Topic:"Capacity Costs" in M08059

Planning Reserve Requirements – Capacity Resource Balance .............................................. 23 Plant Optimization ‐ Model Retirements and Builds ............................................................... 23 3.2. Wholesale...

AI summary The document outlines sections related to capacity resource balance, wholesale revenue requirements, and energy balances. Key topics include planning reserve requirements, plant optimization, cost components (e.g., Maritime Link, DSM, transmission), and NPV/revenue results. It emphasizes capacity factors, energy by fuel type, and cost exclusions in a regulatory context.

Plant Utilization – Capacity Factors – Coal and Tufts Cove Units ............................................ 41 Energy by Fuel Type..............................................................................................................

AI summary The document discusses plant utilization, capacity factors for coal and Tufts Cove units, energy by fuel type, emissions, and sensitivities (e.g., battery costs, gas prices). It also outlines updates since a technical conference, demand-side options, NB transmission, and additional wind resources in the discussion and recommendations section.

ization and Utilization study is to determine the extent to which it is cost‐effective to ratepayers to retain Nova Scotia Power Inc.’s (NSPI) thermal (steam) fleet through, and possibly beyond, 2030. The thermal fleet consists of the Tuft...

AI summary The study evaluates the cost-effectiveness of retaining Nova Scotia Power Inc.'s thermal fleet through 2030, considering its oil, gas, and coal units. It uses Plexos modeling for capacity expansion and production cost analysis, factoring in emissions targets and grid constraints. Lingan 2's retirement depends on the Nova Scotia Block's service via Maritime Link, expected mid-2020.

napse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 3 Final Report enhancing investment. As seen in Table 9a, that range of costs is $90 million to $570 million (NPV).4 The illustrative comparisons are...

AI summary The report analyzes NSPI's thermal generation optimization, highlighting that battery cost sensitivity significantly impacts coal unit retirement timelines and battery build decisions. Lower battery costs accelerate coal unit retirements (e.g., 2019 vs. 2027) and increase battery installations, emphasizing the critical role of battery cost analysis in resource optimization.

nd performance, since battery energy storage systems can replace the increasingly peaking nature of the energy provided by the least cost‐effective of the coal fleet resources.  The bookend scenarios (25, 26) with accelerated coal plant r...

AI summary The text compares NPVRR across scenarios with accelerated coal plant retirements and gas price sensitivity. Accelerated retirements show lower NPVRR, while gas price changes don't significantly alter outcomes. Energy efficiency and wind integration (Scenario 13) are noted as cost-effective alternatives. The Plexos optimization model's results suggest soft-constraint violations in some scenarios.

ia the Maritime Link, is in service. This is currently estimated to occur midway through 2020.11 1.2. Terms of Reference The Terms of Reference for the Study included the following objective: The main objective of the analysis will be to d...

AI summary The study evaluates the cost-effectiveness of retaining NSPI's thermal fleet through 2030, comparing retention costs to alternatives like retirement, capacity utilization, and replacement resources. The analysis includes sensitivity testing and stakeholder feedback rounds, with finalized terms of reference dated August 1, 2017.

nd assumptions in a memo dated October 16, 2017. Additional updates to these assumptions, also based on stakeholder comments, were provided in December 2017 memo. The key aspects are described below. 2.1. Modeling Methodology Plexos Synaps...

AI summary The document outlines Synapse's use of the Plexos Integrated Energy Model to simulate Nova Scotia's energy futures through long-term (LT) capacity expansion and short-term (ST) production cost modeling. Key factors include capital/production costs, system constraints (emissions limits, transmission), and resource optimization under NSPI's planning framework.

evaluated. Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 9 Final Report capacity and generation, or overall operating costs of the system—as well as more detailed, unit or plant‐specific results...

AI summary Synapse Energy Economics evaluates NSPI's thermal generation utilization and optimization, using Plexos to model system costs, including fuel, O&M, and DSM expenses, and calculating annual revenue requirements for different scenarios.

Sust Wind NB HardCode Scen. # Scenario Name Load Capital CapCredit Trans RetirePath 1 Ref Ref Ref Ref No No 2 Med DSM Med DSM Ref Ref No No 4 Ref/HighSusCap Ref High Ref No No 5 Med DSM/HighSusCap Med DSM High Ref No No 7 Ref/HighWindCapCr...

AI summary The table outlines various scenarios with different combinations of demand-side management (DSM), capacity credits, and transmission (NB Trans) options. The note highlights 'RetirePath1,' which includes forced retirements of coal plants beyond Lingan 2 in 2020/21, specifically Lingan 4 (2023) and Lingan 3 (2024), modeled in Plexos.

