Impacts of Large-scale Wind and Solar Power Integration on California s Net Electrical Load

Impacts of Large-scale Wind and Solar Power Integration on California s Net Electrical Load Hamid Shaker a,, Hamidreza Zareipour a, David Wood a a Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada, T2N1N4 Abstract Integration of wind- and solar-based generation into the electric grid has significantly grown over the past decade and is expected to grow to unprecedented levels in coming years. Several jurisdictions have set high targets for renewable energy integration. While electric grid operators have managed the variable and nondispatchable nature of wind and solar power at current levels, large-scale integration of these resources would pose new challenges. In particular, the variable nature of wind and solar may lead to new electric grid operation and planning procedures. Net load in electric grids is defined as the conventional load minus the nondispatchable generation. Net load is the basis of operation planning in day-today delivery of electricity to the consumers. With large-scale integration of wind and solar power, the net load in the system would be significantly affected. In this paper, we focus on characteristics of net load in electric grids when a large amount of wind and solar power generation is integrated into the grid. We use Corresponding author Email address: hshakera@ucalgary.ca (Hamid Shaker) Preprint submitted to Elsevier April 19, 2016

the data from California s power system. California intends to produce 33% of its electricity from renewable resources by 2020, 80% of which is expected to come from wind and solar power. We use both historical data and simulated scenarios of future wind and solar power generation. For future scenarios, we use the data provided by National Renewable Energy Laboratory to generate wind and solar power integration scenarios for years 2018 and 2023. The simulated net load data are analyzed from a variety of perspectives, such as, average daily shapes, load and net load factor, duration curves, volatility, and hourly ramps. The results showed that compared to conventional load, characteristics of net load would be significantly different and need to be taken into account when designing measures and mechanisms for operating electric grids with high penetration of renewables. Keywords: Net load; variable generation; large-scale wind and solar integration; California renewable energy portfolio. 1. Introduction California s renewable portfolio standard mandates the state to supply 33% of its electric energy demand through renewables by 2020 [1]. It is expected that 80% of the mandate to come from solar and wind power [2]. The three main investor owned utilities in California, i.e., Pacific Gas and Electric, San Diego Gas and Electric and Southern California Edison, collectively supplied 22.7% of their retail electricity through renewables in 2013 [1]. This indicates that the utilities are working to meet the mandate. The increasing trend in large-scale integration of renewables, in particular, 2

wind and solar power, is universal. In 2014, the cumulative global installed wind capacity reached 370 GW, which had nearly a 250 GW increase compared to 2008 [3]. The cumulative global wind capacity is higher than the total installed generation capacity of Japan, the third largest power system in the world [4]. China and the USA have the highest wind capacity by reaching 114.6 GW and 65.9 GW in 2014, respectively [3]. Among the United States, California power system with nearly 6 GW wind power capacity ranks second after Texas with about 16.5 GW [5]. Solar Photovoltaic (PV) power generation capacity also increased substantially over the past few years. Global solar PV capacity has grown from 1.3 GW in year 2000 to 138.9 GW at the end of 2013 [6]. According to the International Energy Agency (IEA), solar power could potentially provide one third of the global final energy demand after 2060 [7]. In the USA, on average, the annual solar capacity has grown more than 40% since 2006 [8]. California is the leader in the United States. Only in 2013 California installed 2,746 MW of new solar power capacity, nearly half of the total United States new solar PV installations [9]. By mid 2014, nearly 8 GW of solar PV had received environmental permits to come online in California [2, 10]. The current integration trends indicate an inevitable major role for wind and solar in future power systems. However, both wind and solar are known to be nondispatchable or variable generation sources for the most part. Operation planning in conventional power systems has been based on a forecast of future load, an understanding of the random variations in the load, and the chances of major con- 3

tingencies in the supply system. With the large-scale integration of wind and solar generators, a new source of uncertainty is added to the operation planning problem. Wind and solar generators are often treated as non-dispatchable units that inject power to the grid when available. Thus, in systems with substantial wind and solar power sources, net load, i.e., the non-dispatchable generation subtracted from the conventional load becomes the new planning measure. The inherent variability of the wind and solar power, however, makes the net load time series a more volatile one compared to the conventional load time series. The higher variability and less predictability of the net load induces more cycling of the conventional units, which causes more wear-and-tear and higher maintenance costs [11]. In [12], the challenges of significant wind and solar integration were categorized in their low capacity credit, reduced utilization of dispatchable plants, and overproduced generation. It was mentioned that a system with a higher proportion of generators which are incapable of rapid entry and exit to the electricity market can face more challenges when low or negative net load appears. Moreover, high wind and solar power generation would decrease the average utilization and therefore the life-cycle generation of the dispatchable units. This increases the generation costs of supplying the net load [12]. This could potentially result in losing revenue and in extreme cases bankruptcy of those units. The concept of net load and the challenges that may arise from its variability has been discussed in the literature. In particular, a number of studies on evaluating and modelling the flexibility requirements of power systems with high penetration of renewables have considered net load [13, 14, 15, 16, 17, 18, 19]. 4

It is argued in [13] that with the current level of wind and solar power, the conventional load variations still makes the highest source of variability and uncertainty in power systems. However, higher wind and solar penetrations along with adaptation of smart grid technologies, such as demand response, will significantly increase the variability and uncertainty of the net load. In [14], flexibility requirements of large-scale variable generation were quantified based on simulated values of onshore wind and solar PV power production in 27 European countries. It is shown in [14] that increasing wind and solar power generation above a 30% share in annual electricity consumption will significantly increase flexibility requirements. It is suggested in [15] that in order to decrease the costs associated with the increased variability of net load in future power systems, new market designs with flexible ramping capability may become a necessity. Flexible ramping refers to the ability of the system to ramp up/down the generation or load to stabilize system frequency. In [16], using an insufficient ramp resource expectation algorithm, the flexibility requirements for a system with high variability in the net load were quantified. Transmission network constraints were considered in [17] to assess the flexibility of a power system based on the variability of the net load data. Furthermore, the work in [18] proposed a new power system planning model by considering large integration of renewable energy sources and the corresponding required flexibility of dispatchable generation units. It has been observed that with more wind capacity in the system, a part of the base-load generation units is replaced by mid-load and peak-load generation units due to decrease of the total net load and increasing its volatility. Finally, in [19], flexibility requirements 5

of high wind and solar PV integrated power system of Germany were analyzed considering both production flexibility of conventional power plants and storage. The results showed that high penetration of variable generation does not decrease the peak net load much. However, the equivalent full load hours decrease significantly. The occurrence of hours with zero or negative net load also increases. Moreover, their results suggested that combination of wind and solar PV production leads to less storage requirement compared to only wind power generation. Storage is one of the options to cope with the challenges of renewable power generation. The benefits of energy storage in providing system flexibility and controlling negative net load situations have been investigated in a number of studies [19, 20, 21, 22, 23, 24]. It was found that energy storage can reduce cycling of conventional power plants and improve the efficiency of power system [21]. Reference [22] provided a comprehensive analysis by combining a simulation of the impacts of future variable renewables on net load with a focus on surplusrelated storage requirements in Germany. The author concluded that renewable surpluses can be minimized by decreasing must-run requirements. The storage requirement would also decrease if power curtailment and demand response were utilized. Furthermore, storage in distribution systems in Germany was analyzed in [23]. The results suggested that in the current situation storage may not be economically viable. However, in high penetration levels storage could be one of the options to ensure system security. In fact, an adequate mix of technical initiatives, load shifting and demand side management, and energy storage are required for high penetration of renewables [24]. 6

