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Sensitivity analysis of hybrid power systems using Power Pinch Analysis considering Feed-in Tariff
Energy xxx (2016) 1e9
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Sensitivity analysis of hybrid power systems using Power Pinch Analysis considering Feed-in Tariff Nor Erniza Mohammad Rozali a, Sharifah Rafidah Wan Alwi b, c, *, Zainuddin Abdul Manan b, c, Jirí Jaromír Klemes d a
Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia c Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia d zma ny P Faculty of Information Technology and Bionics, Pa eter Catholic University, H-1083, Budapest, Hungary b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2016 Received in revised form 31 July 2016 Accepted 9 August 2016 Available online xxx
Feed-in Tariff (FiT) has been one of the most effective policies in accelerating the development of renewable energy (RE) projects. The amount of RE electricity in the FiT purchase agreement is an important decision that has to be made by the RE project developers. They have to consider various crucial factors associated with RE system operation as well as its stochastic nature. The presented work aims to assess the sensitivity and profitability of a hybrid power system (HPS) in cases of RE system failure or shutdown. The amount of RE electricity for the FiT purchase agreement in various scenarios was determined using a novel tool called On-Grid Problem Table based on the Power Pinch Analysis (PoPA). A sensitivity table has also been introduced to assist planners to evaluate the effects of the RE system's failure on the profitability of the HPS. This table offers insights on the variance of the RE electricity. The sensitivity analysis of various possible scenarios shows that the RE projects can still provide financial benefits via the FiT, despite the losses incurred from the penalty levied. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Power Pinch Analysis (PoPA) Hybrid power system (HPS) Renewable energy (RE) Feed-in Tariff (FiT) Sensitivity analysis
1. Introduction Power generation using renewable energy (RE) sources as an alternative to fossil fuel can contribute to the security of energy supply, apart from reducing the emissions. In order to support the development of new RE projects, an energy supply policy called the Feed-in Tariff (FiT) has been introduced. The FiT involves a longterm purchase agreement for the RE electricity sale that is offered to the RE project developers. The agreement, which offer a specified price for every kWh generated electricity is typically structured for a contract range of between 10 and 25 y [1]. Hybrid Power System (HPS) generally consists of two or more generation sources to provide electricity supply to load demands. Widespread deployment of RE sources for large scale HPS is, however, a challenging task due to the stochastic nature of RE
* Corresponding author. Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia. E-mail address: [email protected] (S.R. Wan Alwi).
sources. Project developers typically have to commit to the forecasted amount of RE electricity supply. This ensures an efficient grid management while maintaining a reliable and uninterruptible supply. If the actual electricity supplied by the project developers deviates below the committed amount of electricity supply, penalties based on the average electricity price for every kWh of deviation are levied to the project developers [1]. A systematic HPS planning and design is therefore vital in order to maximise the power generation profit and avoid cost penalty. In addition, energy management in HPS can ensure an efficient utilisation of the RE resources. Feroldi and Zumoffen [2] incorporated energy management strategy in sizing a HPS with multiple RE and bioethanol. The proposed concept was able to meet a given loss of power supply probability (LPSP) requirement with the lowest cost. Sowa et al. [3] examined operation strategies for power plants with a high share of RE, with respect to economic and technical aspects, as well as the uncertainties in generation and load. The proposed operation scheduling shows that additional flexibilities for power plant can be achieved. A number of studies implementing mathematical programming and software tools have been conducted for the planning and
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optimization of HPS. Khan et al. [4] scrutinised the effectiveness of Cuckoo Search technique for the optimization of HPS. Optimal system that satisfies the demand reliably with minimum total cost was established considering seasonal variation of load. A mixedinteger linear programming model for a cost-optimal planning of HPS was presented by Ragwitz and Huber [5]. The proposed model minimises the net present value of total electricity supply cost, and considers various applicable energy storage technologies in the design. Esfahani et al. [6] developed a simulated annealing algorithm to determine the optimum sizing of a hybrid PV-wind turbine-fuel cell system. Three decision variables namely the number of storage tanks, total swept area by wind turbine blades, and total PV area were taken into account in the model to satisfy the maximum allowable loss of power supply probability. A quantilebased simulation optimization model was proposed by Chang [7] to size a HPS while taking the upside risk into consideration. The methodology that consists of Monte Carlo simulation, quantile estimation techniques and stochastic optimizer allows an enhanced decision quality for system sizing to be achieved within a reasonable computing time. Researches on the design and optimization of HPS are often supplemented with sensitivity study. Sensitivity analysis was performed by designers to investigate the effects of parameter variations from the typical HPS operational conditions [8]. Among the vital parameters in HPS design, which are typically examined for their sensitivity are RE sources availability, RE surplus selling prices, diesel price and cost of RE systems. Orioli and Di Gangi [9] utilised Hybrid Optimization Model for Electric Renewables (HOMER) software to establish the optimal hybrid system for a range of sensitivity variables. The authors defined four input parameters for the sensitivity study, namely the load profile, annual average stream flow, annual interest rate and diesel price which varies considerably throughout the years. HOMER has also been employed by Sawhney et al. [10] to study the effects of key variables such as wind speed, solar radiation and fuel price on a stand-alone HPS. The changes in the levelised cost of energy, breakeven grid extension distance and emissions due to the variations of these variables were analysed to obtain the optimal system design. The impacts of demand and supply uncertainties on RE system design were scrutinised by Mulder et al. [11] using one-way, two-way and multiway sensitivity analyses. Performance indices of the total annualised cost, CO2 emissions and grid interaction index were developed as a combined objective. Results show that 20% variation in the four input variables gives about 26.2% maximum change of the combined objective. Pietzcker et al. [12] calculated the optimal mix of wind and solar PV power considering the various projections of wind and solar PV prices. Solar PV was found to have high sensitivity to prices, relative to that of wind. Study from Wand and Leuthold [13] however reported that, the design and cost of the optimal RE system is far more sensitive to changes in the cost of wind power, compared to the solar power. Variations in the wind power capacity cost by 25% can contribute to over than 25% changes in the optimal RE mix. Loughlin et al. [14] recently performed demand side management for an integrated renewable energy system (IRES) with varying biomass price, wind turbine cost and battery cost. It was observed that the IRES was very sensitive towards fluctuation of biomass prices. Research by Hsu [15] has been more focused to investigate the effectiveness of incentive policies for solar PV applications. Sensitivity of distributed capacity as well as the investment of distributed PV power, with respect to subsidies of distributed PV power generation, proportion of PV power investment subsidy and self-consumed PV electricity proportion were
examined based on system dynamics approach. The authors concluded that subsidies of distributed PV power could accelerate the proportion of distribute PV capacity, and the implementation should be sustained. Currently, the uncertainties associated with the amount of RE electricity available for FiT sale is one of the most crucial variables that has not received much attention in sensitivity analysis studies. Moreover, insight-based approach for on-grid HPS design considering FiT has not been developed. The insight-based technique such as the Problem Table can provide better engineering understanding through visualisation, and are typically preferred for the unit-wise and step by step build scale integration for many industrial applications. Application of Pinch Analysis for the design of renewable and isolated power systems was initiated by Bandyopadhyay [16]. The author presented the Grand Composite Curves as a representation of storage requirement as a function of time. The concept of Power Pinch Analysis (PoPA) for HPS targeting and design was also utilised by Wan Alwi et al. [17]. PoPA has so far been implemented for the targeting of power and storage allocations considering energy losses [18], to perform load shifting [19], for optimal HPS sizing [20], and for storage optimization [21]. Ho et al. [22] also applied the Pinch Analysis concept to design an off-grid HPS, and studied the effect of power consumption variations on the design variables. Recent work by Liu et al. [23] developed strategies for purchasing and selling of electricity based on the various design parameters including energy related capacity, power related capacity of energy storage and maximum grid power rating. The overall system's sensitivity to the RE system's failure and changes using PoPA has yet to be explored. Besides, previous researches on the design of HPS based on Pinch Analysis have considered the option of storing the excess RE electricity. Those approaches have neither considered the option of importing electricity to the grid nor the incorporation of FiT policy into the design. The work presented in this paper aims to assess the sensitivity of an HPS with respect to an RE system's failure or shutdown that may be due to unforeseen circumstances such as operability problems and unpredicted accidents. The amount of RE electricity for the FiT purchase agreement in various scenarios is determined using a new algebraic PoPA tool called the On-Grid Problem Table. A sensitivity table which offers insights on the variance of the RE electricity is proposed, in order to evaluate the effects of an RE system's failure on the annual profit due to the penalty incurred. 2. Methodology Hybrid power systems can be either grid connected or stand alone, and comprise of two or more generation sources including the RE. One of the major challenges in implementing HPS involving RE is the variation of the supply with time. In addition, this work covers the on-grid HPS that implement the FiT policy, in order to improve the economic benefits of HPS. Therefore, a technique that provide the overview of electricity flow, surplus and deficit is required to plan an optimal HPS incorporating FiT. The electricity generated from the alternating current (AC) and direct current (DC) sources will firstly be supplied to the load, and any surplus will be exported to the grid to get paid via the FiT. Payments for every kWh of electricity sold to the grid are guaranteed to the RE project developers, depending on the FiT rate of each source. Apart from the FiT credit, project developers should also pay attention to the penalty that may be levied for any RE electricity deviation. Though the amount of the RE electricity supply from the HPS has been targeted, unexpected shutdown of RE system may occur and influence the supply. A tool with insights on the variance of the RE
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electricity could assists planners to evaluate the effects of changes in RE system on HPS profitability. The methodology is performed in two key steps i.e. (1) Determination of the amount of the maximum RE electricity, and (2) Sensitivity analysis. 2.1. Determination of the amount of the maximum RE electricity The amount of RE electricity for normal operation without RE system failure or shutdown was determined using an extension of an algebraic PoPA tool called the Power Cascade Table (PCT) e Mohammad Rozali et al. [24]. The conventional PCT was revised to consider the operation of an on-grid HPS with combined ACDC topology, as well as to incorporate the losses from the conversions between the AC and DC electricity. Besides considering the energy losses in the construction, the new revised PCT tool known as the On-Grid Problem Table also considers the effects of FiT incorporation in the HPS. The On-Grid Problem Table provides the allocations of the maximum RE electricity supply available, for sale via the FiT. The incorporation of FiT can generate extra income to the project developers, and thus the maximum RE supply for the FiT sale should be established. Tables 1 and 2 show the power sources and demands for an Illustrative Case Study involving an on-grid HPS, to demonstrate the application of the On-Grid Problem Table. The on-grid HPS includes two RE, i.e. biomass as the AC and solar PV as the DC power sources. Note that to demonstrate the construction of the methodology in the Illustrative Case Study, the average generation from the RE is assumed for a particular time range (e.g. average solar power rating of 70 kW is available from 8 a.m. to 6 p.m.). In actual, the hourly generation from the RE should be used, as the radiation and wind speed varies and not constant throughout the day. Tables 3a and 3b show the On-Grid Problem Table for the Illustrative Case Study. Table 3a was constructed using the procedure described by Mohammad Rozali et al. [24]. Since an AC-DC coupled HPS is considered, the AC and DC electricity are computed separately as described next; 1) Column 1 lists the time interval for power sources and demands in ascending order. Column 2 shows the duration between two adjacent time intervals. 2) The 3rd and 4th columns show arrows representing power sources and power demands according to the time-interval
3
where they exist, and their corresponding power ratings. Note that the arrows are shown in different patterns, to denote the AC and DC power sources and demands. 3) The total sum of ratings for the power sources and power demands for each time-interval are given in Columns 5 and 6. Note that each column consists of both AC and DC electricity that are listed and computed separately. 4) Columns 7 and 8 list the quantities of electricity sources and demands between time intervals, calculated using Eq (1).
