Submission for the NRG Energy Case Study

Transcription

1 Submission for the NRG Energy Case Study Colorado School of Mines Team Westpaw Source: Sundance Solar, accessed March 4, 2017 PREPARED BY: WALTER MEEKER, PHILLIP RUBAN, AUGUST STEINBECK DATE: MARCH 8, 2017

2 Table of Contents List of Figures... 2 Acknowledgements Introduction Model Framework Carbon emission considerations Reliability and cleanliness of power supplied Financing considerations for the project Operational strategy Financial valuation and key metrics Details and specific considerations Sensitivity analysis and Monte Carlo Simulation Synergistic opportunities that warrant further evaluation Trigeneration: Waste heat to chilled water Use of biomass as a substitute for natural gas Conclusion APPENDIX A Financial Statements APPENDIX B Model Assumptions and References List of Figures FIGURE 1 AVERAGE DAILY GENERATION AND DEMAND AFTER FULL SCALE BATTERY... 8 FIGURE 1 CUMULATIVE NPV PROFILE OF PROPOSED INVESTMENT FIGURE 2 RESULTS OF MONTE CARLO ANALYSIS ON THE EXPECTED NPV OF THE PROJECT

3 Acknowledgements Despite our desire to claim sole responsibility for the completion of this report, doing so would neglect several supporting figures who helped us along the way. Therefore, we would like to express our sincere gratitude to the following: The first is Dov Quint, who aided in the modeling process by contributing both his knowledge and advice. The second is Agata Bogucka, who generously donated her time and resources to aid in the filming and editing of our video presentation. The third is the Department of Economics and Business at the Colorado School of Mines, who provided financial support and encouragement for our participation in this project. Finally, we would like to thank The Economist and NRG Energy, because without them we would have missed this great opportunity to learn and develop as students and future industry professionals. Thank you and enjoy the submission. Team WestPAW (Phillip, August, Walter) 3

4 1.0 Introduction The optimal strategy to provide the financial instruments to deliver low-carbon, reliable power to a data center in Texas is one that balances many competing objectives. Carbon-free generation sources are often irregular, which combined with the relatively expensive, risky and currently immature battery storage technologies makes it costly to implement a singular source of generation. Furthermore, the reliability of minimally distorted electricity is of great importance for the proposed data center client. Our proposed model includes natural gas fired microturbines, which provide baseload generation, as well as photovoltaic generation and a two-stage battery storage installation. A staged investment approach provides sufficient time to learn from the early implementation of battery technology while allowing the technology to mature and become less costly prior to the secondary installation. Lastly, we are able to capture an arbitrage opportunity due to a significant amount of cheap wind power generated in the early hours of the morning in west Texas. This holistic solution is the optimal approach to finance the development of low-carbon, reliable power generation for the data center in the deregulated Electric Reliability Council of Texas (ERCOT) market. 1.1 Model Framework The business model we developed serves a theoretical data center named Westpaw as a retail customer of NRG. The contract agreement offered by NRG is a flat rate of 8 cents per kwh with an annual price escalation of 2% over the 20-year duration of the project for all electricity consumed. For Westpaw, the presence of co-located microturbine generation and battery assets provides an unprecedented degree of reliability and superior electrical harmonic mitigation that other data centers would not experience. In addition, this opportunity provides an excellent long- 4

5 term price hedge, which will act as a strong stabilizing factor in their business planning cycles. Therefore, despite this offer being slightly higher than the current average industrial retail rate in Texas of about 6 cents 3 per kwh, we believe that the high degree of reliability, signal purity, and price stability make this offer competitive in the long run Carbon emission considerations The proposed system was designed in consideration of NRG s publically stated goal of a 50% reduction in carbon emissions from their 2014 levels by the year The average emissions over the system s lifetime represent a 48% carbon reduction from 2015 average ERCOT emission levels. In the final years of the project, however, emissions fall to 52% of the same 2015 emission levels. This goal was accomplished in two ways. First, a carbon cost of $25 per ton of CO2 equivalent was imposed on all carbon emissions generated from non-renewable energy sources specifically the microturbine and the majority of purchased power. Secondly, we imposed a carbon emissions target of 50% reduction from the ERCOT 2015 baseline levels to be achieved by In the absence of a carbon cost and target carbon reduction threshold, our modelling efforts would have minimized cost by choosing to meet production entirely by natural gas, and would have excluded all renewable generation resources. When the carbon cost was included, however, the microturbine became more expensive than solar PV (neglecting associated battery costs required to support an entirely solar generation profile). After balancing the goal of providing a reliable baseload and reduced emissions, 13 MW of microturbines and 25 MW solar PV installation with associated battery storage was determined to be the optimal solution. After determining the generation mix to use, a sensitivity analysis was run to evaluate the cost of carbon at which the project is no longer economical. The results of this analysis show that the 5

