Photovoltaics and Demand Side Management Performance Analysis at a University Building

Transcription

1 IEEE Transactions on Energy Conversion, Vol. 8, No., September 99 9 Photovoltaics and Demand Side Management Performance Analysis at a University Building S. Rahman Senior Member B.D. Kroposki Student Member Energy Systems Research Laboratory Bradley Department of Electrical Engineering Virginia Polytechnic Institute and State University Blacksburg, VA, 06, U.S.A. Abstract A performance evaluation of crystalline and amorphous silicon photovoltaic(pv) modules is presented in the light of their ability to perform as a demand side management (DSM) tool. Roof mounted, fixed-axis PV modules provide a very close match between their outputs and building peak load. The data presented is from the Virginia Tech Solar Test Facility (VTSTF) over the two year period from June 989 to May 9. The VTSTF is comprised three types of modules; Solarex SA0 (66 watts), ARCO Solar M55 (95 watts), and ARCO Solar G000 (680 watts). Also the load of a six story academic building, on which the arrays are mounted, is monitored. The meteorological station collects weather information from the top of the building every 0 minutes. During ' the two years, the station has produced over.7 megawatt-hr of DC energy. During the first year of operation the SA0, M55, and the G000 arrays produced %, 0%, and 0% of their rated DC energy. In the second year the corresponding numbers were 0%, 0%, and 5%. These values are normalized with the number of days of operation. A comparative analysis shows that for DSM applications, -axis tracking provides only marginal benefits, and may not be cost effective. Keywords: Crystalline silicon cells, Amorphous silicon cells, Demand side management, Building load data, Performance evaluation..0 Introduction As the availability of fossil fuels declines and energy consumption increases, the cost of electrical energy will continue to rise. Consumers must pay not only for the cost of generating the power they use but also for its transmission, distribution, and the indirect cost of cleanup. The utility in turn tries to fairly apportion these costs to its consumers by setting a series of price schedules. These schedules can reward or penalize the consumer based on their energy usage habits. The consumer thus has incentives ( in the form of lower electric bills) to modify how and when energy is used. 9 SM 50-6 EC A paper recommended and approved by the IEEE Energy Development and Power Generation Conunittee of the IEEE Power Engineering Society for presentation at the IEEEjPES 99 Summer Meeting, Seattle, WA, July -6, 99. Manuscript submitted September, 9; made available for printing May 5, 99. This awareness and effort on the part of the consumer takes the form of demand side management (DSM). DSM can take the form of simply making a greater effort to conserve energy by shutting off lights when not in use, to the installation of very expensive and involved controllers to monitor the entire building and level the load. Other methods include the utilization of auxiliary generators to reduce peak demand and/or energy purchased from the utility. This paper investigates the use of photovoltaics as a demand side management tool. The output of a three-array test facility located on the roof of the Virginia Tech Electrical Engineering building and the electricity demand profile for that same building is observed. Since the building load peaks during daylight hours, it would be expected that photovoltaics may be very useful in shaping the daily demand curve. The relarjve performance of crystalline and amorphous silicon cells and their suitability for demand side management will also be examined. By studying the relative performance of different types of solar modules, one can see which type will perform best for a particular application. Results are presented which compare the outputs of two types of modules, crystalline and amorphous, over two consecutive years at various tilt angles. The outputs of -axis tracking systems are also estimated for comparison. This paper is divided into the following sections; a description of the facility, a summary of the array performance, and a discussion of the applications to demand side management. Detailed discussions follow..0 Description of the Virginia Tech Facility The Virginia Tech Solar Test Facility (VTSTF) consists of three distinct components. These