X
Electricity Source=Demand ¼
X
Power Rating
Time interval duration
(1)
The electricity amounts from RE sources were converted accordingly (AC to DC or DC to AC) before being supplied to the demands at a particular time interval. Conversion losses due to converter inefficiencies were assumed as 5% [25]. Table 3b shows these losses, that are computed as follows; 1) The demands (Column 8, Table 3a) are satisfied by the sources (Column 7, Table 3a) accordingly for the AC and DC electricity. Any surpluses and deficits between time intervals are calculated independently for the AC and DC power using Eq (2), and listed in Column 9. The positive and negative values represent electricity surpluses and electricity deficits respectively.
Electricity surplus=deficit ¼
X
Electricity Source
Electricity Demand
(2)
2) Eqn. (3) calculates the amount of converted electricity surpluses that is listed in Column 10. The AC (or DC) surplus is only converted if there is a deficit in the DC (or AC) demand. For example, 100 kWh of surplus AC electricity (Column 9) is available between time intervals 18e20 h. This surplus is converted to DC using Eqn. (3) to give 95 kWh (Column 10). For DC electricity surplus, similar equation as Eqn. (3) (replace the AC to DC electricity surplus and the rectifier to inverter efficiency) can be used if the amount of surplus is less than the AC deficit. Eqn. (3) is only applicable if the amount of surplus is less than the deficit. However, if the surplus is higher than the deficit, only the exact amount of the required electricity load is converted from the available surplus. This condition applies to the DC surplus between time intervals
Table 1 Power sources for the Illustrative Case Study. Power sources AC
Time, h DC
From
To
Solar
0 8
24 18
Biomass
Time interval, h
Power generated, kW
Electricity generation, kWh
24 10
90 70
2,160 700
Table 2 Power demands for the Illustrative Case Study. Power demand appliances
Time, h
AC
DC
From
To
Appliance 1
0 0 0 0 18
24 10 24 10 20
Appliance 2 Appliance 3 Appliance 4 Appliance 5
Time interval, h
Power consumed, kW
Electricity consumption, kWh
24 10 24 10 2
30 50 20 50 40
720 500 480 500 80
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Table 3 a) On-Grid Problem Table for the Illustrative Case Study. b) On-Grid Problem Table for the Illustrative Case Study (continued). a
1 Time, h
2
3
Time interval duration, h
4
5 ∑Power source rating, kW
Power rating, kW Source 90 70
Demand 20 50
6 ∑Power demand rating, kW
7
8
∑Electricity source, kWh
∑Electricity demand, kWh
AC
DC
AC
DC
AC
DC
AC
DC
2
90
0
100
50
180
0
200
100
6
90
0
100
50
540
0
600
300
2
90
70
100
50
180
140
200
100
8
90
70
0
50
720
560
0
400
2
90
0
40
50
180
0
80
100
4
90
0
0
50
360
0
0
200
30
50
40
0 2 8 10 18 20 24 AC source
DC source
AC demand
DC demand
b 9
10
11
12
Electricity surplus/deficit, kWh
Converted surplus, kWh
RE electricity, kWh
Outsourced electricity, kWh
AC
DC
AC-DC
DC-AC
AC
DC
AC
DC
20.00
100.00
0
0
0
0
20.00
100.00
60.00
300.00
0
0
0
0
60.00
300.00
20.00
40.00
0
21.05
0
18.95
0
0
720.00
160.00
0
0
720.00
160.00
0
0
100.00
100.00
95.00
0
0
0
0
5.00
360.00
200.00
210.53
0
149.47
0
0
0
869.47
178.95
80.00
405.00
8e10 h. The 40 kWh DC surplus is higher than the deficit in AC surplus (20 kWh). Eqn. (4) gives the amount of surplus to be converted in the event that the surplus is higher than the deficit. Instead of converting all 40 kWh of DC to AC, the AC deficit amount is divided by the converter efficiency as in Eqn. (4) to give only 21.05 kWh (see Column 10).
Amount of converted surplus ¼ Electricity surplus converter efficiency
(3)
Amount of electricity surplus to be converted ¼
Amount of deficit Converter efficiency
(4)
3) The amount of RE electricity available for sale via the FiT after load utilisation is shown in Column 11. It is based on the residual electricity amount that is still available, after the entire load has been satisfied in each time interval. For example between time intervals 8e10 h, 18.95 kWh DC electricity remains after an amount of 21.05 kWh is taken from its total 40 kWh surplus, to be supplied to the deficit in AC demand. The values in the last row represent the maximum RE electricity for biomass (AC) and solar (DC) that can be included in the purchase agreement.
4) Column 12 provides the amount of electricity that needs to be outsourced when the demands are not fully satisfied by the sources at each time interval. Note from Table 3b that 869.47 kWh of biomass and 178.95 kWh of solar electricity can be exported to the grid in order to be entitled to the FiT payment. These capacities indicate the targets for normal operation, without an RE system failure. In addition to the RE electricity targets, it is worthy to note that the On-Grid Table also provides the allocations of the outsourced electricity. It can be used along with the RE electricity allocations for strategising load management in the HPS, considering the trade-off between the profit from FiT with the peak and off peak electricity tariffs. 2.2. Sensitivity analysis The flexibility of HPS associated with the RE system is an essential factor to be considered in HPS design, especially for HPS incorporating the FiT policy. This is because, even though the sales of RE electricity could lead to revenues via the FiT payment, penalisation for shortage of the RE electricity may cause substantial amount of economic loss. While the stochastic nature of the RE can cause the deficit, a higher amount of RE electricity shortage, and thereby a higher penalty can occur due to the unexpected RE system failure and shutdown. A numerical tool could offer a good insights to evaluate the sensitivity of each RE system in the HPS. The
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economic feasibility of the system when an RE system fails can be assessed, and suitable measures can be taken during the design and operational stages to ensure other power supplies are not disrupted. To investigate the sensitivity of the HPS with respect to RE system failure or shutdown, two possible scenarios were assumed i.e. (1) Biomass system fails, and (2) Solar PV system fails. The OnGrid Problem Table was constructed for each scenario in order to establish the targets for RE electricity and the outsourced electricity when these scenarios occur. Based on the electricity targets from the On-Grid Problem Table, the sensitivity table that provides the variation in RE electricity and the outsourced electricity, as well as the impact on the economics from different operating scenarios is illustrated (see Table 4). The sensitivity analysis was performed on the basis of 24 h operation, assuming that each RE system takes one whole day (24 h) for the maintenance before it can return to normal operation. The stepwise construction of Table 4 is described below. 1) Column 1 lists all the possible operating scenarios considered. 2) The amount of RE electricity obtained via the On-Grid Problem Table is provided in Column 2 of the sensitivity table. 3) The total FiT prices paid from the sale of the RE electricity is then calculated for each source i.e. biomass and solar using Eqn. (5), and listed in Column 3. Malaysia's FiT rates of 0.24 RM/kWh (0.06 USD/kWh) for biomass and 1.25 RM/kWh (0.31 USD/kWh) for solar PV are used in the computation [26]. For example, the FiT paid for biomass for the normal operation is the available biomass electricity multiplied with the rate of FiT for biomass (869.47 kWh 0.06 USD/kWh ¼ USD 51.09).
