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Optimisation and techno-economic analysis of autonomous photovoltaic–wind hybrid energy systems in comparison to single photovoltaic and wind systems
Energy Conversion and Management 43 (2002) 2453–2468 www.elsevier.com/locate/enconman
Optimisation and techno-economic analysis of autonomous photovoltaic–wind hybrid energy systems in comparison to single photovoltaic and wind systems A.N. Celik * Department of Mechanical Engineering, School of Engineering and Architecture, Mustafa Kemal University, 31050 Antakya, Hatay, Turkey Received 30 June 2001; accepted 26 November 2001
Abstract A techno-economic analysis for autonomous small scale photovoltaic–wind hybrid energy systems is undertaken for optimisation purposes in the present paper. The answer to the question whether a hybrid photovoltaic–wind or a single photovoltaic or wind system is techno-economically better is also sought. Monthly analysis of 8 year long measured hourly weather data shows that solar and wind resources vary greatly from one month to the next. The monthly combinations of these resources lead to basically three types of months: solar-biased month, wind-biased month and even month. This, in turn, leads to energy systems in which the energy contributions from photovoltaic and wind generators vary greatly. The monthly and yearly system performances simulations for different types of months show that the system performances vary greatly for varying battery storage capacities and different fractions of photovoltaic and wind energy. As well as the system performance, the optimisation process of such hybrid systems should further consist of the system cost. Therefore, the system performance results are combined with system cost data. The total system cost and the unit cost of the produced electricity (for a 20 year system lifetime) are analysed with strict reference to the yearly system performance. It is shown that an optimum combination of the hybrid photovoltaic–wind energy system provides higher system performance than either of the single systems for the same system cost for every battery storage capacity analysed in the present study. It is also shown that the magnitude of the battery storage capacity has important bearings on the system performance of single photovoltaic and wind systems. The single photovoltaic system performs better than a single wind system for 2 day storage capacity, while the single wind system performs better for 1.25 day storage capacity for the same system cost. Ó 2002 Elsevier Science Ltd. All rights reserved.
*
Tel.: +90-532-2277353; fax: +90-326-2455499. E-mail address: [email protected] (A.N. Celik).
0196-8904/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 1 9 6 - 8 9 0 4 ( 0 1 ) 0 0 1 9 8 - 4
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Keywords: Solar energy; Wind energy; Single system; Hybrid system; Solar-biased month; Optimisation of the hybrid energy system; Techno-economic analysis; Unit cost of electricity
1. Introduction The search for a more reliable and less costly renewable energy system has brought about the hybrid use of two energy sources: solar and wind energy. For a photovoltaic–wind hybrid system, the techno-economical efficiency is mainly dependent on the solar and wind energy resources, which are highly variable in time and site specific. The problems caused by the variable nature of these resources can be partially overcome by integration of the two resources into an optimum combination. The strength of one source could overcome the weakness of the other during a certain period of time. This is apparent by realising the fact that in many areas, more solar radiation and less wind are available during the summer months, and similarly, more wind and less solar radiation are available during the winter. The primary aim of combining more than one renewable converter is then to design techno-economically more effective systems. As stated by Kellogg et al. [1] and Seeling-Hochmuth [2], reduction, to a minimum, in the required storage capacity, when one of the optimum combinations of photovoltaic and wind energy is used, is another advantage of hybrid systems for a given site. Single or hybrid systems of different energy sources (solar, wind, Diesel, etc.) are the only way to generate electricity in some regions of developing countries. On the other hand, they are an alternative way to supply electricity, especially in remote areas of developed countries. However, only limited experience exists with the operation of photovoltaic–wind hybrid energy systems. Protogeropoulos et al. [3] state that the benefits of combining solar and wind energy resources are obvious. However, there are also problems that stem from the increased complexity of the system in comparison with single energy systems. This complexity, brought about by the use of two different resources together, makes the hybrid systems more difficult to analyse. The solar radiation and wind speed being highly location dependent, the sizing of such hybrid systems requires comprehensive analysis of these variables for a given location in relation to the system cost for different combinations of the two converters. The use of either a single or a hybrid system is strongly dependent on the solar radiation and wind speed potentials and the load demand in a given location. It is necessary to establish the photovoltaic and wind energy contributions to the load in the case of a hybrid system for optimisation purposes. Once decided in favour of a hybrid system, the optimisation of the hybrid system gains importance to run the hybrid system effectively. This is because a certain ratio of energy to load (ELR) can be obtained from several different combinations of photovoltaic panels and wind turbines. Therefore, one of the optimum combinations amongst many different ones must be used. Otherwise, the hybrid system will not be satisfactory in terms of performance cost effectiveness. 2. A literature survey: hybrid energy systems as alternative to single systems The fact that the electrical energy requirements for remote applications may be too great to allow the cost effective use of autonomous single photovoltaic or single wind systems has moti-
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vated researchers to develop more effective systems combining these power sources to form a hybrid system. The system performance and optimisation of such hybrid systems have been the subjects of research in this field. Protogeropoulos et al. [3] present a general methodology for the sizing and optimisation of solar photovoltaic–wind power systems. Two scenarios are examined to illustrate calculation of the relative contributions of photovoltaic and wind energy for stand alone hybrid systems. Scenario 1 uses the annual average monthly values. With Scenario 2, the renewable components are sized with respect to the worst renewable months. Both scenarios take into account the actual energy yield from the renewable energy sources in combination with the energy demand by systematically varying the relative sizes of the renewable energy components size determined by availability of components on the market. A techno-economic combination is then found by applying cost data to disclose the system with the lowest overall system cost. Beyer and Langer present a simplified design method in [4] for photovoltaic–wind hybrid energy systems. They first develop equations for the performance curves of photovoltaic and wind systems separately. An approach is then presented that focuses on determination of the combinations of generators and storage battery that ascertain a given system reliability in a hybrid system. Beyer and Langer conclude that, using the simple measure of investment costs as an optimisation criterion, photovoltaic–wind hybrid energy systems are recommended for an average 20 W load in the entire region of northwestern Europe for the two random sources of energy, which are individually less reliable, could, as a whole, have higher reliability. However, in the Mediterranean region, the hybrid solution is restricted to a smaller number of sites with better wind conditions. Markvart describes a procedure in [5] that theoretically determines the sizes of the photovoltaic array and wind turbine in a photovoltaic and wind hybrid energy system. Using the measured values of solar and wind energy at a given location, the method employs a simple graphical construction of the two generators that satisfies the energy demand throughout the year. If d is assumed to be the average daily load demand, the daily energy condition is given by the following equation, d 6 Waw þ Sas ;
ð1Þ
where W and S are the available yearly average wind and solar energy and aw and as are the sizes of the wind and solar converters, respectively. The main aim is to establish the range of values of aw and as which fulfil the equation at all times of the year using the average values of W , S and d. The system cost is given by hybrid generator cost ¼ cs as þ cw aw
ðshould be minimumÞ;
ð2Þ
where cs and cw represent the costs of photovoltaic and wind energy generators per unit power of the output rating. Considering just winter and summer, if a graph is drawn showing aw and as at different co-ordinates, the boundary of the two lines connecting the winter and summer conditions defines the solution region for the hybrid system. This could be repeated for each of the 12 months. Again, the optimum system is within the boundary of the 12 lines. Combining the
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technical and economical analyses, finally, an optimum solution is derived. Markvart [5] concludes that for a range of costs of the solar and wind energy systems, the hybrid system represents the most cost effective solution. In this model, the system is sized where the energy to load ratio (ELR, i.e. ratio of energy produced by the renewable components to energy demand) is equal to unity. Although the model theoretically proves that the hybrid system is less costly than the single photovoltaic or wind system, the inefficiency of the model lies in the fact that it gives no insight into the level of loss-of-load and the storage problem. Obviously, a photovoltaic–wind combination is not the only hybrid system available. Either of them could be effectively combined with other types of power generators, such as the photovoltaic–Diesel [6] and wind–Diesel [7] generator. Furthermore, depending on the requirement and the availability of energy sources, more than two of the sources could be combined, such as photovoltaic–wind–Diesel generator [8]. The selection process for hybrid power sources at a given site is dependent on a combination of many factors, including the load demand, site topography, seasonal availability of energy sources, cost of energy storage and delivery, seasonal energy requirements, etc.
