Optimal design of an autonomous solar–wind-pumped storage power supply system

Applied Energy xxx (2014) xxx–xxx

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Optimal design of an autonomous solar–wind-pumped storage power supply system q Tao Ma ⇑, Hongxing Yang, Lin Lu, Jinqing Peng Renewable Energy Research Group (RERG), Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China

h i g h l i g h t s  Optimal design of a pumped storage-based renewable energy power generation system.  Assessment on the techno-economic performance of the optimized microgrid system.  Sensitivity analysis on key parameters.

a r t i c l e

i n f o

Article history: Received 6 September 2014 Received in revised form 2 November 2014 Accepted 12 November 2014 Available online xxxx Keywords: Optimal design Pumped storage Hybrid solar and wind system Techno-economic evaluation Remote area power supply

a b s t r a c t Renewable energy, particularly solar and wind power integrated with microgrid technology, offers important opportunities for remote communities to provide power supply, improve local energy security and living conditions. The combination of solar, wind power and energy storage make possible the sustainable generation of energy for remote communities, and keep energy costs lower than diesel generation as well. The purpose of this study is to optimize the system design of a proposed hybrid solar–wind-pumped storage system in standalone mode for an isolated microgrid of a scale of a few hundred kW. The initial design process of the system’s major components is presented, and then optimized based on a techno-economic evaluation. The optimal system configuration under zero loss of power supply probability (LPSP) is further examined. In addition, the system performance of hybrid solar–wind, solar-alone and wind-alone systems with pumped storage under LPSP from 0% to 5% is investigated and compared. Results demonstrate that addition of wind turbine can result in a lower cost of energy (COE) and help reduce the size of energy storage. Sensitivity analysis on several key parameters is also performed to examine their effects on system COE. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Recent years the rising price of fossil fuels and concerns about the environmental consequences of CO2 emissions have resulted in emerging interest in the development of renewable energy applications [1,2]. In particular, the Fukushima nuclear accident was a turning point in the call for a transition from the risky nuclear and CO2 intensive fossil fuels to the sustainable and environmental-friendly renewable energy for power supply [3]. Therefore, a global expansion of solar and wind energy applications has been witnessed in the past decades, meanwhile the cost of renewables continues to drop. q This paper is included in the Special Issue of Energy innovations for a sustainable world edited by Prof. S.k. Chou, Prof. Umberto Desideri, Prof. Duu-Jong Lee and Prof. Yan. ⇑ Corresponding author. Tel.: +852 2766 5863; fax: +852 2765 7198. E-mail address: [email protected] (T. Ma).

However, one challenge of renewable energy utilization is its fluctuation in production and time-dependent characteristic. Flexible demand management [4–6] and smart energy management [7,8] may help but they do not fully suffice in maintaining the balance between production and demand of electricity. In this regard, energy storage technology could be an effective solution to overcome the intermittency problem of the production of renewable energy [9]. It stores the generated energy when production exceeds demand and allows for dispatching the stored energy when production is low than demand. Pumped hydro storage, as a leading energy storage technology, has been widely used in the world [10]. In recent years, a considerable amount of work [11–18] has been carried out to study large-scale pumped storage facilities integrated with the grid-connected wind or solar power system. These studies were carried out with respected to feasibility study, system hybridization, modeling, optimization, operation and management, demonstrating that the pumped storage can be

http://dx.doi.org/10.1016/j.apenergy.2014.11.026 0306-2619/Ó 2014 Elsevier Ltd. All rights reserved.

