Value Analysis of lntermittent Generation Sources From the System Operations Perspective

484 EEE Transactions on Energy Conversion, Vol. 8, No. 3, September 1993 Value Analysis of lntermittent Generation Sources From the System Operations Perspective Mounir Bouzguenda Student Member Saifur Rahman Senior Member Energy Systems Research Laboratory Bradley Department of Electrical Engineering Virginia Polytechnic Institute and State University Blacksburg, VA 24061, U.S.A. e-mail: SRAHMANBVTVMI.cc.vt.edu Abstract The objective of this study is to determine the economic and operational impact on energy cost of incorporating large photovoltaic (PV) and wind energy conversion systems (WECS) into the electric utility generation mix. In most cases, PV and WECS power outputs are subtracted from the utility load with the expectation that conventional generation would meet the residual load. This approach is valid for small penetration levels and/or for PV and WECS facilities connected near load centers. However, several constraints such as thermal generation characteristics, fuel supply and delivery, spinning reserve requirements, and hydro availability are not adequately represented in this process. To determine the optimal value of large-scale PV and WECS applications, a new methodology that would take into account the fcrth-mentioned constraints as well as a more global penetration is being developed. The proposed methodology also handles constrained combustion turbines and hydro power plant generation. While PV systems are considered as a prototype to test the methodology, WECS and other renewable energy sources can be handled similarly. Performance analysis shows that hydro availability, generation mix and characteristics, spinning reserve requirements, maintenance schedule, PV power output dynamics, and load variations influence the economic and operational value of large-scale PV generation. Results indicate that while high hydro availability increases PV penetration levels, high ramping rates can also significantly increase penetration levels. Key Words: Photovoltaics~ Wind Energy Systenls~ lntermittent Generation, Hydro Availability, Energy Value, Penetration Level, and Combustion Turbine Displacement Ratio. 1.0 Introduction Today, more than ever, renewable energy systems are poised to become significant sources of electricity. With increasing fuel prices, environmental concerns, and improving solar energy technology, PV systems can help reduce dependence on fossils fuels. Therefore, they can help reduce emissions, and risks associated with large capacity additions and fuel prices. The system dependent optimum size of PV systems has yet not been determined. It is generally assumed that the major impact on PV and WECS energy value will be due to their short-time fluctuations. The seasonal variations in solar and wind resources, utility load, and generation need to be accounted for through long term generation planning. For this puipose a yearly and weekly generation planning and a unit commitment-production costing package has been developed. This package is designed to handle hourly data and 10-minute load and generation performance for the long-term planning and short term system operation, respectively. The technique is implemented for a typical US electric utility using actual data. Simulation results presented are based on January load and PV output data. The proposed technique presents a comprehensive approach in PV dispatching. It can also handle other renewable energy sources. Combustion turbines and hydro power plants are found to enhance, to a large extent, the economic and operational values of large-scale PV applications. Ramping rates and daily hydro availability constraints reflect the viability of such an energy source. The present work includes a brief review of existing dispatching techniques with photovoltaic and wind energy conversion systems and a description of the proposed technique. 2.0 Review of PV and Wind Energy Systems Dispatch 92 SM 526-4 EC A paper recommended and approved Wind energy conversion systems (WECS) and PV systems by the IEEE Energy Development and Power Generation are being considered as promising alternatives in the near future. committee of the IEEE Power Engineering society for Several research activities indicate the increasing potential of such presentation at the IEEE/PES 1992 Summer Meeting, Seattle, WA, July 12-16, 1992. Manuscript submitted sources. The literature review focuses primarily on existing January-3, 1992;-made available for printing dispatching techniques involving PV and WECS devoted for bulk May 13, 1992. power generation. 0885-8969/93$03.00 0 1992 EEE

485 2.1 Large-scale WECS Integration Schlueter et al. [12] proposed a modified unit commitment and generation control to dispatch wind energy conversion systems. The method addresses problems related to unit commitment and generation control by limiting wind power variation. The proposed procedure would reduce spinning and unloadable generation reserves, thus would significantly reduce fuel and operating costs. The unit commitment is updated on three different levels-daily, quarter-hourly, and one-minute. This is to account for load variations and slow trends in WECS power changes, cyclic changes and quick variations in WECS power, respectively. The proposed unit commitment control utilizes normal automatic generation control, coordinated blade pitch control, supplementary control of peaking units, and regulating and quick pick-up units committed by both the quarter-hourly and one-minute updated unit commitments. The method is reported to have significant advantages such as lower spinning reserve requirements, hourly operating reserve adjustment, and increased WECS penetration levels. 