3. Energy contribution of the components and system performance definitions 3.1. Photovoltaic and wind fractions In a hybrid photovoltaic–wind energy system the term ‘total produced energy’ is non-specific in the sense that the photovoltaic and wind contributions are not known. The term ‘fraction’ specifies this contribution. The photovoltaic fraction fPV and wind fraction fWG are given by the following: fPV ¼
EPV ; ET
fWG ¼
EWG : ET
ð3aÞ
ð3bÞ
Then knowing from ET ¼ EPV þ EWG
ð4Þ
that the total energy (ET ) is determined from the photovoltaic and wind energies (EPV and EWG , respectively), the following is written, fPV þ fWG ¼ 1;
ð5Þ
where the point fPV ¼ 1 corresponds to a single photovoltaic system in which all the energy is contributed by the photovoltaic system. Similarly, the point fPV ¼ 0 corresponds to a single wind system. Therefore, except for these boundary combinations, the remaining combinations correspond to a hybrid system.
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3.2. Autonomy definition The system performance is defined by the term ‘autonomy’, which is one minus the ratio of the total number of hours in which loss-of-load occurs to the total hours of operation, given by A¼1
HLOL : Htot
ð6Þ
In the present paper, the photovoltaic–wind energy system simulation program of the Solar Energy Unit (SEU–ARES) of Cardiff University has been used [3]. The same system settings as in the experimental hybrid system, as described by Celik in [9], installed at Tal-y-Bont (TyB, a remote site near Cardiff, UK) are assumed for the system investigated in this paper. The validated power curves of the photovoltaic modules and the wind generator with respect to solar radiation and wind speed are used in the simulation program.
4. Optimal and non-optimal combinations The optimum combination of photovoltaic and wind energy in a hybrid system varies as the solar radiation and wind speed potentials vary during the time in question: for example, hourly, monthly, seasonally or yearly. Therefore, if the system is designed to supply electricity throughout a year, the hybrid energy system should be designed according to the yearly solar and wind resources rather than those of any other period of time. Similarly, if the system is to supply power in a predetermined season or a month, then the seasonal or monthly solar and wind resources should be considered. Eight year long measured hour-by-hour weather data from five different locations (Cardiff, Canberra, Davos, Athens and Ankara) have been used in the present paper for analysing the optimal and non-optimal combinations of photovoltaic and wind resources in a hybrid system. The monthly statistics of the available 8 year long solar radiation and wind speed data show that the resources vary greatly from one month to the next. This section establishes the fundamentals for defining the terms ‘optimal’ and ‘non-optimal’ combinations in a photovoltaic–wind energy system on a monthly basis. The monthly average daily specific energies produced by the photovoltaic and wind generators are presented in Fig. 1 for three different months representing three main combinations of solar and wind resources, which are:
Fig. 1. Three different months representing three main combinations of solar and wind resources.
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Table 1 Photovoltaic fraction statistics consisting of a total of 96 months fPV
Percentage
0.08–0.4 0.4–0.6 0.6–0.7
33% 52% 15%
1. May of Cardiff 1991, in which the solar energy resource almost equals the wind energy resource in terms of available energy per square metre. This month typifies an optimum combination in which the system performance is enhanced by the use of a hybrid system. 2. January of Cardiff 1996, in which the solar energy resource is very little, while the wind speed potential is quite high. This is an example of a wind-biased month and the lack of solar energy is rectified by the wind energy. 3. August of Canberra, in which the solar energy resource is much higher than the wind energy resource. This is an example of a solar-biased month in which the solar energy rectifies the lack of wind energy. Photovoltaic fraction statistics, consisting of a total of 96 months, are shown in Table 1. The specific photovoltaic energy (photovoltaic energy output per square metre) is quite comparable to the specific wind energy for 52% of the months, similar to May shown in Fig. 1. For 33% of the months, the specific photovoltaic energy is much smaller than the specific wind energy. These months are comparable to January shown in Fig. 1. For the remaining 15% of the months, the specific photovoltaic energy is quite high compared to the specific wind energy, similar to August in Fig. 1. It should be noted that 55% of the months analysed are wind-biased and the remaining 45% are solar-biased in terms of the specific energies. Therefore, in 55% of the months, a possible hybrid system is wind-biased, and in the rest of the months, 45%, it is solar-biased, providing the same amount of photovoltaic and wind converters are used. Overall, a 55% and 45% distribution shows a relatively uniform distribution in terms of the specific photovoltaic and wind energy outputs. The system performances corresponding to each type of month will be analysed next for a varying range of photovoltaic and wind fractions. 4.1. Optimal combination The first of the three main combinations is the May of Cardiff 1991 data, in which the specific solar and wind energy outputs are nearly equal. This is an example month in which the hybrid use of photovoltaic and wind energy is most efficient. Fig. 2 shows some possible combinations of photovoltaic and wind energy, including single photovoltaic and wind systems, for the battery to load ratio (BLR) of 1.5. The simulations are run assuming a 24 h constant 15 W load. Each continuous line represents a fixed value of the photovoltaic fraction. Along any curve, the photovoltaic and wind energy contribution to the hybrid system is constant. The bottom curves correspond to the photovoltaic fraction values of 0.0 and 1.0, respectively, while the upper curves belong to the photovoltaic fractions of 0.4, 0.5 and 0.6. The photovoltaic fraction values of 0.4, 0.5 and 0.6 are the optimal combinations of photovoltaic and wind energy for ELRs larger than a
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Fig. 2. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for an even month.
certain limit. The increase in system autonomy brought about by the hybrid use of photovoltaic and wind energy is evident. After the ELR of 1.0 the hybrid system offers 20–25% more autonomy than the single systems do. However, it is noted that at low ELRs (0.2–0.7), the single photovoltaic system provides higher autonomy figures than a hybrid system. This must be considered in a system that can afford an autonomy level as low as 70%. The hybrid system autonomies, in general, are higher than the single system autonomies for the same values of ELR. This confirms that the use of two different energy resources at the same time generally leads to a more consistent system performance. This is because the wind energy is distributed over 24 hours, which complements the solar energy prevailing only during daylight. Thus, the produced energy and the load demand match more closely, and the system works more efficiently. 4.2. Wind-biased month Fig. 3 shows the monthly autonomy values of the photovoltaic and/or wind energy system for a highly wind-biased month, January of Cardiff 1996, for the BLR of 1.5. The single photovoltaic system can provide a maximum of 27% monthly autonomy, whereas the single wind system provides monthly autonomy values as high as 81% for the ELR of 2.0. The high wind speed prevailing during this month results in 10 times more specific wind energy than the specific photovoltaic energy. Therefore, in order to acquire the same amount of photovoltaic energy (i.e. to obtain an fPV value of 0.5) within the hybrid system in this month, 10 times as much
Fig. 3. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for a wind-biased month.