In other research works, the net load time series has been the basis for building unit commitment studies [25, 26, 27, 20], studying the market impact of a price-maker wind unit [28], determining the optimal level of reserves [29], and managing systems with large numbers of electric vehicles [30]. Despite the increasing importance of understanding how the system net load and its characteristics would change as the share of renewables grows, the literature focusing on this topic is limited. In [31], the annual and seasonal changes of load duration curves after integrating renewable resources, mainly biomass, has been studied. In this work, less than 5% of renewable generation capacity comes from wind and solar power. Moreover, there are several studies carried out by the industry on impacts of large-scale wind and solar power integration on power systems. Examples include the Eastern wind integration study [32], the Western wind and solar integration study [33], the California power system study [34, 35, 36], the Nova Scotia renewable integration study [37] and the New England wind integration study [38]. While net load has been the basis of such studies, neither has particularly focused on characteristics of the net load time series. The objective of the current work is analyzing the impacts of large-scale wind and solar power integration on the characteristics of net load time series. Based on a range of wind and solar integration scenarios generated for the case of California s power system, we study the impacts of renewables on the following aspects: Average hourly values, Load and net load factor 7

Load and net load duration curves, Load and net load volatilities, Hourly ramps. The main contribution of this paper is to provide an in-depth discussion on how the net load characteristics would deviate from what power systems are accustomed to today and highlight the challenges that large-scale integration of these two resources may impose to power systems. Understanding those deviations would be necessary for power system operators to plan and adjust their operation strategies, procedures and policies to ensure power system security and reliability with presence of large-scale wind and solar power. The rest of the paper is organized as follows. The description of net load definition and the data is discussed in Section 2. Methodologies for future generation scenarios are provided in Section 3. Section 4 focuses on the assessment of the net load from different perspectives. Finally, the paper ends with the conclusions in Section 5. 2. Background and Data In this section, first we provide a generic definition for net load. We also provide a description of the data that has been used in this study. 2.1. Net Load Definition Assume the value of the conventional load at time interval t is L t, and the value of negawatt at time interval t to be NG t. The value of net load at time t is 8

defined as NL t = L t NG t. (1) Typical examples of negawatt include the non-disptachable power generated by wind, solar or small of-the-river hydro facilities [26, 25, 16, 14, 15, 31, 39]. For example, in Alberta s electricity market, wind power plants do not bid in the market and inject their available power to the system. The system operator treats them as negative load in scheduling practices [40]. However, in some studies, the non-conventional load in the system (e.g., electric vehicle fleet load [30] or the import/export [13]) has been deducted from the conventional system load to determine system net load. 2.2. Description of the Data In order to analyze the effect of large-scale wind and solar on the net load, we have used the historical hourly load data [41], and wind and solar power generation data [42] of the California power system from year 2000 to the end of year 2013. These data contain the gradual growth of wind and solar power integration over this period. According to the California ISO website [43], California s power system had 60,703 MW of installed capacity with a recorded peak demand of 50,270 MW by January 2014. The year 2013 historical data shows a peak solar PV generation of 2,830 MW and peak wind generation of 4,215 MW. These numbers are relatively small compared to the peak load at this year, i.e., 44,924 MW. However, meeting the 33% renewable goal of year 2020 would require a signifi- 9

cantly larger share of wind and solar generation capacity. To simulate larger levels of wind and solar power integration in California in the present study, we have used the data produced by the National Renewable Energy Laboratory (NREL), which is further discussed next. In the Western wind and solar integration study [33], NREL, in conjunction with a few other institutions such as GE Energy Consulting Group, performed a study to analyze the effect of integrating large amounts of wind and solar power into the Western US power systems. The first phase of this study investigated the benefits and challenges of integrating up to 35% wind and solar energy in the Western Interconnection, by 2017. The second phase evaluated the effect of wind and solar generation on wear-and-tear costs and emissions associated with cycling of fossil-fuelled generation fleet [44]. Over the course of these studies, two datasets have been developed for wind and solar generation [45, 46]. These datasets are both simulated data, validated against some real-life weather data. The Western wind dataset is for the years 2004 to 2006 with a 10-minute resolution. The surface covering the Western interconnection power system was modelled with a resolution of 2 km 2 km grids, and 32,043 locations were selected across the modelled surface. Each grid point was estimated to hold ten Vestas V90 3 MW wind turbines. For the selected grid points, simulated wind speed data, and accordingly wind power, were generated [45, 47]. The solar dataset was created for year 2006 and has the resolution of 5 minutes. It consists of 6,000 simulated PV plants. Potential solar plants have been detected, and simulated solar power generation for the plants were created [46]. 10

We use the actual data of year 2013 for our analysis. In addition, we use the NREL datasets to build two sets of scenarios, one for year 2018 and the other for year 2023, with 5-year intervals from year 2013. The NREL dataset only has solar data for year 2006, and for consistency, we also use the wind data of the NREL dataset for year 2006. In addition, actual system load of 2006 is used as the base for creating the load scenarios for years 2018 and 2023. This is because there is an inherent correlation between weather conditions, wind/solar generation and electrical load in a power system. Thus, the load, wind, and solar data of the same year have been used. Further details on how the wind, solar, and load scenarios are generated are provided next. 3. Generated Simulated Data Scenarios In this section, and based on the data described in the previous section, the generated simulated load, wind power, solar power, and net load scenarios are discussed. 3.1. The Load Scenarios We have used the latest publicly available report on load forecast for California as the basis for our future load scenarios [48]. This report covers the period of year 2012 to 2022. Table 1 summarizes the non-coincident peak load of years 2006, 2018, and 2023 for low, medium, and high load growth rates. The peak load scenarios for year 2023 in this table are extrapolated based on business as usual load growth following year 2022 load forecast. 11

