Value of PV at highpenetration

Transcription

1 Value of PV at highpenetration Applied Project Final Report Bahram Emami August 218

2 Abstract:... 2 Introduction:... 3 Capacity Value:... 3 I. Capacity Value of PV System:... 3 A. First Approach Capacity Credit Method:... 6 B. Second Approach Reliability-based Method: Effective Load Carrying Capability (ELCC) Equivalent Conventional Power (ECP): Calculations & Discussions:... 1 II. Capacity Value of Energy Storage: Calculations & Discussions: LCOE & Net LCOE Calculations & Discussions: Virtual Power Plant I. Modeling & Analyzing: A. HOMER First Case Study: Second Case Study: Third Case Study: B. XENDEE: Power Flow Analysis: Quasi-Static Time-Series (QSTS) studies: Conclusions: Acknowledgments: Appendix Appendix 1: Net-LCOE in 216 and Appendix 2: IEEE 13 Node Test Feeder References

3 Abstract: The resources in a power system must be designed in a way that can meet demand reliably in the long run. The ability of a generator to help reliably serve the demand can be measured as its capacity value. Generator outage can happen due to failure, maintenance, and lack of resource (which is more common for the renewable generators). Therefore, the capacity value for all generators is lower than 1% and they can leave the power system with an insufficient capacity to meet the load in some periods. Determining the capacity value of the variable resources is an issue. For determining that, comparing between their generation and demand is necessary. The share of Distributed Energy Resources (DERs) in the electricity market is growing fast and realizing that the consumers are generating, consuming, or storing the electricity is not an easy task for utility companies. Finding some strategies to allow controlling the new two-way complexity is one of the concerns in this market. Virtual Power Plant (VPP) is one of the most successful strategies that could solve these problems. 2

4 Introduction: Reliability and affordability are the two most significant factors for utilities and consumers. Increasing the share of variable resources like PV and wind turbine can decrease the reliability of the system due to uncertainty in the amount of generation. Finding some methods that can show the ability of these resources for serving the load in different situations is a big concern for utilities and power system planners. In this study the capacity value of the PV system determined by using two different methods for a power system. While the LCOE of the PV system is lower than some other technologies that occupied the most share of the electricity market, but this technology in high penetration could be more expensive due to curtailment. This phenomenon can happen when PV generation is higher than demand or is higher than demand minus minimum generation by conventional power plants. For avoiding this problem some solutions have been presented in this paper and the optimal size of the storage system and optimal penetration of EVs which can mitigate this problem has been determined for a power system. For performing the above solutions communication between utilities and small DERs is vital. The VPP concept is based on these assumptions which lots of smaller generators are connected to the grid and can generate electricity, and some storage systems that are available and can release their stored electrical energy quickly. The other component is controllable loads. These loads could be disconnected from the grid when the amount of demand is more than supply. The software which is controlling all the above components can order to storage systems for charging and discharging, control the amount of generation by DERs, and disconnect controllable loads when it s required. By putting all of these elements together, expanded capacity will be available for the grid. Therefore, using this capacity can allow us to avoid some other requirements like high voltage transmission and distribution lines or some power plants and substation which are designed just for peak hours. VPP by employing the storage system and controllable loads has this ability to accomplish peak shaving as well as peak shifting. I. Capacity Value of PV System: Capacity Value: One of the main issues in the power systems planning is meeting demand in different situations and time. In other words, the amount of generation should be equal to the amount of consumption. The ability of each generator to help the system reliably serve load called capacity 3

5 value[1]. The traditional dispatchable resources can respond to the load variation by increasing and decreasing the amount of generation. But the capacity value of these resources as a result of mechanical failure, maintenance, or lack of resource[2] in the real-time is less than 1%. That means these resources cannot generate and serve the load in 1% of the time. The generator outage may leave the system with the lower amount of generation in comparison to demand and cause a power outage. Capacity and technology are the factors that can determine the capacity value of the conventional generators. For example, the capacity value of the gas-fired generator is different by the coal-fired generator even when the capacity of the units are the same. These capacity values also are different for the same technology with different nameplate capacity. On the other hand, renewable energy resources have intermittent nature. The amount of generation in these kinds of resources depends on the time of day, season, and weather pattern. This dependency made the risk of lack of resource for these resources higher. As a result, the capacity value for these technologies not only is a function of technology and nameplate capacity but also depends on geography and demand pattern[1]. Two different methods for determining the renewables capacity value are employed. First one is focusing on the output of the system on the subset of periods that power system has the highest risk of the outage. Generally, 1 to 1 hours of highest demand load represent these periods[3]. The second method which has higher accuracy and more accepted by utilities and power system planners is the reliability-based approach. Unlike the first one in this method, the capacity value of the system should be measured in the whole year. As mentioned earlier, determining the technology, location, and comparing the real-time generation and demand are the pre-requirements for capacity value calculation. These data have presented in Table 1. But the other factor is nameplate capacity. In this study, different penetration for the PV systems analyzed. This penetration varies from 5% to 5% of the total capacity of the all power plants that located in the Southwest region (39.4 GW). The list of all power plants in the U.S. is published by EIA - Form 86 - annually. In this study the power plants that located in the Southwest region only considered. Also, we intentionally eliminated all the renewable power plants in Southwest region due to this fact that their capacity value has a direct effect on determining the capacity value of our system and these data are not available for their capacity value. The calculation of their capacity value needs a separate study. Technology Photovoltaic Sites Phoenix, Las Vegas, Tucson, El Paso, Albuquerque Demand Pattern Southwest region (216 & 217) Table 1 Technology and location The share of each site determined based on the number of roofs in each city that has a good condition for PV system installation[4]. This number and the size of the systems under different scenarios has presented in Table 2. 4

6 Location No. of Roofs Penetration (DC Capacity) 5% 1% 15% 2% 3% 4% 5% Phoenix 43K 777 MW 1.55 GW 2.3 GW 3.1 GW 4.6 GW 6.2 GW 7.7 GW Las Vegas 12 K 231 MW 463 MW 694 MW 926 MW 1.3 GW 1.85 GW 2.3 GW El Paso 149 K 287 MW 574 MW 862 MW 1.15 GW 1.7 GW 2.3 GW 2.8 GW Tucson 162 K 312 MW 625 MW 937 MW 1.2 GW 1.87 GW 2.5 GW 3.1 GW Albuquerque 188 K 362 MW 725MW 1.8 GW 1.4 GW 2.17 GW 2.9 GW 3.6 GW Total 1,22 K 1.97 GW 3.9 GW 5.9 GW 7.8 GW 11.8 GW 15.7 GW GW Table 2 Location and the size of systems under different scenarios These PV systems have been modeled by National Renewable Energy Laboratory s System Advisor Model (SAM). SAM takes some information like weather data, solar irradiation, and temperature and model hourly AC electricity output of the system. The model of module and inverter also should be determined. In this study, we used a module with a low-temperature coefficient to mitigate the effect of the high temperature in this area, especially in summers. DC to AC ratio is 1.2 and the tilt angle is equal to the site s latitude. Azimuth angle also assumed zero. Hourly southwest electricity demand and supply balance are published by EIA and available on [5]. Southwest region has shown in Figure 1 and its border has been bolded. This area includes Arizona, most parts of New Mexico, parts of Nevada, and El Paso County in Texas. Figure 1 Southwest region 5