2.2 Large-scale PV Integration Chowdhury [2] investigated the effect of central station photovoltaic plants on the power system security. He proposed a 7-step procedure that combined a modified dispatch strategy with power flow algorithms to determine the optimal economic operation and system conditions under specific load scenarios, respectively. The author suggested that the value of the PV plant can only be useful if the additional power does not cause line overloading and/or bus voltage deviations that would otherwise not occur. Similarly, Chowdhury [3] recognized the need for a single definitive optimization method to assess the optimal size, operation, performance, and economics of a PV system. He proposed a five module optimization methodology. The most distinctive ones are the power system security analysis and realtime system control identification modules. The objective of the methodology is to determine the optimal PV penetration levels and the most effective form of PV operation without causing security violations. Rahman [ 111, and Bouzguenda and Rahman [ 11 studied the parameters that would affect the value of large-scale PV systems. Such parameters include PV power profile, seasonal load and PV power variations, and dispatching assumptions. It is reported that PV power output intermittency increases start-up costs and reduces spinning reserves. Jewell and Raniakumar [9] presented the results of a simulation designed to assess the maximum possible changes in PV generation expected over certain time interval. The simulation can be used with power flow to study the actual effects of dispersed residential PV generation on the electric utility. It is reported that maximum changes in PV generation usually occur within 1-2 minutes and during off-peak times. Longer intervals would not result in greater changes. The results indicate that the effects of PV generation changes will be the greatest at the substation level, therefore proper protection and control are necessary. Jewell, Raniakumar and Hill [B] presented results of a study of the Public Service Company of Oklahoma (PSO) system with simulated dispersed generation. It was found that for high PV penetration because of insolation changes significant variations in power flows would occur on transmission and sub-transmission lines. This would require changes in system protection and voltage control practices. In this study, the optimal penetration level was not assessed. Jewell and Unruh [lo] investigated how a utility would determine its maximum allowable PV capacity and its value. The study allows estimation of the maximum fluctuations in PV generation a utility can withstand before inadvertent tie line flows occur and cause operation costs to increase. Results indicate that the utility needs to limit the size of the central station PV system to 1.51% of the system load to avoid inadvertent tie-line flows. For a diversified PV system, this limit could approach 40%. 2.3 Issues in PV and Wind Energy Systems Integration In Schlueter et al. [12], generation mix was not reported. In the study by Chowdhury [3], hydro and pumped storage hydro plants were not included in the generation mix. In the study by Jewell er al. [ 101, no fast ramping generation was used in spite of the fact that combustion turbines (CT) would absorb PV fluctuations and thus would enhance large-scale PV applications. Energy sale/purchase from neighboring utilities was not considered either. It is expected that CT, hydro, pumped storage hydro, and power interchange would indeed enhance the value of WECS and PV power systems. Since CT, hydro and pumped storage hydro have short start-up times and high ramp rates, their outputs can be controlled easily to respond to any load, and WECS and PV power output variations. In addition, tie-lines are capable of transferring excess power output to neighboring utilities to reduce Area Control Error (ACE). In this respect, the studies by Chowdhury [2,3] are useful for intermittent generation plant siting, optimal penetration level assessment, and system security evaluation. In the studies by Rahman [ll], and Bouzguenda and Rahnian [ 11, hydro and pumped-storage hydro were dispatched to meet peak load, but without hydro constraints. Constant generating mix and high ramping rates are assumed throughout the study. The previous studies revealed the viability of WECS and P?I systems for bulk power generation in terms of penetration potential, operational mode, and economic value. However, there is no comprehensive methodology that takes into account all the issues related to WECS and PV systems such as generation mix, output fluctuations, system load, and dispatching practices. The objective of the present work is to develop and implement such a methodology. 3.0 Model Development A long term generation planning method using a modified unit commitment, economic dispatch, and generation control is developed and implemented. The objectives are i) to determine the optimal PV penetration level, ii) to decide the operation of the PV system, and iii) to study the parameters and factors that would enhance both economical and operational values of integrating PV systems. The methodology can be applied to other nonconventional energy sources. The proposed methodology consists of the following five modules: 'Yearly generation planning, Weekly hydro-thermal coordination, '24-hour unit commitment, 'Production cost, capacity displacement, energy value, and *Dispatch emissions post-processor.