are: () photovoltaic test bed, () building load data acquisition system and () meteorological station. Brief descriptions of these systems are provided in the following.. Photovoltaic Test Bed This test bed is located on the uppermost roof of Whittemore Hall on the Virginia Tech campus. It is comprised of three independent photovoltaic arrays. They are; Array A: ARCO Solar M55 single crystal silicon - 95 watts peak (DC), Array B: ARCO Solar G000 amorphous silicon watts peak (DC), Array C: Solarex SA0 amorphous silicon - 66 watts peak (DC). The DC rating is found under standard test conditions (STC) and given by the manufacturer. Each array is completely independent of the others. Each one has its own protection equipment, source combiner box, meters, transducers, and power conditioning unit (PCU). The PCUs used are Omnion series 00 units which are rated at kw each (DC input). The-AC outputs of the PCUs are fed directly into the electrical grid of the building. They are automatically shut down in case of significant voltage and frequency fluctuations. A detailed description of the VTSTF is given in Rahman et a [l] /9$ IEEE - ~~

2 9 All modules comprising the arrays are facing due south and are mounted in a fixed tilt mode which can be easily adjusted. The PCUs are designed to handle 50 volt DC input (i.e. 5 VDC from positive to neutral and 5 VDC from neutral to negative connection) and provide a 5 VAC output. Since the M55 array only has approximately 0 VDC output, its PCU was modified to handle the reduced input voltage by the addition of a transformer to boost the output to the appropriate level.. Building Load Data Acquisition Station One of the major uses of the VTSTF has so far been to study the application of photovoltaic power for building load management. A high resolution building electrical load data acquisition system has been installed for this purpose. This system consists of a Dranetz 808 Electric PowedDemand Analyzer, its associated peripherals and a portable personal computer. The demand analyzer stores and displays volts, amps, power factor, kilowatt, kilowatt-hour, kilovolt-amp, kilovolt-amp-hour, demand, projected demand, time, and date. This system has been used to collect the building load data for Whittemore Hall. Whittemore Hall is a six story academic building located on the Virginia Polytechnic Institute and State University campus in Blacksburg, Virginia. It houses the majority of the Electrical Engineering and Industrial Engineering departments. Real power demand and power factor for Whittemore Hall have been collected at ten minute intervals since June 989. A profile of a typical day of the week can be developed by averaging the available data for each ten minute period for that particular day.. Meteorological Station The meteorological station at the Virginia Tech Solar Test Facility is used to gather high resolution data for solar radiation, ambient temperature and wind speed. The following instrumentation is used for this purpose.. Plane-of-array precision spectral pyranometer (PSP). Global horizontal PSP pyranometer. Normal incidence pyrheliometer. Multi-vane wind data logger 5. Multi-sensor remote temperature detector (RTD) probes These instruments are used for the collection of data at ten second to one hour intervals.. Overall Performance.0 VTSTF Performance The VTSTF has completed two years of data acquisition as of June 9. The facility was completed and connected to the electrical grid in March 989. It must be noted that the output reported for the amorphous modules reflects the period after their initial "burn-in" period, i.e. after the initial efficiency losses occur. For the most part, the facility has performed well, with only a few cases of data loss or malfunction. The performance of the three subfields is discussed in this section, but first a Table of tilt angle changes is given. Such data is available from April 989 to August 9. Table shows the tilt angles and when they were changed. The latitude for the site is 8". Initially the tilt angles were different for studying their impact on the PV output.. Then starting in the third and fourth quarters, the angles are brought to around 7". This provides a better way of comparing each module's performance. The angles were changed to 0" in the fourth quarter of 9 for n better winter tilt angle. In the third quarter of 9 they were changed to " for a better summer tilt. Table