7 A. First Approach Capacity Credit Method: The probability of outage has a direct relationship with increasing the demand. If renewable resources can provide a higher amount of electricity in these periods, the conventional generators will face to lower pressure and the probability of outage will be decreased. On the other hand, in terms of stability in the power systems, the amount of generation must be exactly equal to demand. Higher generation by renewables means lower generation is required by conventional resources. Therefore, we need to define the net-load which is equal to total load minus generation from variable renewables. Total generation of conventional resources must be equal to net-load. In our case, the only variable resource is a PV system. Figure 2 shows total load and net-load in the Southwest region in a 7 days period (6/17/17-6/23/17). The peak amount of consumption in 217 occurred in this period (6/2/17 4: PM 5: PM). The penetration of PV system, in this case, is 2%. Total Load Generation from PV Net Load Figure 2 Total load, generation from PV, and net-load For determining the capacity value of the PV system by this method, first, load pattern must be converted from the normal style to load-duration curve[6]. In the load-duration curve, the highest amount of consumption is plotted in the left and the other loads will be sorted based on their amount. Time axis in this curve also illustrates the number of hours that each amount of generation is required. 4 2 Total Load (MWh) Figure 3 Load-duration curve in 217 Figure 4 shows the amount of generation by the PV system (2% penetration), total load and netload in the 1 hours of highest load in 217. The capacity value of the PV system will be equal 6

8 to the fraction of its generation in this period to its AC nameplate capacity. For example, if A 1 GW PV system has 1% capacity value, then it can provide 1 GW electricity for the grid. In other words, the amount of capacity that required by other power plants will be reduced 1 GW in compare to a situation that the PV system has not been installed Total Load (MWh) PV Generation (MWh) Net Load (MWh) Figure 4 Load duration curve, PV generation, and net-load The maximum nameplate capacity of a PV system is not necessarily its maximum capacity. In some conditions like higher irradiance or lower temperature (in compare to STC conditions) it can provide even higher than 1% capacity. By comparing the output of the system in the 1 and 1 hours of highest load in 217 the capacity value (credit) of the system in the 1 hours of highest load in average was 5.43% (with the standard deviation of 24.25%). This value for the 1 hours of the highest load was 46.24% (with the standard deviation of 26.72%). These results have been shown in Figure 5. Histogram of CC-1, CC-1 Normal Variable CC-1 CC-1 Mean StDev N Density % 2.% 4.% 6.% 8.% 1.% Data Figure 5 Capacity credit (value) for the PV system in the 1 hours (CC-1) and 1 hours (CC- 1) of the highest load in 217 In 216 capacity value (credit) of the system in the 1 hours of highest load in average was 5.8% (with the standard deviation of 13.93%). This value for the 1 hours of the highest load was 48.14% (with the standard deviation of 26.66%). Figure 6 shows these results. 7

9 Histogram of CC-1, CC-1 Normal 5 4 Variable CC-1 CC-1 Mean StDev N Density % 2.% 4.% 6.% 8.% 1.% Data Figure 6 Capacity credit (value) for the PV system in the 1 hours (CC-1) and 1 hours (CC- 1) of the highest load in 216 B. Second Approach Reliability-based Method: The capacity value of the PV system is dependent on coincide of sunlight and load pattern. By increasing the share of PV systems in a power system the problem can appear. In fact, PV systems decrease the net-load during the day and shift the time of greatest demand to the evening when the generation capacity of the PV systems have dropped significantly. Therefore, analyzing the effect of PV system on the power system in a whole year can illustrate their real capacity value for the power system. The reliability-based method is accepted and employed by utility companies and power system planners. In this method, reliability-based method and statistical approximation are used. By the estimation of the probability of outage event in a power system which explained as the generation capacity lower than load; the capacity value of the system can be determined. For this estimation, we need to determine loss of load probability (LOLP) and loss of load expectation (LOLE) in our power system. LOLP is defined as the probability transmission lines, power plants, or generators outage leaves the system with lower capacity in comparison to the load in a period of time[1]. The sum of LOLPs in a planning period can determine LOLE. Generally, LOLP modeled at hourly time steps and LOLE measured over a year time step[1]. Power system planners plan for a certain LOLE in a year like.1 day/year[3] (1 day in 1 years). In the real condition due to the export and import of the electricity between utilities, LOLE of the system in some years is even less than.1 day/year. The probability of a failure of conventional generators in any given time is modeled by the equivalent forced outage rate (EFOR). This rate normally expresses as a percentage and varies by the technology and the size of the generator. The availability of a conventional generator is equal 1% minus its EFOR. 8

10 1. Effective Load Carrying Capability (ELCC) The amount of the load that can be added to the system by installing a new generator while maintaining the same LOLE is the ELCC of a generator. For calculating the EFOR of PV systems first step is determining LOLPs of the power system without PV system: P prob{ G L} t t t Where G t is conventional generating capacity available in hour t, L t is load in hour t. This probability function explains the failure of generators which has been modeled in EFOR. The LOLP of the base system can be measured by: Pt In the third step, PV system output ( PV ) and a fixed load ( L ) should be added to each hour: ELCC t t T prob{ G PV L L} t T t t t By adding the PV system LOLE in the system will be decreased. Therefore, the fixed load should keep increasing till LOLE of the system after installation of PV system be equal to LOLE before the installation: ELCC The final amount of L define as the PV system s ELCC and its fraction to the AC capacity of the system can determine the capacity value of the system in this method. 2. Equivalent Conventional Power (ECP): As mentioned earlier, the ELCC of conventional generators also is less than 1 due to maintenance, mechanical failures, and lack of resource. So comparing the ELCC of a PV system with a benchmark unit can help us to determine the capacity value for the system and compare that with other technologies. In this technique, once the ECP of the system should be calculated for the PV system. This step is the same as the calculations that have been done in the ELCC calculation: PV L L L t t prob{ G PV L } t T t t t Then instead of PV system add another technology and calculate its ECP: Benchmark prob{ G B L } t T t t t 9

11 In fact, in this formula instead of changing the load, the size of the benchmark unit varies until the ECP of benchmark unit be equal to ECP of the PV system: PV Benchmark It is important to note, B t is available generating capacity of the benchmark unit in hour t and it is not the nameplate capacity. So converting that to the nameplate capacity is necessary. The fraction of AC nameplate capacity of the PV system to the nameplate capacity of the benchmark unit would be the capacity value of the PV system in this method. 3. Calculations & Discussions: The rated capacity of generators obtained from EIA Form 86. The summer and winter capacity of generators due to the difference in ambient temperature is different. This difference also considered in our calculations. EFORs of the existing generators also have been estimated by using the North American Electric Reliability Corporation s Generating Availability Data System (GADS). Gas turbine generator considered as the benchmark unit in this study which is commonly used as a peaking generator. The size of our benchmark also considered over 5 MW. The EFOR of this kind of units is 38.7%. Therefore, its availability is equal to 61.93%. The generation capacity of Southwest region is higher than its demand. It is caused by the exportation of energy from this area to other states and regions. For example, the Palo Verde Nuclear Generating Station is located in Arizona and has 5 units. 4% of the generation of unit 1, 2, and 3 and 6% of the generation of unit 4 and 5 are exported to other states[7]. Finding all of this data and employing them in the calculations without accessing to the all utility companies data in this region is impossible. On the other hand, for determining the capacity value of the PV system, determining the LOLE of the base system is pre-requirement. In another word, the calculation of capacity value for the PV system when the LOLE is equal to zero is pointless. So LOLE for our power system assumed 1 hours. These outages assumed to occur in the 1 hours of highest load. Table 3 and 4 shows the size of the PV system, added load and capacity value of the PV system in different penetrations in 217 and 216 respectively. Penetration AC Size of PV System Added Load CV - ELCC CV - ECP 5% 1.6 GW 277 MW 16.86% 27.22% 1% 3.3 GW MW 13.8% 22.29% 15% 4.9 GW 59.5 MW 1.34% 16.69% 2% 6.6 GW 55.5 MW 8.38% 13.53% 3% 9.9 GW MW 5.61% 9.7% 4% 13.1 GW MW 4.38% 7.7% 5% 16.4 GW 613 MW 3.73% 6.2% Table 3 Capacity Value of the PV System in different penetrations in 217 1