486 The methodology takes advantage of existing generation planning and short-term system operation methods. The following parameters are considered: *Daily and seasonal load and solar variations, *Generation mix characteristics,.unit maintenance and repair schedule, *Unit minimum and maximum generation levels, *Unit response rates, 'Minimum on-time and off-time, *Unit start-up time and costs, *Boiler cooling time, 'Operating and maintenance costs, *System spinning reserve requirements, *Unit spinning reserve contribution, *Hydroelectric water availability, 'Geographical PV distribution, and *Automatic generation control practices. 3.1 Methodology Figure 1 illustrates the functional breakdown of the longterm planning and short term operation. The subproblems are hierarchical where yearly generation planning generates weekly maintenance and repair schedules, and seasonal hydro availability constraints for the weekly hydro-thermal coordination which in turn imposes new constraints for the daily unit commitment. Figure 1. Functional Breakdown of Operation Scheduling 3.1.1 Long-Term Generation Planning The objective of long-term planning is to determine weekly hydro dispatching, weekly maintenance, and sale/purchase contracts. It uses one-year load and renewable energy power output forecasts, generation mix characteristics, and capacity additions and retirements. 3.1.2 Weekly Hydro-Thermal Coordination The weekly hydro-thermal scheduling is needed to account for hydro availability, maintenance and repair schedules, and operating schedule for each unit. It would also account for fuel availability and delivery, and power interchange with neighboring utilities. Its main function is to determine the scheduling of generating units over several days on a hourly basis. This is done by optimizing the hourly schedules based on the weekly load and PV forecasts. It also determines the start-up and shut-down schedules of all thermal units subject to minimum running-time, off-time and maintenance schedules. The hydro-thermal schedule and interchange with neighboring utilities are obtained subject to hydro availability, and line transfer capabilities. The weekly hydro-thermal algorithm uses weekly fuel schedules, weekly unit availability and weekly water availability. 3.1.3 24 Hour-Unit Commitment The 24-hour unit commitment (UC) sets up the hourly commitment schedule based on the daily load and PV resource forecasts and maintenance schedules to satisfy the load demand, system losses, and spinning reserve requirements. It uses the heuristic schemes to guide the commitment of each unit until the entire period is scheduled. 3.1.4 Economic Dispatch The Economic Dispatch (ED) is performed every 1 to 2 minutes to determine the generation level of each committed unit such that fuel, operation and maintenance and start-up costs are minimized. The ED problem is solved using the Lagrangian method which is based on the equal incremental cost criterion, subject to water dispatching availability and spinning reserve requirements. 3.1.5 Automatic Generation Control The Automatic Generation Control (AGC) is executed every 1 to 10 seconds. AGC tracks system load, generation level of each committed unit, and PV power output by minimizing the area control error. It is expected that instantaneous PV power output can change by up to 100 percent due to significant weather changes. Therefore, AGC must be able to account for such instantaneous power fluctuations and load variations as well. For example, if hydro and thermal generations cannot absorb excess PV power, then that excess power needs to be either stored or sold to neighboring utilities subject to storage and interchange constraints. However, if PV power output drops to zero and hydro-thermal generation cannot be adjusted to make up for the generation drop, then fast ramping generation is needed. Due to their high ramping rates and quick start, hydro and pumped storage hydro power plants can be adjusted to cushion PV power fluctuations. 