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a good partner to renewable energy integration, especially for the island power supply system. However, none of these studies explored the potential of using small-scale pumped storage of a scale at a few hundred kW in standalone hybrid renewable energy power generation systems for remote areas. Inspired by pumped storage for conventional power plants, this paper presents a novel pumped storage-based solar–wind power generation system for a remote island. In our previous study, a technical feasibility of such hybrid system has been examined in [19], demonstrating that technically the pumped storage-based renewable energy system can support a 100% energy autonomy in remote communities, while there was no economic evaluation in that work. A follow-up study on the economic analysis of the two energy storage technologies was performed in [20], showing that the pumped storage has significantly lower life cycle cost compared to battery storage, particularly when some variables are controlled such as increased storage capacity and days of autonomy, whereas this was just a case study and thus it might not be the optimal system in terms of technical and economic performance. Furthermore, the system modeling and techno-economic optimization of the pumped storage based solar PV system has been investigated in [21]. However, the wind energy contribution was not considered in that study. The present study focuses on optimizing the configuration of a standalone solar–wind-pumped storage power system through evaluating its techno-economic performance. The proposed system schematic is illustrated in Fig. 1. It consists of photovoltaic (PV) array and wind turbine (WT), pumped hydro storage, end-user and control station. The whole system is isolated from the utility grid, hence called standalone/autonomous system, aiming for remote areas where utility extension is very expensive or impossible. The physical model and operating principle of the microgrid solar–wind-pumped storage system has been examined in [19].

generator, power grid extension by undersea cable or overhead, and renewable energy, have been examined. In addition, different energy storage technologies, primarily battery and pumped storage, have been investigated [20]. The final decision was to take renewable energy, i.e. solar and wind energy or their combination, since it would be the lowest cost option when compared with remote diesel power generation. In addition, renewable energy technology is now mature enough to provide utility quality power supply at a reasonable cost [22]. In Stage 1 of this project, a 19.8 kWp PV system with battery was installed in 2008 [23]. Given that the power generation and storage capacity in stage 2 will increase to tenfold of stage 1, some problems will arise if energy is stored in battery, such as high cost, environmental problems particularly in ultimate disposal. Therefore, the pumped storage is proposed for this project to be an alternative to battery. 3. Methodology The mathematical models of PV, wind turbine, and pumped storage have been presented in another published paper of the authors [19]. The relationship between output current I and output voltage V of a PV array is modeled using the five-parameter model (Eq. (1)), which has been validated through outdoor tests [24] and in a real PV power plant [25]. 1 Vt

I ¼ Np Iph  Np I0 e



V þIRs Ns Np



! 1 

  Np V IRs þ Rp Ns Np

ð1Þ

where Iph is the photo current (A); I0 is the diode saturation current (A); Rs is the series resistance (O); Rp is the shunt/parallel resistance (O); Vt = nKT/q is the diode thermal voltage; n is the diode ideality factor; k is the Boltzmann’s constant (J/K); q is absolute value of electron’s charge (C) and T is the cell temperature (K); Ns represents the number of cells in series and Np is the number of PV strings in parallel. The power curve of the specified wind turbine (Fig. 2) is used to model its power output under different wind speeds. The pumped storage subsystem consists of a separated pump/ motor unit and a turbine/generator unit, and they are modeled based on the principle of conservation of mechanical energy,

2. Background of this study This study is based on a collaborative research project between the research group and a local power supply company for power supply in a remote island in Hong Kong. Before this study, some potential power supply solutions for this island, such as diesel

Upper reservoir

Wind turbine PV array

DC/AC End-use

Inverter Generating

Electricity out during discharging

Pumping

Generator Control station

Double penstock Turbine

Pump/Motor Lower reservoir/sea

Pumping

Electricity in during charging Fig. 1. A microgrid solar–wind system with pumped storage system [19].

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T. Ma et al. / Applied Energy xxx (2014) xxx–xxx

Solar irradiation (kWh/m 2/day)

Power output (kW)

5

4

3

2

1

700

Solar irradiation Wind resource

6

600

5

500

4

400

3

300

2

200

1

100 0

0 0

Jan Feb Mar Apr May Jun 0

2

4

6

8

10

12

14

16

Wind resource (W/m 2)

7 6

18

Jul Aug Sep Oct Nov Dec

Month

Wind speed (m/s) Fig. 3. Monthly energy density of solar and wind energy on the island [26]. Fig. 2. Power curve of the wind turbine.