Table 1: Historical and forecasts of California non-coincident peak load. Forecast (MW) Increase over 2006 (%) Year Actual (MW) Low Medium High Low Medium High 2006 64,000 2018 64,500 67,500 69,000 0.78 5.47 7.81 2023 67,200 71,500 74,900 5.00 11.72 17.03 To limit the number of scenarios in this study, we only use the medium growth case. Load scenarios for years 2018 and 2023 are generated by scaling up the hourly load values of year 2006 at the medium growth rates of Table 1, i.e., 5.47% and 11.72%, respectively. The two future load scenarios are referred to as L18 and L23 for years 2018 and 2023, respectively. 3.2. The Wind Power Production Scenarios The California power system has three major zones, namely NP15, ZP26 and SP15. These zones cover the north, central and southern parts of California, respectively. Zone SP15 covers parts of Nevada and Arizona too. These three zones are marked in Fig. 1. At the time of writing this paper, there were 2,823 MW of wind capacity in NP15 and 4,969 MW in SP15. ZP26 currently does not have any wind facilities [49]. Note that these numbers are the nameplate capacity of the facilities. Some of them are not fully operational at the time. We assumed that they would be fully operational by year 2018. In addition, the generation queue of California Independent System Operator (Cal-ISO) [50] shows 660 MW new wind capacity planned in the NP15 to be commissioned by the end of 2018. It also shows 3,036 MW new wind capacity for ZP26 and 1,270 MW new capacity in SP15. Although 12

Table 2: The existing capacity and the amount in the generation queue for wind plants in each zone or county for the California electric power system. County Zone Existing wind New wind cap. capacity (MW) in the queue (MW) NP15 2,823 SP15 4,969 Alameda NP15 Southwest 36 Kern ZP26 South 3,036 Riverside SP15 Centre 150 San Diego NP15 Southwest 419 Solano NP15 Centre 205 Baja California SP15 1,120 Sum (MW) 12,758 7,792 4,966 the generation queue only shows the list of projects under study or approval and it does not guarantee the projects to be online by the expected online date, it provides a reasonable indication of what will happen in the future. Hence, we have used the generation queue as the basis to build our future scenarios, i.e., we assume the generation queue will be fully realized. Table 2 summarizes the existing and future wind plants for each zone or county. It shows that currently California has 7,792 MW wind capacity and it is expected to add 4,966 MW new wind capacity by the end of year 2018. The data of Table 2 is used in the following sections to select appropriate places to represent the year 2018 and also year 2023 wind generation. The objective here is to select the sites in each zone or sub zone that represent the wind generation of future scenarios as closely as possible to the future expected situation in line with the generation queue. 13

3.2.1. Selecting Wind Power Plant Sites for Year 2018 Assuming that all of the existing and new wind facilities will be online by year 2018, there will be 3,483 MW capacity in NP15, 3,036 MW capacity in ZP26, and 6,239 MW in SP15. It will sum up to 12,758 MW of total wind generation capacity for year 2018, which we call it Scenario W18. There are no wind sites for ZP26 in the NREL wind dataset. To address this, the closest sites to the southern border of zone ZP26 will be considered as the ZP26 sites. The selected wind capacity is a multiplier of 30 MW. We chose 116 sites in NP15 and 309 sites in SP15, each with a capacity of 30 MW. These were the sites with better wind regimes. Figure 1 represents the location of all selected sites from the NREL dataset for year 2018. 3.2.2. Selecting Wind Power Plant Sites for Year 2023 In order to study higher wind penetration levels, six scenarios have been developed for year 2023. The expected capacity grows by 4,966 MW over the period of years 2013 to 2018. For generating the year 2023 scenarios, we consider two cases for the capacity growth: one at a moderate level of 4,000 MW, and another one with an extreme growth of 8,000 MW. The second case is to observe an unexpected extreme growth in wind power installations and quantify the expected net load impacts. The resulting total wind capacity for the year 2023 is 16,750 MW and 20,750 MW, for the two cases. We consider three scenarios in terms of where the additional capacity will be located. Those three include: (i) the new capacity is equally distributed in NP15 and SP15; (ii) 75% of all new capacity is 14

44 NP15 SP15 42 Latitude [degrees] 40 38 36 NP15 ZP26 34 SP15 32-126 -124-122 -120-118 -116-114 -112 Longitude [degrees] Figure Figure1: 1: Selected wind windsites sitesfor foryear year2018. located where the in NP15 additional and 25% capacity in SP15; will be and, located. (iii) 75% Those of all three new include: capacity (i) is the located new in capacity SP15 and is equally 25% is distributed located in NP15. in NP15 The and latter SP15; two (ii) cases 75% are of extreme all new capacity growth in is one located area in versus NP15 the and other, 25% and in SP15; would and, allow (iii) investigation 75% of all of new the capacity consequences is located of such in SP15 cases and on 25% net load. is located We name in NP15. these The scenarios latter two as W23M-EQ, cases are extreme W23M-NP, growth and in W23M-SP one area versus for the the case other, with and 16,750 would MW allow total investigation capacity and of W23E-EQ, the consequences W23E-NP, of and such W23E-SP cases on net for the load. case We with name 20,750 these MW scenarios total capacity, as W23M-EQ, respectively. W23M-NP, and 3.3. W23M-SP The Solar for the PV case Power with Production 16,750 MW Scenarios total capacity and W23E-EQ, W23E-NP, and W23E-SP for the case with 20,750 MW total capacity, respectively. Currently, the installed solar PV capacity in NP15, SP15, and ZP26 is, respectively, 1,257 MW, 3,108 MW and 26 MW [49]. The generation queue of Cal-ISO shows 12,160 MW new solar PV capacity under study to be added to the system 15

Table 3: The existing capacity and the amount in the generation queue for solar PV plants in each zone or country for the California electric power system. Country Zone Existing solar PV New solar PV cap. capacity (MW) in the queue (MW) NP15 1,257 1,008 SP15 3,108 ZP26 26 4,572 Arizona SP15 South 455 Nevada SP15 East 577 SP15 centre 2,960 SP15 South 1,731 SP15 West 857 Sum (MW) 16,551 4,391 12,160 by the end of year 2018 [50]. Similar to wind scenarios we have used this data to build the future scenarios. Table 3 summarizes the aggregated existing and expected solar PV plants for each zone. This information will be used to select appropriate locations to represent year 2018 and also year 2023 solar PV power generation. Assuming that all of the existing and new solar PV facilities will be online by year 2018, the expected total solar PV capacity in NP15, SP15, and ZP26 will be 2,265 MW, 8,656 MW and 4,598 MW, respectively. Moreover, the expected capacity additions in the areas in Nevada and Arizona that are within the Cal-ISO jurisdiction are 577 MW and 455 MW, respectively. Thus, the total solar PV generation capacity for year 2018 is expected to be 16,551 MW. 3.3.1. Selecting Solar Power Plant Sites for Year 2018 The NREL solar database [46] considers different solar plants with a variety of sizes from 4 MW to 200 MW capacity depending on the characteristics of the 16