FiT paid ¼ RE electricity FiT rate
(5)
4) The capacity of electricity deviation was specified as beyond 5% of the forecasted biomass electricity, and 20% of the solar electricity during normal operation [1]. Column 4 provides the magnitude of the deviation in each scenario. The values were obtained using Eqn. (6). For example in the event of failure of PV system occurs, the deviation in biomass is; 869.47(0.05 869.47)448.42 ¼ 337.58 kWh.
Electricity deviation ¼ REn ðd REn Þ REs
(6)
where REn ¼ RE electricity during normal operation; REs ¼ RE electricity during scenarios 1 and 2; d ¼ the maximum allowable deviation (5% for biomass and 20% for solar). 5) The penalty listed in Column 5 was specified as 10% of the average electricity price for every kWh of deviation, and will be levied to the project developers [1]. This translates to the penalty cost of 0.006 USD/kWh for each kWh of biomass electricity deviation, and 0.031 USD/kWh for each kWh of solar PV
5
electricity deviation. Eqn. (7) gives the amount of the levied penalty. If we have biomass deviation of 337.58 kWh, the penalty is 337.58 kWh 0.006 USD/kWh ¼ USD 2.22.
Penalty ¼ Deviation Penalty rate
(7)
6) Column 6 lists the amount of outsourced electricity, which can be obtained from the On-Grid Problem Table (see Column 12 in Table 3b). Since the power grid supplies AC electricity, the total DC electricity that need to be imported are divided by the conversion efficiency (0.95), e.g. 80 þ 405/0.95 ¼ 506.32 kWh for the normal operation. 7) The cost that need to be paid for the outsourced electricity is listed in Column 7. Each kWh of electricity outsourced is charged 0.02 USD/kWh [27]. When 506.32 kWh is outsourced, the cost of electricity is 506.32 kWh 0.02 USD/kWh ¼ USD 9.92. 8) Column 8 gives the net profit after the penalty and the outsourced electricity cost are subtracted from the total FiT prices. For example when biomass system fails, the net profit is 48.97e4.85 e 34.85 ¼ USD 9.27. Based on the sensitivity table, it can be observed that the impact of the malfunction of solar PV system on the total profit is bigger than the impact caused by the biomass system failure. This is because, when the PV system is not working, higher penalty is charged on the solar electricity deviation due to the higher solar FiT rates. On the other hand, no penalty is levied for the solar electricity when the biomass system fails, because the deviations are less than 5%. Even though the fault in RE systems requires project developers to pay for the penalty, the profit from the RE electricity sales are higher. The penalties only account for 25% of the total sales when the solar PV system fails (6.60/ 26.35 100), and 10% of the total sales when the biomass system fails (4.85/48.97 100). 3. Case Study The Case Study from Ref. [28], featuring a wind and solarpowered hybrid system is shown in Table 5. The efficiencies of solar and wind generators are assumed to be 16.4% and 95% respectively [29]. The maximum capacity for wind and solar electricity during the normal operation is first determined using the On-Grid Problem Table. Table 6a and b shows the On-Grid Problem Table for the Case Study. Note that, up to 1,994.74 kWh of wind electricity and 1,873.68 kWh of solar electricity can be sold to the grid when there is no breakdown in the RE systems. Using the targeted amount of RE electricity and the outsourced electricity from the On-Grid Problem Table, sensitivity study was performed for two scenarios i.e. (1) Wind system fails, and (2) Solar PV system fails. Malaysia's FiT rates for wind is 0.23 RM/kWh (0.06 USD/kWh) [26]. Similar to the solar PV system, a deviation of the wind electricity of above 20% could result in the
Table 4 Sensitivity table for the Illustrative Case Study. 1
2
3
4
5
6
7
8
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Biomass
Solar
Biomass
Solar
Total
Biomass
Solar
Biomass
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
869.47 0 448.42
178.95 160.00 0
51.09 0 26.35
54.77 48.97 0
105.86 48.97 26.35
e 826.00 377.58
e 0 143.16
e 4.85 2.22
e 0 4.38
e 4.85 6.60
506.32 1,778.84 631.58
9.92 34.85 12.37
95.95 9.27 7.38
Normal Biomass system fails Solar PV system fails
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Table 5 Power sources and demands for the Case Study [28]. Time, h
Time interval, h
From
To
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Source
Demand
Wind (AC)
Solar (DC)
AC load
DC load
Power rating, kW
Electricity generated, kWh
Power rating, kW
Electricity generated, kWh
Power rating, kW
Electricity consumed kWh
Power rating, kW
Electricity consumed kWh
350 1,100 100 400 800 50 150 450 200 50 300 400 400 800 600 400 400 0 300 600 0 150 200 400
350 1,100 100 400 800 50 150 450 200 50 300 400 400 800 600 400 400 0 300 600 0 150 200 400
0 0 0 0 0 0 100 150 150 400 800 350 1,000 300 700 900 300 200 150 100 50 0 0 0
0 0 0 0 0 0 100 150 150 400 800 350 1,000 300 700 900 300 200 150 100 50 0 0 0
200 100 100 200 200 200 250 300 600 500 650 550 800 600 1,050 600 450 100 300 100 600 400 700 200
200 100 100 200 200 200 250 300 600 500 650 550 800 600 1,050 600 450 100 300 100 600 200 500 200
0 100 100 0 0 0 200 150 0 100 0 200 200 500 0 0 100 450 400 800 400 650 300 300
0 100 100 0 0 0 200 150 0 100 0 200 200 500 0 0 100 450 400 800 400 650 500 300
project developers being charged a penalty of 10% of its FiT rate. Table 7 shows the results of the sensitivity analysis for the Case Study. From the analysis, it can be concluded that both RE systems can still provide financial benefits via FiT despite the losses incurred from the penalty levied. Failure in the solar PV system contributes to a higher penalty (USD 45.88), and thereby lower total profit (USD 41.94) as compared to the other scenario. The key reason of the gap is the difference in the FiT rates for both RE. Analogous to the Illustrative Case Study, RE with higher FiT rates, i.e. solar has a bigger impact on the total charged penalty even when the total deviation is lower. The penalties share over the total sales when no system shutdown takes place however is fairly high in this Case Study. When the wind system is not operating, the project developers are charged a penalty of 37% (43.11/117.59 100) from the total FiT price, while 44% (45.88/103.89 100) of the FiT profit is penalised when PV system failure occurs. The penalties can be minimised by reducing the amount of RE electricity in the FiT purchase agreement. Table 8 shows the effect of reducing the RE capacity during the normal operation by 37% for the wind and 44% for the solar electricity. Even though this solution can reduce the amount of the levied penalty, but it also affects the FiT revenue significantly. Assuming that the normal operation can run for up to 300 days annually, the annual revenue has reduced from USD 178,734 to only USD 90,399. On the other hand, merely USD 2,848 of the penalties managed to be reduced annually. Therefore, in order to maximise the economic benefits, it is more worthwhile to maintain the maximum RE capacity as targeted using the On-Grid Problem Table. 3.1. Effects of weather patterns The previous section shows the sensitivity analysis results for the daily average solar and wind energy generations. However in real cases, solar irradiation and wind speed are of stochastic nature
and may affect the results. In order to incorporate the effects of unpredictable weather throughout the year, five different weather patterns have been considered by taking into account the complementary characteristics of the solar and wind resources. The best-case scenario for the solar PV system is during sunny days. Table 5 gives the hourly solar generation. The wind energy generation is assumed to be zero (the worst case scenario for the wind system) in this climate. During the rainy days, on the other hand, the load would be solely supplied by the wind system (see AC electricity generation in Table 5) instead. The five annual cases considering days are: (1) (2) (3) (4) (5)
70% 30% 50% 60% 40%
sunny and 30% rainy. sunny and 70% rainy. sunny and 50% rainy. sunny and 40% rainy. sunny and 60% rainy.