6 project remains economical up to a carbon cost of $97 per ton of CO2. This provides sufficient leeway to accommodate potential future increases in the price of carbon while remaining an economically viable solution. Another sensitivity analysis determined a required price of carbon of to be $125 per ton of CO2 equivalent in order to completely avoid the use of electricity generation from natural gas Reliability and cleanliness of power supplied As cloud computing and web-based services play an ever increasing role in our economy, a reliable and uninterrupted flow of electricity to data centers can help IT companies prevail over competitors. Onsite microturbines, batteries, and solar PV allows Westpaw to electrically island itself during periods of irregular service. In addition, solar generation coupled with energy storage provides extremely clean power relative to traditional power generation methods, which suffer from frequency excursions, voltage deviations, and increased harmonic dissonance that can damage equipment. 9 Furthermore, the delivery of clean power will eliminate the need of costly filters that are typically required to be installed for clients with highly sensitive electronics, such as data centers. Therefore, Westpaw will receive a resilient stream of clean power, giving them a significant competitive and financial advantage in the market Financing considerations for the project A key challenge in the consideration of new and non-mature technologies is the ability to gain acceptance from the traditional finance vehicles. In order to address this concern, our proposed solution involves a two staged investment approach. The initial stage at time zero, acquires and deploys the full microturbine and photovoltaic arrays, which are mature technologies and carry little risk from the project finance perspective. In this initial stage, we also intend to install a pilot 6

7 scale 10 MWh capacity of lithium-ion battery storage. This is intended to provide time to implement, optimize and ultimately assess the full characteristics of the battery storage technology. If the technology proves to be successful and meets its performance criterion, a second phase of battery capacity will be installed in year four for use in year five and beyond. Another benefit of this additional timeframe is that it will allow for further capital cost reduction and technological improvements in lithium-ion battery storage. This will ultimately serve to de-risk the technology and provide access to conventional finance vehicles, such as bank debt with the acknowledgement of higher interest rates due to the relatively higher risk profile. Had it not been for this staged approach, we believe it would have been impossible to access these traditional and relatively lower cost finance vehicles. 1.2 Operational strategy Operationally, NRG will meet Westpaw's forecasted load with a variety of solutions including purchasing wholesale power, baseload microturbines, large-scale behind the meter solar, and the optimal dispatch of a battery storage system. For our model, we have assumed that NRG will take advantage of congestion constrained electricity opportunities and make strategic power purchases in such instances. After analyzing the real time market price data 5 at the Sweetwater ERCOT node during the last two years, we used an average price of $5.89 per MWh for hours ending (HE) 1 6. When this variable was subsequently modeled in stochastic analysis, an upper limit of $20 per MWh was assumed - this was consistent with the current levelized cost of energy (LCOE) of wind 1 and with recent PPA's signed in ERCOT. 4 The second operational phase begins with the construction of an additional 120 MWh of battery storage. Through rigorous market and financial analysis, we discovered that if battery storage technology prices 7

8 fall below the $280 per kwh threshold, we can economically arbitrage the nearly free wholesale power in the morning to cover peak load in the evening. In this phase, early morning power purchases will be used to charge the batteries and cover any demand the baseload microturbines cannot supply. In the midday, solar generation will be used to supply the bulk of consumption and excess generation will go towards keeping the batteries topped off in anticipation of the evening peak demand requirements. This has been graphically represented below in Figure 1. Figure 1 Average daily generation and demand after full scale battery This guaranteed load obligation can also be viewed as a hedge from NRG's perspective as the company operates merchant wind plants in the region and can opt to sink power to the contracted load at Westpaw rather than have the wind plants curtailed or dispatched below optimal maximum capacity. The initial rate of 8 cents per kwh charged to Westpaw translates to $80 per MWh (escalated annually), which provides a sufficient margin above the calculated 20-year 8