12 Penetration AC Size of PV System Added Load CV - ELCC CV - ECP 5% 1.6 GW 393 MW 23.92% 38.62% 1% 3.3 GW 462 MW 14.6% 22.7% 15% 4.9 GW 462 MW 9.37% 15.14% 2% 6.6 GW 462 MW 7.3% 11.35% 3% 9.9 GW 462 MW 4.69% 7.57% 4% 13.1 GW 51 MW 3.81% 6.15% 5% 16.4 GW 51 MW 3.5% 4.92% Table 4 Capacity Value of the PV System in different penetrations in 216 Unlike the first approach; in the reliability-based method, the capacity value of the system varies by changing the size of the system. It is due to this fact that the system cannot generate electricity in the nights and the amount of generation in the early morning and late evening is significantly lower. When the amount of added load increase, our PV system may help to the system to serve the load in the most critical hours (1 hours of highest load) but it cannot add any capacity to the system in the night. For example, the 1 hours of highest load in 216 were June 19 (4 5 PM), June 2 (3 6 PM), July 22 (3 6 PM), July 27 (3 5 PM) and July 28 (4 5 PM) and we assumed outage occurred in these periods. So the maximum available capacity of the system in June and July was lower than demand. But in other months the maximum available capacity considered only 1% higher than maximum demand on that month. By increasing the available capacity to the system (by adding PV system) and adding the load, some outage can occur in other periods and even in other months. In 216 case, the PV system could not generate any electricity in the 5%, 7%, 7%, 7%, 7%, 8%, and 8% of the time that outage happened (where the penetration were 5%, 1%, 15%, 2%, 3%, 4%, and 5% respectively). But in 217 case, the number of hours that the PV system could not generate any electricity was 2, 2, 4, 5, 6, 6, and 7 out of 1 where the PV penetration varies between 5%, 1%, 15%, 2%, 3%, 4%, and 5% respectively. II. Capacity Value of Energy Storage: In fact, by increasing the penetration of PV systems in a power system the problem can occur on the time that the PV system cannot generate any electricity. So, this problem and very low capacity value of the system in high penetration of PV systems encourage us to combine the PV system with the storage system. The storage system can convert the non-dispatchable resources (like PV) to dispatchable resources. But on the other hand, each storage system depends on its size and the time that can be discharged has its own capacity value and this value also should be determined. 11

13 1. Calculations & Discussions: Energy storage systems have energy limitation and time dependency. These two factors make the estimation of the capacity value for the storage system more complicated[8]. The ability of the storage system for serving the load at time t depends on its prior operation. So, if a storage system cannot serve the load in time t, it cannot serve the load in the further time until it starts charging again. Selecting the best time for charging and discharging of the storage system is a significant issue. The best time for charging and discharging can be dictated to the storage systems by controllers. In this part of our study the controller designed to start charging the batteries when supply is higher than demand and start discharging when supply is lower than demand. The system is idle when the system is full charge or the supply and demand are equal. This method can show us the maximum amount of capacity value that a storage system can provide for the system. The size of the storage system assumed 25% of the PV system. The size of the system varies by increasing the size of the PV system. This system has the ability to charge in 2 hours and discharge for 2 hours and round-trip efficiency neglected. The amount of capacity value has been estimated base on ELCC and ECP methods which described earlier. In this case, also the gas turbine generator considered as a benchmark unit in the ECP calculation. The power system at this level has the PV system and storage system. Therefore, before adding the storage system to the power system, the amount of added load which calculated for the PV system must be added to demand. The result of this estimation for 217 (Table 5) and 216 (Table 6) has been presented. Penetration of PV Size of the Storage Added Load CV - ELCC CV - ECP 5% 41.7 MW 41.5 MW 99.94% % 1% MW 672 MW 81.8% 132.9% 15% 1.2 GW 1.55 GW 85.62% % 2% 1.6 GW 1.2 GW 75.23% % 3% 2.5 GW 1.5 GW 6.95% 98.41% 4% 3.3 GW 1.7 GW 53.23% 85.95% 5% 4.1 GW 1.9 GW 46.38% 74.89% Table 5 Capacity Value of the Storage System in different penetrations in

14 Penetration of PV Size of the Storage Added Load CV - ELCC CV - ECP 5% 41.7 MW MW 99.91% % 1% MW 716 MW 87.16% 14.74% 15% 1.2 GW 1.1 GW 89.19% 144.1% 2% 1.6 GW 1.3 GW 81.13% 131.1% 3% 2.5 GW 1.5 GW 62.45% 1.84% 4% 3.3 GW 1.7 GW 53.26% 86.% 5% 4.1 GW 1.96 GW 47.84% 77.25% Table 6 Capacity Value of the Storage System in different penetrations in 216 The estimation of the capacity value of the storage system by the ELCC method when the penetration is 5% is very close to 1% in both years. The capacity value of the system by ECP technique in both of these years is higher than 1% in some cases. This excess is due to the higher capacity that storage system can provide for the power system in comparison to gas turbine generators. The controller of the storage system designed to order for discharging only when demand is higher than supply and this factor has a significant effect on the high amount of capacity value that obtained. In other words, this capacity value is the maximum amount of capacity value which these storage systems can provide for the power system. LCOE & Net LCOE One of the most important factors that can limit the PV deployment is curtailment. Sometimes PV energy must be rejected due to the supply-demand balance of the system. This problem will be more notable when the penetration of PV systems increases dramatically. The most important element that can dictate the amount of the curtailment is the minimum amount of energy that must be generated by other conventional generators. Some technologies like nuclear plants and coal plants do not have enough flexibility to decrease their generation when PV is generating a high amount of energy and increase it when the amount of generation by PV systems dropped significantly. In other words, a power system with high flexibility can reduce the amount of curtailment by PV systems and low flexibility raise it. Figure 7 and 8 show the effect of other generators on the amount of curtailment on January 1, 217, in the Southwest region. In these cases, the penetration of PV system is 5%. 13