44 42 NP15 ZP26 SP15 Latitude [degrees] 40 38 36 34 32-126 -124-122 -120-118 -116-114 -112 Longitude [degrees] Figure 2: Selected solar PV sites for year 2018. modelled sites. It shows a total of 405 sites in California, 24 sites in Nevada, and 5 sites in Arizona, all within Cal-ISO jurisdiction. In each zone we have chosen the number of required sites from the candidate locations of that zone to reach the desired total solar PV capacity for year 2018. We refer to this scenario as S18. Since there is no certainty about the operation of any of the available locations in the future and since all of them have similar solar regimes, we have chosen the required sites randomly. The resulting total capacity in this scenario is 16,593 MW for year 2018. Figure 2 represents the locations of selected sites for this scenario. 17

3.3.2. Selecting Solar Power Plant Sites for Year 2023 Two scenarios for year 2023 were generated to evaluate the effect of increased solar PV capacity in this year. Removing the sites that were considered for year 2018, a total of 9,027 MW capacity would remain to be realized. Hence, we have divided this remaining capacity by two and made two additional scenarios for year 2023. The first one is built using half the remaining sites and has a total solar PV capacity of 21,137 MW. This would represent a moderate solar PV capacity increase, and is referred to as S23M. Considering that more than 12,000 MW solar PV was expected to come online over the pervious period of years 2013 to 2018, a 4,513 MW increase in the following five years could be considered moderate. The sites for this scenario are randomly selected from the remaining choices. The last scenario includes all of the possible solar PV plants from all of the considered zones. This leads to a total capacity of 25,620 MW and is referred to as S23E. This scenario is extremely optimistic, i.e., by the year 2023, all of the potentials solar power production sites are developed. This, of course, is not unrealistic considering the extreme solar PV growth over the past years in California and other places around the world. 3.4. The Net Load Scenarios Following the aforementioned scenarios for load, wind, and solar PV, there will be one net load scenario for year 2018 but 12 different net load scenarios for year 2023. A general description of the net load scenarios is provided in Table 4. Figure 3 summarizes the net load scenarios for year 2023. 18

Figure 3: The procedure of building net load scenarios using the NREL datasets for year 2023. The naming style of year 2023 net load scenarios is: (Solar growth symbol)(wind growth symbol)-(wind growth zonal ratio symbol). In this naming, symbols M and E represent the Moderate and Extreme growth rates for solar PV and wind, respectively. Wind growth zonal ratio symbol could be EQ, NP or SP as the equal wind growth rate, 75% new wind growth rate in NP15, and 75% new wind growth rate in SP15 zones, respectively. For example, Scenario EM-NP represents the extreme solar PV capacity and moderate wind capacity, where 75% of new wind installations over year 2018 are considered in zone NP15. 19

Table 4: Description of year 2023 net load scenarios. MM-EQ means moderate solar growth and extreme wind growth equally distributed between NP15 and SP15 over year 2018. Growth Rate Over Year 2018 New Wind Capacity Share (%) Scenario Name Solar Wind NP15 SP15 Net Load Solar Wind Moderate (M) Moderate (M) 50 50 MM-EQ S23M W23M-EQ Moderate (M) Moderate (M) 75 25 MM-NP S23M W23M-NP Moderate (M) Moderate (M) 25 75 MM-SP S23M W23M-SP Extreme (E) Moderate (M) 50 50 EM-EQ S23E W23M-EQ Extreme (E) Moderate (M) 75 25 EM-NP S23E W23M-NP Extreme (E) Moderate (M) 25 75 EM-SP S23E W23M-NP Moderate (M) Extreme (E) 50 50 ME-EQ S23M W23E-EQ Moderate (M) Extreme (E) 75 25 ME-NP S23M W23E-NP Moderate (M) Extreme (E) 25 75 ME-SP S23M W23E-SP Extreme (E) Extreme (E) 50 50 EE-EQ S23E W23E-EQ Extreme (E) Extreme (E) 75 25 EE-NP S23E W23E-NP Extreme (E) Extreme (E) 25 75 EE-SP S23E W23E-SP Table 5 summarizes the all employed scenarios in this study. In this table, energy penetration level is defined as the annual electricity energy generated by wind/solar facilities divided by the annual electrical load. The historical solar and wind capacities in this table are retrieved from [51]. The capacities in the table only include the large-scale renewable generation. We believe the developed wind and solar PV capacity growth scenarios over the period of years 2018 to 2023 are realistic. Based on Table 5, 1,922 MW of new solar PV capacity was added to the California s power system over one year from 2012 to 2013. This means that with the same rate, over a five year period capacity addition of 9,610 MW is possible. This is even lower than the extreme solar PV growth of year 2023 in scenarios starting with EM or EE. Moreover, the extreme wind power growth in our scenarios has 8,000 MW of new installations. This is equal to the annual average of 1,600 MW, which is not too unrealistic. Texas alone installed 2,760 MW of new wind capacity during year 2008 [5]. 20

Table 5: Summary of historical and future net load scenarios. Year/ Peak Capacity (MW) Capacity Penetration (%) Energy Penetration (%) Scenario Load (MW) Solar Wind Solar Wind Solar Wind Total 2006 50,198 0 0 0 0 0 2011 45,569 622 3,992 1.4 8.8 0.2 0.8 1.0 2012 46,654 1,150 4,967 2.5 10.6 0.9 4.0 4.9 2013 44,924 3,072 6,205 6.8 13.8 2.1 5.4 7.5 2018 52,944 16,593 12,750 31.3 24.1 11.0 12.8 23.8 MM-EQ 56,081 21,137 16,770 37.7 29.9 13.5 15.7 29.2 MM-NP 56,081 21,137 16,740 37.7 30.0 13.5 15.5 29.0 MM-SP 56,081 21,137 16,740 37.7 30.0 13.5 15.9 29.4 EM-EQ 56,081 25,620 16,770 45.7 29.9 17.3 15.7 33.0 EM-NP 56,081 25,620 16,740 45.7 30.0 17.3 15.5 32.8 EM-SP 56,081 25,620 16,740 45.7 30.0 17.3 15.9 33.2 ME-EQ 56,081 21,137 20,730 37.7 37.0 13.5 19.4 32.9 ME-NP 56,081 21,137 20,760 37.7 37.0 13.5 19.2 32.7 ME-SP 56,081 21,137 20,760 37.7 37.0 13.5 19.6 33.1 EE-EQ 56,081 25,620 20,730 45.7 37.0 17.3 19.4 36.7 EE-NP 56,081 25,620 20,760 45.7 37.0 17.3 19.2 36.5 EE-SP 56,081 25,620 20,760 45.7 37.0 17.3 19.6 36.9 As Table 5 shows, all of year 2023 scenarios agree with the 33% renewable goal of year 2020. In other words, at least 33% 0.8 = 26.4% of total system load is supplied from wind and solar power in all of the year 2023 scenarios. Some of them, such as EE-EQ, EE-NP, and EE-SP represent very high energy penetration of wind and solar for extreme case analyses. The energy penetrations are calculated based on the assumption that all of the available energy is injected to the grid and no curtailment is used. As can be seen, in order to supply any amount of load from wind and solar, a much higher capacity of these resources is needed. For instance, Scenario EE-SP shows that for supplying 36.9% of load from wind and solar, a total capacity of 82.7% of these resources compared to the peak load is required. 21

