni, Optimization procedure for design of heliostat field layout of a 1MW e solar tower thermal power plant, Solid State Light. Sol. Energy Technol. 6841 (2008) 684119, , https://doi.org/10.1117/12.755285. [29] X. Wei, Z. Lu, Z. Wang, W. Yu, H. Zhang, Z. Yao, A new method for the design of the heliostat field layout for solar tower power plant, Renew. Energy 35 (2010) 1970–1975, https://doi.org/10.1016/j.renene.2010.01.026. [30] P. Schwarzbözl, U. Eiden, R. Pitz-Paal, S. Jones, A TRNSYS Model Library for Solar Thermal Electric Components (STEC) A Reference Manual Release 2.2, (2002), pp. 1–53. [31] Q. Yu, Z. Wang, E. Xu, Simulation and analysis of the central cavity receiver's performance of solar thermal power tower plant, Sol. Energy 86 (2012) 164–174, https://doi.org/10.1016/j.solener.2011.09.022. [32] D.L. Siebers, J.S. Kraabel, Estimating Convective Energy Losses from Solar Central Receivers, (1984), p. 69 Sandia Report Sand84-8717. [33] Z. Yao, Z. Wang, Z. Lu, X. Wei, Modeling and simulation of the pioneer 1 MW solar thermal central receiver system in China, Renew. Energy 34 (2009) 2437–2446, https://doi.org/10.1016/j.renene.2009.02.022. [34] L. Xu, W. Stein, J.S. Kim, Y.C.S. Too, M. Guo, Z. Wang, Transient numerical model for the thermal performance of the solar receiver, Appl. Therm. Eng. 141 (2018) 1035–1047, https://doi.org/10.1016/j.applthermaleng.2018.05.112. [35] E.B. Hellström G., Stratified Temperature Model, Manual for Computer Code, Univ. Lund, 1989. [36] M. Guo, F. Sun, Z. Wang, The backward ray tracing with effective solar brightness used to simulate the concentrated flux map of a solar tower concentrator, AIP Conf. Proc. 1850, 2017, https://doi.org/10.1063/1.4984366. [37] B. Rezaie, B.V. Reddy, M.A. Rosen, Exergy analysis of thermal energy storage in a district energy application, Renew. Energy 74 (2015) 848–854, https://doi.org/10. 1016/j.renene.2014.09.014. [38] I. Dincer, M.A. Rosen, Heat storage systems, Exergy Analysis of Heating, Refrigerating and Air Conditioning, (2015), https://doi.org/10.1016/b978-0-12417203-6.00006-5. [39] A. Lake, B. Rezaie, Energy and exergy efficiencies assessment for a stratified cold thermal energy storage, Appl. Energy 220 (2018) 605–615, https://doi.org/10. 1016/j.apenergy.2018.03.145. [40] P. Steinert, S. Göppert, B. Platzer, Transient calculation of charge and discharge cycles in thermally stratified energy storages, Sol. Energy 97 (2013) 505–516, https://doi.org/10.1016/j.solener.2013.08.039. [41] J. Fernández-Seara, F.J. Uhía, J. Sieres, Experimental analysis of a domestic electric hot water storage tank. Part I: static mode of operation, Appl. Therm. Eng. 27 (2007) 129–136, https://doi.org/10.1016/j.applthermaleng.2006.05.006.

Fig. 17. Comparison of operating state of the receiver in different control strategies at the end of the non-heating season.

compared with the temperature difference control at the end of the non-heating season. 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. Acknowledgements The authors would like to thank the “Transformational Technologies for Clean Energy and Demonstration”, Strategic Priority Research Program of the Chinese Academy of Sciences for funding the Project [No: XDA21050200]. References [1] H. Tao, Analysis of solar space heating in various areas of China, Proceedings of ISES World Congress 2007 (Vol. I – Vol. V), Springer Berlin Heidelberg, 2008. [2] J. Xu, R.Z. Wang, Y. Li, A review of available technologies for seasonal thermal energy storage, Sol. Energy 103 (2014) 610–638, https://doi.org/10.1016/j. solener.2013.06.006. [3] A. Hesaraki, S. Holmberg, F. Haghighat, Seasonal thermal energy storage with heat pumps and low temperatures in building projects—a comparative review, Renew. Sustain. Energy Rev. 43 (2015) 1199–1213. [4] A.V. Novo, J.R. Bayon, D. Castro-Fresno, J. Rodriguez-Hernandez, Review of seasonal heat storage in large basins: water tanks and gravel-water pits, Appl. Energy 87 (2010) 390–397, https://doi.org/10.1016/j.apenergy.2009.06.033. [5] A. Dahash, F. Ochs, M.B. Janetti, W. Streicher, Advances in seasonal thermal energy storage for solar district heating applications: a critical review on large-scale hotwater tank and pit thermal energy storage systems, Appl. Energy 239 (2019) 296–315, https://doi.org/10.1016/j.apenergy.2019.01.189. [6] A. Heller, 15 Years of R&D in central solar heating in Denmark, Sol. Energy 69 (2000) 437–447, https://doi.org/10.1016/S0038-092X(00)00118-3. [7] W. Lin, Z. Ma, H. Ren, S. Gschwander, S. Wang, Multi-objective optimisation of thermal energy storage using phase change materials for solar air systems, Renew. Energy 130 (2019) 1116–1129, https://doi.org/10.1016/j.renene.2018.08.071. [8] D. Bauer, R. Marx, J. Nußbicker-Lux, F. Ochs, W. Heidemann, H. MüllerSteinhagen, German central solar heating plants with seasonal heat storage, Sol. Energy 84 (2010) 612–623, https://doi.org/10.1016/j.solener.2009.05.013. [9] D. Bauer, R. Marx, H. Drück, Solar district heating for the built environment technology and future trends within the European project Einstein, Energy Procedia 57 (2014) 2716–2724, https://doi.org/10.1016/j.egypro.2014.10.303. [10] M. Lundh, J.O. Dalenbäck, Swedish solar heated residential area with seasonal storage in rock: initial evaluation, Renew. Energy 33 (2008) 703–711, https://doi. org/10.1016/j.renene.2007.03.024. [11] T. Tao, F. Zhang, W. Zhang, P. Wan, X. Shen, H. Li, Low cost and marketable operational experiences for a solar heating system with seasonal thermal energy storage (SHSSTES) in Hebei (China), Energy Procedia 70 (2015) 267–274, https://doi. org/10.1016/j.egypro.2015.02.123.

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