Table 9 summarises the total profit and penalty for the six weather patterns considered, including the case with the daily average sources data. The results are calculated by assuming that the normal operation (without any of the RE systems having breakdown) can run for up to 300 d annually. Considering the profit from Table 7, the annual profit for analysis using the average RE data is USD 592.73 300 d ¼ 177,821 USD/y. Based on the penalty obtained for the different climate i.e. sunny and rainy days, it was found that the penalty levied during the sunny days is higher than the one during rainy days. Therefore, the penalty for the five weather patterns investigated is set to the maximum value by assuming that system failure (throughout the 65 d) occurs only during this climate. For the average RE data analysis, maximum annual penalty assuming wind and solar system breakdown for 65 d is (43.11 65) þ (45.88 65) ¼ 5,755 USD/y. Analysis of the five weather patterns shows that lower annual profit is achieved, indicating that there is lower amount of RE
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9 Table 6 On-Grid Problem Table for the Case Study. P P Time Time Power source Power demand h interval h rating, kW rating, kW
7
P Electricity source, kWh
P Electricity demand, kWh
Electricity surplus/deficit, kWh
Converted surplus, kWh
RE electricity, kWh
Outsourced electricity, kWh
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
1
350
0
200
0
350
0
200
0
150
0
0
0
150.00
0
0
0
1
1100
0
100
100
1100 0
100
100
1000
100
105.26 0
894.74
0
0
0
1
100
0
100
100
100
0
100
100
0
100
0
0
0
0
0
100.00
1
400
0
200
0
400
0
200
0
200
0
0
0
200.00
0
0
0
1
800
0
200
0
800
0
200
0
600
0
0
0
600.00
0
0
0
1
50
0
200
0
50
0
200
0
150
0
0
0
0
0
150.00
0
1
150
100
250
200
150
100
250
200
100
100
0
0
0
0
100.00
100.00
1
450
150
300
150
450
150
300
150
150
0
0
0
150.00
0
0
0
1
200
150
600
0
200
150
600
0
400
150
0
142.50 0
0
257.50
0
1
50
400
500
100
50
400
500
100
450
300
0
285.00 0
0
165.00
0
1
300
800
650
0
300
800
650
0
350
800
0
368.42 0
431.58
0
0
1
400
350
550
200
400
350
550
200
150
150
0
142.50 0
0
7.50
0
1
400
1,000
800
200
400
1,000
800
200
400
800
0
421.05 0
378.95
0
0
1
800
300
600
500
800
300
600
500
200
200
190.00 0
0
0
10
1
600
700
1050
0
600
700
1050
0
450
700
0
473.68 0
226.32
0
0
1
400
900
600
0
400
900
600
0
200
900
0
210.53 0
689.47
0
0
1
400
300
450
100
400
300
450
100
50
200
0
52.63
0
147.37
0
0
1
0
200
100
450
0
200
100
450
100
250
0
0
0
0
100
250
1
300
150
300
400
300
150
300
400
0
250
0
0
0
0
0
250
1
600
100
100
800
600
100
100
800
500
700
475.00 0
0
0
0
225
1
0
50
600
400
0
50
600
400
600
350
0
0
0
0
600
350
1
150
0
400
650
150
0
400
650
250
650
0
0
0
0
250
650
1
200
0
700
300
200
0
700
300
500
300
0
0
0
0
500
300
1
400
0
200
300
400
0
200
300
200
300
190.00 0
0
0
0
110
a 0 1 2 3 4 5 6 7 8 9 10 11 b 12 13 0
14 15 16 17 18 19 20 21 22 23 24 1,994.74 1,873.68 2,130
2,345
Table 7 Sensitivity table for the Case Study. 1
2
3
4
5
6
7
8
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Wind
Solar
Wind
Solar
Total
Wind
Solar
Wind
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
1,994.74 0 1,844.74
1,873.68 384.21 0
112.33 0 103.89
573.46 117.59 0
685.80 117.59 103.89
e 1,595.79 0
e 1,114.73 1,498.95
e 8.99 0
e 34.12 45.88
e 43.11 45.88
4,598.42 978.87 820.53
90.08 19.18 16.07
592.73 55.06 41.73
Normal Wind system fails Solar PV system fails
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8
N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9
Table 8 Sensitivity Table after RE electricity reduction. 1
2
3
4
5
6
7
6
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Wind
Solar
Wind
Solar
Total
Wind
Solar
Wind
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
Normal Wind system fails Solar PV system fails
309.38 0 451.68
256.18 94.07 0
71.16 0 103.89
320.21 117.59 0
391.38 117.59 103.89
e 1,010.84 0
e 452.79 837.00
e 23.25 0
e 13.86 25.62
e 19.55 25.62
4,598.42 978.87 820.53
90.08 19.18 16.07
299.90 78.47 61.88
system failure.
Table 9 Economics of the different weather patterns. Cases
Annual profit, USD/y
Annual penalty, USD/y
Average RE data Weather pattern Weather pattern Weather pattern Weather pattern Weather pattern
177,821 29,377 25,287 35,101 35,101 31,010
5,755 1,217 1,075 1,217 1,217 1,075
1 2 3 4 5
electricity available for the FiT sale. Consequently, the levied penalty is also less than the one charged when the average data is used. Based on the results obtained, it can be concluded that the wind velocity and solar radiation variations have substantial effects on the system performance. Both are highly dependent on the weather variation, which is inconsistent and unpredictable. Changes in the generation profile could influence the amount of RE electricity supply, which in turns affect the economics of the HPS. It is therefore important for the designers to collect information on the time series meteorological data and weather patterns throughout the year, in order to decide on the best sets of data to be used for the analysis.
4. Conclusion The effects of the failure of RE systems on the economics of HPS incorporating the FiT policy have been explored. A new algebraic PoPA tool called the On-Grid Problem Table has been proposed to establish the maximum RE electricity targets for the FiT purchase agreement. A sensitivity table has been introduced to provide vital insights on the changes of RE electricity for various scenarios. The sensitivity table enables the analysis of the profitability of the FiT policy implementation for an HPS by considering the profit gained from the sales as well as the penalty charged due to the deviation. The investigated Case Study have shown that despite the penalties levied, the total RE electricity sales were always higher and more profitable. Since RE electricity sales have bigger influence on the total profit as compared to the penalties reduction, reducing the amount of RE electricity to eliminate the penalties is therefore unnecessary. The weather patterns should also be considered, especially in a tropical country like Malaysia where the cloud pattern is rather inconsistent. Due to the complexity in distinguishing the electricity fed into the grid from diverse sources, the methodology presented in this paper has been limited only for HPS systems with two RE sources (AC and DC source each). Further studies to include RE electricity from more than two sources are of interest in order to enhance the profit from the FiT, and also to consider the reduction of the environmental footprints [30]. In addition, other approaches such as probabilistic technique should be explored to systematically examine the effects of various weather patterns on the HPS sensitivity with respect to RE
Acknowledgement The authors would like to thank Universiti Teknologi Malaysia (UTM) for providing the financial support through the Research University Grant under the Vote No. Q.J130000.3009.00M77 as well ny Pe ter Catholic University (PPKE), Faculty of Inforas the P azma mation Technology and Bionics, Budapest, Hungary. The authors also acknowledge the financial support from Universiti Teknologi PETRONAS, under the Short Term Internal Research Funding (0153AA-F06). References [1] Couture TD, Cory K, Kreycik C, Williams E. A policymaker's guide to feed-in tariff policy design. Washington DC, USA: National Renewable Energy Laboratory; 2010. Tech. Rep. NREL/TP-6A2e44849. [2] Feroldi D, Zumoffen D. Sizing methodology for hybrid systems based on multiple renewable power sources integrated to the energy management strategy. Int J Hydrogen Energy 2014;39(16):8609e20. [3] Sowa T, Krengel S, Koopmann S, Nowak J. Multi-criteria operation strategies of power-to-heat-systems in virtual power plants with a high penetration of renewable energies, 8th international renewable energy storage conference and exhibition (IRES 2013). Energy Procedia 2014;46:237e45. [4] Khan MRB, Jidin R, Pasupuleti J, Shaaya SA. Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea. Energy 2015;82:80e97. http:// dx.doi.org/10.1016/jenergy201412072. [5] Ragwitz M, Huber C. Feed-in systems in Germany and Spain and a comparison. Karlsruhe, Germany: Fraunhofer. Institute Systems and Innovation Research; 2005. [6] Esfahani IJ, Lee S, Yoo C. Extended-power pinch analysis (EPoPA) for integration of renewable energy systems with battery/hydrogen storages. Renew Energy 2015;80:1e14. [7] Chang K-H. A quantile-based simulation optimization model for sizing hybrid renewable energy systems. Simul Model Pract Theory 2016;66:94e103. [8] Yoshida S, Ito K, Yokoyama R. Sensitivity analysis in structure optimization of energy supply systems for a hospital. Energy Convers Manag 2007;48(11): 2836e43. [9] Orioli A, Di Gangi A. Review of the energy and economic parameters involved in the effectiveness of grid-connected PV systems installed in multi-storey buildings. Appl Energy 2014;113(0):955e69. [10] Sawhney R, Thakur K, Venkatesan B, Ji S, Upreti G, Sanseverino J. Empirical analysis of the solar incentive policy for Tennessee solar value chain. Appl Energy 2014;131(0):368e76. [11] Mulder G, Six D, Claessens B, Broes T, Omar N, Mierlo JV. The dimensioning of PV-battery systems depending on the incentive and selling price conditions. Appl Energy 2013;111(0):1126e35. [12] Pietzcker RC, Stetter D, Manger S, Luderer G. Using the sun to decarbonize the power sector: the economic potential of photovoltaics and concentrating solar power. Appl Energy 2014;135:704e20. [13] Wand R, Leuthold F. Feed-in tariffs for photovoltaics: learning by doing in Germany? Appl Energy 2011;88(12):4387e99. [14] Loughlin DH, Yelverton WH, Dodder RL, Miller CA. Methodology for examining potential technology breakthroughs for mitigating CO2 and application to centralized solar photovoltaics. Clean Technol Environ Policy 2013;15(1): 9e20. [15] Hsu C-W. Using a system dynamics model to assess the effects of capital subsidies and feed-in tariffs on solar PV installations. Appl Energy 2012;100(0):205e17. [16] Bandyopadhyay S. Design and optimization of isolated energy systems through pinch analysis. Asia-Pacific J Chem Eng 2011;6(3):518e26. [17] Wan Alwi SR, Mohammad Rozali NE, Manan ZA, Klemes JJ. A process integration targeting method for hybrid power systems. Energy 2012;44(1):6e10.