3.2 Procedure In the first stage, the yearly generation planning package is run using hourly load and generation data for the year. The output includes weekly maintenance schedule for each generating unit, the expected loss of load probability, and the average spinning reserves. In the second stage, the weekly generation planning is performed using the maintenance schedule obtained in the first stage, water availability forecast, required maintenance schedule, and energy sales contracts with neighboring utilities. The objective is to determine generation schedules that would satisfy system load, losses, and spinning reserves. The output includes start-up and shut down timetables for each unit. At this stage, water availability is accounted for in the optimization process. At the third stage, the 24-hour unit commitment program is run using data obtained at stage 2, and the 24-hour load and PV forecasts. At this stage minimum up and down times, and boiler cooling times are considered. Hourly schedules are then utilized by the economic dispatch and automatic generation control modules. The fourth and fifth stages deal with assessing the economical values of the PV system. This includes spinning reserve variations, production costs savings, combustion turbine energy displacement, water discharge variations, and emissions reductions. 4.0 System Description In this study, the photovoltaic system discussed in section 4.3 is used as a renewable energy system prototype. The generating system and load are presented in Sections 4.1 and 4.2..

4.1 System Generator Data The simulated utility system used for this study consists of nuclear, coal and oil steam (large and small), combustion turbines, pumped-storage hydro, and hydroelectric units. Generation mix characteristics are shown in Table 1. Here, the heat rate function for unit i is given by Hi=Ai+Bi*Pi+Ci*Pi2 (MBtu/hour) (1) where Ai, Bi, and Ci are the constant, linear, and quadratic heat rate coefficients and Pi is the power output of unit i. Table 2 lists generation mix, fuel ($1985), and O&M costs. In Table 2, the fuel cost is based on the average heat rate. 4.2 Load Data The 1990 load data consists of hourly and 10-minute data for a southeastern utility. The hourly data are used for the yearly and weekly generation planning. The 10-minute data are interpolated from actual 15-minute data and used for short-term operation. The 10-minute time resolution is appropriate for high load variations and intermittent generation dynamics. 4.3 Photovoltaic System The PV system, located in Virginia, is rated 20 kw(ac) and consists of polycrystalline modules arranged on fixed-axis structures. The 1990 PV output data are actual lo-n~inute data. The PV system is assumed to consist of several identical solar facilities scattered throughout the service area. A total PV capacity of 4000 MW is assumed. The purpose is to determine the maximum possible upper limit of PV penetration under various load and weather conditions. Solar day type selection takes into account the peak PV output, the ac energy, and the number of fluctuations during daylight hours. For example, a solar day that has the highest peak output and daily energy and least fluctuations is considered to be the "sunny" day. In this study, the O&M cost for PV system is chosen to be $O.OOS/kWhr. This seems to be in line with future O&M costs [4]. 5.0 Performance Analysis The proposed methodology is implemented using 1990 load and PV data and the simulated utility listed in Tables 1 and 2. The following definitions would also apply for wind energy conversion systems. 5.1 Assessment of Maximum Penetration Level The objective is to determine the maximum PV penetration level, energy value (PVEV) and combustion turbines displacement ratio (CTDR) under different ramp rates and hydro availability, for various load and solar power output profiles. For this purpose five different solar days and four load types are considered. These are raidsnow, sunny, partly sunny, variable cloudy, and cloudy. The load day types correspond to low, medium, and high weekday loads, and weekend load. The following performance indices are used. 