4. Numerical Results and Discussions In this section, we analyze the developed net load scenarios from a number of viewpoints. Those include annual and seasonal average shapes, load and net load factors, load and net load duration curves, renewable energies curtailment potential, volatility, and hourly ramp analyses. While the specific discussions are based on the developed scenarios for California, the general directions and methodologies presented in the paper may be used to evaluate similar issues in other systems. 4.1. Daily Shoulder and Valley Hours: Load Versus Net Load In this section, we discuss the shape of net load time series, specifically looking at the valley and shoulder hours, based on annual and seasonal average hourly values. Figure 4 represents the annual average values of net load along with other variables for each hour of the day for the year 2018 and year 2023 scenarios. We have also presented the same values for the actual data of years 2012 and 2013 as references. In this figures, the left vertical axis represents the values of load and net load, whereas the right vertical axis measures wind and solar PV generation. We only bring the results for Scenario EE-NP for year 2023. However, other scenarios also presented similar features. As this figure shows, comparing the average hourly net load shape for year 2012 with 13.1% wind and solar capacity penetration to year 2023 with 82.7% in scenario EE-NP, a mid day valley is appearing and growing. For all four years, 22

#10 4 4 3.5 3 Load Net load Solar PV Wind Year 2012 #10 4 2 1.75 1.5 #10 4 4 3.5 3 Year 2013 #10 4 2 1.75 1.5 2.5 1.25 2.5 1.25 MW 2 1 MW 2 1 1.5 0.75 1.5 0.75 1 0.5 1 0.5 0.5 0.25 0.5 0.25 2 4 6 8 10 12 14 16 18 20 22 24 Hour 2 4 6 8 10 12 14 16 18 20 22 24 Hour #10 4 4 Year 2018 #10 4 2 #10 4 4 Year 2023-(EE-NP) #10 4 2 3.5 1.75 3.5 1.75 3 1.5 3 1.5 2.5 1.25 2.5 1.25 MW 2 1 MW 2 1 1.5 0.75 1.5 0.75 1 0.5 1 0.5 0.5 0.25 0.5 0.25 2 4 6 8 10 12 14 16 18 20 22 24 Hour 2 4 6 8 10 12 14 16 18 20 22 24 Hour Figure 4: Average annual values for each hour of the day in different years. the load has a morning ramp up that starts at around 5 am. This upward ramp continues until the daily peak load occurs and then the downward afternoon ramp starts. However, looking at the net load shape for years 2018 and 2023, a morning ramp down is forming when solar power generation grows. Thus, in the morning hours, the impact of solar generation on the average net load shape is dominant compared to wind power. The higher the solar capacity the deeper will the valley become. Compared to the shape of conventional load, a morning ramp down is new phenomenon for power system operators. Furthermore, the load and net load shapes are more alike during the night when wind blows and there is no solar PV 23

power generation. Hence, the overall impact of solar power on the net load shape is more significant than that of wind power. This is because the average hourly wind does not vary significantly over a day, whereas the opposite is the case for solar power generation. The new net load shape, i.e., a morning downward ramp and an evening upward ramp, would require new power systems operation strategies. Currently, the system is designed and operated such that enough capabilities for upward ramping in the morning hours and downward ramping in the evenings are available. Also, the dispatched units during the morning ramp would be needed for a longer part of the day as the load grows in the morning and is sustained during the day. However, with significant solar and wind power integration, the operators must plan for sufficient capability to ramp down in the morning and ramp up in the evening. Moreover, since the net load in the high penetration scenarios is generally lower than the load, conventional generators will be dispatched less often. This may limit the ramping capability available to the operators since fewer conventional units may find themselves in the merit order. Thus, the future operation strategies would need to be adjusted, for both system operators and generation companies, to ensure system security and economic sustainability. Another observation from Fig. 4 is that, on average, wind generation peaks at night time and drops to minimal levels around mid-day. On the other hand, solar generation peaks during the mid-day when the demand is also high. The opposite directions of wind and solar generation patterns result in a smoother net load shape and reduce the morning downward ramp and the evening upward 24

ramp. A smoother net load shape would be more desirable from a planning point of view because the available transmission and generation infrastructure would be more evenly utilized. Thus, simultaneous growth of wind and solar power mitigates some of the challenges associated with these intermittent resources. The balance between the wind and solar generation capacities are presented in Fig. 5 for Scenario MM-EQ as an example. It depicts the energy penetration level of wind and solar PV generation together for different load deciles. The decile penetration of wind and solar individually are not shown in the interests of brevity. The individual penetrations show the opposite behaviour for the wind and solar generation. Hence, as Fig. 5 shows, the opposite behaviour of wind and solar results in an almost flat penetration level for all load levels. This shows the benefit of simultaneous growth of wind and solar resources in the power system. Figure 6 presents the seasonal hourly average values for the load and net load in year 2023. We only bring Scenario MM-EQ as a representation since other scenarios behaved similarly. Comparing the four quarters, the upward and downward net load ramp is significantly higher is Q1 and Q4, i.e., from October to March. During the period of July to September, i.e., Q3, the net load ramps are the lowest, and the system would require the least ramping capability. On the other hand, comparing the load and net load patterns for the four quarters in Fig. 6 one can observe that the highest ramping capability for conventional load is required during Q3. This would call for revisiting the problem of maintenance scheduling in generation facilities. In particular, maintenance of units that provide the system with ramping may be shifted to Q3 when the system can spare some units. In 25

40 35 30 40% 50% 60% 70% 80% 90% 100% Penetration (%) 25 20 15 10 5 0 2013 2018 2023 (MM-EQ) Year Figure 5: Total renewable penetration levels by load decile. general, the variations in the net load shapes in the four seasons would require alternative arrangements for providing the system with enough flexibility to deal with the upward and downward ramps during the day. In [30], the authors discuss how proper planning of electric vehicle charging could compensate the net load valley at times. An understanding of the net load shape variations would improve such plans and help the system in dealing with various issues, such as, high ramps and low net load at some periods. 4.2. Load Factor and Net Load Factor Load factor is an indication of how much load changes within a specific period, typically a year. The ideal load factor for cost and environmental considerations 26