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9 [18] Mohammad Rozali NE, Wan Alwi SR, Abdul Manan Z, Klemes JJ, Hassan MY. Process integration of hybrid power systems with energy losses considerations. Energy 2013;55:38e45. [19] Mohammad Rozali NE, Wan Alwi SR, Manan ZA, Klemes JJ, Hassan MY. Peakoff-peak load shifting for hybrid power systems based on Power Pinch Analysis. Energy 2015;90:128e36. [20] Mohammad Rozali NE, Wan Alwi SR, Abdul Manan Z, Klemes JJ, Hassan MY. Optimal sizing of hybrid power systems using power pinch analysis. J Clean Prod 2014;71:158e67. [21] Mohammad Rozali NE, Wan Alwi SR, Manan ZA, Klemes JJ, Hassan MY. A process integration approach for design of hybrid power systems with energy storage. Clean Technol Environ Policy 2015;17(7):2055e72. [22] Ho WS, Khor CS, Hashim H, Macchietto S, Klemes JJ. SAHPPA: a novel power pinch analysis approach for the design of off-grid hybrid energy systems. Clean Technol Environ Policy 2013;16(5):957e70. [23] Liu WH, Wan Alwi SR, Hashim H, Lim JS, Mohammad Rozali NE, Ho WS. Sizing of Hybrid Power System with varying current type using numerical probabilistic approach. Appl Energy 2016. http://dx.doi.org/10.1016/ j.apenergy.2016.06.035. [24] Mohammad Rozali NE, Wan Alwi SR, Manan ZA, Klemes JJ, Hassan MY. Process
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Sensitivity analysis of hybrid power systems using Power Pinch Analysis considering Feed-in Tariff Nor Erniza Mohammad Rozali a, Sharifah Rafidah Wan Alwi b, c, *, Zainuddin Abdul Manan b, c, Jirí Jaromír Klemes d a
Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia c Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia d zma ny P Faculty of Information Technology and Bionics, Pa eter Catholic University, H-1083, Budapest, Hungary b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2016 Received in revised form 31 July 2016 Accepted 9 August 2016 Available online xxx
Feed-in Tariff (FiT) has been one of the most effective policies in accelerating the development of renewable energy (RE) projects. The amount of RE electricity in the FiT purchase agreement is an important decision that has to be made by the RE project developers. They have to consider various crucial factors associated with RE system operation as well as its stochastic nature. The presented work aims to assess the sensitivity and profitability of a hybrid power system (HPS) in cases of RE system failure or shutdown. The amount of RE electricity for the FiT purchase agreement in various scenarios was determined using a novel tool called On-Grid Problem Table based on the Power Pinch Analysis (PoPA). A sensitivity table has also been introduced to assist planners to evaluate the effects of the RE system's failure on the profitability of the HPS. This table offers insights on the variance of the RE electricity. The sensitivity analysis of various possible scenarios shows that the RE projects can still provide financial benefits via the FiT, despite the losses incurred from the penalty levied. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Power Pinch Analysis (PoPA) Hybrid power system (HPS) Renewable energy (RE) Feed-in Tariff (FiT) Sensitivity analysis
1. Introduction Power generation using renewable energy (RE) sources as an alternative to fossil fuel can contribute to the security of energy supply, apart from reducing the emissions. In order to support the development of new RE projects, an energy supply policy called the Feed-in Tariff (FiT) has been introduced. The FiT involves a longterm purchase agreement for the RE electricity sale that is offered to the RE project developers. The agreement, which offer a specified price for every kWh generated electricity is typically structured for a contract range of between 10 and 25 y [1]. Hybrid Power System (HPS) generally consists of two or more generation sources to provide electricity supply to load demands. Widespread deployment of RE sources for large scale HPS is, however, a challenging task due to the stochastic nature of RE
* Corresponding author. Process Systems Engineering Centre (PROSPECT), Research Institute of Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia. E-mail address: [email protected] (S.R. Wan Alwi).
sources. Project developers typically have to commit to the forecasted amount of RE electricity supply. This ensures an efficient grid management while maintaining a reliable and uninterruptible supply. If the actual electricity supplied by the project developers deviates below the committed amount of electricity supply, penalties based on the average electricity price for every kWh of deviation are levied to the project developers [1]. A systematic HPS planning and design is therefore vital in order to maximise the power generation profit and avoid cost penalty. In addition, energy management in HPS can ensure an efficient utilisation of the RE resources. Feroldi and Zumoffen [2] incorporated energy management strategy in sizing a HPS with multiple RE and bioethanol. The proposed concept was able to meet a given loss of power supply probability (LPSP) requirement with the lowest cost. Sowa et al. [3] examined operation strategies for power plants with a high share of RE, with respect to economic and technical aspects, as well as the uncertainties in generation and load. The proposed operation scheduling shows that additional flexibilities for power plant can be achieved. A number of studies implementing mathematical programming and software tools have been conducted for the planning and
http://dx.doi.org/10.1016/j.energy.2016.08.063 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9
optimization of HPS. Khan et al. [4] scrutinised the effectiveness of Cuckoo Search technique for the optimization of HPS. Optimal system that satisfies the demand reliably with minimum total cost was established considering seasonal variation of load. A mixedinteger linear programming model for a cost-optimal planning of HPS was presented by Ragwitz and Huber [5]. The proposed model minimises the net present value of total electricity supply cost, and considers various applicable energy storage technologies in the design. Esfahani et al. [6] developed a simulated annealing algorithm to determine the optimum sizing of a hybrid PV-wind turbine-fuel cell system. Three decision variables namely the number of storage tanks, total swept area by wind turbine blades, and total PV area were taken into account in the model to satisfy the maximum allowable loss of power supply probability. A quantilebased simulation optimization model was proposed by Chang [7] to size a HPS while taking the upside risk into consideration. The methodology that consists of Monte Carlo simulation, quantile estimation techniques and stochastic optimizer allows an enhanced decision quality for system sizing to be achieved within a reasonable computing time. Researches on the design and optimization of HPS are often supplemented with sensitivity study. Sensitivity analysis was performed by designers to investigate the effects of parameter variations from the typical HPS operational conditions [8]. Among the vital parameters in HPS design, which are typically examined for their sensitivity are RE sources availability, RE surplus selling prices, diesel price and cost of RE systems. Orioli and Di Gangi [9] utilised Hybrid Optimization Model for Electric Renewables (HOMER) software to establish the optimal hybrid system for a range of sensitivity variables. The authors defined four input parameters for the sensitivity study, namely the load profile, annual average stream flow, annual interest rate and diesel price which varies considerably throughout the years. HOMER has also been employed by Sawhney et al. [10] to study the effects of key variables such as wind speed, solar radiation and fuel price on a stand-alone HPS. The changes in the levelised cost of energy, breakeven grid extension distance and emissions due to the variations of these variables were analysed to obtain the optimal system design. The impacts of demand and supply uncertainties on RE system design were scrutinised by Mulder et al. [11] using one-way, two-way and multiway sensitivity analyses. Performance indices of the total annualised cost, CO2 emissions and grid interaction index were developed as a combined objective. Results show that 20% variation in the four input variables gives about 26.2% maximum change of the combined objective. Pietzcker et al. [12] calculated the optimal mix of wind and solar PV power considering the various projections of wind and solar PV prices. Solar PV was found to have high sensitivity to prices, relative to that of wind. Study from Wand and Leuthold [13] however reported that, the design and cost of the optimal RE system is far more sensitive to changes in the cost of wind power, compared to the solar power. Variations in the wind power capacity cost by 25% can contribute to over than 25% changes in the optimal RE mix. Loughlin et al. [14] recently performed demand side management for an integrated renewable energy system (IRES) with varying biomass price, wind turbine cost and battery cost. It was observed that the IRES was very sensitive towards fluctuation of biomass prices. Research by Hsu [15] has been more focused to investigate the effectiveness of incentive policies for solar PV applications. Sensitivity of distributed capacity as well as the investment of distributed PV power, with respect to subsidies of distributed PV power generation, proportion of PV power investment subsidy and self-consumed PV electricity proportion were