15 Load (GW) PV (GW) Curtailment Figure 7 Minimum amount of curtailment Min. Gen. 7 GW Load (GW) PV (GW) Curtailment Figure 8 Curtailment when the minimum amount of generation by conventional generators is 7 GW As we can see in figure 7, curtailment in a power system when the penetration of PV system increase is unavoidable. In general, in a region like Southwest region, the amount of consumption in winters is lower than summers. As a result, curtailment also can happen more common in this period. But in figure 8, the amount of curtailment increased due to this fact that all power plants cannot be shut down when PV is generating electricity. In this case, the minimum amount of generation by other generators in the system assumed 7 GW. Definitely, by decreasing the amount of minimum generation by conventional power plants amount of curtailment can be decreased. In this study the amount of curtailment for the PV systems when their share varies between 5% and 5% calculated. The flexibility of the power system also varies between 7 GW (very low flexibility), 3.5 GW (low flexibility), 3 GW (medium flexibility), and 2.5 GW (high flexibility). 14

16 On the other hand, curtailment has a direct effect on Levelized Cost of Energy (LCOE). When the total amount of generation by a system as a result of curtailment decrease, the LCOE of that system increase. While all the stakeholders in the PV industry have tried to make this technology comparable with conventional resources in terms of cost, this factor can hurt all of these efforts. For addressing this problem the concept of Net-LCOE is defined which means the cost of energy that can be used by grid after considering the curtailment[9]: Net LCOE = Base LCOE (1 Curtailment Rate) While unsubsidized LCOE of Solar PV (Crystalline Silicon) in the utility-scale varies between $ 46 and 53 $ [1], this number can be increased due to the high amount of curtailment. In MWh MWh $ this study the LCOE of PV systems considered.5 which is equal to the average kwh unsubsidized LCOE of Solar PV. Figure 1 and 11 shows the effect of curtailment on the Net LCOE for PV systems in 217 and 216 and compare the LCOE of the system after curtailment with gas, coal, and gas peaking power plants. LCOE of these technologies considered.6 $, kwh $.1, and.183 $ respectively. These amounts also are the average unsubsidized LCOE of kwh kwh these technologies in 217[1]. Figure 9 also shows the average unsubsidized LCOE for these technologies Coal Gas Gas Peaking Solar Figure 9 - Average Unsubsidized LCOE ($/kwh) 15

17 $.2 $.15 $.1 $.5 $. % 5% 1% 15% 2% 3% 4% 5% 6% Very Low Flexiblity Low Flexiblity Midum Flexiblity High Flexiblity Gas Coal Gas Peaking $.2 Figure 1 Net LCOE of PV systems in 217 $.15 $.1 $.5 $. % 5% 1% 15% 2% 3% 4% 5% 6% Very Low Flexiblity Low Flexiblity Midum Flexiblity High Flexiblity Gas Coal Gas Peaking Figure 11 Net LCOE of PV systems in 216 Avoiding 1% of curtailment when the pentation of PV system is very high is a very hard task and sometimes is impossible. Therefore, we should try to decrease it to an acceptable level. The 16

18 acceptable level is considered the amount that the Net LCOE of the PV system be comparable with other technologies. In Figure 1 and 11 is obvious even in 5% penetration of PV system and very low flexible power system the Net LCOE of PV systems is lower than gas peaking and coal plants. But in some cases, the Net LCOE of the system is higher than gas plants. The goal of this research is decreasing the Net LCOE of the system to the level of gas plants or.6 $ kwh. For achieving that employing storage system, charging the electric vehicles (EV) when the PV system can generate electricity, demand response, and exporting the extra generation to other states or countries are the solutions that can be used[9]. In this study two first solutions (storage system and EV) considered. 1. Calculations & Discussions: The storage system is assumed to have 8-hours discharging capacity. Round-trip efficiency of the storage system in this study neglected. In fact, this factor can decrease the size of the storage system due to a higher amount of energy that would be needed for charging in compare to the available energy for discharging. But, this element could not be modeled in our MATLAB code. The number of EV also determined based on the number of cars in Arizona, New Mexico, and Nevada. The total number of cars in this three states is more than 4 million[11]. Storages of the EV assumed have 3kWh energy with 4 hours charging time and 156 miles driving range[12]. With considering average 37 miles per day driving distance[13], these cars need to recharge approximately every 4 days. The controller of the storage system designed to allow the battery to charge only with the electricity that is generated by the PV system. But if EV s couldn t be full charge with the PV s generation during the day, they can be charged with the grid s electricity. By employing this model the amount of curtailment can be decreased more. With considering the above conditions, the Net-LCOE of the PV system only is higher than gas plants when its penetration is higher than 3%. If our system has low flexibility (minimum generation = 3.5 GW) and medium flexibility (minimum generation = 3 GW), this problem can appear only when the share of PV reaches 5%. Also, by increasing the flexibility of the system to the high level (minimum generation = 2.5 GW) can avoid this issue even when penetration of PV system is 5%. When the penetration of PV reaches to 5% the required storage varies between.45 GW, 1 GW, and GW and the penetration of EVs were 5%, 15%, and 25% for the medium flexible, low flexible, and very low flexible power systems respectively. For the very low flexible power system the required storage system and penetration of EVs are 1 GW and 5%, and 4.4 GW and 1% when PV penetration is 3% and 4% respectively. The required storage system and penetration of EVs for 217 are as shown in figure12 and

19 Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration Figure 12 EVs penetration and Required Storage - : Very Low (217) Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration Figure 13 EVs penetration and Required Storage - PV Penetration: 5% (217) For 216 the penetration of EVs varies between 5% (3% - very low flexibility), 1% (4% - very low flexibility), 5% (5% - medium flexibility), 15% (5% - low flexibility), and 2% (5% - very low flexibility) wherein the parentheses the penetration of PV and the flexibility of the power system has expressed. The size of the required storage systems is 1 GW, 3.9 GW,.5 GW, 1 GW, and 6.25 GW respectively. Figure 14 and 15 shows these results. 18

20 Storage Required (GW) % 5% 1% 15% 2% 25% EV Penetration Figure 14 EVs penetration and Required Storage - : Very Low (216) Storage Required (GW) % 5% 1% 15% 2% 25% EV Penetration Figure 15 EVs penetration and Required Storage - PV Penetration: 5% (216) Virtual Power Plant The complexity of the grid is increasing due to higher penetration of distributed energy resources (DERs). The size of these resources is small and as a result, they cannot participate in the electricity market. On the other hand, these resources can generate, consume, and store the electricity in different periods of time. As mentioned earlier, curtailment sometimes in the power system is an unavoidable task and without two-way communication with the small units, it can be impossible. Also, selecting the best time for charging and discharging the storage systems depends on many factors that without considering the situation of the whole system is impossible 19