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photovoltaic converter as wind converter should be used. Even then, as seen from the figure, the autonomy of the hybrid system for the fPV value of 0.5 is well under that of the single wind system (fPV ¼ 0). Evidently, in the case of using such a large size of photovoltaic converter for the sake of a month, a possible hybrid system would be immensely oversized for the rest of the year. The system cost would be extensively high too. Two important points are worth making. The first is the advantage of a hybrid system over the single systems. If a hybrid system was not an option and the project had to use only the photovoltaic converter, a high level of autonomy could be achieved only by excessively increasing the photovoltaic converter size or/and the storage capacity. The latter possibility would not contribute to achieving a high level of autonomy, knowing that the previous month was December, which is a poor solar month. Therefore, the battery storage would have most probably been depleted. The only option left is to increase both the photovoltaic converter size and the battery storage size, which will, in turn, result in too costly a system. The second point is that the single wind system is already able to achieve a monthly system autonomy level as high as 81%. While the best option is the single wind system for this highly wind-biased month, the photovoltaic fraction values between 0.2 and 0.0 also produce as high autonomy figures as the single wind system. 4.3. Solar-biased month The monthly autonomy values of the photovoltaic and/or wind energy system for a solar-biased month, February of Canberra, are presented in Fig. 4 for the BLR of 1.5. During this month, the specific photovoltaic energy output was more than twice that of the specific wind energy output. Compared to 10 times the difference in the wind-biased month, a ratio of 2 is the case for the solar-biased month. The single photovoltaic system provides autonomy values up to 91%, while the bottom curve represents the single wind system, providing up to 66% of monthly autonomy. The top curves correspond to the photovoltaic fraction values between 0.7 and 0.95, supplying over 95% monthly autonomy values for the ELR of 2.0 and higher. The highest monthly autonomy values occur where the fPV ¼ 0:77. For the lower ELRs (less than 0.6), the single wind system returns higher autonomy figures than the hybrid and single photovoltaic systems. It is observed from Figs. 2–4 that the monthly system autonomies rise rapidly where the ELR is between 0.2 and 1.0. A slow increase in the system autonomy follows up to an ELR of 2.0. After
Fig. 4. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for a solar-biased month.
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this point, the system autonomies increase slightly. Therefore, it could be concluded that systems must be sized for ELRs between 1.0 and 2.0. Beyond the ELR of 2.0, the system cost would increase sharply to gain a further few percent of autonomy. Therefore, systems sized beyond the ELR of 2.0 would be techno-economically non-optimal. 5. Annual system autonomy simulations Having analysed the optimal and non-optimal combinations of solar and wind resources in a hybrid energy system on a monthly basis, the system autonomy is studied on a yearly basis in this part of the paper. One year long measured hourly weather data of Cardiff 1996, measured at the TyB site, in which the yearly average specific photovoltaic and wind energy outputs were 1.90 and 2.52 kWh/m2 , is used. The monthly photovoltaic and wind energy outputs are given in Fig. 5. The annual autonomy values are presented for various photovoltaic fractions for the BLR of 1.5 in Fig. 6. It is observed from the figure that while the lower curves represent the boundary combinations of solar and wind resources (0.0, 0.1, 0.9, 1.0), the upper curves correspond to the optimum photovoltaic fraction range where the fPV values are between 0.4 and 0.5. The difference between the upper and the lower curve autonomy levels is as high as 16%, accentuating the importance of the photovoltaic fraction used. It is observed that the hybrid system provides a higher autonomy value than a single photovoltaic or a single wind system at this ELR. At small
Fig. 5. Monthly energy outputs of Cardiff 1996 weather data.
Fig. 6. Yearly system autonomies versus ELR for different fractions of photovoltaic energy for the BLR of 1.5.
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ELRs (0.0–0.5), the single photovoltaic and the hybrid systems with high photovoltaic fraction values (0.9–0.8) provide higher autonomy figures than the hybrid energy system with lower photovoltaic fractions (0.4, 0.5, 0.6). At this low ELR, therefore, a single photovoltaic or a hybrid system with a high photovoltaic fraction value (0.9–0.8) should be preferred. It should, however, be noted that the autonomy level at this ELR range is too small for an autonomous hybrid system for many applications.
6. Performance–total cost analysis for hybrid and single systems It was shown that at high ELRs, the hybrid photovoltaic–wind energy system provides more autonomy than the single systems. However, the performance analysis alone is insufficient, for the system cost is mostly the governing design criterion. Therefore, the performance data are combined with the cost data in this part of the paper. The costs are £58.75 per photovoltaic panel (rated output of 10 W at 1 kW h/m2 ) of 0.3 m2 each and £327 per wind generator (rated output of 50 W at 10 m/s) of 0.65 m2 swept area. The characteristic parameters for a 24 A h lead acid battery with a unit cost of £25 will be used. A total of £200 battery controller cost is also added into the cost of the system. The component capital costs refer to 2001 prices in the UK. In calculating the cost of the system a 20 year lifetime and a 5% installation, maintenance and engineering cost of the initial hardware cost are also assumed. In the following figures of cost versus performance for varying BLRs, each data point, of a total of four, along the lines is determined by a different sizing scenario. The first point is by the ‘yearly average scenario’, which sizes the systems at a point where the ELR is equal to unity. The second point is sized by the ‘plus standard deviation scenario’. This scenario uses the yearly average areas of photovoltaic and wind converters (as in the yearly average scenario) plus the corresponding standard deviations (rPV and rWG ) of the monthly areas. The third point is by the ‘worst month scenario’. This scenario chooses the worst month in which the largest total area of photovoltaic and wind generator occurs. The fourth point is by the ‘worst months scenario’. The worst months are the ones in a year that require the largest photovoltaic and wind converter sizes to meet the load. Fig. 7 shows the yearly autonomy levels and the corresponding system costs for the single photovoltaic, single wind and one of the near optimal combinations of the hybrid energy system for the BLR of 2. For the same system cost, the hybrid system returns the highest annual autonomy values and proves most optimal in terms of the performance–cost relationship. For example, looking at Fig. 7, for the cost of £3000, the hybrid system provides 97% yearly autonomy, while the single photovoltaic system provides 90% and the single wind system provides only 73%. The single photovoltaic system returns higher autonomy figures than the single wind system for the same system cost. For example, 80% autonomy level is achieved with a £1600 system cost for the photovoltaic system, while the same system autonomy can be achieved with £3600 for the wind system. On average, the hybrid system provides 96% autonomy while the single photovoltaic and wind systems supply 87% and 77%, respectively, for the BLR of 2. One of the most important points Fig. 7 shows is that achieving a further small increase in the system autonomy in the high autonomy region (over 95%) means a sharp increase in the system cost, especially for the hybrid and single photovoltaic systems. For example, the hybrid system can achieve 97% yearly au-
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Fig. 7. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0.
tonomy level with a system cost of £3000, and an extra 2% autonomy requires an extra £1500 investment. A similar analysis for a lower BLR (1.50) shows that the hybrid system is still superior to the single systems, as shown in Fig. 8. In comparison to the previous figure the hybrid system supplies even higher autonomy values than the single systems. On average, the hybrid system provides 86% autonomy, while the single photovoltaic and wind systems supply 67% and 68%, respectively. The most noticeable difference, when compared to the previous BLR, is that amongst the single systems, the photovoltaic system no longer provides higher autonomy values than the wind energy system at all cost levels. At around £3700, where the photovoltaic and wind lines intersect, the photovoltaic and the wind energy systems both provide the same level of yearly autonomy. Therefore, the photovoltaic system with the total cost less than £3700 would have provided more yearly autonomy than the single wind system for this particular location. Beyond the total system cost of £3700, the wind system provides higher autonomy values than the photovoltaic system. The yearly system autonomy values and the system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25 are presented in Fig. 9. For this BLR, the single wind system is now superior to the single photovoltaic system. The hybrid energy system still returns
Fig. 8. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5.
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Fig. 9. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25.
higher autonomy values in comparison to the single systems. While the single wind system provides, on average, 59% yearly autonomy, the photovoltaic system provides only 46%, 13% less yearly autonomy than the single wind system, thus being the least likely option to choose for this BLR. The hybrid system, with an optimal combination of photovoltaic and wind generator, provides an average of 71% yearly autonomy for the BLR of 1.25.
7. Performance–unit cost analysis for hybrid and single systems Unit cost of the produced energy is analysed as a function of the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for varying BLRs. Fig. 10 shows the unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0. For the same yearly system autonomy, the unit cost of electricity is most by the single wind system and least by the hybrid system. It is seen from the same figure that the hybrid system returns the lowest unit cost values to supply the same level of autonomy as the single systems. In other words, the hybrid system supplies more autonomy than the single systems for the same unit cost value. For example, as seen in the figure,
Fig. 10. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0.