examined based on system dynamics approach. The authors concluded that subsidies of distributed PV power could accelerate the proportion of distribute PV capacity, and the implementation should be sustained. Currently, the uncertainties associated with the amount of RE electricity available for FiT sale is one of the most crucial variables that has not received much attention in sensitivity analysis studies. Moreover, insight-based approach for on-grid HPS design considering FiT has not been developed. The insight-based technique such as the Problem Table can provide better engineering understanding through visualisation, and are typically preferred for the unit-wise and step by step build scale integration for many industrial applications. Application of Pinch Analysis for the design of renewable and isolated power systems was initiated by Bandyopadhyay [16]. The author presented the Grand Composite Curves as a representation of storage requirement as a function of time. The concept of Power Pinch Analysis (PoPA) for HPS targeting and design was also utilised by Wan Alwi et al. [17]. PoPA has so far been implemented for the targeting of power and storage allocations considering energy losses [18], to perform load shifting [19], for optimal HPS sizing [20], and for storage optimization [21]. Ho et al. [22] also applied the Pinch Analysis concept to design an off-grid HPS, and studied the effect of power consumption variations on the design variables. Recent work by Liu et al. [23] developed strategies for purchasing and selling of electricity based on the various design parameters including energy related capacity, power related capacity of energy storage and maximum grid power rating. The overall system's sensitivity to the RE system's failure and changes using PoPA has yet to be explored. Besides, previous researches on the design of HPS based on Pinch Analysis have considered the option of storing the excess RE electricity. Those approaches have neither considered the option of importing electricity to the grid nor the incorporation of FiT policy into the design. The work presented in this paper aims to assess the sensitivity of an HPS with respect to an RE system's failure or shutdown that may be due to unforeseen circumstances such as operability problems and unpredicted accidents. The amount of RE electricity for the FiT purchase agreement in various scenarios is determined using a new algebraic PoPA tool called the On-Grid Problem Table. A sensitivity table which offers insights on the variance of the RE electricity is proposed, in order to evaluate the effects of an RE system's failure on the annual profit due to the penalty incurred. 2. Methodology Hybrid power systems can be either grid connected or stand alone, and comprise of two or more generation sources including the RE. One of the major challenges in implementing HPS involving RE is the variation of the supply with time. In addition, this work covers the on-grid HPS that implement the FiT policy, in order to improve the economic benefits of HPS. Therefore, a technique that provide the overview of electricity flow, surplus and deficit is required to plan an optimal HPS incorporating FiT. The electricity generated from the alternating current (AC) and direct current (DC) sources will firstly be supplied to the load, and any surplus will be exported to the grid to get paid via the FiT. Payments for every kWh of electricity sold to the grid are guaranteed to the RE project developers, depending on the FiT rate of each source. Apart from the FiT credit, project developers should also pay attention to the penalty that may be levied for any RE electricity deviation. Though the amount of the RE electricity supply from the HPS has been targeted, unexpected shutdown of RE system may occur and influence the supply. A tool with insights on the variance of the RE
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9
electricity could assists planners to evaluate the effects of changes in RE system on HPS profitability. The methodology is performed in two key steps i.e. (1) Determination of the amount of the maximum RE electricity, and (2) Sensitivity analysis. 2.1. Determination of the amount of the maximum RE electricity The amount of RE electricity for normal operation without RE system failure or shutdown was determined using an extension of an algebraic PoPA tool called the Power Cascade Table (PCT) e Mohammad Rozali et al. [24]. The conventional PCT was revised to consider the operation of an on-grid HPS with combined ACDC topology, as well as to incorporate the losses from the conversions between the AC and DC electricity. Besides considering the energy losses in the construction, the new revised PCT tool known as the On-Grid Problem Table also considers the effects of FiT incorporation in the HPS. The On-Grid Problem Table provides the allocations of the maximum RE electricity supply available, for sale via the FiT. The incorporation of FiT can generate extra income to the project developers, and thus the maximum RE supply for the FiT sale should be established. Tables 1 and 2 show the power sources and demands for an Illustrative Case Study involving an on-grid HPS, to demonstrate the application of the On-Grid Problem Table. The on-grid HPS includes two RE, i.e. biomass as the AC and solar PV as the DC power sources. Note that to demonstrate the construction of the methodology in the Illustrative Case Study, the average generation from the RE is assumed for a particular time range (e.g. average solar power rating of 70 kW is available from 8 a.m. to 6 p.m.). In actual, the hourly generation from the RE should be used, as the radiation and wind speed varies and not constant throughout the day. Tables 3a and 3b show the On-Grid Problem Table for the Illustrative Case Study. Table 3a was constructed using the procedure described by Mohammad Rozali et al. [24]. Since an AC-DC coupled HPS is considered, the AC and DC electricity are computed separately as described next; 1) Column 1 lists the time interval for power sources and demands in ascending order. Column 2 shows the duration between two adjacent time intervals. 2) The 3rd and 4th columns show arrows representing power sources and power demands according to the time-interval
3
where they exist, and their corresponding power ratings. Note that the arrows are shown in different patterns, to denote the AC and DC power sources and demands. 3) The total sum of ratings for the power sources and power demands for each time-interval are given in Columns 5 and 6. Note that each column consists of both AC and DC electricity that are listed and computed separately. 4) Columns 7 and 8 list the quantities of electricity sources and demands between time intervals, calculated using Eq (1).
X
Electricity Source=Demand ¼
X
Power Rating
Time interval duration
(1)
The electricity amounts from RE sources were converted accordingly (AC to DC or DC to AC) before being supplied to the demands at a particular time interval. Conversion losses due to converter inefficiencies were assumed as 5% [25]. Table 3b shows these losses, that are computed as follows; 1) The demands (Column 8, Table 3a) are satisfied by the sources (Column 7, Table 3a) accordingly for the AC and DC electricity. Any surpluses and deficits between time intervals are calculated independently for the AC and DC power using Eq (2), and listed in Column 9. The positive and negative values represent electricity surpluses and electricity deficits respectively.
Electricity surplus=deficit ¼
X
Electricity Source
Electricity Demand
(2)
2) Eqn. (3) calculates the amount of converted electricity surpluses that is listed in Column 10. The AC (or DC) surplus is only converted if there is a deficit in the DC (or AC) demand. For example, 100 kWh of surplus AC electricity (Column 9) is available between time intervals 18e20 h. This surplus is converted to DC using Eqn. (3) to give 95 kWh (Column 10). For DC electricity surplus, similar equation as Eqn. (3) (replace the AC to DC electricity surplus and the rectifier to inverter efficiency) can be used if the amount of surplus is less than the AC deficit. Eqn. (3) is only applicable if the amount of surplus is less than the deficit. However, if the surplus is higher than the deficit, only the exact amount of the required electricity load is converted from the available surplus. This condition applies to the DC surplus between time intervals
Table 1 Power sources for the Illustrative Case Study. Power sources AC
Time, h DC
From
To
Solar
0 8
24 18
Biomass
Time interval, h
Power generated, kW
Electricity generation, kWh
24 10
90 70
2,160 700
Table 2 Power demands for the Illustrative Case Study. Power demand appliances
Time, h
AC
DC
From
To
Appliance 1
0 0 0 0 18
24 10 24 10 20
Appliance 2 Appliance 3 Appliance 4 Appliance 5
Time interval, h
Power consumed, kW
Electricity consumption, kWh
24 10 24 10 2
30 50 20 50 40
720 500 480 500 80
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Table 3 a) On-Grid Problem Table for the Illustrative Case Study. b) On-Grid Problem Table for the Illustrative Case Study (continued). a
1 Time, h
2
3
Time interval duration, h
4
5 ∑Power source rating, kW
Power rating, kW Source 90 70
Demand 20 50
6 ∑Power demand rating, kW
7
8
∑Electricity source, kWh
∑Electricity demand, kWh
AC
DC
AC
DC
AC
DC
AC
DC
2
90
0
100
50
180
0
200
100
6
90
0
100
50
540
0
600
300
2
90
70
100
50
180
140
200
100
8
90
70
0
50
720
560
0
400
2
90
0
40
50
180
0
80
100
4
90
0
0
50
360
0
0
200
30
50
40
0 2 8 10 18 20 24 AC source
DC source
AC demand
DC demand
b 9
10
11
12
Electricity surplus/deficit, kWh
Converted surplus, kWh
RE electricity, kWh
Outsourced electricity, kWh
AC
DC
AC-DC
DC-AC
AC
DC
AC
DC
20.00
100.00
0
0
0
0
20.00
100.00
60.00
300.00
0
0
0
0
60.00
300.00
20.00
40.00
0
21.05
0
18.95
0
0
720.00
160.00
0
0
720.00
160.00
0
0
100.00
100.00
95.00
0
0
0
0
5.00
360.00
200.00
210.53
0
149.47
0
0
0
869.47
178.95
80.00
405.00
8e10 h. The 40 kWh DC surplus is higher than the deficit in AC surplus (20 kWh). Eqn. (4) gives the amount of surplus to be converted in the event that the surplus is higher than the deficit. Instead of converting all 40 kWh of DC to AC, the AC deficit amount is divided by the converter efficiency as in Eqn. (4) to give only 21.05 kWh (see Column 10).