#10 4 4.5 4 3.5 3 Year 2023-(EE-NP)-Q1 #10 4 Load Net load Solar PV Wind 2 1.75 1.5 #10 4 4.5 4 3.5 3 Year 2023-(EE-NP)-Q2 #10 4 2 1.75 1.5 MW 2.5 2 1.25 1 MW 2.5 2 1.25 1 1.5 0.75 1.5 0.75 1 0.5 1 0.5 0.5 0.25 0.5 0.25 2 4 6 8 10 12 14 16 18 20 22 24 Hour 2 4 6 8 10 12 14 16 18 20 22 24 Hour #10 4 4.5 Year 2023-(EE-NP)-Q3 #10 4 #10 4 4.5 Year 2023-(EE-NP)-Q4 #10 4 4 2 4 2 3.5 1.75 3.5 1.75 3 1.5 3 1.5 MW 2.5 2 1.25 1 MW 2.5 2 1.25 1 1.5 0.75 1.5 0.75 1 0.5 1 0.5 0.5 0.25 0.5 0.25 2 4 6 8 10 12 14 16 18 20 22 24 Hour 2 4 6 8 10 12 14 16 18 20 22 24 Hour Figure 6: Quarterly average values for each hour of the day for Scenario EE-NP. is when the load is constant in all times [30]. Although wind and solar power have relatively similar capacity factors over different years, when their penetration increases, net load factor decreases significantly compared to the conventional load. For year 2013, the net load capacity factor was 2% lower than the load factor. However, the load factor of year 2018 is 6.7% lower than that of the conventional load. This decrease is even more for different scenarios of year 2023. Figure 7 represents the net load factors for the 12 scenarios of year 2023 along with the load factor without any wind and solar power in this year. Observe that the net load factor of year 2023 is 10-15% lower 27

60 50 Load/Net load factor (%) 40 30 20 10 0 Load MM-EQ MM-NP MM-SP EM-EQ EM-NP EM-SP ME-EQ ME-NP ME-SP EE-EQ EE-NP EE-SP Figure 7: Load and net load factor of different scenarios for year 2023. than the load factor in different scenarios. In general, small net load factors are not favorable. Low net load factor means that the transmission and generation infrastructure will be under utilized during a large number of hours over a year. In particular, a big portion of the installed generating capacity will be offline or generating at the allowable minimum very often. This could increase the cycling cost of generators [21, 44] and their emissions, especially for slower thermal units [39]. This would also impact the economics of peaking generators significantly in competitive markets, and may lead to long-term system reliability concerns. 28

4.3. Duration Curves Figures 8(a) and 8(b) show load duration curves and net load duration curves of years 2013 to 2023, respectively. Duration curves show the variations without a timestamp. Scenario EE-NP is chosen as a representative of the 2023 scenarios. Similar to other cases, different scenarios exhibit similar patterns and thus only one of them are shown here. As Fig. 8 shows, since the renewable integration increases from year 2013 to year 2023, the net load duration curves move down compared to their corresponding load duration curves. For instance, for year 2023, the load duration curve in Fig. 8(a) is above those of the other years, whereas the net load duration curves in Fig. 8(b) for this year is below those of other years. Thus, the peak system net load has decreased as a result of high solar and wind power penetration. The expected peak load, not considering the renewables for year 2023 is 56 GW. The peak net load could be as low as 48 GW. However, the nearly 8 GW net load peak shaving comes from more than 40 GW of total wind and solar power installations. Thus, renewable capacity only contributes up to approximately one fifth of its capacity to peak net load shaving. One important observation from Fig. 8 is that low net load values are more frequent compared to the low load values. The minimum load was around 19 GW in year 2013. However, in year 2023, the net load is less than 19 GW over more than 3,000 hours. Thus, system operators will need to deal with low net load periods very often. In addition, observe from the figure that there will be several hours with a negative net load, i.e., the hours during which the generated renewable en- 29

MW MW MW MW #10 4 6 #10 4 6 2013 5 2013 2018 5 2018 2023-(EE-NP) 4 2023-(EE-NP) 4 3 3 2 2 1 1 0 0-1 -1 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 #10 4 6 #10 4 6 5 5 4 4 3 3 2 2 1 1 0 0 Hours Hours (a) -1-1 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Hours Hours (b) Figure 8: Duration Curve s: a) Load; b) Net load. 2013 2018 2023-(EE-NP) ergy would be more than system demand. Low and negative net load could lead to potentially challenging security, reliability, and economic sustainability issues. At very low demand, and to securely plan system operation, many units may be required to run at their minimum output to ensure system ramping requirements. Moreover, very low net loads result in difficulties in finding optimum schedules 30

for the dispatchable units [26]. Thus, running at minimum output is inefficient from the emissions and economics point of view. Negative net load would also require curtailment of renewable resources or may lead to negative electricity prices to encourage consumption and discourage generation. Hence, under high penetration of renewables, the full environmental benefits of wind and solar power may not be realized. Also, low or negative prices may force some of the peaking units out of business, which is a system reliability concern in the long-term [29]. For example, on June 16, 2013 between 2 pm and 3 pm, the wholesale electricity price of Germany fell to -100 e /MWh. At that time, solar and wind generators produced 28.9 GW of power, while the total generation was over 51 GW. Since the grid could not cope with more than 45 GW without becoming unstable, prices went negative to encourage cutbacks and protect the grid from overgenerating. The burden of this adjustment fell on gas-fired and coal power plants by decreasing their output to only about 10% of capacity. This means that these units were losing money on electricity generation [52]. The top 20 energy utilities in Europe were worth e 1 trillion in year 2008. At the end of year 2013 they were worth less than half of it. This decrease is due to the changes that wind and solar electricity generation brought into the grid [52]. In particular, Table 6 summarizes the total energy associated with the negative net loads for different scenarios for year 2023. The table also shows the number of hours in each scenario when negative net load occurs. Observe that the excess energy in the system could be as high as 149 GWh under high renewable penetration. 31

Comparing the renewable integration scenarios, the amount of excess energy in the system grows significantly for Scenarios EE-EQ, EE-NP, and EE-SP. The energy penetration levels for these scenarios are around 36% versus those of the others that are around 33%. This may indicate that renewable integration to a certain threshold level may pose significantly less challenges. This means that after such threshold, adding more renewables may not yield the same incremental value and needs to be carefully justified. Also, observe that among scenarios with the same total installed capacity, the ones with less wind power installations in the south, i.e., SP15, would lead to less excess energy. For example, the total wind capacity in Scenarios EE-EQ, EE-NP, and EE-SP is around 21 GW. However, Scenarios EE-NP, where 25% of the wind additions are in SP15 and the other 75% are in NP15, lead to significantly less excess energy. This point may be taken into account when providing incentives for wind power integration programs. The excess energy in the system may justify the integration of bulk electric energy storage. Energy storage can reduce cycling and improve the efficiency of the system as a whole trough significant operating cost savings [21]. At this time, California has mandated to have 1,325 MW of energy storage capacity by the end of 2020, which is the United States first energy storage mandate [53, 54]. 4.4. Volatility Volatility is a well-known index for measuring changes in a time series. It measures the standard deviation of changes in a time series over a specific time 32