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with the unit cost of £1.4 the single wind system supplies only 58% yearly autonomy, while the single photovoltaic system supplies 92%. For the same unit cost value, the hybrid system provides 98% yearly system autonomy. For the single photovoltaic system, the unit cost of electricity rises sharply from £1.0 to over £1.5 between the yearly autonomy levels of 87% and 92%. This means that the unit cost of the electricity rises sharply in return for a little increase in the system autonomy. A similar sharp increase is observed for the hybrid system too: the unit cost is doubled from £0.85 to £1.70 between the yearly system autonomy values of 89% and 99%. Another notable point in the figure is that the unit cost of electricity for 80% yearly system autonomy is £0.75 when produced by the single photovoltaic system, while the unit cost is £1.75 for the same level of yearly autonomy when produced by the single wind system. The unit cost of the produced electricity versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5 is presented in Fig. 11. The hybrid system is the best option for this BLR, for it produces the least costly electricity for the same level of yearly autonomy. For this particular BLR and location, if an autonomous system is expected to supply a yearly autonomy level of 90%, the hybrid system is the only option because the single systems cannot supply that level of autonomy. The single photovoltaic system supplies 60% yearly system autonomy with a unit cost of £1.0. It is £1.7 when the same level of yearly autonomy is supplied by the single wind system. At around the yearly autonomy level of 73%, the electricity produced by the single photovoltaic and wind systems both costs £2.0. Beyond this level of autonomy, the single wind system produces the electricity at a lower cost. Fig. 12 shows the unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25. The hybrid system provides much higher yearly system autonomies than the single systems for the same unit cost of electricity. For example, with a unit cost of £1.8, the single photovoltaic and wind systems return 44% and 46% yearly autonomies, respectively, while the hybrid system provides 75% yearly autonomy. Overall, the single photovoltaic system produces the most costly electricity for this BLR. As the yearly autonomy level goes from 40% to 50%, the unit cost of the electricity increases rapidly from £1.35 to £2.85. The unit cost of the electricity by the single photovoltaic system increases exponentially for an autonomy level more than 50%. The cost of the electricity from the single wind system increases from £1.75 to £2.25 linearly between the autonomy levels of 43% and
Fig. 11. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5.
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Fig. 12. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25.
Table 2 For a 20 year lifetime, the ratio of cost to the system autonomy (cost per percent autonomy) for single photovoltaic, single wind and hybrid energy systems for varying BLRs Battery to load ratio (BLR) Photovoltaic system Wind system Hybrid system
2.0
1.5
1.25
1.55 1.45 2.07
1.99 1.65 2.31
2.90 2.02 2.65
64%. Beyond this level of autonomy, the unit cost of the electricity rises sharply for the single wind system. A further design parameter that combines the unit cost and yearly system autonomy is defined here as the ‘cost per percent autonomy’, which is calculated over a 20 year system lifetime. The ratios of cost to the system autonomy (cost per percent autonomy) are presented in Table 2 for single photovoltaic, single wind and hybrid energy systems for varying BLRs. The cost per percent autonomy increases as the BLR decreases for the single and hybrid systems. This is especially accentuated in the single photovoltaic system, where the cost per percent autonomy increases 87% as the BLR decreases from 2.0 to 1.25. The cost per percent autonomy increases only 28% for the single wind system, while it increases 39% for the hybrid energy system from the BLR of 2.0 to 1.25. Overall, the per percent autonomy is least costly when produced by the hybrid system for every BLR and highest when produced by the single wind system. This is due to the fact that the site studied is a typical poor wind site with the yearly average hourly wind speed value of 2.18 m/s.
8. Conclusions This paper has addressed the optimisation of photovoltaic–wind hybrid energy systems in terms of a performance–cost relationship. A similar performance–cost relationship analysis has been performed to determine whether the hybrid photovoltaic–wind energy system or the single
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photovoltaic or wind system is better. The numerical analysis has been based on 1996 weather data from the TyB site of Cardiff, UK, and considers the basic system settings at this experimental site. Three main combinations of solar and wind energy have been analysed on a monthly basis: an even month, a highly wind-biased month and a solar-biased month. The system autonomies have been derived for each case, using an example month. For the even month, it was shown that the photovoltaic fractions of 0.4, 0.5 and 0.6 are the optimal combinations for the hybrid photovoltaic–wind energy system. The example month for the wind-biased case has shown that the smaller values of the photovoltaic fraction (0.0–0.2) provide the highest autonomy figures. For the solar-biased month, it was observed that the photovoltaic fraction values between 0.7 and 0.9 offer the highest monthly autonomy values. A careful examination of the monthly and yearly autonomy curves suggests that the optimum design point is in the range where the ELR is between 1.0 and 2.0 for any BLR. Therefore, the photovoltaic–wind hybrid energy systems sized beyond this point would fall into the non-optimal range, resulting in techno-economically ineffective systems. Hybrid systems sized within the optimum range of ELR return, on average, 93% annual autonomy, while the single photovoltaic and wind systems supply 87% and 65%, respectively, for the BLR of 2, for the same system cost. However, as the BLR decreases to 1.5, the difference between the single photovoltaic and wind system reduces to 8%. The single wind system supplies 5% more autonomy than the single photovoltaic system for the BLR of 1.25 for the same system cost. The second design parameter used to evaluate the quality of the system in terms of technoeconomics has been the unit cost of the produced electricity. Amongst the BLRs analysed, the unit cost of the produced electricity is lowest for 2 day battery storage. For this BLR, the single wind system returns the highest unit cost values. For the single photovoltaic system, the unit cost of electricity rises sharply from £1.0 to over £1.5 between the yearly autonomy levels of 87% and 92%. This means that the unit cost of the electricity rises sharply in return for a little increase in the system autonomy. Overall, the hybrid system returns the lowest unit cost values. Looking at the relationship between the system performance–system cost and system performance–unit cost, the hybrid system proves to be techno-economically better than either of the single systems for every BLR analysed in the present paper. This is a consequence, as expected, of the more reliable hybrid system behaviour by combining two less reliable resources. References [1] Kellogg W, Nehrir MH, Venkataramanan G, Gerez V. Optimal unit sizing for a hybrid wind/photovoltaic generating system. Electr Power Syst Res 1996;39:35–8. [2] Seeling-Hochmuth GC. A combined optimisation concept for the design and operation strategy of hybrid-PV energy systems. Sol Energy 1997;61(2):77–87. [3] Protogeropoulos C, Brinkworth BJ, Marshall RH. Sizing and techno-economical optimisation for hybrid solar photovoltaic/wind power systems with battery storage. Int J Energy Res 1997;21:1–15. [4] Beyer HG, Langer C. A method for the identification of configurations of PV/wind hybrid systems for the reliable supply of small loads. Sol Energy 1996;57(5):381–91. [5] Markvart T. Sizing of hybrid photovoltaic–wind energy systems. Sol Energy 1996;57(4):277–81. [6] Ashari M, Nayar CV. An optimum dispatch strategy using set points for a photovoltaic (PV)–Diesel–battery hybrid power system. Sol Energy 1999;66(1):1–9.
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[7] Elhadidy MA, Shaahid SM. Optimal sizing of battery for hybrid (wind þ Diesel) power systems. Renew Energy 1999;18:77–86. [8] Elhadidy MA, Shaahid SM. Parametric study of hybrid (wind þ solar þ Diesel) power generating systems. Renew Energy 2000;21:129–39. [9] Celik AN. The system performance and sizing of autonomous photovoltaic, wind and the hybrid energy systems. Ph.D. Thesis, Division of Mechanical Engineering and Energy Studies, University of Wales, Cardiff, 1998 [Chapter 2, p. 19–51].