21 to be selected. Therefore, a new method which allows us to communicate with DERs is necessary. This communication allows utilities to perform some technical aspects like frequency support and reactive power support. On top of that, it can enable the participation in the market for DERs[14].For addressing these challenges and enabling the opportunities different approaches like microgrid, Virtual Utilities, and Virtual Power Plant has been presented. Virtual Power Plant (VPP) is a decentralized energy management system that aggregates DERs and controllable loads. In fact, VPP is a grid-connected microgrid where DERs and controllable loads are controlled by power system planners[15]. VPP can act as a single power plant and it has its own operating conditions like ramp rates, voltage regulation capability, limit of generation and etc. It also can communicate with controllable loads and turn them on or off depends on the balance between demand and supply. DERs in a VPP can vary from renewable resources like Solar PV and Wind to small hydro stations and diesel generators. All in all, VPP is a good approach for controlling renewable energy sources (RES) and normally employs in the places that share of RES is significant. Energy Storage System (ESS) is the other component which can be combined with the above components and make the VPP more flexible and reliable. Energy Management System (EMS) located in the core of VPP. All the data from all components that connected to the VPP like DERs, ESS, and controllable loads and the grid requirements collect with EMS. Based on these data and some forecasting algorithms EMS provides some bids in the market and sends signals to the components of VPP[14]. Based on the situation of the system EMS can set the operation mode of the system. No power exchange mode, grid export mode, and grid import mode are three different modes that system can vary between them. EMS can find the best time for charging and discharging the storage systems, exporting the excess generation to the grid, turning on and off the controllable loads, and if it was necessary, the curtailment of the extra generation by PV. This algorithm is shown in Figure 16. Figure 16 EMS algorithm for VPP[14] 2

22 I. Modeling & Analyzing: Modeling a power system can help power system planners and utility companies to find the problems and issues before performing. In this study, a VPP has been modeled in HOMER and XENDEE. In fact, by employing HOMER the optimize size of PV system and storage system can be determined and XENDEE can show the condition of the power system after adding these components. A. HOMER In this study a power system modeled in HOMER. Electrical load of this system is a combination of loads of 12 ASU s buildings in 217. The list of the buildings, the type of building, and peak demand of each building in this period has been listed below: Name Category Peak Art Arts kw Barrett Honors College - Residential kw Family Studies Academic kw Hassayampa Village 2 Residential kw Health Services Administration kw ISTB4 Research kw ISTB5 Research kw McClintock Hall Residential kw Parking Structure 3 Parking kw Social Science Academic kw Student Service Administration kw Verde Dickey Dome Athletics kw Table 7 Name, type and peak demand of ASU s buildings in 217 The categories of buildings are different. Therefore, the load patterns of them are different as well. For example, a residential building like Hassayampa Village 2 has more fluctuation in its average daily load profile while a research building has a flatter average daily load profile. This difference can be seen in Figure

23 Figure 17 - Average daily load profile of Hassayampa Village 2(left) and ISTB 4 (right) The combination of these loads provided the total electrical load for our modeling. Some of the load profiles such as Hassayampa Village 2 used more than once to provide 15 load profiles for modeling in the next step (XENDEE). Unlike the normal load pattern in Phoenix area, the amount of demand in June and July is lower than other months. Summer vacation of the university in these months can interpret this differences. Figure 18 Monthly load profile The optimal size of a PV system and storage system in HOMER are depended on many different factors like the capital cost of each component, demand rates, electricity rates, and sell back rates of excess PV generation. Three different case studies with different variables in HOMER have been simulated and their conditions and results are reported below. The storage system that employed in all of these case studies is Tesla Powerwall 2 and net metering was enabled. 22

24 1. First Case Study: The condition of first modeling was considered like the real situation[16]: On-Peak Hours Off-Peak Hours Monday-Friday 3 PM to 8 PM Monday-Friday 8 PM to 3 PM+ Weekends Winter On-Peak kwh $.475 Winter Off-Peak kwh $.475 Winter On-Peak Demand Charge per kw $ Winter Off-Peak Demand Charge per kw $ 6.5 Summer On-Peak kwh $.575 Summer Off-Peak kwh $.475 Summer On-Peak Demand Charge per kw $ 2.25 Summer Off-Peak Demand Charge per kw $ 6.5 Sellback per kwh $.129 Capital Cost of PV per kw $ 21 Capital Cost of Battery per Unit $ 65 Capital Cost of Converter per kw $ 3 In the above conditions, the optimal system that offered by HOMER was installing a PV system. The size of an optimal PV system was 184 kw. The storage system did not offer by HOMER due to the higher profit that can be obtained by selling back the excess of PV generation to the grid. The sell back price is higher than on-peak rates. LCOE of the system, in this case, $ decreased from.883 in the grid only situation to.879 $ in the grid + PV situation. kwh kwh Renewable fraction which defined as the fraction of the energy delivered to the load that originated renewable power sources was 1.95%. Storage systems can mitigate demand charge rate and absorb parts of the excess generation of PV system. All in all, installing ESS won t be a good decision until the sell back rates are higher than on-peak rates or big jumps in the load pattern (that can cause high demand charge) do not occur in the load pattern. 2. Second Case Study: The conditions in this case study are as following: 23

25 On-Peak Hours Off-Peak Hours Monday-Friday 1 PM to 8 PM Monday-Friday 8 PM to 1 PM+ Weekends On-Peak kwh $.18 Off-Peak kwh $.5 On-Peak Demand Charge per kw $ 3.5 Off-Peak Demand Charge per kw $ 1.5 Sellback per kwh $.3 Capital Cost of PV per kw $ 18 Capital Cost of Battery per Unit $ 65 Capital Cost of Converter per kw $ 3 In this situation, the sell back rates dropped significantly. Despite, the load pattern is the same, but higher on-peak energy charge encourages the consumers to install ESS. The optimized system for this situation is 2,311 kw PV system and 13 Tesla Powerwall units. LCOE of the system in the grid only situation was.918 was.853 $ kwh $ kwh. Renewable fraction in this case was 23.7%., but after installing the DERs and ESS LCOE 3. Third Case Study: In this study, the situation designed in a way that extremely encourages consumers to install DERs and ESS: On-Peak Hours Off-Peak Hours Monday-Friday 1 PM to 8 PM Monday-Friday 8 PM to 1 PM+ Weekends On-Peak kwh $.25 Off-Peak kwh $.5 On-Peak Demand Charge per kw $ 5.5 Off-Peak Demand Charge per kw $ 1.5 On-Peak Sellback per kwh $.19 Off-Peak Sellback per kwh $.3 Capital Cost of PV per kw $ 18 24

26 Capital Cost of Battery per Unit $ 65 Capital Cost of Converter per kw $ 3 The higher rates for on-peak demand charge, on-peak charge, and sell back rates are the factors that caused increasing the size of our system in this situation. Optimize system, in this case, is 24,65 kw PV system which combined with 4,27 Tesla Powerwall units. The LCOE of the system in the grid only situation was $ kwh B. XENDEE: $ kwh, but after installing DERs and ESS it dropped to. Finally, Renewable fraction of the system reached to the level of 88.9%. The mentioned load profile modeled in XENDEE for analyzing the load flow and effect of the installation of PV and PV + Storage in the whole year. These loads modeled in one of the IEEE test cases (13 Node Test Feeder) that are available in [17]. In all the cases the power rating of the transformer increased from 2MVA to 5MVA for avoiding the overvoltage on the secondary side of the transformer. The system modeled based on the second study case situation. But for analyzing the effect of high penetration of PV system and storage system on the power system the size of components assumed as following: PV system 6 kw, Converter 5 kw, Storage 396 kwh (3 Tesla Powerwall 2). Renewable Fraction is 55.7% in this condition. Figure 19 IEEE 13 Node Test Feeder 25

27 Figure 19 IEEE 13 Node Test Feeder after installing PV and Battery 1. Power Flow Analysis: In the base case power flow analysis shows an overvoltage fault on the secondary side of the transformer (higher than 5%) but this fault does not appear after adding PV, and PV + Storage to the system. So DERs and ESS could improve power flow and voltage stability of the power system. Figure 21 Overvoltage fault in the base case 26