9 LCOE of our proposed solution, which is $59 per MWh. This represents a project life average profit of $38 per MWh delivered. The data center demand was modelled based on a study from Cornell University 6 and adapted to meet the demand requirements in the case data. The solar generation profile was modeled based on a real data captured from an installed solar array 7 in an area of similar solar insolation. Although not selected as a part of our implemented solution, a wind generation profile was modeled and analyzed based off data from a wind farm in Texas Financial valuation and key metrics The implementation of the proposed solution is a strong investment opportunity realizing $27.8 million of net present value with a 27% internal rate of return (IRR). The LCOE was calculated to be $59.2 per MWh for the 20-year implementation horizon considered and was consistent with the methodology used in the Lazard V2 LCOE analysis. Our solution is proven to be robust as the variability of several key inputs were simulated through Monte Carlo simulation to find the optimal generation strategy in the face of given uncertainty Details and specific considerations The basis for this financial analysis was that it was considered as an isolated part of the larger NRG Energy corporation. The cash flows were reported in an isolated environment, however other income is assumed to have been generated to allow for the immediate capture of the investment and depreciation tax credits. An initial equity injection was assumed to have been received from NRG in the period zero to cover a portion of the initial capital investment. Once the project realized a positive profit, a 10% dividend was distributed back to NRG. Lastly, the 9

10 Millions of cumulative NPV project was able to cover the equity of the second portion of capital investment in time period four with cash generated from the first few years of operation. The cumulative NPV profile is featured below in Figure 2, where a key advantage of the delayed investment option is clearly visualized. The maximum capital exposure is minimized, to a year zero value of $23.9 million, thus increasing the present value ratio (PVR) for the project to As previously discussed, the profits generated in the early years of the project generate a sufficient cash position to cover the $14 million required to offset the equity portion of the second (additional battery) stage of investment. This strategy reduces the overall capital exposure and is expected to be viewed favorably by the investment committee. $30.0 $25.0 $20.0 $15.0 $10.0 $5.0 $- $(5.0) $(10.0) $(15.0) $(20.0) $(25.0) $(30.0) Cumulative NPV profile Project year Figure 1 Cumulative NPV profile of proposed investment The staged investments were assumed to have different costs of capital (debt and equity) in accordance with their associated risk levels. The initial capital investment was made in predominantly mature and proven microturbine and photovoltaic technologies and carried a cost of debt of 6% and an expected return on equity of 10%. The additional investment required for 10

11 the additional battery capacity in year four was seen as higher risk than the typical investment and was thus assumed to require a cost of debt and equity of 9% and 13%, respectively Sensitivity analysis and Monte Carlo Simulation Monte Carlo analysis or stochastic modeling is often performed to test the robustness of a given economic analysis. This technique involves varying key input parameters within their expected ranges by a randomized draw and monitoring the outcomes of various performance metrics. This process is typically repeated several hundred thousand times to get a resulting distribution of the outcome variables. Key input parameters that were analyzed in our analysis were: the price of natural gas, the degree of battery cost reduction anticipated, the price paid for purchased electricity in the early morning hours of the day, the fuel cost escalation rate, the fixed and variable operations and maintenance cost escalation rates. This analysis produced the probability distribution function as shown in Figure 3 below over 100,000 randomized iterations. The mean expected NPV value was $24.4 million, with a 95% confidence interval of expected NPV of $17.1 million to $26.1 million. This provides significant robustness and credibility to the decision to support the investment in this project. 11

12 Figure 2 Results of Monte Carlo analysis on the expected NPV of the project 1.4 Synergistic opportunities that warrant further evaluation Technologies exist that could be integrated into the proposed system that would reduce both its carbon footprint and its overall electric demand. The first technology to be considered is trigeneration, which has the potential to reduce the data center s overall load by decreasing its dependence on water chillers. The second is the installation of a biomass digester, which would capture methane produced from agricultural waste to fuel the microturbines. Despite the apparent benefits of these technologies, they were not included in the proposed solution due to their relative technological maturity at this scale of implementation and the high capital costs associated with their installation Trigeneration: Waste heat to chilled water Combined heating, cooling, and power (CCHP) or trigeneration could be installed with the microturbine array to reduce the electric demand of the data center. A CCHP system captures 12