Optimisation and techno-economic analysis of autonomous photovoltaic–wind hybrid energy systems in comparison to single photovoltaic and wind systems A.N. Celik * Department of Mechanical Engineering, School of Engineering and Architecture, Mustafa Kemal University, 31050 Antakya, Hatay, Turkey Received 30 June 2001; accepted 26 November 2001
Abstract A techno-economic analysis for autonomous small scale photovoltaic–wind hybrid energy systems is undertaken for optimisation purposes in the present paper. The answer to the question whether a hybrid photovoltaic–wind or a single photovoltaic or wind system is techno-economically better is also sought. Monthly analysis of 8 year long measured hourly weather data shows that solar and wind resources vary greatly from one month to the next. The monthly combinations of these resources lead to basically three types of months: solar-biased month, wind-biased month and even month. This, in turn, leads to energy systems in which the energy contributions from photovoltaic and wind generators vary greatly. The monthly and yearly system performances simulations for different types of months show that the system performances vary greatly for varying battery storage capacities and different fractions of photovoltaic and wind energy. As well as the system performance, the optimisation process of such hybrid systems should further consist of the system cost. Therefore, the system performance results are combined with system cost data. The total system cost and the unit cost of the produced electricity (for a 20 year system lifetime) are analysed with strict reference to the yearly system performance. It is shown that an optimum combination of the hybrid photovoltaic–wind energy system provides higher system performance than either of the single systems for the same system cost for every battery storage capacity analysed in the present study. It is also shown that the magnitude of the battery storage capacity has important bearings on the system performance of single photovoltaic and wind systems. The single photovoltaic system performs better than a single wind system for 2 day storage capacity, while the single wind system performs better for 1.25 day storage capacity for the same system cost. Ó 2002 Elsevier Science Ltd. All rights reserved.
*
Tel.: +90-532-2277353; fax: +90-326-2455499. E-mail address: [email protected] (A.N. Celik).
0196-8904/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 1 9 6 - 8 9 0 4 ( 0 1 ) 0 0 1 9 8 - 4
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Keywords: Solar energy; Wind energy; Single system; Hybrid system; Solar-biased month; Optimisation of the hybrid energy system; Techno-economic analysis; Unit cost of electricity
1. Introduction The search for a more reliable and less costly renewable energy system has brought about the hybrid use of two energy sources: solar and wind energy. For a photovoltaic–wind hybrid system, the techno-economical efficiency is mainly dependent on the solar and wind energy resources, which are highly variable in time and site specific. The problems caused by the variable nature of these resources can be partially overcome by integration of the two resources into an optimum combination. The strength of one source could overcome the weakness of the other during a certain period of time. This is apparent by realising the fact that in many areas, more solar radiation and less wind are available during the summer months, and similarly, more wind and less solar radiation are available during the winter. The primary aim of combining more than one renewable converter is then to design techno-economically more effective systems. As stated by Kellogg et al. [1] and Seeling-Hochmuth [2], reduction, to a minimum, in the required storage capacity, when one of the optimum combinations of photovoltaic and wind energy is used, is another advantage of hybrid systems for a given site. Single or hybrid systems of different energy sources (solar, wind, Diesel, etc.) are the only way to generate electricity in some regions of developing countries. On the other hand, they are an alternative way to supply electricity, especially in remote areas of developed countries. However, only limited experience exists with the operation of photovoltaic–wind hybrid energy systems. Protogeropoulos et al. [3] state that the benefits of combining solar and wind energy resources are obvious. However, there are also problems that stem from the increased complexity of the system in comparison with single energy systems. This complexity, brought about by the use of two different resources together, makes the hybrid systems more difficult to analyse. The solar radiation and wind speed being highly location dependent, the sizing of such hybrid systems requires comprehensive analysis of these variables for a given location in relation to the system cost for different combinations of the two converters. The use of either a single or a hybrid system is strongly dependent on the solar radiation and wind speed potentials and the load demand in a given location. It is necessary to establish the photovoltaic and wind energy contributions to the load in the case of a hybrid system for optimisation purposes. Once decided in favour of a hybrid system, the optimisation of the hybrid system gains importance to run the hybrid system effectively. This is because a certain ratio of energy to load (ELR) can be obtained from several different combinations of photovoltaic panels and wind turbines. Therefore, one of the optimum combinations amongst many different ones must be used. Otherwise, the hybrid system will not be satisfactory in terms of performance cost effectiveness. 2. A literature survey: hybrid energy systems as alternative to single systems The fact that the electrical energy requirements for remote applications may be too great to allow the cost effective use of autonomous single photovoltaic or single wind systems has moti-
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vated researchers to develop more effective systems combining these power sources to form a hybrid system. The system performance and optimisation of such hybrid systems have been the subjects of research in this field. Protogeropoulos et al. [3] present a general methodology for the sizing and optimisation of solar photovoltaic–wind power systems. Two scenarios are examined to illustrate calculation of the relative contributions of photovoltaic and wind energy for stand alone hybrid systems. Scenario 1 uses the annual average monthly values. With Scenario 2, the renewable components are sized with respect to the worst renewable months. Both scenarios take into account the actual energy yield from the renewable energy sources in combination with the energy demand by systematically varying the relative sizes of the renewable energy components size determined by availability of components on the market. A techno-economic combination is then found by applying cost data to disclose the system with the lowest overall system cost. Beyer and Langer present a simplified design method in [4] for photovoltaic–wind hybrid energy systems. They first develop equations for the performance curves of photovoltaic and wind systems separately. An approach is then presented that focuses on determination of the combinations of generators and storage battery that ascertain a given system reliability in a hybrid system. Beyer and Langer conclude that, using the simple measure of investment costs as an optimisation criterion, photovoltaic–wind hybrid energy systems are recommended for an average 20 W load in the entire region of northwestern Europe for the two random sources of energy, which are individually less reliable, could, as a whole, have higher reliability. However, in the Mediterranean region, the hybrid solution is restricted to a smaller number of sites with better wind conditions. Markvart describes a procedure in [5] that theoretically determines the sizes of the photovoltaic array and wind turbine in a photovoltaic and wind hybrid energy system. Using the measured values of solar and wind energy at a given location, the method employs a simple graphical construction of the two generators that satisfies the energy demand throughout the year. If d is assumed to be the average daily load demand, the daily energy condition is given by the following equation, d 6 Waw þ Sas ;
ð1Þ
where W and S are the available yearly average wind and solar energy and aw and as are the sizes of the wind and solar converters, respectively. The main aim is to establish the range of values of aw and as which fulfil the equation at all times of the year using the average values of W , S and d. The system cost is given by hybrid generator cost ¼ cs as þ cw aw
ðshould be minimumÞ;
ð2Þ
where cs and cw represent the costs of photovoltaic and wind energy generators per unit power of the output rating. Considering just winter and summer, if a graph is drawn showing aw and as at different co-ordinates, the boundary of the two lines connecting the winter and summer conditions defines the solution region for the hybrid system. This could be repeated for each of the 12 months. Again, the optimum system is within the boundary of the 12 lines. Combining the
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technical and economical analyses, finally, an optimum solution is derived. Markvart [5] concludes that for a range of costs of the solar and wind energy systems, the hybrid system represents the most cost effective solution. In this model, the system is sized where the energy to load ratio (ELR, i.e. ratio of energy produced by the renewable components to energy demand) is equal to unity. Although the model theoretically proves that the hybrid system is less costly than the single photovoltaic or wind system, the inefficiency of the model lies in the fact that it gives no insight into the level of loss-of-load and the storage problem. Obviously, a photovoltaic–wind combination is not the only hybrid system available. Either of them could be effectively combined with other types of power generators, such as the photovoltaic–Diesel [6] and wind–Diesel [7] generator. Furthermore, depending on the requirement and the availability of energy sources, more than two of the sources could be combined, such as photovoltaic–wind–Diesel generator [8]. The selection process for hybrid power sources at a given site is dependent on a combination of many factors, including the load demand, site topography, seasonal availability of energy sources, cost of energy storage and delivery, seasonal energy requirements, etc.