including kinetic energy and potential energy. The models of energy storage system charging and discharging mode are:

gp  PRE!p ðtÞ ¼ cp  PRE!p ðtÞ qgh

ð2Þ

Pt ðtÞ ¼ gt qgh  qt ðtÞ ¼ ct  qt ðtÞ

ð3Þ

where qp(t) and qt(t) is the water flow rate in the charging and discharging model (m3/s), respectively; PRE?p(t) is the charging power from the hybrid RE generator to the pump (W); Pt(t) is the output power from turbine generator (W); gp and gt is the overall efficiency of pump/motor unit and turbine/generator unit; cp and ct is the water pumping coefficient (m3/kW h) and turbine generating coefficient(kW h/m3); h is the elevating head (m); g is the acceleration due to gravity (m/s2); and q is the density of water (kg/m3). So the quantity of water stored in the upper reservoir (UR) is:

Q UR ðtÞ ¼ Q UR ðt  1Þð1  aÞ þ

Z

t

qp ðtÞdt 

t1

Z

t

qt ðtÞdt

ð4Þ

t1

where a is the evaporation and leakage loss, which has been ignored in this study for simplification. The technical specifications of PV and wind turbine are presented in [19]. The rated power of a single PV module and a single wind turbine is 200 W and 5.2 kW, respectively. The cost information of the key components and their corresponding lifespan is summarized in Table 1. In this study, the replacement cost for the key components in the system is considered as the same as the initial capital cost, and the study period is 25 years. Hourly weather data was collected by the Hong Kong Observatory on a nearby island, including solar radiation, wind speed and ambient temperature. The yearly average solar irradiation and wind speed are 4.34 kW h/m2/day and 5.2 m/s, respectively. Fig. 3 presents the complementary nature of solar and wind energy resources by month. The summer provides a relatively good solar

Table 1 A summary of cost and specification of the major components. Items

Unit

Initial cost (US$)

Lifetime (year)

PV module Wind turbine Pump Turbines and pipes Reinforced concrete (reservoir) Inverter

200 W 5.2 kW 45 kW kW m3 5 kW

300 20,000 10,749 1000 170 4480

25 20 10 10 35 15

3.1. Initial design of the system configuration Before undertaking system simulation and optimization, initial values of the key components must be determined. Three major parameters, the number of PV module NPV, number of wind turbine NWT, and the volume of upper reservoir VUR, were initially determined based on the calculation flowchart in Fig. 5. The methods are explained as follows. Based on the mathematical models and input data, the power production from renewable energy generator can be calculated. One objective of system optimization is to minimize the magnitude of the difference between the generated power Pgen and the load demand Pdem, in order to reduce required energy storage capacity.

  DP ¼ Pgen  Pdem  ¼ jNPV PPV þ NWT PWT  Pdem j

ð5Þ

where PPV and PWT are the power generated by a single PV panel and a specified wind turbine, respectively; NPV and NWT represent the number of PV panels and wind turbines employed.

22

Spring Summer Autumn Winter

20

Electricity consumption (kW)

qp ðtÞ ¼

energy resource but poor wind condition, while the winter has a crosscurrent. The electricity consumption on this island is estimated as 250 kW h/day. Fig. 4 presents an example of load profile during four different seasons. The peak load demand appears in the late afternoon and evening.

18 16 14 12 10 8 6 4 2 4

8

12

16

20

24

Time (hour) Fig. 4. Electricity consumption of four typical days in a year.

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Wind speed

Solar radiation

Modelling of PV array output

Load demand

Modelling of wind turbine output

The energy curve of Eq. (9) can be used to find the initial volume of UR VUR. On an average day, the pump and turbine are required to cycle between the positive and negative peaks of the energy curve. Therefore, the upper reservoir in the pumped storage system should have a capacity at least equal to the difference between the positive and negative peaks of the energy curve.

Z  Z  EPHS  Max Dpdt  Min Dpdt

initial value of NWT

initial value of NPV

Hence, the required volume of the upper reservoir can be initially calculated:

V UR ¼

initial value of VUR

ð10Þ

EPHS

ð11Þ

gt qgh

3.2. Optimization

Modelling of pumped storage system Fig. 5. Flowchart to determine initial values of components (NPV, NWT, VUR).