'Penetration level is defined as the ratio of the maximum PV capacity to the peak load for the day. The maximum penetration level is considered non feasible for one of two reasons: i) the economic dispatch fails. This occurs if load variations, canbined with PV power fluctuations, can not be absorbed by conventional generation, or (ii) the baseload nuclear generation has Production Cost Savings PVEV = (PV Capacity) * (Day Light Hours) and CTDR = CT Generation Displacement (PV Capacity) *(Day Light Hours) *Low (high) hydro availability implies that no more than 4 (8) percent of the daily available water can be discharged. Zero hydro availability means that the hydro is off. Low and high ramp rates correspond to the minimum and maximum ramping rates. 5.1.1 Case Studies PV penetration levels are studied for low, medium and high weekday load profiles; weekend load; lowhigh ramp rates; zero, low, and high hydro availability. At this point, high ramp rates and high hydro availability combination will not be studied because of it is very unlikely. The study will, however, focus on the following cases: (2) (3)

~ 488 Technology Total Fuel Cost (MW) ($/MBTU) Fuel Cost* (millslkwhr) Nuclear 3000 0.69 8.93 Coal I 6750 I 1.90 I 17.77 Oil (residual) Natural Gas Hydro Interchange Pumped Storage Hydro 500 Photovoltaics I 4000 I -- 1900 3.50 27.60 290 3.50 31.45 1200 2.00 100 50.00 20.00 5.00 5.2 Results The following discussion is based on results in Tables 3-8. The PV system rated capacity is 4000 MW. The average daylight hours of 9 hours and 21 minutes is used. This is the daily average daylight hours for the month of January. Tables 3 and 4 provide the system operation summary for the low weekday load (peak load=6950 MW) with low and zero hydro availability. The purpose of conducting Cases I and I1 is to II 1 7 19 31 43 55 67 79 91 103 115 127 139 Time (10-minute) Figure 2. Load and PV Power Output Profiles for PV Rated @ 95OMW. PV Size Rainy Sunny Partly Cloudy Variable Sunny Cloudy 0 2000 2000 2000 900 (MW) I 0 I 20001 20001 20001. 2000 CT Gen. 01 01 01 01 0 I 01 01 01 01 0 HrdroGen.1 1179 I 1101 I 1135 I 1352 I 1455 imwhr) I 1323 I 1364 I 1429 I 1 505 I 1307 PVG~~. Prod. Costs ($1 CTDR (%I PVEV :millskwhr, I 0 I 13411 I 11566 I 2198 I 2816 0 13411 11566 2198 6257 1972 641 1799271 1 819 196 1 951 390 1949673 1963 632 1 808 723 1 828 184 1 940 637 1 879 910 NA 0 0 0 0 NA 0 0 0 0 NA 4.71 4.17 0.58 0.62 NA 4.20 3.68 0.62 2.27 Note: Top (bottom) entry in each row indicates low (high) r mip rates.

PV Size (MW) CT Gen. Hydro Gen. PV Gen. Table 4. System Operation Summary for Case II (Low Weekday Load) I Rainy I Sunny I Partly I Cloudy I Variable I Sunny Cloudy 0 1100 1300 1300 800 0 2000 2000 2000 2000-0 7376 7518 1429 2503 I 0 I 13411 I 11566 I 2198 I 6257 Prod. Costs 11 960 341867 44311 866 00411 945 20211 933 259 6) 11 948 52311 790 39211 809 84311 923 66811 867 588 CTDR 1 NA I 01 01 01 0 (%I PVEV (millskwhr) NA 0 0 0 0 NA 2.52 2.56 0.41 0.74 NA 4.29 3.76 0.67 2.20 Note: Top (bottom) entry in each row indicates low (high) ramp rates. To establish the relationship between PV dispatching and hydro availability for higher peak loads, Case 111 was run. The results are shown in Table 5. All conditions reported here are for low ramp rates. Results indicate that by increasing hydro availability from zero to 4 percent, CT generation was reduced by a factor of 7. However, PV penetration levels were improved and reduced PV energy values decreased. In fact, the PV penetration levels increased by 60 and 29 percent under clear and variable cloudy weather conditions, respectively. In addition, CTDR and PV energy values have increased significantly. To further explore the interactions between ramp rates, hydro availability, load profiles, and weather conditions, Cases IV and V were conducted. Relevant results are shown in Tables 6 and 7. In these cases only extreme weather conditions were considered. These were rainy (zero PV output), sunny and variable cloudy conditions. Table 6 includes system operation summary for the weekend load (peak load=7024 MW). Table 7 provides those for the