13 waste heat from the microturbine and uses it to produce a stream of heated and chilled water. The respective temperature streams are then used for either heating or cooling purposes. A distinct advantage of this system is realized when co-located with a data center. To keep servers from overheating, data centers typically cycle chilled water through the stacks of servers and other electrical equipment. Chilling the water used to keep the servers cool requires onsite chillers, which can account for 40% of the total power consumption of a data center. These chiller systems supply the approximately 1.8 liters of chilled water required per kwh of electricity consumed in a data center. Using the chilled water produced by a CCHP process in place of traditional chillers will allow the data center to significantly reduce their required electrical load. The use of a CCHP process could provide half of the chilled water required, and thus reduce electric consumption by up to 20%. As waste heat is a natural byproduct of microturbines, there is no additional carbon cost associated with implementing a CCHP process. Therefore, in addition to a reduction in the total electric load of the data center, the CO2 equivalent emissions per kwh generated would also be reduced Use of biomass as a substitute for natural gas Another method by which carbon emissions could be further reduced is through the installation of a biomass digester. A digester would allow the data center to burn methane captured from agricultural waste that would otherwise escape into the atmosphere. This would ultimately serve to reduce dependency on traditionally sourced natural gas. Burning methane captured from agriculture waste is viewed as more environmentally favorable than allowing it to escape into the atmosphere as it has a greenhouse gas potential of approximately 20 times that of CO2. Even though the microturbine would continue to emit CO2 as a result of this process, the net contribution to global warming would be negative. 13

14 Presently, investment in a biomass digester for this scale of implementation is not economically viable. However, given the data center s proximity to abundant biomass resources in northwest Texas, as well as potential tax incentives, investing in a digester could become a viable economic option in the near future. As greenhouse gas avoidance policies and government regulations become more stringent, the economics become increasingly favorable. This is evidenced by the popularity of biomass digesters in Europe, where carbon prices can be upwards of $100 per ton of CO2 equivalent. 1.5 Conclusion In conclusion, NRG will employ a combination of co-located distributed generation including microturbines, a solar photovoltaic array, grid scale lithium-ion batteries, as well as wholesale power purchases to supply the power required by the data center. The proposed solution contains realistic assumptions about CAPEX, OPEX, and liabilities, while also accounting for decreasing technology costs and variable energy prices. The full scale implemented solution represents a 52% reduction in carbon emissions from 2015 ERCOT levels and provides electricity for the data center at a competitive rate of 8 cents/kwh escalating for inflation. This is accompanied by an expected internal rate of return of 27% and a net present value of $27.8 million. This model serves as a template for NRG to employ future technologies and forge new partnerships in the information-technology industry. 14