28 2. Quasi-Static Time-Series (QSTS) studies: In this study, the power system has been modeled in three different situations. The duration of the study was 1 year and power system in the base case, after installing the PV system, and after installing the PV system and storage system was analyzed. The results of these studies are as below: Figure 22 Consumption of the system in the base case 27

29 Figure 23 Consumption of the system after installing PV Figure 24 Consumption of the system after installing PV and Battery The total electricity that provided by utility decreased from 2.7 GWh to 11.3 GWh in only PV scenario and to 11.5 GWh in PV plus storage case. The higher demand in the PV plus storage case happened due to charging batteries by grid electricity and round-trip efficiency of the storage system Grid + PV + Battery Grid Grid + PV Figure 25 Load pattern of the system in the first 48 hours 28

30 By analyzing the load pattern of the system in the first 28 hours the importance of storage system can appear. The storage system can store some part of energy before the on-peak hours (1: PM - 8: PM) and discharge that before the end of on-peak hours and when the PV system can not generate enough energy. The logic of the storage system in the power system after installation of PV and storage can be seen in figure 26 which shows the provided electricity by the grid, PV, and storage system. The negative amounts for storage system show discharging time and positive amounts show charging time for the storage system. The negative amounts for the grid also mean the system sets in the exporting mode while the positive amounts show importing mode Grid PV Battery Figure 26 Logic of VPP in the first 48 hours The higher amount of peak demand in the system after adding storage system happened due to higher demand for charging the storage system. Sometimes the PV system cannot provide enough electricity for charging the storage system due to failure or lack of resource (e.g. in a cloudy day). The system designed to charge the batteries before the on-peak hours. This extra demand can increase the peak demand in some periods. Highest demand in this study happened on a day that the generation of PV before the noon was neglectable, but batteries were charging from 8: AM to 1: PM (Figure 27). 29

31 Grid PV Battery Figure 27 Peak demand in the power system after installing PV and battery One of the advantages of DERs is decreasing the amount of the losses in the power system due to the lower distance between the generation site and demand. This reduction in our system was significant. Figure 28 shows the line losses, transforms losses, and total losses for the power system in the all three different situations Line Losses (MWh) Transformer Losses (MWh) Total Losses (MWh) Base Case PV PV+Battery Figure 28 Line, transformer, and total losses in the power system Finally, PV and PV plus storage could slightly improve the voltage profile of the system in phase A and voltage profile in these two cases is closer to 1. But the PU amounts of voltage for phase B and C before the replacement of PV and storage had a better condition. Although, in all the phases and all the condition the voltage profile of the system is acceptable and the system does not have any significant issue. 3

32 Histogram of PU Phase A (Base), PU PHase A (PV), PU Phase A (PV+Bat) Normal Density Variable PU Phase A (Base) PU PHase A (PV) PU Phase A (PV+Bat) Mean StDev N Data Figure 29 Voltage profile of Phase A Histogram of PU Phase B (Base), PU PHase B (PV), PU Phase B(PV+Bat) Normal Density Variable PU Phase B (Base) PU PHase B (PV) PU Phase B(PV+Bat) Mean StDev N Data Figure 3 Voltage profile of Phase B 31

33 Histogram of PU Phase C (Base), PU PHase C (PV), PU Phase C (PV+Bat) Normal Density Variable PU Phase C (Base) PU PHase C (PV) PU Phase C (PV+Bat) Mean StDev N Data Figure 31 Voltage Profile of Phase C Conclusions: Calculating capacity value allows us to quantify the effect of a resource on the reliability of the system and plan for meeting the load by other resources. The capacity value of renewable resources in comparison to other resources in high penetration is lower. The main reason is lack of resource which for conventional resources is not very common. One method for increasing the capacity value of the renewable resources is installing the storage system. Definitely, the storage systems increase this value and reliability of the system. In fact, employing PV plus storage can help us to convert the PV systems from non-dispatchable resources to dispatchable. Also, determining the capacity value helps us to optimize the size of our storage system. By increasing the share of variable resources curtailment in some periods of time will be inevitable. It will be more critical when the minimum generation of the power system by conventional power plants is considered. For mitigating this effect (which can increase the Net LCOE for the PV systems) installing more storage systems, charging the EVs during the day (when PV can generate electricity), exporting excess electricity to other states or countries, and shifting demand from evening and night to the noon and morning are the solutions that can be used. On the other hand, by increasing the share of DERs a platform that allows us to perform some tasks such as load shifting, charging the batteries when supply is higher than demand and discharging them when demand is higher than supply, supporting the frequency and so on, will be necessary. VPP is a smart grid that provides a platform for linking retail markets to wholesale markets. By employing VPP can find a balance between the stability of the grid and high profit for DER s owners. For the consumers and grid, it is exactly like a single power plant that is generating electricity for the grid, and consumers use it. However, unlike conventional power plants, these DERs located in the distribution network and the level of voltage is low or medium. Different small scale DERs such as wind turbines, Photovoltaics 32

34 systems, and biomass; energy storage systems (ESS) like batteries; and controllable and flexible loads; are different components of a VPP. Software that connects DERs and storage systems together and controls all of the components and controllable loads is the last and most significant element of a VPP. Reliability in the grid is the most significant factor. Before using a new idea like VPP, modeling that, and examination of the model is a necessary task. Modeling can help us to recognize the problems and challenges that can be happened. Fortunately, with the new developments, modeling the systems before running is a feasible task. The results of the modeling in this study proved VPP can help to power system by decreasing the losses and lower demand. It also can provide lots of benefits for consumers and connect them to the wholesale market. Acknowledgments: The author would like to thank the following individuals and company for their contributions: Dr. Nathan Johnson and Mr. Shammya Saha who helped me to understand the concept of microgrid and VPP; Sunny Energy Company that allowed me to have lots of meeting with its director; Mr. Erik Ellis who provided some information for me and guided me in some parts of project which the completion of this project without those supports was impossible. I also acknowledge the supports I received from Arizona State University (ASU), Mrs. Karen Dada, and Dr. Govindasamy Tamizhmani. Finally, my deepest appreciation goes to my advisors. I gratefully acknowledge Dr. Ronald Roedel for his help and supports and Mr. Joseph Cunningham for all of his advice and assists. Their generous dedication of time and energy to long discussions and constant guidance and encouragement has come to rescue. 33

35 Appendix Appendix 1: Net-LCOE in 216 and 17 Amount of curtailment and Net-LCOE of the system in different penetration and different flexibility of the system in 216 and 217: Net LCOE in 217: 5% Penetration Total Generation Very Low Low Medium High PV Generation 3.77 TWh 3.77 TWh 3.77 TWh 3.77 TWh 3.77 TWh Rate of Curtailment % % % % % LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation 7.54 TWh 7.52 TWh 7.54 TWh 7.54 TWh 7.54 TWh Rate of Curtailment %.18% % % % LCOE ($/kwh).5 ~ % Penetration Total Generation Very Low Low Medium High PV Generation 11.3 TWh 1.86 TWh 11.3 TWh 11.3 TWh 11.3 TWh Rate of Curtailment % 3.9% % % % LCOE ($/kwh)