high weekday load (peak load=10159 MW). The impact of PV power fluctuations on PV system penetration can be seen in both tables. For low ramp rates, the PV penetration level was much lower for variable cloudy conditions than for sunny weather conditions, regardless of hydro availability. However, for high ramp rates much more PV was absorbed by conventional generation. For high ramp rates, all the PV system rated capacity of 4 000 MW was dispatched regardless of the load profile and peak load. For low ramp rates, however, the PV penetration level depended very much on the peak load and load shape. Finally, increasing hydro generation improved the penetration level for clear sky, but not for the variable cloudy sky. The impact of ramping rates, weather conditions, and load shape and peak laod on the economical and operational values of dispatching PV is listed in Table 8. CTDR and PV energy values for the selected load types are computed using equations 2 and 3. 6.0 Discussion 6.1 PV Penetration Levels Results indicate that higher ramping rates and higher hydro availability increase maximum PV penetration levels. These penetration levels increase as the peak load increases. 489 The penetration level can also become significant as the peak load and the match between the peak load and peak PV output increases. Table 5. System Operation Summary for Case III (Medium Weekday Load) Sunny Cloudy PV Size (MW) CTGen. HvdroGen. 0 0 2653 18361 1270 3200 2000 2433 8603 1208 3200 2000 2433 9039 1265 3200 2400 2593 14482 1201 2200 1700 2608 12072 1287 PV Gen. 0 21457 18506 3517 6883 0 13411 11 566 2638 5318 Prod. Costs 2 844 340 2 587 819 2 621 337 2 801 560 2 770 126 Note: Top (bottom) entry in each row indicates low (zero) hydro availability. Table 6. System Operation Summary for Case IV (Weekend Load):Sunny vs. Variable Cloudy Days ~LOW ramp rates and low hydro 2High ramp rates and low hydro jlow ramp rates and high hydro Table 7. System Operation Summary for Case v (High Weekday Load): Sunny vs. Variable Cloudy Days llow ramp rates and low hydro 2High ranip rates and low hydro jlow ramp rates and high hydro

490 Table 8. Summary of CTDR and PV Energy Values. Low Hydro Availability. Low ramp rates vs. high ramp rates. Peak PVSize Load (MW) (MW) Sunny VCldy weekday 2000 900 (6950) 2 000 2 000 weekday 3200 2200 (9009) 3 800 3 800 weekday 3500 2200 (1015% 4000 4000 weekend 2 000 1 100 (7 024) 4 000 3 400 6.2 PV Energy Values CTDR (%) Sunny V Cldy 0 0 0 0 0.60 0.12 0.45 0.01 16.88 11.38 16.96 15.74 23.22 13.18 23.64 19.74 PVEV (mills/ kwhr) Sunny V Cldy 4.71 0.62 4.20 2.27 6.96 2.02 8.90 3.61 17.77 9.06 19.69 13.03 18.15 9.06 21.78 15.25 While penetration levels increase, PV energy values tend to decrease. In addition, as higher hydro availability increases penetration levels, PV energy values decrease. Moreover, higher CTDR and PV energy values can be obtained due to a better match between the peak load and peak PV power output which causes higher CT generation displacement, as is the case for the weekend load. 6.3 Production Costs Lower production costs result from start-up costs, O&M costs, and fuel cost savings. It is found that production cost reductions are spread over the 24-hour period. It is also found that production cost savings depend on the penetration level, ramp rates, and hydro availability. Moreover, production cost savings become more significant if the peak load and peak PV power output concur. This is reflected through higher CT generation displacement as is the case for the weekend load mode. 7.0 Conclusion Results obtained in this study show the validity of the proposed method and the interrelationships among peak load, ramping rates, hydro availability, and PV power dynamics. Higher PV penetration levels could be obtained if fast ramping generation and higher hydro generation are allowed. First, it is found that the PV energy values depend to a large extent on the match between thz peak load and the peak PV power output. Second, for a particular day, maximum penetration level depends on the weather conditions, hydro availability, and thermal generation ramping rates. Results obtained in this study are useful in choosing the operating strategy to optimize PV penetration levels under various weather conditions by appropriately selecting the ramping rates and hydro generation dispatching. 