15 APPENDIX A Financial Statements 15

16 Actual Income Statement Year Revenue USD 000's Revenue - Sales to WestPaw $ - $ 16,169 $ 16,493 $ 16,822 $ 17,159 $ 21,041 $ 21,462 $ 21,891 $ 22,329 $ 22,776 $ 23,231 $ 23,696 $ 24,170 $ 24,653 $ 25,146 $ 25,649 $ 26,162 $ 26,685 $ 27,219 $ 27,763 $ 28,319 Revenue - Sales to the Grid $ - $ 566 $ 577 $ 589 $ 601 $ 89 $ 91 $ 93 $ 94 $ 96 $ 98 $ 100 $ 102 $ 104 $ 106 $ 108 $ 111 $ 113 $ 115 $ 117 $ 120 Total Revenue $ - $ 16,735 $ 17,070 $ 17,411 $ 17,760 $ 21,130 $ 21,553 $ 21,984 $ 22,423 $ 22,872 $ 23,329 $ 23,796 $ 24,272 $ 24,757 $ 25,252 $ 25,757 $ 26,273 $ 26,798 $ 27,334 $ 27,881 $ 28,438 Operating Costs USD 000's Fixed Costs (O&M) / yr $ - $ (524) $ (535) $ (546) $ (556) $ (1,230) $ (1,255) $ (1,280) $ (1,305) $ (1,331) $ (1,358) $ (1,385) $ (1,413) $ (1,441) $ (1,470) $ (1,499) $ (1,529) $ (1,560) $ (1,591) $ (1,623) $ (1,655) Variable Costs (O&M)/yr $ - $ (7,535) $ (7,649) $ (7,765) $ (7,883) $ (6,538) $ (6,641) $ (6,746) $ (6,853) $ (6,962) $ (7,074) $ (7,187) $ (7,303) $ (7,421) $ (7,541) $ (7,664) $ (7,790) $ (7,918) $ (8,048) $ (8,181) $ (8,317) Total Operating Costs $ - $ (8,060) $ (8,184) $ (8,311) $ (8,440) $ (7,768) $ (7,896) $ (8,026) $ (8,158) $ (8,294) $ (8,432) $ (8,572) $ (8,716) $ (8,862) $ (9,011) $ (9,164) $ (9,319) $ (9,477) $ (9,639) $ (9,804) $ (9,972) EBITDA USD 000's $ - $ 8,676 $ 8,886 $ 9,101 $ 9,320 $ 13,362 $ 13,657 $ 13,958 $ 14,265 $ 14,578 $ 14,898 $ 15,224 $ 15,556 $ 15,895 $ 16,241 $ 16,594 $ 16,954 $ 17,321 $ 17,695 $ 18,077 $ 18,466 Depreciation $ - $ (13,880) $ (22,208) $ (13,325) $ (7,995) $ (14,951) $ (15,127) $ (6,678) $ (4,007) $ (4,007) $ (2,003) $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Interest $ - $ (2,498) $ (2,430) $ (2,358) $ (2,282) $ (4,079) $ (3,937) $ (3,784) $ (3,620) $ (3,444) $ (3,256) $ (3,053) $ (2,836) $ (2,603) $ (2,353) $ (2,085) $ (1,796) $ (1,487) $ (1,154) $ (797) $ (413) Tax Benefit (Liability) $ 3,900 $ 3,081 $ 6,301 $ 2,633 $ 383 $ 2,268 $ 2,163 $ (1,398) $ (2,655) $ (2,851) $ (3,856) $ (4,868) $ (5,088) $ (5,317) $ (5,555) $ (5,804) $ (6,063) $ (6,334) $ (6,616) $ (6,912) $ (7,221) Net Profit USD 000's $ 3,900 $ (4,622) $ (9,451) $ (3,950) $ (574) $ (3,401) $ (3,244) $ 2,098 $ 3,983 $ 4,276 $ 5,783 $ 7,302 $ 7,632 $ 7,975 $ 8,333 $ 8,705 $ 9,094 $ 9,500 $ 9,924 $ 10,368 $ 10,832 16