3. Energy contribution of the components and system performance definitions 3.1. Photovoltaic and wind fractions In a hybrid photovoltaic–wind energy system the term ‘total produced energy’ is non-specific in the sense that the photovoltaic and wind contributions are not known. The term ‘fraction’ specifies this contribution. The photovoltaic fraction fPV and wind fraction fWG are given by the following: fPV ¼
EPV ; ET
fWG ¼
EWG : ET
ð3aÞ
ð3bÞ
Then knowing from ET ¼ EPV þ EWG
ð4Þ
that the total energy (ET ) is determined from the photovoltaic and wind energies (EPV and EWG , respectively), the following is written, fPV þ fWG ¼ 1;
ð5Þ
where the point fPV ¼ 1 corresponds to a single photovoltaic system in which all the energy is contributed by the photovoltaic system. Similarly, the point fPV ¼ 0 corresponds to a single wind system. Therefore, except for these boundary combinations, the remaining combinations correspond to a hybrid system.
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3.2. Autonomy definition The system performance is defined by the term ‘autonomy’, which is one minus the ratio of the total number of hours in which loss-of-load occurs to the total hours of operation, given by A¼1
HLOL : Htot
ð6Þ
In the present paper, the photovoltaic–wind energy system simulation program of the Solar Energy Unit (SEU–ARES) of Cardiff University has been used [3]. The same system settings as in the experimental hybrid system, as described by Celik in [9], installed at Tal-y-Bont (TyB, a remote site near Cardiff, UK) are assumed for the system investigated in this paper. The validated power curves of the photovoltaic modules and the wind generator with respect to solar radiation and wind speed are used in the simulation program.
4. Optimal and non-optimal combinations The optimum combination of photovoltaic and wind energy in a hybrid system varies as the solar radiation and wind speed potentials vary during the time in question: for example, hourly, monthly, seasonally or yearly. Therefore, if the system is designed to supply electricity throughout a year, the hybrid energy system should be designed according to the yearly solar and wind resources rather than those of any other period of time. Similarly, if the system is to supply power in a predetermined season or a month, then the seasonal or monthly solar and wind resources should be considered. Eight year long measured hour-by-hour weather data from five different locations (Cardiff, Canberra, Davos, Athens and Ankara) have been used in the present paper for analysing the optimal and non-optimal combinations of photovoltaic and wind resources in a hybrid system. The monthly statistics of the available 8 year long solar radiation and wind speed data show that the resources vary greatly from one month to the next. This section establishes the fundamentals for defining the terms ‘optimal’ and ‘non-optimal’ combinations in a photovoltaic–wind energy system on a monthly basis. The monthly average daily specific energies produced by the photovoltaic and wind generators are presented in Fig. 1 for three different months representing three main combinations of solar and wind resources, which are:
Fig. 1. Three different months representing three main combinations of solar and wind resources.
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Table 1 Photovoltaic fraction statistics consisting of a total of 96 months fPV
Percentage
0.08–0.4 0.4–0.6 0.6–0.7
33% 52% 15%
1. May of Cardiff 1991, in which the solar energy resource almost equals the wind energy resource in terms of available energy per square metre. This month typifies an optimum combination in which the system performance is enhanced by the use of a hybrid system. 2. January of Cardiff 1996, in which the solar energy resource is very little, while the wind speed potential is quite high. This is an example of a wind-biased month and the lack of solar energy is rectified by the wind energy. 3. August of Canberra, in which the solar energy resource is much higher than the wind energy resource. This is an example of a solar-biased month in which the solar energy rectifies the lack of wind energy. Photovoltaic fraction statistics, consisting of a total of 96 months, are shown in Table 1. The specific photovoltaic energy (photovoltaic energy output per square metre) is quite comparable to the specific wind energy for 52% of the months, similar to May shown in Fig. 1. For 33% of the months, the specific photovoltaic energy is much smaller than the specific wind energy. These months are comparable to January shown in Fig. 1. For the remaining 15% of the months, the specific photovoltaic energy is quite high compared to the specific wind energy, similar to August in Fig. 1. It should be noted that 55% of the months analysed are wind-biased and the remaining 45% are solar-biased in terms of the specific energies. Therefore, in 55% of the months, a possible hybrid system is wind-biased, and in the rest of the months, 45%, it is solar-biased, providing the same amount of photovoltaic and wind converters are used. Overall, a 55% and 45% distribution shows a relatively uniform distribution in terms of the specific photovoltaic and wind energy outputs. The system performances corresponding to each type of month will be analysed next for a varying range of photovoltaic and wind fractions. 4.1. Optimal combination The first of the three main combinations is the May of Cardiff 1991 data, in which the specific solar and wind energy outputs are nearly equal. This is an example month in which the hybrid use of photovoltaic and wind energy is most efficient. Fig. 2 shows some possible combinations of photovoltaic and wind energy, including single photovoltaic and wind systems, for the battery to load ratio (BLR) of 1.5. The simulations are run assuming a 24 h constant 15 W load. Each continuous line represents a fixed value of the photovoltaic fraction. Along any curve, the photovoltaic and wind energy contribution to the hybrid system is constant. The bottom curves correspond to the photovoltaic fraction values of 0.0 and 1.0, respectively, while the upper curves belong to the photovoltaic fractions of 0.4, 0.5 and 0.6. The photovoltaic fraction values of 0.4, 0.5 and 0.6 are the optimal combinations of photovoltaic and wind energy for ELRs larger than a
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Fig. 2. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for an even month.
certain limit. The increase in system autonomy brought about by the hybrid use of photovoltaic and wind energy is evident. After the ELR of 1.0 the hybrid system offers 20–25% more autonomy than the single systems do. However, it is noted that at low ELRs (0.2–0.7), the single photovoltaic system provides higher autonomy figures than a hybrid system. This must be considered in a system that can afford an autonomy level as low as 70%. The hybrid system autonomies, in general, are higher than the single system autonomies for the same values of ELR. This confirms that the use of two different energy resources at the same time generally leads to a more consistent system performance. This is because the wind energy is distributed over 24 hours, which complements the solar energy prevailing only during daylight. Thus, the produced energy and the load demand match more closely, and the system works more efficiently. 4.2. Wind-biased month Fig. 3 shows the monthly autonomy values of the photovoltaic and/or wind energy system for a highly wind-biased month, January of Cardiff 1996, for the BLR of 1.5. The single photovoltaic system can provide a maximum of 27% monthly autonomy, whereas the single wind system provides monthly autonomy values as high as 81% for the ELR of 2.0. The high wind speed prevailing during this month results in 10 times more specific wind energy than the specific photovoltaic energy. Therefore, in order to acquire the same amount of photovoltaic energy (i.e. to obtain an fPV value of 0.5) within the hybrid system in this month, 10 times as much
Fig. 3. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for a wind-biased month.