36 2% Penetration Total Generation Very Low Low Medium High PV Generation 15.7 TWh TWh 15.7 TWh 15.7 TWh 15.7 TWh Rate of Curtailment % 1.48% % % % LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation TWh TWh TWh 22.3 TWh TWh Rate of Curtailment % 23.17% 3.96% 2.56% 1.5% LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation 3.15 TWh 2.15 TWh TWh TWh TWh Rate of Curtailment % 33.15% 12.22% 1.7% 8.16% LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation TWh TWh TWh 3.84 TWh TWh Rate of Curtailment % 41.65% 2.54% 18.17% 15.94% LCOE ($/kwh)

37 Net LCOE in 216: 5% Penetration Total Generation Very Low Low Medium High PV Generation 3.77 TWh 3.77 TWh 3.77 TWh 3.77 TWh 3.77 TWh Rate of Curtailment % % % % % LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation 7.54 TWh 7.52 TWh 7.54 TWh 7.54 TWh 7.54 TWh Rate of Curtailment %.17% % % % LCOE ($/kwh).5 ~ % Penetration Total Generation Very Low Low Medium High PV Generation 11.3 TWh 1.86 TWh 11.3 TWh 11.3 TWh 11.3 TWh Rate of Curtailment % 3.96% % % % LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation 15.7 TWh TWh 15.7 TWh 15.7 TWh 15.7 TWh Rate of Curtailment % 1.59%.2% % % LCOE ($/kwh)

38 3% Penetration Total Generation Very Low Low Medium High PV Generation TWh TWh TWh TWh TWh Rate of Curtailment % 23.58% 4.11% 2.74% 1.66% LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation 3.15 TWh 2. TWh TWh 27.8 TWh TWh Rate of Curtailment % 33.65% 12.35% 1.17% 8.24% LCOE ($/kwh) % Penetration Total Generation Very Low Low Medium High PV Generation TWh TWh TWh 3.68 TWh TWh Rate of Curtailment % 42.32% 21.1% 18.6% 16.31% LCOE ($/kwh)

39 Appendix 2: IEEE 13 Node Test Feeder Overhead Line Configuration Data: Confi g. Phasin g Phase Neutral Spacin g ACSR ACSR ID 61 B A C N 556,5 26/7 4/ 6/ C A B N 4/ 6/1 4/ 6/ C B N 1/ 1/ A C N 1/ 1/ C N 1/ 1/ 51 38

40 Underground Line Configuration Data: Config. Phasing Cable Neutral Space ID 66 A B C N 25, AA, CN None A N 1/ AA, TS 1/ Cu 52 Line Segment Data: Node A Node B Length(ft.) Config XFM Switch Transformer Data: kva kv-high kv-low R - % X - % Substation: 5, D 4.16 Gr. Y 1 8 XFM Gr.W.48 Gr.W

41 Capacitor Data: Node Ph-A Ph-B Ph-C kvar kvar kvar Total

42 References: [1] S. H. Madaeni, S. Member, R. Sioshansi, and S. Member, Comparing Capacity Value Estimation Techniques for Photovoltaic Solar Power, vol. 3, no. 1, pp , 213. [2] S. Energy, C. Provide, and V. Capacity, Solar Energy and Capacity Value Solar Energy Can Provide Valuable Capacity to Utilities and Power System Operators. [3] J. Katz and P. Denholm, Using Wind and Solar To Reliably Meet Electricity Demand, pp. 1 2, 215. [4] Project Sunroof - Data Explorer. [Online]. Available: [Accessed: 18-Feb-218]. [5] U.S. Electric System Operating Data. [Online]. Available: regions=8. [Accessed: 2-Jan-218]. [6] International Energy Agency, Modelling the capacity credit of renewable energy sources, Iea, p. 6, 211. [7] Arizona Not Sure It Wants Largest U.S. Nuclear Plant - The Washington Post. [Online]. Available: it-wants-largest-us-nuclear-plant/f8459b58-ec1f-4595-bfb- 66bc24f81d/?utm_term=.5b45af7cc59. [Accessed: 9-Jul-218]. [8] R. Sioshansi, S. Member, S. H. Madaeni, and P. Denholm, A Dynamic Programming Approach to Estimate the Capacity Value of Energy Storage, IEEE Trans. Power Syst., vol. January, no. 1, pp , 214. [9] P. Denholm and R. Margolis, Energy Storage Requirements for Achieving 5 % Solar Photovoltaic Energy Penetration in California, Nrel, no. August, 216. [1] Lazard, Levelised Cost of Energy Analysis, no. November, pp. 21, 217. [11] Total number of automobiles in the U.S. by state in 216 Statistic. [Online]. Available: [Accessed: 18-Jul-218]. [12] BU-13: Electric Vehicle (EV) Battery University. [Online]. Available: [Accessed: 19-Jul-218]. [13] The Average American Drives This Much Each Year -- How Do You Compare? [Online]. Available: [Accessed: 19-Jul-218]. [14] P. M. Naina, H. Rajamani, and K. S. Swarup, Modeling and Simulation of Virtual Power Plant in Energy Management System Applications, 217 7th Int. Conf. Power Syst., pp. 2 7, 217. [15] Fahimeh Kazempour, A Robust Hierarchical Control Structure for Virtual Power Plants, Toronto,

43 [16] RATE SCHEDULE R-TECH RESIDENTIAL SERVICE PILOT TECHNOLOGY RATE SAVER CHOICE TECH. [17] IEEE Test Cases cloud computing for microgrids. [Online]. Available: [Accessed: 2-Jul-218]. 42

44 8/7/18 Value of PV at highpenetration Presented By: Bahram Emami Academic Adviser: Dr. Ronald Roedel Industrial Advisor: Mr. Joe Cunningham Agenda Capacity Value LCOE & Net- LCOE Virtual Power Plant Modeling 2 1

45 8/7/18 1 Capacity Value Capacity Value For PV & Storage 3 Capacity Value -Capacity Value: Ability of a generator to help reliably serve load -Less than 1% Due to: Maintenance, Failure, and Lack of Resource -Conventional Resources: Technology and Capacity -Renewable Resources: Variability in resource -Solution: How RE can serve the load in the regional demand patterns 4 2

46 8/7/18 Map 5 First Approach Capacity Credit: Focusing on the output of the system on the periods that power system has the highest risk of the outage. (1 to 1 hours) Methods of Estimation Second Approach Reliability-based method: Measuring the capacity value of the system in the whole year. ELCC & ECP 6 3

47 8/7/18 Capacity Credit Load-Duration $ $ $ $) $ $ $ $ $) $ Time of the day Duration of the load 7 Capacity Credit Net-Load 5 G8 1 8 Day 1 Day2 Day 3 Day 7 8 4

48 8/7/18 Capacity Credit Net-Load 5 G8 1 8 C 8G C 46 Day 1 Day2 Day 3 Day 7 9 Capacity Credit Net-Load 5 G8 1 8 C 8G C 46 3 G 1 8 Net-load: Reduction in the capacity of the Conventional Resources 1 5

49 8/7/18 Capacity Credit 217 ( $( $ ( $ $( ) ( ( ) $ ( 5 G C 8G C 27 3 G Capacity Credit = Average output of PV Nameplate capacity 11 Capacity Credit 216 & 217 Histogram of CC-1, CC-1 Normal Histogram of CC-1, CC-1 Normal 5 Variable CC-1 CC-1 3. Variable CC-1 CC-1 4 Mean StDev N Mean StDev N Density 3 Density % 2.% 4.% 6.% 8.% 1. % Data..% 2.% 4.% 6.% 8.% 1. % Data