gives the hours of operation of the PV arrays and their respective outage rates. The hours of light are defined as the period when the global horizontal insolation was greater than 50 Wlm. This level of insolation is the minimum at which the arrays could produce useable AC power. The hours of operation are defined as the number of hours per month when the arrays produced useable energy. The outage rates, in parenthesis, are the percent of time that the arrays did not produce energy, even though there was enough insolation. This is found by the following formula: Outage rate = (0 f m r n Hours of Light Table. Tilt Angle Changes Year Quarter SA0 M55 G First NA NA NA Second " " no Third " no 7" Fourth 5" no no 9 First 5" 7" no Second 5" no no Thud 5" no 7" Fourth 0" 0" 0" 9 First 0" 0" 0" Second 0" 0" 0" Third " " " Note - Quarters are three month periods, beglnning January, 989. Table. Hours of PV System Operation and Outage Rates Year Month Hours of Hours of Operation (outage rate %) Light SA0 M55 ( donthly Average: rear 89-5(.) 9(.6) 8(.9) 66(.9) 59( 5.8) 55( 6.8) ( 7.) 8( 9.) 5(0.0) 58(.5) 78( 5.7) 8(.7) 6(.0) (.7) 80( 6.) 85(5.) 6(.) ( 6.5) 66(68.) 8(.) 8(.9) 9(.6) 0.9) 5 6.7) 9 (.) 98(5.) 50( 9.) 5( 0.) ( 7.5) 69( 9.7) (.) 0(.5) 5(5.9) ( 5.8) (.) 97(.0) 7( 6.8) (.8) 59( 6.8) 6( 8.) (.7) 8.) 6( 0.0) 78( 0.0) 0 l(.) 06( 8.) ( 8.) 86( 6.5) 0(.) 8( 9.) 56( 7.5) 6( 8.) 8( 5.5) 85(.) lg(5.) (9.) 77(.9) 8(0.8) (.9) 06(0.) ls(.) 76( 0.5) 6( 7.) (8.0) 78( 5.) 60(.5) 7(6.8) 00(7.9) 89(.) (7.9) U.) 6(.) 9(9.7) 8(5.8) 7U7.8) Outage rates are caused by hardware malfunctions in the system. This includes the module, electrical connections, and the PCU. The PCUs will disconnect due to voltage and frequency fluctuations in the building. This gives a good indication of the reliability of the PV system. There are some very high outage rates (i.e. SA0 in - and G000 in -). These were caused by module failures.

3 9 The outage rates can also be a function of non-optimal tilt angles. This can be seen in April and May of 9. The outage rates are increasing because the arrays are at a high tilt, 0'. During that time of the year the tilt should be around 7'. Since the summer sun rises more to the east than the winter sun, the arrays did not receive early morning or late afternoon insolation, thus causing a lower output even though insolation was available. This loss of output due to non-optimal tilt is of significant importance for any fixed axis PV system used for DSM applications. It is unlikely that such systems will employ tracking mechanisms (especially on the east coast) because there may not be much additional energy available during the peak demand hours (between 000 and 600 hours). More importantly, the additional energy available through tracking may not be enough to offset the cost of such systems. The study the effect of such two-axis tracking through simulation is presented in section.0. Table contains the energy outputs (kw-hr AC) for each of the three subfields. It is easy to see that the M55 array produces the most energy. This is this array has the highest rating of the three (see section.). Along with the actual AC outputs from the arrays, is a listing of the total monthly global horizontal insolation (INS) in kw-hr/m. This helps to show the weather variation during these months. Table. Actual Energy Outputs (kw-hr AC) from Subfields Year Month SA0 M55 G000 INS I : Year Year Table. Normalized Energy Output (kw-hr AC) from Subfields Year Month SA0 M55 G000 INS : Year Year Figures and provide a graph of the normalized AC energy. From these graphs some observations can be made. For example, in October 989, there is a high output of energy from the three subfields. This can be explained by the rise in global insolation coupled with a more favorable tilt for that season. This sudden rise in energy can be seen again in November of 9. Here the rise in energy output is attributed to the changing of tilt angles. Since the angle was raised for a better winter tilt, the arrays produced more energy. In general the PV outputs follow the global horizontal insolation. These graphs also show the generally higher energy outputs during the summer. This is a good match with a high summer air conditioning demand. Since the large NC load matches the energy output, demand side management can be used here to take advantage of the extra energy to reduce the higher load. The overall performance