Table 6: Energies associated with negative net loads in scenarios of year 2023. Scenario Hours with Negative Excess Energy Total Renewable Net Load (GWh) Energy Penetration (%) MM-EQ 3 2.1 29.2 MM-NP 2 1.0 29.0 MM-SP 3 2.2 29.4 EM-EQ 20 27.0 33.0 EM-NP 16 22.3 32.8 EM-SP 21 33.5 33.2 ME-EQ 11 18.1 32.9 ME-NP 8 16.4 32.7 ME-SP 19 29.4 33.1 EE-EQ 45 111.9 36.7 EE-NP 45 97.2 36.5 EE-SP 55 149.0 36.9 window. To calculate the volatility usually the logarithmic return of the time series over the time period h, denoted by r t,h is used, which is calculated by r t,h = ln( L t L t h ) = ln(l t ) ln(l t h ). (2) L t is the parameter being assessed at time t. Then, standard deviation of logarithmic returns over a time window T, denoted by σ h,t, is defined as the historical volatility and calculated by σ h,t = T t=1 (r t,h r T,h ) 2, (3) T 1 where r T,h is average of the logarithmic returns over T. Since electricity load follows daily and weekly periodicities, the return time series are highly correlated 33

and therefore, the time window T should be chosen short enough (e.g. 24 h) so as to have negligible return correlations [55]. Thus, the historical volatility for each studied day, denoted by σ h,24 (d), is calculated as σ h,24 (d) = 24 d t=1+24 (d 1) (r t,h r t,h (d)) 2 23 100 (%), (4) where d stands for the studied day or period of time, and r t,h (d) is the logarithmic return s average over the specified day d. Considering hourly, daily, and weekly logarithmic returns, the averages of σ h,24 (d) over all studied days, i.e., σ 1,24, σ 24,24 and σ 168,24 are volatility indices. For instance, σ 1,24 quantifies the electricity load or any other time series changes from one hour to another during a day. Because in some cases we have very low or negative net loads and zero solar generation, volatility calculation results in very large amounts for those instances. Hence, we have assumed the floor of 1,000 MW for the time series for the purpose of volatility analysis. Table 7 summarizes the volatilities of load and net load over the studied years and scenarios. Although volatility of load, wind and solar PV do not change significantly over the years and different scenarios, it is clear from the figure that with increased level of wind or solar penetration, volatility of the net load notably increases compared to that of the load. For instance, σ 1,24 for the net load in year 2013 is 5%, which is very close to the load volatility, i.e., 4%. However, in year 2023, σ 1,24 of the net load is 2-3 times higher than the conventional load. In addition, daily and weekly volatilities of the net load could be up to eight times 34

higher than that of the load in some scenarios. Higher volatility is an indication of lower predictability for a time series [56]. Thus, with the increasing penetration of renewables, predicting the net load for operation planning purposes would become more challenging. Low accuracy in net load forecasts could lead to inefficient operation schedules, and thus, lower economic and environmental benefits. The other observation from Table 7 is that the highest net load volatilities are associated with higher wind capacity in the SP15 zone, i.e., scenarios ending with -SP. The reason is that aggregated wind time series are smoother with higher geographically dispersed wind sites in the systemwide values [57]. The scenarios with the highest net load volatilities are the ones with high wind power installations in the South. As discussed before, the geographical location of new installations and the associated impacts on overall success of the renewable integration program need to be carefully analyzed to gain the highest benefits. 4.4.1. Hourly Volatilities Volatility indices are also calculated based on each hour of the day for different scenarios. This will reveal the most volatile hours of the day and the results could be used later to develop appropriate forecasting engines based on the volatility of each hour. Since all of the scenarios show similar patterns for each hour, Fig. 9 only shows the load and net load volatilities of Scenario EE-EQ as a sample along with the results of years 2013 and 2018. To calculate these volatilities, first the original time series is filtered by each hour and 24 separate time series are generated. With the new filtered time series, hourly volatilities are based on the 35

Table 7: Annual volatilities for load (L) and net load (NL). All numbers are in %. σ 1,24 σ 24,24 σ 168,24 Scenario / Year L NL L NL L NL 2013 4.3 4.7 3.0 4.4 2.9 4.4 2018 4.8 7.4 3.1 10.3 2.7 10.1 MM-EQ 4.8 9.2 3.1 13.8 2.7 13.6 MM-NP 4.8 9.2 3.1 13.2 2.7 13.0 MM-SP 4.8 9.3 3.1 14.6 2.7 14.6 EM-EQ 4.8 10.2 3.1 17.9 2.7 17.7 EM-NP 4.8 10.1 3.1 17.0 2.7 16.8 EM-SP 4.8 10.5 3.1 19.5 2.7 19.5 ME-EQ 4.8 12.3 3.1 17.3 2.7 17.3 ME-NP 4.8 12.3 3.1 16.6 2.7 16.6 ME-SP 4.8 12.4 3.1 18.2 2.7 18.4 EE-EQ 4.8 13.7 3.1 21.9 2.7 21.9 EE-NP 4.8 13.6 3.1 20.9 2.7 20.9 EE-SP 4.8 14.0 3.1 23.7 2.7 24.0 changes of one specific hour from one day to another. Hence, all of the volatilities are actually averaged over a weekly time frame, i.e., T=7. As Fig. 9(a) depicts, load volatilities are nearly similar for all hours from year 2013 to 2023. However, net load volatilities in Fig. 9(b) show a significant increase with the increased level of renewable integration. Note that Fig. 9(b) shows one of the most volatile cases for year 2023. However, the least volatile case, which is Scenario MM-NP, shows the peak volatility of 33.6%, which is still much higher than the 2018 case, i.e., 23.8%. The highest net load volatility is for Scenario EE-SP with the maximum of 64.5%. This confirms the observations from Table 7. Another point from Fig. 9 is that load volatility is higher in the early morning 36