Therefore, the total generated and required energy over a period T can be written in terms of the generated solar/wind power Egen and the load demand Edem as follows:

Egen ¼

T X ðN PV PPV þ NWT PWT Þ  t

ð6Þ

t¼1

Edem ¼

T X

Pdem  t

ð7Þ

t¼1

where T is the total time period, and t is the time interval between the successive samples taken, which in this case is 1 h. 3.1.1. The initial value of PV panels and wind turbines number In the proposed system, solar panels and the wind turbines are seen as hybrid renewable energy power generator supplying the load. The average power obtained from the power generator should be larger than the average load power required:

Pgen  Pdem

ð8Þ

Therefore a series of combination of the required number of solar panels and wind turbines can be calculated based on above principle. As the wind turbine number is expected to not too many due to its high cost, so letting it changes from 0 to 10, the corresponding PV panel numbers are then be determined. In this way, the number of the PV panels and wind turbines are initially obtained. 3.1.2. The initial volume of upper reservoir It was observed in the literature [27] that the most significant variables in the design of pumped hydro systems are the volume of the upper reservoir and the height difference between the upper and lower reservoirs. In the current study, the height difference was fixed at 60 m based on the geography feature of the island concerned. The sea provided the lower reservoir. Only the volume of the upper reservoir, therefore, had to be initially determined. In order for a generated electricity and load to be balanced over a given period of time, the curve of DE over that time must average at least zero. Note that positive values of DE indicate the availability of surplus power, and negative DE indicates a shortage of generated power. An equation relating energy to time DE can be obtained by integrating DP

DE ¼

Z

DPdt ¼ Egen  Edem

ð9Þ

3.2.1. Rationale for system optimization Standalone autonomous renewable energy systems are usually unreliable power sources due to the intermittent nature of weather conditions. A combination of renewable energy generation and energy storage can provide reliable and sustainable energy autonomy for remote areas. The combination of the two should be optimized, however, to ensure that the load can be met at all times, but still achieve the optimum balance between generator and storage capacities [28]. If a large pumped storage system is used and the renewable energy generation system is underdesigned, the upper water reservoir may be left in a discharged state for long periods of time resulting in unmet loads. This situation would be serious, especially for the primary load. In contrast, if the renewable energy generation system is oversized, the overcharging of upper reservoir may occur and waste of energy will result. Therefore, system optimization should specifically aim to size the system components as sufficient to simultaneously meet load requirement and minimize system costs. 3.2.2. Index for optimization The process of system optimization will simulate the operation of technically possible configurations, to meet load consumption and determine the most optimal one in terms of economic cost. This is the co-called technical–economic optimization [21,29]. The technical constraint is about system reliability, evaluated based on the well-known loss of load probability (LPSP) concept. LPSP is defined in Eq. (12), i.e. the percentage of time when load demand may not be satisfied under low solar irradiation, low wind speed and low stored energy in upper reservoir.

LPSP ¼

1 T

Z

T

Pfail dt

ð12Þ

0

where Pfail, either 1 or 0, is the power failure of the system. The other optimization objective is the lifecycle cost, including initial capital cost, replacement cost, maintenance cost and residual value as well. In this study, the cost of energy (COE) [30] is used to analyze the system’s economic performance. The future cash flows are discounted back to the present value using the discount rate. The calculation procedure has been presented in [21]. 3.2.3. Optimization methodology and algorithm After determination of the initial values, the performance of the system was simulated based on the input solar radiation, ambient temperature, wind speed, and load profile. System optimization was then carried out based on the techno-economic evaluation results (Fig. 6). Firstly, two fitness functions, LPSP and COE, are evaluated. If its resultant LPSP can meet the requirement and it

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has the lowest COE, the optimal system configuration is then obtained. If the system configuration cannot meet the targeted LPSP or has a higher COE, the combination is discarded, and it goes to the genetic algorithm (GA) optimization process, i.e. selection, crossover and mutation operation. A new system combination is then generated and goes to the cycle: simulation, evaluation and optimization, repeating until the desired LPSP value is met simultaneously with the lowest cost value. The parameters of GA optimization process are summarized in Table 2.

Table 2 GA optimization parameters. No.