of these subfields is shown in tables 5 and 6. The peak solar hour is defined as the hour during which the highest amount of AC energy was collected from each PV system. This peak solar hour is usually different from the peak load hour for the building. Table contains the normalized enerzv outdut for the three subfields, and the total monthly global horizvdntal hsolation (INS) from table. The normalization factor on the energy is two fold. First all subfields are normalized to a rating of 000 watts (DC). This eliminates the discrepancy in the ratings of the modules. Then the array output is normalized with respect to the outage rate. This allows comparisons as if the arrays experienced equal outages Month Figure. Normalized AC Energy for 6-89 to 5-

4 G000. Crystalline vs. Amorphous Silicon Cell Performance 0 I a a I 0 li 5 ib 7 Month Figure. Normalized AC Energy for 6- to 5- These tables cover the incident solar energy, DC energy output, array efficiency, AC output, PCU efficiency, capacity factor, and capability factor. Capacity factor is defined as: Capacity Factor = Monthlv AC Energy Output (Rated Power)(Total Hours in Month) While Capability factor is defined as: Capability Factor - = (Rated Power)(Hours of Light in Month) A good comparison of crystalline and amorphous cells can be made using the data presented in Tables 5 and 6. The major differences are in the cell efficiencies, which average 0% for the single crystalline cells and around % for the amorphous cells. Although the single crystalline cells have a higher efficiency, they may not work as well during non-optimal tilts. For example in July and August of 9 the G000 array actually outperformed the M55 array on a normalized comparison (see figure ). During this season the tilt of the arrays was not optimal so the crystalline modules did not perform as well. This non-optimal tilt effect can also be seen in April and May of 9, when the available insolation increased. The relative performance of the G000 array improved while that of the M55 array remained about the same. Thus the effect of nonoptimal tilts is more pronounced on crystalline modules than on amorphous modules. The monthly average capability factor is low for several reasons. One is the fact that the arrays experienced PCU or module failures. This down time decreases the capability factor by not allowing the arrays to produce energy, even when there is enough insolation. The PCUs efficiency is also considered low. This is because they are often running at less than 50% capacity. Table 5. PV System Operation Summary 6-89 to 5- Incident Solar Energy (kwwm) Rated Capacity (STC) (DC, kw) PV Array output (DC, kwh) Daily Average h Y Efficiency (%) * PV System AC output (kwh) PCU Efficiency (%) Capacity Factor (%) SA0 M55 G00C * Capability Factor (%) Note: The peak solar hour of AC energy was recorded on June 0,989 between the hours of and (EST) for SA0; on Feb 5, 9 between the hours of and for M55; and on Sept, 989 between the hours of and for G000. Table 6. PV System Operation Summary 6- to 5- SA0 M55 (000 Incident Daily Average Rated Capacity (STC) (DC, kw) PV Array output (DC, kwh) Array Efficiency (%) * PV System AC output (kwh) PCU Efficiency (%) Capacity Factor (%) Capability Factor (%) Note: The peak solar hour of AC energy was recorded on June,9 between the hours of and (EST) for SA0; on Feb 79 between the hours of and for M55; and on Sept, 9 between the hours of and for (000. * - Normalized for down time.

5 95.0 Demand Side Management Now that the performance of the three different module technologies under various seasonal and sky conditions has been observed, their role as DSM tools should be investigated. In studying this, the shape of building load in different seasons are examined first. Unlike the load shape for the whole electric utility system, which peaks at different times during different seasons, the typical weekday building load peaks between 000 and 600 hours in all seasons (see figure ). It can be shown that 95% or more of rated PV outputs are available between the hours of 000 and 600, by looking at the peak solar day AC outputs from the three arrays in different seasons of 9 (see figures, 5 and 6). This shows the natural match between the PV output and the building load in all seasons and all three types of PV modules provide this good match. However, the size of outputs from these three modules are significantly different. Usually