Amount of converted surplus ¼ Electricity surplus converter efficiency
(3)
Amount of electricity surplus to be converted ¼
Amount of deficit Converter efficiency
(4)
3) The amount of RE electricity available for sale via the FiT after load utilisation is shown in Column 11. It is based on the residual electricity amount that is still available, after the entire load has been satisfied in each time interval. For example between time intervals 8e10 h, 18.95 kWh DC electricity remains after an amount of 21.05 kWh is taken from its total 40 kWh surplus, to be supplied to the deficit in AC demand. The values in the last row represent the maximum RE electricity for biomass (AC) and solar (DC) that can be included in the purchase agreement.
4) Column 12 provides the amount of electricity that needs to be outsourced when the demands are not fully satisfied by the sources at each time interval. Note from Table 3b that 869.47 kWh of biomass and 178.95 kWh of solar electricity can be exported to the grid in order to be entitled to the FiT payment. These capacities indicate the targets for normal operation, without an RE system failure. In addition to the RE electricity targets, it is worthy to note that the On-Grid Table also provides the allocations of the outsourced electricity. It can be used along with the RE electricity allocations for strategising load management in the HPS, considering the trade-off between the profit from FiT with the peak and off peak electricity tariffs. 2.2. Sensitivity analysis The flexibility of HPS associated with the RE system is an essential factor to be considered in HPS design, especially for HPS incorporating the FiT policy. This is because, even though the sales of RE electricity could lead to revenues via the FiT payment, penalisation for shortage of the RE electricity may cause substantial amount of economic loss. While the stochastic nature of the RE can cause the deficit, a higher amount of RE electricity shortage, and thereby a higher penalty can occur due to the unexpected RE system failure and shutdown. A numerical tool could offer a good insights to evaluate the sensitivity of each RE system in the HPS. The
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9
economic feasibility of the system when an RE system fails can be assessed, and suitable measures can be taken during the design and operational stages to ensure other power supplies are not disrupted. To investigate the sensitivity of the HPS with respect to RE system failure or shutdown, two possible scenarios were assumed i.e. (1) Biomass system fails, and (2) Solar PV system fails. The OnGrid Problem Table was constructed for each scenario in order to establish the targets for RE electricity and the outsourced electricity when these scenarios occur. Based on the electricity targets from the On-Grid Problem Table, the sensitivity table that provides the variation in RE electricity and the outsourced electricity, as well as the impact on the economics from different operating scenarios is illustrated (see Table 4). The sensitivity analysis was performed on the basis of 24 h operation, assuming that each RE system takes one whole day (24 h) for the maintenance before it can return to normal operation. The stepwise construction of Table 4 is described below. 1) Column 1 lists all the possible operating scenarios considered. 2) The amount of RE electricity obtained via the On-Grid Problem Table is provided in Column 2 of the sensitivity table. 3) The total FiT prices paid from the sale of the RE electricity is then calculated for each source i.e. biomass and solar using Eqn. (5), and listed in Column 3. Malaysia's FiT rates of 0.24 RM/kWh (0.06 USD/kWh) for biomass and 1.25 RM/kWh (0.31 USD/kWh) for solar PV are used in the computation [26]. For example, the FiT paid for biomass for the normal operation is the available biomass electricity multiplied with the rate of FiT for biomass (869.47 kWh 0.06 USD/kWh ¼ USD 51.09).
FiT paid ¼ RE electricity FiT rate
(5)
4) The capacity of electricity deviation was specified as beyond 5% of the forecasted biomass electricity, and 20% of the solar electricity during normal operation [1]. Column 4 provides the magnitude of the deviation in each scenario. The values were obtained using Eqn. (6). For example in the event of failure of PV system occurs, the deviation in biomass is; 869.47(0.05 869.47)448.42 ¼ 337.58 kWh.
Electricity deviation ¼ REn ðd REn Þ REs
(6)
where REn ¼ RE electricity during normal operation; REs ¼ RE electricity during scenarios 1 and 2; d ¼ the maximum allowable deviation (5% for biomass and 20% for solar). 5) The penalty listed in Column 5 was specified as 10% of the average electricity price for every kWh of deviation, and will be levied to the project developers [1]. This translates to the penalty cost of 0.006 USD/kWh for each kWh of biomass electricity deviation, and 0.031 USD/kWh for each kWh of solar PV
5
electricity deviation. Eqn. (7) gives the amount of the levied penalty. If we have biomass deviation of 337.58 kWh, the penalty is 337.58 kWh 0.006 USD/kWh ¼ USD 2.22.
Penalty ¼ Deviation Penalty rate
(7)
6) Column 6 lists the amount of outsourced electricity, which can be obtained from the On-Grid Problem Table (see Column 12 in Table 3b). Since the power grid supplies AC electricity, the total DC electricity that need to be imported are divided by the conversion efficiency (0.95), e.g. 80 þ 405/0.95 ¼ 506.32 kWh for the normal operation. 7) The cost that need to be paid for the outsourced electricity is listed in Column 7. Each kWh of electricity outsourced is charged 0.02 USD/kWh [27]. When 506.32 kWh is outsourced, the cost of electricity is 506.32 kWh 0.02 USD/kWh ¼ USD 9.92. 8) Column 8 gives the net profit after the penalty and the outsourced electricity cost are subtracted from the total FiT prices. For example when biomass system fails, the net profit is 48.97e4.85 e 34.85 ¼ USD 9.27. Based on the sensitivity table, it can be observed that the impact of the malfunction of solar PV system on the total profit is bigger than the impact caused by the biomass system failure. This is because, when the PV system is not working, higher penalty is charged on the solar electricity deviation due to the higher solar FiT rates. On the other hand, no penalty is levied for the solar electricity when the biomass system fails, because the deviations are less than 5%. Even though the fault in RE systems requires project developers to pay for the penalty, the profit from the RE electricity sales are higher. The penalties only account for 25% of the total sales when the solar PV system fails (6.60/ 26.35 100), and 10% of the total sales when the biomass system fails (4.85/48.97 100). 3. Case Study The Case Study from Ref. [28], featuring a wind and solarpowered hybrid system is shown in Table 5. The efficiencies of solar and wind generators are assumed to be 16.4% and 95% respectively [29]. The maximum capacity for wind and solar electricity during the normal operation is first determined using the On-Grid Problem Table. Table 6a and b shows the On-Grid Problem Table for the Case Study. Note that, up to 1,994.74 kWh of wind electricity and 1,873.68 kWh of solar electricity can be sold to the grid when there is no breakdown in the RE systems. Using the targeted amount of RE electricity and the outsourced electricity from the On-Grid Problem Table, sensitivity study was performed for two scenarios i.e. (1) Wind system fails, and (2) Solar PV system fails. Malaysia's FiT rates for wind is 0.23 RM/kWh (0.06 USD/kWh) [26]. Similar to the solar PV system, a deviation of the wind electricity of above 20% could result in the
Table 4 Sensitivity table for the Illustrative Case Study. 1
2
3
4
5
6
7
8
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Biomass
Solar
Biomass
Solar
Total
Biomass
Solar
Biomass
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
869.47 0 448.42
178.95 160.00 0
51.09 0 26.35
54.77 48.97 0
105.86 48.97 26.35
e 826.00 377.58
e 0 143.16
e 4.85 2.22
e 0 4.38
e 4.85 6.60
506.32 1,778.84 631.58
9.92 34.85 12.37
95.95 9.27 7.38
Normal Biomass system fails Solar PV system fails
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Table 5 Power sources and demands for the Case Study [28]. Time, h
Time interval, h
From
To
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Source
Demand
Wind (AC)
Solar (DC)
AC load
DC load
Power rating, kW
Electricity generated, kWh
Power rating, kW
Electricity generated, kWh
Power rating, kW
Electricity consumed kWh
Power rating, kW
Electricity consumed kWh
350 1,100 100 400 800 50 150 450 200 50 300 400 400 800 600 400 400 0 300 600 0 150 200 400
350 1,100 100 400 800 50 150 450 200 50 300 400 400 800 600 400 400 0 300 600 0 150 200 400
0 0 0 0 0 0 100 150 150 400 800 350 1,000 300 700 900 300 200 150 100 50 0 0 0
0 0 0 0 0 0 100 150 150 400 800 350 1,000 300 700 900 300 200 150 100 50 0 0 0
200 100 100 200 200 200 250 300 600 500 650 550 800 600 1,050 600 450 100 300 100 600 400 700 200
200 100 100 200 200 200 250 300 600 500 650 550 800 600 1,050 600 450 100 300 100 600 200 500 200
0 100 100 0 0 0 200 150 0 100 0 200 200 500 0 0 100 450 400 800 400 650 300 300
0 100 100 0 0 0 200 150 0 100 0 200 200 500 0 0 100 450 400 800 400 650 500 300
project developers being charged a penalty of 10% of its FiT rate. Table 7 shows the results of the sensitivity analysis for the Case Study. From the analysis, it can be concluded that both RE systems can still provide financial benefits via FiT despite the losses incurred from the penalty levied. Failure in the solar PV system contributes to a higher penalty (USD 45.88), and thereby lower total profit (USD 41.94) as compared to the other scenario. The key reason of the gap is the difference in the FiT rates for both RE. Analogous to the Illustrative Case Study, RE with higher FiT rates, i.e. solar has a bigger impact on the total charged penalty even when the total deviation is lower. The penalties share over the total sales when no system shutdown takes place however is fairly high in this Case Study. When the wind system is not operating, the project developers are charged a penalty of 37% (43.11/117.59 100) from the total FiT price, while 44% (45.88/103.89 100) of the FiT profit is penalised when PV system failure occurs. The penalties can be minimised by reducing the amount of RE electricity in the FiT purchase agreement. Table 8 shows the effect of reducing the RE capacity during the normal operation by 37% for the wind and 44% for the solar electricity. Even though this solution can reduce the amount of the levied penalty, but it also affects the FiT revenue significantly. Assuming that the normal operation can run for up to 300 days annually, the annual revenue has reduced from USD 178,734 to only USD 90,399. On the other hand, merely USD 2,848 of the penalties managed to be reduced annually. Therefore, in order to maximise the economic benefits, it is more worthwhile to maintain the maximum RE capacity as targeted using the On-Grid Problem Table. 3.1. Effects of weather patterns The previous section shows the sensitivity analysis results for the daily average solar and wind energy generations. However in real cases, solar irradiation and wind speed are of stochastic nature
and may affect the results. In order to incorporate the effects of unpredictable weather throughout the year, five different weather patterns have been considered by taking into account the complementary characteristics of the solar and wind resources. The best-case scenario for the solar PV system is during sunny days. Table 5 gives the hourly solar generation. The wind energy generation is assumed to be zero (the worst case scenario for the wind system) in this climate. During the rainy days, on the other hand, the load would be solely supplied by the wind system (see AC electricity generation in Table 5) instead. The five annual cases considering days are: (1) (2) (3) (4) (5)
70% 30% 50% 60% 40%
sunny and 30% rainy. sunny and 70% rainy. sunny and 50% rainy. sunny and 40% rainy. sunny and 60% rainy.