Parameters

Value

1 2 3 4 5 6 7

Population size Algorithm Reproduction type Crossover rate Selector Mutation rate Number of generations

100 NSGA II Simple multi point 0.95 Crowded tournament 0.05 300

4. Results and discussion 4.1. Overall simulation results Together with the wind capacity ranging from 0 to 20.8 kW in steps of 5.2 kW (the rated power of one WT) and PV size from 100 to 170 kWp in steps of 10 kWp, 40 cases in total were

simulated in this section. During simulation, a zero energy deficit was required for all cases, and the initial quantity of water in the UR was assumed to be at maximum capacity. Using the simulation program developed in this work, the required minimum UR capacity can be derived for each PV and wind turbine combination. For example, in theory, if the number of wind turbines is two and PV

Solar radiation & temperature

Wind speed

initial value of NPV

initial value of NWT

Modeling PV array output

Modeling wind turbine output

Initial system configuration initial value of VUR

Load demand

Meet load ?

RE>load

Dump load

N

RE
SOC
SOC>SOCmin RE=load

Y

N

Loss of load

Y

Turn on pump

Turn on turbine Evaluating fitness function (1) LPSP

Target LPSP achieved? N

Y

Evaluating fitness function (2) COE Increase component values Lowest COE?

Y

Optimal system configuration NPV, NWT , VUR

N

Selection operation Crossover and Mutation operation New generation of system configuration Fig. 6. Flowchart of the system optimization process.

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100

80

80

25,000 60

60

20,000

SOC (%)

UR capacity (m

3

)

30,000

15,000

oversized system optimized system undersized system

40

40

10,000

110

120

PV c

130

apac

3 140

ity (k

Wp)

150

160

nu m

1 2

be r

0

W T

0 100

20

20

5,000

4

0 3096

0 3120

3144

3168

3192

Time (hour)

170

Fig. 8. SOC of the energy storage system under different system configurations. Fig. 7. Overall simulation results.

size is 120 kWp, the required minimum UR size was calculated at 6100 m3. The overall results are presented in Fig. 7. For the first scenario, there were no wind turbines, i.e. a PV-alone system with pumped storage. The lowest UR capacity (6100 m3) can be achieved when the PV size is 170 kW. The UR capacity increases gradually as the PV size decreases. For a PV of 120 kW, for example, the UR soars up to 12,700 m3. If PV size is further decreased, the UR becomes unreasonable high at over 31,300 m3. For the second scenario, a single wind turbine was assumed. Thanks to the complementary characteristics in the timing of solar and wind energy outputs, the required UR size can be obviously reduced. For the same PV size, the UR size could be reduced by about 30% when compared to the PV-alone system. Similar trends can be found for the wind turbine numbers from 2 to 4. More wind turbine number reduces both PV and UR capacity. For instance, 70 kW PV with four wind turbines can also achieve the zero power supply target, however, the UR reach to almost 25,000 m3 for this case. 4.2. Performance analysis of the optimized, overdesigned and underdesigned system The results of system optimization demonstrate that the optimal system configuration consisted of 553 PV panels (110.6 kWp), 9220 m3 upper reservoir and 1 wind turbine (5.2 kW), with a resultant COE of US$0.286/kW h (HK$2.219/ kW h). The use of only one wind turbine is because of its relatively high cost of the selected turbine, which needs to survive typhoons. If the system were applied in other areas, more wind turbines may be adopted as wind turbine cost will be reduced greatly. Currently, the general service tariff in Hong Kong is 0.973– 1.206 HK$/kW h depending on the amount of electricity consumed and power supply company fuel costs [31]. However, the tariff, including basic tariff and fuel clause charge, increases at for about 5% annually due to wider use of cleaner but more expensive fuel, and as existing electricity generation facilities are to be retired [32]. Therefore, the COE of the proposed hybrid systems on this island will be probably lower than the retail electricity price in 15-year time. Besides, if considering the construction fee of undersea cable or overhead line for extending power gird to this island, the economic benefit of the proposed autonomous renewable energy system would be significant. To examine the effectiveness of optimization, two other system configurations, i.e. oversized and undersized, were studied and