a building's peak demand occurs during the hottest days of the summer when insolation levels are high. The reason that the building load for Whittemore Hall is lower in the summer than the winter is the fact that the air conditioning is run from a chilled water plant outside the building and therefore the electrical demand for the chilled water plant is not included in the building load. Also the building load is higher during the academic year ( September thru May ). Once it is demonstrated that the PV modules can be used as a DSM tool, one might ask what will be the impact of -axis tracking on the ability of the PV modules to follow the building load. For this purpose a simulation was done using PVFORM. []. Here the plane-of-may insolation between 000 and 600 under different tilt conditions (see Table 7) is presented. In the first case, the tilt angle is changed twice a year. In the second case the angle is changed once a month. In the third case, it is -axis tracking. It is interesting to note that the largest gain (in insolation capture) due to -axis tracking over a semi-annual tilt change, is.% while the smallest gain is only.9%. The corresponding gain over the monthly tilt angle change and the -axis tracking is 0.7% at the most. This leads to two interesting observations. First, the additional benefit of monthly tilt angle change is marginal at best. Second, only.% more energy can be captured between 000 and 600 hours by -axis tracking as opposed to fixed tilt (with twice-a-year angle change). Given the fact that many of these PV based DSM tools will be roof mounted, and may be used for peak load displacement, the marginal benefit of -axis tracking may not be cost effective. "., , T,mP(hr) Figure. Whittemore Building Load for Typical Weekday in Four Seasons o m cu iaxr m rm 800 IBW 000 zm Time (hr) Figure 5. M55 Peak Solar Day Outputs in Four Seasons I >. a. a- -a,.,.,. *....,......,.,, 0 xx) W mo M) sdo CO M Figure. SA0 Peak Solar Day Outputs in Four Seasons M - 0.,.,.,.,.,.,.,.,.,.,.,. a m m 800 ma ma im iuo rm rm 000 m i Time (hr) Figure 6. G000 Peak Solar Day Outputs in Four Seasons X

6 96 Table 7. Plane of Array Insolation ( hr) Month Semi-annual Angle Monthly Angle -axis Change Change tracking (8.5) (8.5) 85.5 (8.5) (5.5).0 (5.5) 6.70 (5.5) (5.5) (5.5) (5.5) (8.5) (8.5) 65.8 (8.5) (59.) 078. (5.) 75. (0.) (8.) 7.00 (9.0) 5.80 (.7) 7.00 (6.7) (.6) 07. (6.) (8.5) 6. (57.) 88.0 (6.) Figure 9. Building Load and PV Power in July Now that the performance of three modules in all four seasons has been shown, the match between building loads and typical solar outputs in those four seasons are examined. In Figures 7 thru 0, typical daily solar AC outputs and a typical weekday building load curves are presented. Even though the PV fluctuations are evident, the envelope of the PV output shows a good match with the daytime load shape. The significant difference in outputs from the amorphous silicon and the crystalline silicon modules are obvious under clear sky conditions in April and October. In July, however (when the sky is cloudy and hazy) the distinction is not as prominent , -, -, -, -, -, -, -, -, - I - I - I o z e 8 0 (e 8 0 n The (hr) Figure 0. Building Load and PV Power in October Figure 7. Building Load and PV Power in January I,A- Buildirm Load I I 5.0 Conclusion Roof mounted, fiied-axis PV modules provide a very close match between their outputs and the building load. The relative contributions of crystalline and amorphous silicon modules have shown that even though crystalline technology is more suitable; under less-than-optimal tilt conditions and cloudy/hazy skies, amorphous modules provide some advantages. Also the marginal benefits from -axis tracking arrays may not be able to justify the necessary cost of tracking. In fact, changing the tilt twice a year is almost as good as changing the tilt every month for DSM (peak shaving) applications so? I o e 8 0 iz ie 8 0 Time (hr) Figure 8. Building Load and PV Power in April References S. Rahman, J. Jockell, and S.Lahouar, Analysis of the Value of Photovoltaics for Demand Side Management, Proc. IEEE Photovoh c Soeci&ts Conference, Kissemmee, FL, May 9, vol pp D.F. Menicucci and J.P. Fernandez, User s Manual For PVFORM: A Photovoltaic System simulation Program for Stand-Alone and Grid-Interactive Applications, Sandia Report SAND85076 UC-76, Sandia National Laboratories, Alberquerque, New Mexico, 985.