8.0 References 1 M. Bouzguenda and S. Rahman, "Integration of Customer- Owned Generation Into the Electric Utility Load Dispatching Technique," Proceedings of the IEEE Southeastcon Conference, April 1989, Columbia, South Carolina, pp. 814-819, vol. 2. 2 B.H. Chowdhury, "Effect of Central Station Photovoltaic Plants on Power System Security, Conference Records of the 21st IEEE Photovoltaic Specialists Conference, May-2 1-25, 1990, Kissimmee, Florida, pp. 831-835, vol. 2. 3 B.H. Chowdhury, "Optimizing The Integration of Photovoltaic Systems with Electric Utilities," IEEElPES 1991 Summer Meeting, San Diego, CA, July 1991, paper no. 91SM 329-3EC. 4 Electric Power Research Institute (EPRI), "Photovoltaic Operation and Maintenance", EPRI GS-6625, Prqject 1607-5, Final Report, December 1989. 5 Electric Power Research Institute (EPRI), "TAG Technical Assessment Guide", EPRI P-6587-L, Vol. 1: Rev. 6, Special Report, September 1989. 6 Electric Power Research Institute (EPRI), "The EPRI Regional Systems Data Base: Version 3. 0", EPRI P-6211, Project 1678, Final Report, January 1989. 7 Hachiro Isoda, "On-Line Load Dispatching Method Considering Load Variation Characteristics and Response Capabilities of Thermal Units," Transactions on Power Apparatus and Systems, August 1982. 8 Jewell, R. Ramakumar, and S.R. Hill "A Study of Dispersed Photovoltaic Generation on the PSO System," IEEE Transactions on Energy Conversion, Vol. 3, no. 3, September 1988, p. 473-478. 9 T.W. Jewell and R. Ramakumar, "The Effects of Moving Clouds on Electric Utilities With Dispersed Photovoltaic Generation," Transactions on Energy Conversion, vol. EC-2, no. 4, December 1987, pp. 570-76. 10 T.W. Jewell and T.D. Unruh, "Photovoltaic Power Output Fluctuation Caused by Clouds: Cost to Utility and Effect on Ability to Serve Load," Transactions on Energy Conversion, March 1990, pp. 8-14. 11 S. Rahman, "Economic Impact of Integrated Photovoltaics with Conventional Electric Utility Operation, "IEEE Transactions on Energy Conversion, September 1990, pp. 422-428. 12 R.A. Schlueter et al., "A Modified Unit Commitment and Generation Control for Utilities with Large Wind Generation Penetrations," IEEE Transactions on Power Apparatus and Systems, Vol. PAS-104, no. 7, July 1985. 9.0 Acknowledgment Parts of the research reported in this paper were sponsored by the DOE Photovoltaics Program administered by Sandia National Laboratories. Saifur Rahman (IEEE S-75, M-78, SM-83) graduated from the Bangladesh University of Engineering and Technology in 1973 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 1975. His Ph.D. degree (1978) is in Electrical Engineering from the Virginia Polytechnic Institute and State University. Saifur Rahman has taught in the Department of Electrical Engineering, 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 System Research Laboratory at VPI. His industrial experience includes work at the 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 areas of interest are demand side management, power system planning, alternative energy systems and expert systems. He has authored more than 150 technical papers and reports in these areas. Mounir Bouzguenda (S-85) graduated from the Pennsylvania State University, University Park, in May 1985 with a B.S. degree in Electrical Engineering. He obtained his M.S. degree in Electrical Engineering from the Virginia Polytechnic Institute and State University, Blacksburg, in December 1987. At present, he is pursuing his Ph.D. degree in Electrical Engineering at Virginia Polytechnic Institute and State University where he is expected to get his degree in July 1992. His areas of interest are generation planning, unit commitment and economic dispatch, load control, and renewable energy systems. He has authored and co-authored several technical papers and reports in these areas.