Volatility (%) Volatility (%) 12 10 8 6 4 2 0 60 50 40 30 20 10 0 2013 2018 2023 Year (a) 2013 2018 2023-(EE-EQ) Year (b) Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Figure 9: Hourly volatilities for year 2013 to 2023, Scenario EE-EQ: a) Load; b) Net load. hours for all of the scenarios, especially from hour 7 to 9. After that the volatilities decrease and the minimum volatile hours are midnight times. However, the most volatile net load hours are in the afternoon, typically hours 13 and 14. These are moments with a high level of solar generation. This means that solar generation shifts the most volatile hours from morning to early afternoon. One consequence 37

of this situation is that net load forecasts will be less accurate at the time of very high solar generation, which coincides with the net load valley. This could impose operation costs since inaccuracy in the net load forecast will result in non-optimal reserve scheduling for these hours. A detailed analysis on the effect of high afternoon volatilities and forecasting error on the system operation and reserve costs would be essential to determine the operational challenges associated with these moments. 4.5. Hourly Ramp Analysis In this section, hourly ramps, which are changes from one hour to the next hour for load and net load scenarios are evaluated. The analyses are performed for each scenario. However, net load results are almost similar to each other in scenarios with the same capacity of wind and solar, no matter where the sites are placed. Hence, similar to previous studies, only one scenario, i.e., ME-NP will be reported as representative of all cases. Duration curves of hourly ramps are shown in Fig. 10 for load. As can be seen, upward hourly ramps with the magnitude of up to 1,000 MW/h are slightly more frequent compared to downward ramps, specially for years 2018 and 2023. On the other hand, hourly downward ramps of higher than 1,000 MW/h are more frequent. Thus, one can conclude that for the load, the duration curve has a longer tail on the left side, meaning that large downward load ramps are more frequent than the upward ramps. Net load hourly ramp duration curves are depicted in Fig. 11. As this figure 38

4500 4000 2013 2018 2023 3500 Number of hours 3000 2500 2000 1500 1000 500 0-5000 -4000-3000 -2000-1000 0 1000 2000 3000 4000 Ramp magnitude (MW) Figure 10: Hourly load ramp duration curves for years 2013 to 2023 in all scenarios. shows, net load ramps have the opposite direction compared to that of the load, i.e., the net load has more frequent very large upward hourly ramps. The other observation is that as the penetration level of renewables increase, both upward and downward hourly ramps are far more often compared to that of the load. Figure 10 shows that the load hourly ramps could be as high as 4,000 MW/h. However, high penetration level of renewables can lead to upward hourly ramps of up to 8,000 MW/h, see Fig. 11. This magnitude of hourly ramps would require careful security measures to avoid any catastrophic system issues. In order to analyze the ramps in more details, all of the hourly ramps greater than 1,500 MW/h are counted and depicted in Fig. 12 for upward and Fig. 13 for 39

5000 4500 4000 2013 2018 2023-(ME-NP) Number of hours 3500 3000 2500 2000 1500 1000 500 0-8000 -6000-4000 -2000 0 2000 4000 6000 8000 Ramp magnitude (MW) Figure 11: Hourly net load ramp duration curves for years 2013 to 2023, Scenario ME-NP. downward ramps for each hour of the day. These figures only show the results of Scenario MM-NP for year 2023 as an example. They show that in all of the cases, increased renewable penetration decreases the occurrence of upward ramp events. More renewables also introduce midday large downward ramp events and afternoon upward ramp events for the net load. Comparing Figs. 12(a) and 12(b), the net load has far less upward ramps in the mornings compared to the load. This is the time that solar generation starts feeding the grid. On the other hand, new upward ramps start to appear in the evening hours. This is due to the decrease in solar PV generation in late afternoon. Also, comparing Figs. 13(a) and 13(b), new downward ramp events in the mornings 40

Occurrence Occurrence 400 350 300 250 200 150 100 50 0 350 300 250 200 150 100 50 0 2013 2018 2023 Year (a) 2013 2018 2023-(MM-NP) Year (b) Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Figure 12: Occurrence of upward ramps higher than 1,500 MW/h by hour for year 2013 to 2023, Scenario MM-NP: a) Load; b)net load. start to appear as the penetration of renewables grows. This is associated with the growth of solar power generation in the morning. On the other hand, the midnight downward ramp events decrease slightly for net load compared to the load, due to wind power generation. Thus, it can be observed that renewables clearly shift the upward ramp events from morning to afternoon. Also, solar power is a more 41

Occurrence Occurrence 400 350 300 250 200 150 100 50 0 400 350 300 250 200 150 100 50 0 2013 2018 2023 Year (a) 2013 2018 2023-(MM-NP) Year (b) Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Hour 13 Hour 14 Hour 15 Hour 16 Hour 17 Hour 18 Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 24 Figure 13: Occurrence of downward ramps higher than 1,500 MW/h by hour for year 2013 to 2023, Scenario MM-NP: a) Load; b)net load. dominant factor in shifting the ramp events timing over a day compared to wind power. A shift in the timing of ramping events needs to be taken into account when operation planning procedures are revisited in systems with high renewables penetration. 42

5. Conclusions In this paper, we have evaluated the impacts of large-scale wind and solar PV power integration in the California s power system on the characteristics of the net load in the system. The analyses were performed using both historical data, i.e., years 2013 and before, and also future simulated data scenarios for years 2018 and 2023. The future scenarios are based on simulated data provided by NREL. The simulated net load time series are analyzed from a variety of perspectives, such as, average daily shapes, load and net load factor, duration curves, renewables curtailment, volatility, and hourly ramps. The main contribution of this paper is to provide an in-depth discussion on how the net load characteristics would deviate from what power systems are accustomed to today when a significant amount of wind and solar power generation is integrated into the grid. The average daily shapes are analyzed in Section 4.1. The results showed that high solar PV penetration level would create a valley in the net load during the day time. This changes the ramping behaviour of net load compared to conventional load and shifts the morning hourly upward ramps to the afternoon. This change would require new power systems operation and reserve scheduling strategies. The analyses in Section 4.2 showed that net load factor decreases compared to the conventional load when the wind and solar PV penetrations grow. Consequently, the transmission and generation infrastructure will be under utilized during a large number of hours over a year, which would also impact the economics of peaking generators significantly in competitive markets, and may lead to long-term system reliability concerns. 43

Another observation was made on load and net load duration curves in Section 4.3. Negative and very low net loads in scenarios with significant wind and solar PV power integration is expected in future years. The negative net loads were shown to occur more frequently if the majority of new wind capacity installations take place in the southern areas of the California power system. Negative net loads would lead to curtailment of renewables or negative prices for electricity in competitive markets. Under high penetration of renewables, the full environmental benefits of wind and solar power may not be realized. It also leads to inefficient system operation from the emissions and economics point of views. The volatility analysis of Section 4.4 showed that high integration of wind and solar power also results in up to eight times higher volatility of the net load in future scenarios compared to the conventional load, specially in the case of high wind capacity growth in southern California. This is an indication of lower predictability for the net load time series. Forecast of net load time series are the basis of many operation-planning decisions, and thus, inaccurate net load forecasts would be costly to the system. Finally, an analysis of hourly ramp events in Section 4.5 revealed an increased level of upward net load ramp events compared to that of conventional load in future scenarios that have a high penetration level of wind and solar PV generation. The ramping behaviour of the net load calls for new strategies for market and system operation to ensure the security of the grid. 44

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