compared with the optimized system. For the oversized system, there must be large amount of dumped energy, and for the undersized system, there must exist some unmet load. Fig. 8 shows the state of charge (SOC) distribution of the energy storage system for the three systems. System configurations and the resultant performance indices, including COE, LPSP and excess energy percentage, are summarized in Table 3. It can be seen that the oversized system has a high reliability but the COE is significantly higher than the optimized system. UR for most of the time was under fully charged and therefore about 43% of excess energy had to be dumped. The undersized system cost was much lower but reliability was badly affected. The LPSP was 17%, meaning no power supply for about 4 h per day. However, 8% of the electricity produced was dumped as the energy storage capacity was limited. It is obvious, therefore, that only the optimized system under study balances both technical and economic targets to produce the optimal/best solution. 4.3. Energy surplus and deficit of the optimized system The performance of the optimal system configuration under zero LPSP was examined. Fig. 9 presents the power surplus and deficit duration over the simulated year. As seen in Fig. 9, the sum of areas B and C under the power surplus curve gives the total energy absorbed by the pumped storage charging system. Similarly, the sum of areas A and B under the power deficit curve gives the total energy provided by the pumped storage discharging system. By integrating the areas over time, the charged and discharged energy in the year was 66,902 and 35,174 kW h, respectively. Therefore, the overall efficiency was 52.5%. In addition, the required sizes of the pump and turbine can be determined based on the maximum surplus power (79.2 kW) and the maximum power deficit (27.1 kW). 4.4. Energy distribution analysis for the optimized system The energy flow for a typical day for the optimal system configuration is presented in Fig. 10. It is obvious that during daytime, PV panels produced power, while wind speed was very low, meaning that there was no wind power production, showing good complementary between the two resources. In Fig. 10, above the zero line represents the load demand, which was totally covered by the PV panels during the sunshine hours, and ensured by the wind power and energy storage system at other times. In summary, 52% of the energy demand was covered by PV panels, 2% by wind turbine and

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T. Ma et al. / Applied Energy xxx (2014) xxx–xxx Table 3 System configuration and resultant performance index.

Oversized system Optimized system Undersized system

PV (kW)

UR (m3)

WT (kW)

COE ($)

LPSP (%)

Excess energy (%)

140 110.6 80

14,000 9220 40,000

10.4 5.2 5.2

0.369 0.286 0.233

0 0 16.9

42.9 19.7 8.0

6000

Power surplus Power deficit 5000

Hours per year (h)

4000

A

3000

2000

B 1000

C 0 0

10

20

30

40

50

60

70

80

Power (kW) Fig. 9. Annual duration curves of power surplus and deficit.

46% by the energy storage system. In such a way, the combined system contributes a continuous power supply. In addition, below the zero line in Fig. 10 represents the charging power, totally provided by the PV surplus power (38% of PV production). No power was stored in the evening because of low wind speed and the absence of solar radiation. 4.5. Comparison with PV-pumped storage and wind-pumped storage system After the double-objective optimization, the lowest COE values for the hybrid solar–wind-pumped storage system and the solarpumped storage system [21] for different power supply reliabilities were obtained. Fig. 11 depicts the COE values as a function of LPSP

from 0% to 5%. For a critical load-the power supply should be uninterruptible such as telecommunication equipment, the LPSP should be set at 0, while for a noncritical load which can be unmet or deferred later such as ice-maker, the LPSP maybe set in the range of 0–5%, to balance costs against reliability. It is obvious that the hybrid solar and wind system can achieve lower costs compared to the solar system alone. The COE difference becomes significant as the LPSP increases. The cost of the wind turbines was quite high because they had to have sufficient stability to survive typhoons. Only one wind turbines was used in the hybrid system and hence the wind power contribution to total renewable energy production was not very high. However, the benefit of just that adding one wind turbine was significant, in that it made such a hybrid system more economically and technically viable. Therefore, to design an optimal power supply system, a combination of wind and solar energy sources should be considered. Wind powered pumped storage was also studied. However, as displayed in Table 4, a wind turbine alone system would be extremely expensive. The number of wind turbines and required energy storage capacity were also high due to the different distributions during the 24 h of wind power generation and load demand. The study has demonstrated that the feasibility of hybrid solar– wind energy system heavily depends on solar radiation and wind energy availability at the site, and the cost of the PV panels and wind turbines. It should be acknowledged that if the wind speed is extremely low or the wind turbine cost is remarkably high, the solar-pumped system may be better than a solar–wind-pumped storage system, but usually integrating PV and wind energy produced in a complementary manner at different periods in the day can reduce energy storage capacity and lead to high energy supply reliability [33–35]. 4.6. Sensitivity analysis Finally, a sensitivity analysis was performed in relation to some input parameters: solar panel cost, wind turbine cost, UR