50 8/7/18 13 High penetration: decreasing the net-load during the day shifting the time of greatest demand to the evening! Reliability-Based ELCC Loss of load expectation (LOLE) Acceptable level:.1 day/year or 1 day in 1 years Building new plant: Lower LOLE Adding load to the system (All time steps) Until: LOLE after installation = LOLE before installation Capacity Value = Added load Nameplate capacity Effective Load Carrying Capability (ELCC) 14 7

51 8/7/18 Reliability-Based ELCC 3.% 2AOAC RU A SE 25.% 2.% 15.% 1.% 5.%.% 5% 1% 15% 2% 3% 4% 5% EMERPAR NM ) (- ) (. 15 Reliability-Based ECP ELCC of conventional generators also is less than 1 Comparing the ELCC of a PV system with a benchmark unit How much should be the capacity of the benchmark? Benchmark Unit: Gas Turbine Generator (Capacity Value 61.93%) Equivalent Conventional Power (ECP) 16 8

52 8/7/18 Reliability-Based ECP 2AOAC RU A SE 5.% 4.% 3.% 2.% 1.%.% 5% 1% 15% 2% 3% 4% 5% EMERPAR NM ) (- ) (. 17 Capacity Value Storage Size of the system: 25% of the size of PV system (Based on optimization) 2 hours charging 2 hours discharging Charging: Demand is lower than supply Discharging: Demand is higher than supply 18 9

53 8/7/18 Capacity Value Storage ELCC 15.% 2AOAC RU A SE 1.% 5.%.% 41 7 $ $ 1%) 5 $ ( 1%- 5 $ 2% 5 $ 3% 5 $ 4%( 5 $ 5%) 1%) 2%) 3%) 4%) VE N RNPAGE EMERPAR NM ) (- ) (. 19 Capacity Value Storage ECP ) % 2AOAC RU A SE 15.% 1.% 5.%.% 41 7 $ $ 1%) 5 $ ( 1%- 5 $ 2% 5 $ 3% 5 $ 4%( 5 $ 5%) 1%) 2%) 3%) 4%) VE N RNPAGE EMERPAR NM ) (- ) (. Capacity value higher than 1% : Capacity value of Storage is higher than benchmark 2 1

54 8/7/18 2 LCOE & Net-LCOE Curtailment 21 LCOE 1 EPAGE M S D VED 6294 & W $ 8N % ) (. %) %( %( % 2NA 5A 5A EA MG N AP 1 EPAGE 6294 & One of the most important factors that can limit the PV deployment is curtailment 22 11

55 8/7/18 Curtailment Rejection of PV generation ) ) ( ( ( ( ( ) -. / ( (( () ( ( ( (- (. (/ ( ) )( )) ) ( ) -. / ( (( () ( ( ( (- (. (/ ( ) )( )) ) 6NAD 5 5 2SPRA LEMR 7 M% 5EM%. 5 6NAD 5 5 2SPRA LEMR 23 Net-LCOE Curtailment = Lower PV Generation = Higher LCOE Base LCOE Net LCOE = (1 Curtailment Rate) Most significant factor: of the power system Very Low Low Medium High Min. Gen. Conventional 7 GW 3.5 GW 3 GW 2.5 GW 24 12

56 8/7/18 Net-LCOE Compare Penetration $( $ ( % 5% 1% 15% 2% 3% 4% 5% 6% G 1. 9 G 2 H. 9 G. 9 G 8F 8 8F 4 8 C 25 Net-LCOE Goal: decreasing Net-LCOE to an acceptable level Net-LCOE of PV = LCOE Gas Plant Storage EV Export Demand Response 26 13

57 8/7/18 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 3% : Very Low Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration 27 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 4% : Very Low Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration 28 14

58 8/7/18 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 5% : Very Low Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration 29 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 5% : Medium 3 15

59 8/7/18 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 5% : Low 31 Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration Storage Required (GW) % 5% 1% 15% 2% 25% 3% EV Penetration PV Penetration: 5% : Very Low 32 16

60 8/7/18 Storage Required (GW) Storage Required (GW) Storage+EV For Avoiding Curtailment % 5% 1% 15% 2% 25% 3% EV Penetration % 5% 1% 15% 2% 25% 3% EV Penetration 217 VLF 3% 4% 5% 217 5% MF LF VLF Charging & Discharging : 8h EV s Battery 3kWh Storage Required (GW) Storage Required (GW) % 5% 1% 15% 2% 25% EV Penetration % 5% 1% 15% 2% 25% EV Penetration 216 VLF 3% 4% 5% 216 5% MF LF VLF 33 3 Virtual Power Plant Concept & Components 34 17

61 8/7/18 Virtual Power Plant (VPP) Size of DERs is small = DERs cannot participate in the electricity market Prosumer : Producing?! Consuming?! Storing?! Best time for charging & discharging the storage system?! Curtailment 35 Virtual Power Plant (VPP) Communication with DERs is Solution Frequency support Reactive power support Participation in the market 36 18

62 8/7/18 Virtual Power Plant (VPP) -VPP : Decentralized energy management system that aggregates DERs and controllable loads -Grid-connected microgrid -VPP = single power plant -Communication with controllable loads: Turn on or off -VPP is a good approach for controlling renewable energy sources Ref. :esig.energy 37 VPP Components Flowchart DERs + ESS + Controllable loads + EMS Modes: -No power exchange -Grid export -Grid import 38 19

63 8/7/18 4 Modeling HOMER & XENDEE 39 HOMER Optimize size of each component based on: Capital Cost, Load Pattern, Demand Charge, Electricity Cost, and 4 2

64 8/7/18 HOMER On-Peak Hours Off-Peak Hours Monday-Friday 1 PM to 8 PM Monday-Friday 8 PM to 1 PM+ Weekends On-Peak kwh $.18 Off-Peak kwh $.5 On-Peak Demand Charge per kw $ 3.5 Off-Peak Demand Charge per kw $ 1.5 Sellback per kwh $.3 Capital Cost of PV per kw $ 18 Capital Cost of Battery per Unit $ 65 Capital Cost of Converter per kw $ 3 41 HOMER Optimal Size: VPP: PV 2,311 kw PV 6, kw Storage 13 Tesla Powerwall Storage 3 Tesla Powerwall LCOE $.853 #$% (7% ) Internal Rate of Return (IRR) 6.6% Renewable Fraction 23.7% LCOE $.663 #$% (27% ) Internal Rate of Return (IRR) 6.5% Renewable Fraction 55.7% 42 21

65 8/7/18 XENDEE Modeled in IEEE 13 Node Test Feeder 1-Grid Only 2-Grid + PV 3-Grid + PV + Storage Grid + PV + Battery Grid Grid + PV 43 XENDEE Grid + PV + Storage Grid PV Battery 44 22

66 8/7/18 XENDEE Grid: Grid + PV: Grid + PV + Storage: 45 Thanks! Any Questions? 46 23

67 8/7/18 Capacity Value NREL 47 Grid + PV + Storage Peak Grid PV Battery 48 24