17 Cash Flow Statement Units Operating USD 000's EBITDA $ - $ 8,676 $ 8,886 $ 9,101 $ 9,320 $ 13,362 $ 13,657 $ 13,958 $ 14,265 $ 14,578 $ 14,898 $ 15,224 $ 15,556 $ 15,895 $ 16,241 $ 16,594 $ 16,954 $ 17,321 $ 17,695 $ 18,077 $ 18,466 Interest $ - $ (2,498) $ (2,430) $ (2,358) $ (2,282) $ (4,079) $ (3,937) $ (3,784) $ (3,620) $ (3,444) $ (3,256) $ (3,053) $ (2,836) $ (2,603) $ (2,353) $ (2,085) $ (1,796) $ (1,487) $ (1,154) $ (797) $ (413) Taxes $ 3,900 $ 3,081 $ 6,301 $ 2,633 $ 383 $ 2,268 $ 2,163 $ (1,398) $ (2,655) $ (2,851) $ (3,856) $ (4,868) $ (5,088) $ (5,317) $ (5,555) $ (5,804) $ (6,063) $ (6,334) $ (6,616) $ (6,912) $ (7,221) Total Operating Cashflows $ 3,900 $ 9,258 $ 12,757 $ 9,375 $ 7,420 $ 11,550 $ 11,883 $ 8,776 $ 7,990 $ 8,283 $ 7,787 $ 7,302 $ 7,632 $ 7,975 $ 8,333 $ 8,705 $ 9,094 $ 9,500 $ 9,924 $ 10,368 $ 10,832 Financing USD 000's Net Debt (Repayments)/Disbursement $ 41,640 $ (1,132) $ (1,200) $ (1,272) $ 19,520 $ (2,061) $ (2,204) $ (2,357) $ (2,521) $ (2,697) $ (2,885) $ (3,088) $ (3,305) $ (3,538) $ (3,788) $ (4,056) $ (4,344) $ (4,654) $ (4,987) $ (5,344) $ (5,728) Net Investments $ (69,400) $ - $ - $ - $ (34,781) $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Equity Injections/(Withdrawls) $ 27,760 $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - Dividends $ - $ - $ - $ - $ - $ - $ - $ (210) $ (398) $ (428) $ (578) $ (730) $ (763) $ (798) $ (833) $ (871) $ (909) $ (950) $ (992) $ (1,037) $ (1,083) Total Financing Cashflows $ - $ (1,132) $ (1,200) $ (1,272) $ (15,261) $ (2,061) $ (2,204) $ (2,567) $ (2,919) $ (3,124) $ (3,464) $ (3,818) $ (4,068) $ (4,335) $ (4,621) $ (4,927) $ (5,254) $ (5,604) $ (5,979) $ (6,381) $ (6,811) Cash Flow Movement USD 000's Opening Cashflow $ - $ 3,900 $ 12,026 $ 23,583 $ 31,686 $ 23,846 $ 33,335 $ 43,014 $ 49,222 $ 54,293 $ 59,452 $ 63,775 $ 67,259 $ 70,823 $ 74,463 $ 78,175 $ 81,954 $ 85,794 $ 89,690 $ 93,636 $ 97,623 Net Cash Inflow/(Outflow) $ 3,900 $ 8,126 $ 11,557 $ 8,103 $ (7,840) $ 9,488 $ 9,679 $ 6,209 $ 5,071 $ 5,159 $ 4,323 $ 3,484 $ 3,564 $ 3,640 $ 3,712 $ 3,779 $ 3,840 $ 3,896 $ 3,945 $ 3,987 $ 4,021 Closing Cashflows $ 3,900 $ 12,026 $ 23,583 $ 31,686 $ 23,846 $ 33,335 $ 43,014 $ 49,222 $ 54,293 $ 59,452 $ 63,775 $ 67,259 $ 70,823 $ 74,463 $ 78,175 $ 81,954 $ 85,794 $ 89,690 $ 93,636 $ 97,623 $ 101,644 Cashflow Analysis USD 000's Period Discounted Cashflows $ (23,860) $ 7,388 $ 9,551 $ 6,088 $ (5,355) $ 5,882 $ 5,398 $ 3,279 $ 2,586 $ 2,410 $ 1,982 $ 1,625 $ 1,533 $ 1,444 $ 1,359 $ 1,277 $ 1,198 $ 1,123 $ 1,051 $ 982 $ 917 Total NPV 27,857 Cumulative Discounted Cashflows $ (23,860) $ (16,472) $ (6,922) $ (833) $ (6,188) $ (306) $ 5,093 $ 8,371 $ 10,957 $ 13,367 $ 15,349 $ 16,973 $ 18,506 $ 19,951 $ 21,310 $ 22,586 $ 23,784 $ 24,907 $ 25,957 $ 26,940 $ 27,857 17