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photovoltaic converter as wind converter should be used. Even then, as seen from the figure, the autonomy of the hybrid system for the fPV value of 0.5 is well under that of the single wind system (fPV ¼ 0). Evidently, in the case of using such a large size of photovoltaic converter for the sake of a month, a possible hybrid system would be immensely oversized for the rest of the year. The system cost would be extensively high too. Two important points are worth making. The first is the advantage of a hybrid system over the single systems. If a hybrid system was not an option and the project had to use only the photovoltaic converter, a high level of autonomy could be achieved only by excessively increasing the photovoltaic converter size or/and the storage capacity. The latter possibility would not contribute to achieving a high level of autonomy, knowing that the previous month was December, which is a poor solar month. Therefore, the battery storage would have most probably been depleted. The only option left is to increase both the photovoltaic converter size and the battery storage size, which will, in turn, result in too costly a system. The second point is that the single wind system is already able to achieve a monthly system autonomy level as high as 81%. While the best option is the single wind system for this highly wind-biased month, the photovoltaic fraction values between 0.2 and 0.0 also produce as high autonomy figures as the single wind system. 4.3. Solar-biased month The monthly autonomy values of the photovoltaic and/or wind energy system for a solar-biased month, February of Canberra, are presented in Fig. 4 for the BLR of 1.5. During this month, the specific photovoltaic energy output was more than twice that of the specific wind energy output. Compared to 10 times the difference in the wind-biased month, a ratio of 2 is the case for the solar-biased month. The single photovoltaic system provides autonomy values up to 91%, while the bottom curve represents the single wind system, providing up to 66% of monthly autonomy. The top curves correspond to the photovoltaic fraction values between 0.7 and 0.95, supplying over 95% monthly autonomy values for the ELR of 2.0 and higher. The highest monthly autonomy values occur where the fPV ¼ 0:77. For the lower ELRs (less than 0.6), the single wind system returns higher autonomy figures than the hybrid and single photovoltaic systems. It is observed from Figs. 2–4 that the monthly system autonomies rise rapidly where the ELR is between 0.2 and 1.0. A slow increase in the system autonomy follows up to an ELR of 2.0. After
Fig. 4. Monthly autonomy versus the ELR for different fractions of photovoltaic and wind energy for a solar-biased month.
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this point, the system autonomies increase slightly. Therefore, it could be concluded that systems must be sized for ELRs between 1.0 and 2.0. Beyond the ELR of 2.0, the system cost would increase sharply to gain a further few percent of autonomy. Therefore, systems sized beyond the ELR of 2.0 would be techno-economically non-optimal. 5. Annual system autonomy simulations Having analysed the optimal and non-optimal combinations of solar and wind resources in a hybrid energy system on a monthly basis, the system autonomy is studied on a yearly basis in this part of the paper. One year long measured hourly weather data of Cardiff 1996, measured at the TyB site, in which the yearly average specific photovoltaic and wind energy outputs were 1.90 and 2.52 kWh/m2 , is used. The monthly photovoltaic and wind energy outputs are given in Fig. 5. The annual autonomy values are presented for various photovoltaic fractions for the BLR of 1.5 in Fig. 6. It is observed from the figure that while the lower curves represent the boundary combinations of solar and wind resources (0.0, 0.1, 0.9, 1.0), the upper curves correspond to the optimum photovoltaic fraction range where the fPV values are between 0.4 and 0.5. The difference between the upper and the lower curve autonomy levels is as high as 16%, accentuating the importance of the photovoltaic fraction used. It is observed that the hybrid system provides a higher autonomy value than a single photovoltaic or a single wind system at this ELR. At small
Fig. 5. Monthly energy outputs of Cardiff 1996 weather data.
Fig. 6. Yearly system autonomies versus ELR for different fractions of photovoltaic energy for the BLR of 1.5.
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ELRs (0.0–0.5), the single photovoltaic and the hybrid systems with high photovoltaic fraction values (0.9–0.8) provide higher autonomy figures than the hybrid energy system with lower photovoltaic fractions (0.4, 0.5, 0.6). At this low ELR, therefore, a single photovoltaic or a hybrid system with a high photovoltaic fraction value (0.9–0.8) should be preferred. It should, however, be noted that the autonomy level at this ELR range is too small for an autonomous hybrid system for many applications.
6. Performance–total cost analysis for hybrid and single systems It was shown that at high ELRs, the hybrid photovoltaic–wind energy system provides more autonomy than the single systems. However, the performance analysis alone is insufficient, for the system cost is mostly the governing design criterion. Therefore, the performance data are combined with the cost data in this part of the paper. The costs are £58.75 per photovoltaic panel (rated output of 10 W at 1 kW h/m2 ) of 0.3 m2 each and £327 per wind generator (rated output of 50 W at 10 m/s) of 0.65 m2 swept area. The characteristic parameters for a 24 A h lead acid battery with a unit cost of £25 will be used. A total of £200 battery controller cost is also added into the cost of the system. The component capital costs refer to 2001 prices in the UK. In calculating the cost of the system a 20 year lifetime and a 5% installation, maintenance and engineering cost of the initial hardware cost are also assumed. In the following figures of cost versus performance for varying BLRs, each data point, of a total of four, along the lines is determined by a different sizing scenario. The first point is by the ‘yearly average scenario’, which sizes the systems at a point where the ELR is equal to unity. The second point is sized by the ‘plus standard deviation scenario’. This scenario uses the yearly average areas of photovoltaic and wind converters (as in the yearly average scenario) plus the corresponding standard deviations (rPV and rWG ) of the monthly areas. The third point is by the ‘worst month scenario’. This scenario chooses the worst month in which the largest total area of photovoltaic and wind generator occurs. The fourth point is by the ‘worst months scenario’. The worst months are the ones in a year that require the largest photovoltaic and wind converter sizes to meet the load. Fig. 7 shows the yearly autonomy levels and the corresponding system costs for the single photovoltaic, single wind and one of the near optimal combinations of the hybrid energy system for the BLR of 2. For the same system cost, the hybrid system returns the highest annual autonomy values and proves most optimal in terms of the performance–cost relationship. For example, looking at Fig. 7, for the cost of £3000, the hybrid system provides 97% yearly autonomy, while the single photovoltaic system provides 90% and the single wind system provides only 73%. The single photovoltaic system returns higher autonomy figures than the single wind system for the same system cost. For example, 80% autonomy level is achieved with a £1600 system cost for the photovoltaic system, while the same system autonomy can be achieved with £3600 for the wind system. On average, the hybrid system provides 96% autonomy while the single photovoltaic and wind systems supply 87% and 77%, respectively, for the BLR of 2. One of the most important points Fig. 7 shows is that achieving a further small increase in the system autonomy in the high autonomy region (over 95%) means a sharp increase in the system cost, especially for the hybrid and single photovoltaic systems. For example, the hybrid system can achieve 97% yearly au-
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Fig. 7. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0.
tonomy level with a system cost of £3000, and an extra 2% autonomy requires an extra £1500 investment. A similar analysis for a lower BLR (1.50) shows that the hybrid system is still superior to the single systems, as shown in Fig. 8. In comparison to the previous figure the hybrid system supplies even higher autonomy values than the single systems. On average, the hybrid system provides 86% autonomy, while the single photovoltaic and wind systems supply 67% and 68%, respectively. The most noticeable difference, when compared to the previous BLR, is that amongst the single systems, the photovoltaic system no longer provides higher autonomy values than the wind energy system at all cost levels. At around £3700, where the photovoltaic and wind lines intersect, the photovoltaic and the wind energy systems both provide the same level of yearly autonomy. Therefore, the photovoltaic system with the total cost less than £3700 would have provided more yearly autonomy than the single wind system for this particular location. Beyond the total system cost of £3700, the wind system provides higher autonomy values than the photovoltaic system. The yearly system autonomy values and the system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25 are presented in Fig. 9. For this BLR, the single wind system is now superior to the single photovoltaic system. The hybrid energy system still returns
Fig. 8. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5.
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Fig. 9. Yearly system autonomy versus system cost for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25.
higher autonomy values in comparison to the single systems. While the single wind system provides, on average, 59% yearly autonomy, the photovoltaic system provides only 46%, 13% less yearly autonomy than the single wind system, thus being the least likely option to choose for this BLR. The hybrid system, with an optimal combination of photovoltaic and wind generator, provides an average of 71% yearly autonomy for the BLR of 1.25.
7. Performance–unit cost analysis for hybrid and single systems Unit cost of the produced energy is analysed as a function of the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for varying BLRs. Fig. 10 shows the unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0. For the same yearly system autonomy, the unit cost of electricity is most by the single wind system and least by the hybrid system. It is seen from the same figure that the hybrid system returns the lowest unit cost values to supply the same level of autonomy as the single systems. In other words, the hybrid system supplies more autonomy than the single systems for the same unit cost value. For example, as seen in the figure,
Fig. 10. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 2.0.