Table 9 summarises the total profit and penalty for the six weather patterns considered, including the case with the daily average sources data. The results are calculated by assuming that the normal operation (without any of the RE systems having breakdown) can run for up to 300 d annually. Considering the profit from Table 7, the annual profit for analysis using the average RE data is USD 592.73 300 d ¼ 177,821 USD/y. Based on the penalty obtained for the different climate i.e. sunny and rainy days, it was found that the penalty levied during the sunny days is higher than the one during rainy days. Therefore, the penalty for the five weather patterns investigated is set to the maximum value by assuming that system failure (throughout the 65 d) occurs only during this climate. For the average RE data analysis, maximum annual penalty assuming wind and solar system breakdown for 65 d is (43.11 65) þ (45.88 65) ¼ 5,755 USD/y. Analysis of the five weather patterns shows that lower annual profit is achieved, indicating that there is lower amount of RE
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N.E. Mohammad Rozali et al. / Energy xxx (2016) 1e9 Table 6 On-Grid Problem Table for the Case Study. P P Time Time Power source Power demand h interval h rating, kW rating, kW
7
P Electricity source, kWh
P Electricity demand, kWh
Electricity surplus/deficit, kWh
Converted surplus, kWh
RE electricity, kWh
Outsourced electricity, kWh
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
AC
DC
1
350
0
200
0
350
0
200
0
150
0
0
0
150.00
0
0
0
1
1100
0
100
100
1100 0
100
100
1000
100
105.26 0
894.74
0
0
0
1
100
0
100
100
100
0
100
100
0
100
0
0
0
0
0
100.00
1
400
0
200
0
400
0
200
0
200
0
0
0
200.00
0
0
0
1
800
0
200
0
800
0
200
0
600
0
0
0
600.00
0
0
0
1
50
0
200
0
50
0
200
0
150
0
0
0
0
0
150.00
0
1
150
100
250
200
150
100
250
200
100
100
0
0
0
0
100.00
100.00
1
450
150
300
150
450
150
300
150
150
0
0
0
150.00
0
0
0
1
200
150
600
0
200
150
600
0
400
150
0
142.50 0
0
257.50
0
1
50
400
500
100
50
400
500
100
450
300
0
285.00 0
0
165.00
0
1
300
800
650
0
300
800
650
0
350
800
0
368.42 0
431.58
0
0
1
400
350
550
200
400
350
550
200
150
150
0
142.50 0
0
7.50
0
1
400
1,000
800
200
400
1,000
800
200
400
800
0
421.05 0
378.95
0
0
1
800
300
600
500
800
300
600
500
200
200
190.00 0
0
0
10
1
600
700
1050
0
600
700
1050
0
450
700
0
473.68 0
226.32
0
0
1
400
900
600
0
400
900
600
0
200
900
0
210.53 0
689.47
0
0
1
400
300
450
100
400
300
450
100
50
200
0
52.63
0
147.37
0
0
1
0
200
100
450
0
200
100
450
100
250
0
0
0
0
100
250
1
300
150
300
400
300
150
300
400
0
250
0
0
0
0
0
250
1
600
100
100
800
600
100
100
800
500
700
475.00 0
0
0
0
225
1
0
50
600
400
0
50
600
400
600
350
0
0
0
0
600
350
1
150
0
400
650
150
0
400
650
250
650
0
0
0
0
250
650
1
200
0
700
300
200
0
700
300
500
300
0
0
0
0
500
300
1
400
0
200
300
400
0
200
300
200
300
190.00 0
0
0
0
110
a 0 1 2 3 4 5 6 7 8 9 10 11 b 12 13 0
14 15 16 17 18 19 20 21 22 23 24 1,994.74 1,873.68 2,130
2,345
Table 7 Sensitivity table for the Case Study. 1
2
3
4
5
6
7
8
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Wind
Solar
Wind
Solar
Total
Wind
Solar
Wind
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
1,994.74 0 1,844.74
1,873.68 384.21 0
112.33 0 103.89
573.46 117.59 0
685.80 117.59 103.89
e 1,595.79 0
e 1,114.73 1,498.95
e 8.99 0
e 34.12 45.88
e 43.11 45.88
4,598.42 978.87 820.53
90.08 19.18 16.07
592.73 55.06 41.73
Normal Wind system fails Solar PV system fails
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Table 8 Sensitivity Table after RE electricity reduction. 1
2
3
4
5
6
7
6
Operation
RE electricity, kWh
FiT paid, USD
Deviation, kWh
Penalty, USD
Wind
Solar
Wind
Solar
Total
Wind
Solar
Wind
Solar
Total
Outsourced electricity, kWh
Electricity cost, USD
Total profit, USD
Normal Wind system fails Solar PV system fails
309.38 0 451.68
256.18 94.07 0
71.16 0 103.89
320.21 117.59 0
391.38 117.59 103.89
e 1,010.84 0
e 452.79 837.00
e 23.25 0
e 13.86 25.62
e 19.55 25.62
4,598.42 978.87 820.53
90.08 19.18 16.07
299.90 78.47 61.88
system failure.
Table 9 Economics of the different weather patterns. Cases
Annual profit, USD/y
Annual penalty, USD/y
Average RE data Weather pattern Weather pattern Weather pattern Weather pattern Weather pattern
177,821 29,377 25,287 35,101 35,101 31,010
5,755 1,217 1,075 1,217 1,217 1,075
1 2 3 4 5
electricity available for the FiT sale. Consequently, the levied penalty is also less than the one charged when the average data is used. Based on the results obtained, it can be concluded that the wind velocity and solar radiation variations have substantial effects on the system performance. Both are highly dependent on the weather variation, which is inconsistent and unpredictable. Changes in the generation profile could influence the amount of RE electricity supply, which in turns affect the economics of the HPS. It is therefore important for the designers to collect information on the time series meteorological data and weather patterns throughout the year, in order to decide on the best sets of data to be used for the analysis.
4. Conclusion The effects of the failure of RE systems on the economics of HPS incorporating the FiT policy have been explored. A new algebraic PoPA tool called the On-Grid Problem Table has been proposed to establish the maximum RE electricity targets for the FiT purchase agreement. A sensitivity table has been introduced to provide vital insights on the changes of RE electricity for various scenarios. The sensitivity table enables the analysis of the profitability of the FiT policy implementation for an HPS by considering the profit gained from the sales as well as the penalty charged due to the deviation. The investigated Case Study have shown that despite the penalties levied, the total RE electricity sales were always higher and more profitable. Since RE electricity sales have bigger influence on the total profit as compared to the penalties reduction, reducing the amount of RE electricity to eliminate the penalties is therefore unnecessary. The weather patterns should also be considered, especially in a tropical country like Malaysia where the cloud pattern is rather inconsistent. Due to the complexity in distinguishing the electricity fed into the grid from diverse sources, the methodology presented in this paper has been limited only for HPS systems with two RE sources (AC and DC source each). Further studies to include RE electricity from more than two sources are of interest in order to enhance the profit from the FiT, and also to consider the reduction of the environmental footprints [30]. In addition, other approaches such as probabilistic technique should be explored to systematically examine the effects of various weather patterns on the HPS sensitivity with respect to RE
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