Charging power Discharging power WT power PV power

30 25

0.30

20

PV-WT-pumped storage PV-pumped storage

0.29

15 10

COE ($)

Power (kW)

0.28 5 0

0.27

-5 -10 0.26

-15 -20

0.25

-25 2

4

6

8

10

12

14

16

18

Time (hour) Fig. 10. Energy distributions for a typical day.

20

22

24

0

1

2

3

4

5

LPSP (%) Fig. 11. System COE under various LPSP.

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Table 4 Optimal system configurations of different system types. PV (kW)

WT (kW)

UR (m3)

COE ($)

Excess energy (%)

LPSP = 0% Solar–wind–pumped storage Solar-pumped storage Wind-pumped storage

110.6 119 0

5.2 0 286

9220 13,205 29,552

0.286 0.289 1.376

19.7 12.2 85.6

LPSP = 5% Solar–wind–pumped storage Solar-pumped storage Wind-pumped storage

98.8 112.8 0

5.2 0 20

5032 7200 28,800

0.257 0.262 0.621

15.1 11.4 61.4

Decrease by 25% Increase by 25%

Wind energy

Solar energy

UR cost

WT cost

PV panel cost

Load demand -25

-20

-15

-10

-5

0

5

10

15

20

25

alone system, and significantly lower than the wind-alone system. Only one wind turbine was considered in the hybrid system as the wind turbine cost was extremely high and it has to withstand the typhoon on the island. However, the benefit of adding one wind turbine was significant in that it makes such a hybrid system more economically and technically viable. Therefore, to design an optimal power supply system, a combination of wind and solar energy should be considered. In addition, energy balance analysis indicates that the overall efficiency of the pumped storage was 52.5%. Sensitivity analysis shows that the key contribution to system cost was the load demand. In conclusion, the renewable energy based pumped storage systems could provide stable and continuous power output for remote areas. Acknowledgements

COE ($) Fig. 12. Sensitivity analysis results on several key parameters (the influence factor is ±25%).

construction cost, load demand, solar energy and wind energy resources. The effects of a ±25% deviation in the parameter values on the COE results were calculated, and the results are displayed in Fig. 12. The load demand was a key parameter in determining the system capacity and cost. It can be seen that a 25% increase in load had a higher impact (+25%) on COE than the impact (20%) of a 25% decrease. For the costs of the key components, COE was quite sensitive to PV cost. In particular a 25% increase in PV panel cost produces a 10% higher COE value. However, the changes in COE for the 25% deviation of wind turbine cost were small because the single wind turbine was always needed in the optimal system configurations, and it is worthy of note that the cost of the wind turbine was only 68% of that of the whole system. The 25% variation in UR construction cost resulted in a 4% increase or decrease in COE. A deviation of 25% in wind speed affected the COE by 1% in either direction, and the effect of solar energy variation was more significant as regards system cost. A 13% increase in COE resulted from a 25% decrease in solar radiation. Overall, the results in Fig. 12 demonstrate that some significant contributors to the system economic cost include load demand, RE resource availabilities, and the cost of key components.

5. Conclusions This paper presents a techno-economic analysis of the standalone hybrid solar–wind-pumped storage system for an isolated microgrid. The effectiveness of the proposed system and optimization method was examined through comparison with undersized and oversized system. This study demonstrates that the system after optimization can achieve reasonable performance. COE of the hybrid solar–wind system was slightly lower than the solar-

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Please cite this article in press as: Ma T et al. Optimal design of an autonomous solar–wind-pumped storage power supply system. Appl Energy (2014), http://dx.doi.org/10.1016/j.apenergy.2014.11.026