18 Balance Sheet Units Assets USD 000's Long Term Assets Net Plant Utility Assets 69,400 55,520 33,312 19,987 46,773 31,822 16,695 10,017 6,010 2, Current Asset Cash 3,900 12,026 23,583 31,686 23,846 33,335 43,014 49,222 54,293 59,452 63,775 67,259 70,823 74,463 78,175 81,954 85,794 89,690 93,636 97, ,644 Other Current Assets Total Current Assets 3,900 12,026 23,583 31,686 23,846 33,335 43,014 49,222 54,293 59,452 63,775 67,259 70,823 74,463 78,175 81,954 85,794 89,690 93,636 97, ,644 Total Assets 73,300 67,546 56,895 51,674 70,620 65,157 59,708 59,239 60,303 61,455 63,775 67,259 70,823 74,463 78,175 81,954 85,794 89,690 93,636 97, ,644 Liabilities USD 000's Long Term Liabilities Long Term Debt (41,640) (40,508) (39,308) (38,036) (57,557) (55,495) (53,291) (50,934) (48,413) (45,717) (42,831) (39,744) (36,439) (32,901) (29,114) (25,057) (20,713) (16,059) (11,072) (5,728) (0) Current Liabilities Other current liabilities Total Current Liabilities Total Liabilities (41,640) (40,508) (39,308) (38,036) (57,557) (55,495) (53,291) (50,934) (48,413) (45,717) (42,831) (39,744) (36,439) (32,901) (29,114) (25,057) (20,713) (16,059) (11,072) (5,728) (0) Equity USD 000's Opening Equity - 31,660 27,038 17,587 13,637 13,063 9,662 6,417 8,305 11,890 15,739 20,944 27,516 34,384 41,562 49,062 56,896 65,081 73,632 82,564 91,895 Equity Injections/(Withdrawls) 27, Dividend (210) (398) (428) (578) (730) (763) (798) (833) (871) (909) (950) (992) (1,037) (1,083) Profit 3,900 (4,622) (9,451) (3,950) (574) (3,401) (3,244) 2,098 3,983 4,276 5,783 7,302 7,632 7,975 8,333 8,705 9,094 9,500 9,924 10,368 10,832 Closing Equity 31,660 27,038 17,587 13,637 13,063 9,662 6,417 8,305 11,890 15,739 20,944 27,516 34,384 41,562 49,062 56,896 65,081 73,632 82,564 91, ,644 Key Metrics Actual Fair Value Rate 5.6% -8.3% -28.4% -19.8% -1.2% -10.7% -19.4% 20.9% 66.3% 213.5% NA NA NA NA NA NA NA NA NA NA NA Return on Equity NA -14.6% -35.0% -22.5% -4.2% -26.0% -33.6% 32.7% 48.0% 36.0% 36.7% 34.9% 27.7% 23.2% 20.0% 17.7% 16.0% 14.6% 13.5% 12.6% 11.8% Profit Margin NA -28% -55% -23% -3% -16% -15% 10% 18% 19% 25% 31% 31% 32% 33% 34% 35% 36% 36% 37% 38% Debt to EBITDA NA (4.67) (4.42) (4.18) (6.18) (4.15) (3.90) (3.65) (3.39) (3.14) (2.88) (2.61) (2.34) (2.07) (1.79) (1.51) (1.22) (0.93) (0.63) (0.32) (0.00) Debt to Equity 417.2% 300.7% 181.0% 155.9% 129.4% 121.1% 113.7% 119.5% 132.6% 152.5% 195.7% 325.0% % % % -78.7% -46.7% -27.9% -15.5% -6.6% 0.0% 18

19 APPENDIX B Model Assumptions and References 19

20 # Subject 1 CAPEX/OPEX Wind, Solar, Microturbine Value ($/MWh) Multiple Values Source Lazard LCOE v lysis-100/ 2 CAPEX/OPEX Batteries Multiple Values Lazard Battery LCOE v TX Industrial Rates 60 EIA pher.cfm?t=epmt_5_6_a 4 ERCOT Wind PPAs 20 DOE 5 HE1-6 Average Price 5 ERCOT RT LMPs =12300&reportTitle=LMPs%20by%20Resource%20Nodes,%2 0Load%20Zones%20and%20Trading%20Hubs&showHTMLV iew=&mimickey 6 Data Center Demand Profile - Lakshmi Ganesh Cornell University 7 Solar Generation Profile - International Scholarly Research Notices 8 Wind Generation Profile - ERCOT Hourly Wind Data TypeId=13424&reportTitle=Hourly%20Aggregated%2 0Wind%20Output&showHTMLView=&mimicKey 9 Electricity Harmonics - Mitigating Data Center Harmonics file:///z:/adit/downloads/wp_mitigatingharmonics_20 14.pdf 20