7 Acknowledgements Parts of this research were made possible by grants from the Core Research Program (Coal and Energy Research) at Virginia Tech and the Center for Innovative Technologies in Virginia. Saifur Rahman (IEEE S-75, M-78, SM-8) graduated from the Bangladesh University of Engineering and Technology in 97 with a B. Sc. degree in Electrical Engineering. He obtained his M.S. degree in Electrical Sciences from the State University of New York at Stony Brook in 975. His Ph.D. (978) is in Electrical Engineering from the Virginia Polytechnic Institute and State University. Saifur Rahman has taught in the Department of Electrical Engineering at the Bangladesh University of Engineering and Technology, the Texas A&M University, and the Virginia Polytechnic Institute and State University, where he is a full professor. He also directs the Energy Systems Research Laboratory at VPI. His industrial experience includes work at Brookhaven National Laboratory, New York and the Carolina Power and Light Company. He is a member of the IEEE Power Engineering and Computer Societies. He serves on the System Planning and Demand side Management subcommittees, and the Long Range System Planning, the Load Forecasting and the Photovoltaics working groups of the IEEE Power Engineering Society. His mas of interest are demand side management, power system planning, alternative energy systems and expert systems. He has authored more than 50 technical papers and reports in these areas. Benjamin Kroposki (IEEE S-) graduated with a B.S. degree from Virginia Polytechnic Institute and State University in 9. He is currently pursuing his M.S. degree at VPI. He is a member of the IEEE Power Engineering Society. His interests include photovoltaic systems, demand side management, and expert systems.

8 98 Discussion Yaw-Juen WANG (Laboratoire delectrotechnique de Grenoble, CNRS URA No 55, BP. 6, Domaine Universitaire, 80 St. Martin d'hkres, France) The authors have presented a useful paper of evaluating photovoltaic (PV) modules' performance based on onsite data. Demand side using PV electricity has also been proposed by the authors in view of the fact that the energy output of PV modules matches the load profile well. I would like to raise the following questions: ) Cost-effectiveness of two-axis tracking - There are several techniques that aim to enhance the efficency of PV systems, such as solar tracking, focusing lens, reflectors, maximum power tracking circuits...etc. Their viability depends heavily on the costeffectiveness. I agree with the authors that two-axis tracking is unlikely to be cost effective. However, this conclusion may not be correct if electricity prices increase to some extent. In fact, the economic feasibility of tracking systems (and of other techniques also) depends strongly on the price of its competitor - grid electricity. Have the authors considered this possibility? ) Optimum sizing of PV modules - One of the most important problems of PV system design is the sizing of PV modules. With current electricity prices, have the authors considered an optimum size (i.e., capacity) of PV modules that gives minimum total cost of electricity consumption? Again, the answer U, this question would be most appropriate if variations of electricity prices were taken into account [I,. I hope these commentdquestions could bring another perspective to the authors' (future) work, in particular the consideration of economic factors. Reference [ Y. J. Wang, "Sizing of a stand-alone photovoltaic system", Research Study Report, Asian Institute of Technology, Bangkok, 987 [] F. Lasnier and T. G. Ang, "Photovoltaic Engineering Handbook", Adam Hilger, New York, 9 (Part V: Sizing procedure, pp. 7-70) Manuscript received August 6, 99. Saifur Rahman and Ben Kroposki (Electrical Engineering. 0 Whittemore Hall, Virginia Tech, Blacksburg, VA 06-0) The authors appreciate the interest shown by Mr. Yaw-Juen Wang On the issues of cost-effectiveness of tracking systems and optimum sizing of PV systems. His questions and comments will be answered in the order presented. ) Extensive studies have shown that the additional energy Output from Weaxis tracking systems may not justify the cost of trackers and the extra energy required to run them in Virginia and similar climates (. The increase in the cost of electricity will not affect this balance much. The decrease in tracker costs, and increase in cell efficiencies will contribute much more to this balance. ) The second set of comments is the subject of a recent paper by the same authors which has been submitted for presentation at the 99 IEEE Winter Power Meeting (. The authors agree that there is an optimum PV ske for a certain building. This depends on the shape and size of the building load. PV Output characteristics (e.g. module performance under different sky conditions, tracking or non-tracking), cost of PV modules. the cost and size of storage, the level of demand side management available and the cost of electricity. References:. S. Rahman, et al. "Analysis of the VISTA Photovoltaic Facility System Performance", / E Truns, Energy Conversion, vol. 5. no., June 9, pp, S. Rahman and B. Kroposki, "Photovoltaics and Storage as DSM Tools: System and Cost Implications", submitted for presentation at the 99 IEEE Winter Power Meeting, 6 p. Manuscript received September, 99.