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with the unit cost of £1.4 the single wind system supplies only 58% yearly autonomy, while the single photovoltaic system supplies 92%. For the same unit cost value, the hybrid system provides 98% yearly system autonomy. For the single photovoltaic system, the unit cost of electricity rises sharply from £1.0 to over £1.5 between the yearly autonomy levels of 87% and 92%. This means that the unit cost of the electricity rises sharply in return for a little increase in the system autonomy. A similar sharp increase is observed for the hybrid system too: the unit cost is doubled from £0.85 to £1.70 between the yearly system autonomy values of 89% and 99%. Another notable point in the figure is that the unit cost of electricity for 80% yearly system autonomy is £0.75 when produced by the single photovoltaic system, while the unit cost is £1.75 for the same level of yearly autonomy when produced by the single wind system. The unit cost of the produced electricity versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5 is presented in Fig. 11. The hybrid system is the best option for this BLR, for it produces the least costly electricity for the same level of yearly autonomy. For this particular BLR and location, if an autonomous system is expected to supply a yearly autonomy level of 90%, the hybrid system is the only option because the single systems cannot supply that level of autonomy. The single photovoltaic system supplies 60% yearly system autonomy with a unit cost of £1.0. It is £1.7 when the same level of yearly autonomy is supplied by the single wind system. At around the yearly autonomy level of 73%, the electricity produced by the single photovoltaic and wind systems both costs £2.0. Beyond this level of autonomy, the single wind system produces the electricity at a lower cost. Fig. 12 shows the unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25. The hybrid system provides much higher yearly system autonomies than the single systems for the same unit cost of electricity. For example, with a unit cost of £1.8, the single photovoltaic and wind systems return 44% and 46% yearly autonomies, respectively, while the hybrid system provides 75% yearly autonomy. Overall, the single photovoltaic system produces the most costly electricity for this BLR. As the yearly autonomy level goes from 40% to 50%, the unit cost of the electricity increases rapidly from £1.35 to £2.85. The unit cost of the electricity by the single photovoltaic system increases exponentially for an autonomy level more than 50%. The cost of the electricity from the single wind system increases from £1.75 to £2.25 linearly between the autonomy levels of 43% and
Fig. 11. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.5.
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Fig. 12. Unit cost of the produced energy versus the yearly system autonomy for single photovoltaic, single wind and hybrid energy systems for the BLR of 1.25.
Table 2 For a 20 year lifetime, the ratio of cost to the system autonomy (cost per percent autonomy) for single photovoltaic, single wind and hybrid energy systems for varying BLRs Battery to load ratio (BLR) Photovoltaic system Wind system Hybrid system
2.0
1.5
1.25
1.55 1.45 2.07
1.99 1.65 2.31
2.90 2.02 2.65
64%. Beyond this level of autonomy, the unit cost of the electricity rises sharply for the single wind system. A further design parameter that combines the unit cost and yearly system autonomy is defined here as the ‘cost per percent autonomy’, which is calculated over a 20 year system lifetime. The ratios of cost to the system autonomy (cost per percent autonomy) are presented in Table 2 for single photovoltaic, single wind and hybrid energy systems for varying BLRs. The cost per percent autonomy increases as the BLR decreases for the single and hybrid systems. This is especially accentuated in the single photovoltaic system, where the cost per percent autonomy increases 87% as the BLR decreases from 2.0 to 1.25. The cost per percent autonomy increases only 28% for the single wind system, while it increases 39% for the hybrid energy system from the BLR of 2.0 to 1.25. Overall, the per percent autonomy is least costly when produced by the hybrid system for every BLR and highest when produced by the single wind system. This is due to the fact that the site studied is a typical poor wind site with the yearly average hourly wind speed value of 2.18 m/s.
8. Conclusions This paper has addressed the optimisation of photovoltaic–wind hybrid energy systems in terms of a performance–cost relationship. A similar performance–cost relationship analysis has been performed to determine whether the hybrid photovoltaic–wind energy system or the single
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photovoltaic or wind system is better. The numerical analysis has been based on 1996 weather data from the TyB site of Cardiff, UK, and considers the basic system settings at this experimental site. Three main combinations of solar and wind energy have been analysed on a monthly basis: an even month, a highly wind-biased month and a solar-biased month. The system autonomies have been derived for each case, using an example month. For the even month, it was shown that the photovoltaic fractions of 0.4, 0.5 and 0.6 are the optimal combinations for the hybrid photovoltaic–wind energy system. The example month for the wind-biased case has shown that the smaller values of the photovoltaic fraction (0.0–0.2) provide the highest autonomy figures. For the solar-biased month, it was observed that the photovoltaic fraction values between 0.7 and 0.9 offer the highest monthly autonomy values. A careful examination of the monthly and yearly autonomy curves suggests that the optimum design point is in the range where the ELR is between 1.0 and 2.0 for any BLR. Therefore, the photovoltaic–wind hybrid energy systems sized beyond this point would fall into the non-optimal range, resulting in techno-economically ineffective systems. Hybrid systems sized within the optimum range of ELR return, on average, 93% annual autonomy, while the single photovoltaic and wind systems supply 87% and 65%, respectively, for the BLR of 2, for the same system cost. However, as the BLR decreases to 1.5, the difference between the single photovoltaic and wind system reduces to 8%. The single wind system supplies 5% more autonomy than the single photovoltaic system for the BLR of 1.25 for the same system cost. The second design parameter used to evaluate the quality of the system in terms of technoeconomics has been the unit cost of the produced electricity. Amongst the BLRs analysed, the unit cost of the produced electricity is lowest for 2 day battery storage. For this BLR, the single wind system returns the highest unit cost values. For the single photovoltaic system, the unit cost of electricity rises sharply from £1.0 to over £1.5 between the yearly autonomy levels of 87% and 92%. This means that the unit cost of the electricity rises sharply in return for a little increase in the system autonomy. Overall, the hybrid system returns the lowest unit cost values. Looking at the relationship between the system performance–system cost and system performance–unit cost, the hybrid system proves to be techno-economically better than either of the single systems for every BLR analysed in the present paper. This is a consequence, as expected, of the more reliable hybrid system behaviour by combining two less reliable resources. References [1] Kellogg W, Nehrir MH, Venkataramanan G, Gerez V. Optimal unit sizing for a hybrid wind/photovoltaic generating system. Electr Power Syst Res 1996;39:35–8. [2] Seeling-Hochmuth GC. A combined optimisation concept for the design and operation strategy of hybrid-PV energy systems. Sol Energy 1997;61(2):77–87. [3] Protogeropoulos C, Brinkworth BJ, Marshall RH. Sizing and techno-economical optimisation for hybrid solar photovoltaic/wind power systems with battery storage. Int J Energy Res 1997;21:1–15. [4] Beyer HG, Langer C. A method for the identification of configurations of PV/wind hybrid systems for the reliable supply of small loads. Sol Energy 1996;57(5):381–91. [5] Markvart T. Sizing of hybrid photovoltaic–wind energy systems. Sol Energy 1996;57(4):277–81. [6] Ashari M, Nayar CV. An optimum dispatch strategy using set points for a photovoltaic (PV)–Diesel–battery hybrid power system. Sol Energy 1999;66(1):1–9.
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[7] Elhadidy MA, Shaahid SM. Optimal sizing of battery for hybrid (wind þ Diesel) power systems. Renew Energy 1999;18:77–86. [8] Elhadidy MA, Shaahid SM. Parametric study of hybrid (wind þ solar þ Diesel) power generating systems. Renew Energy 2000;21:129–39. [9] Celik AN. The system performance and sizing of autonomous photovoltaic, wind and the hybrid energy systems. Ph.D. Thesis, Division of Mechanical Engineering and Energy Studies, University of Wales, Cardiff, 1998 [Chapter 2, p. 19–51].