15.0 8.8 25 2030 4.50 20.0 8.8 30 19 Maritime Link Interim Cost Assessment M07718. 20 NSPI, response to discovery request DR‐15. Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 17 Final Report Not...

AI summary The document discusses the available resource new build costs for NSPI, including wind, gas CC, gas CT, battery storage, and solar PV. Costs were annualized based on economic life, capital cost, and a 7% weighted average cost of capital for NSPI.

of 17 percent for NRIS resources and zero for ERIS resources. For all “high wind capacity credit” scenarios (7, 8, 16, 17) a value of 20 percent is used for the capacity credit for all wind resources. The differential capacity credits asso...

AI summary The document discusses capacity credit values for wind resources in Nova Scotia, comparing different scenarios (17%, 20%, and 12.4%). It highlights the impact of using different capacity credit values on the overall capacity contribution and planning reserve margins, especially with coal plant retirements and increased wind deployment.

ick load of 50 percent, given the benefits accruing to NB from such 345 kV reinforcement; a book life of 30 years, a cost of capital of 7 percent, and a resulting fixed charge rate of 8.06 percent. 22 GE. 2016. “Pan Canadian Wind Integrati...

AI summary The text discusses the calculation of a fixed charge rate based on a 50 percent load, 30-year book life, and a 7 percent cost of capital. It also references several studies on wind integration and renewable energy, including reports by GE and Hatch.

ing capital costs; alternative resource cost declines) render these bookend scenarios valuable. At a minimum, they provide a bounding estimate on a more aggressive coal plant retirement policy option. Sensitivities We have run additional s...

AI summary The document discusses sensitivity analyses on capital costs, battery costs, natural gas prices, and unit availability, as well as scenarios involving coal plant retirements and transmission flexibility. These analyses aim to evaluate the impact of various factors on energy utilization and system efficiency in Nova Scotia.

TC 1-3 TC 4-6 0% Source: Plexos Generation Output Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 43 Final Report Table 11: Capacity Factors – Thermal Fleet Scenario 1, Reference Case

AI summary The document presents a table titled 'Capacity Factors – Thermal Fleet' under Scenario 1, Reference Case, sourced from Synapse Energy Economics, Inc. and related to NSPI Thermal Generation Utilization and Optimization M08059.

Final Report Table 14: Capacity Factors – Thermal Fleet Scenario 17, Medium DMS, NB Transmission, High Wind Capacity Credit

AI summary The document presents a table titled 'Capacity Factors – Thermal Fleet' under Scenario 17, which includes Medium DMS, NB Transmission, and High Wind Capacity Credit. This scenario explores the impact of various factors on the capacity of the thermal fleet.

capital costs incurred a range of 6.5% to 10.4% of total NPVRR costs in our main scenarios. It is critical to continue to assess the pattern of these costs and project future costs. 5. Establish requirements to allow increased levels of wi...

AI summary The document discusses capital costs and the need to assess their future impact. It also outlines requirements for increasing wind energy on the NSPI system, including infrastructure improvements and coordination with neighboring provinces for reliability with increased wind resource utilization.

Appendix 5.1 Terms of Reference Response to Stakeholder Comments on Proposed Terms of Reference Plexos Optimization Analysis / NSPI Thermal Fleet Retention August 1, 2017 Synapse has reviewed comments received in response to the June 29, 2...

AI summary Synapse Energy Economics adjusted the Terms of Reference for a Plexos Optimization Analysis to assess the cost-effectiveness of retaining NSPI’s thermal fleet through 2030. Key changes include extended stakeholder input periods, scenario analysis with varied assumptions, and clarification that the study is a planning exercise, not an operational assessment. The analysis will compare thermal fleet retention against alternatives like retirement and renewable integration.

Notation Scenario Name Load Capital CapCredit NB Trans RetirePath 1 NSPI Reference Ref Ref Ref Ref No No 2 Change Case Med DSM Med DSM Ref Ref No No 3 Change Case High DSM High DSM Ref Ref No No 4 Change Case Ref/HighSusCap Ref High Ref No...

AI summary The table outlines multiple regulatory scenarios (e.g., Med DSM, HighSusCap) with varying load, capital, and credit parameters. Each scenario combines different demand-side management (DSM) levels, capital credit thresholds, and transmission considerations, reflecting potential regulatory options under analysis.

No 19 Change Case Ref/HighSusCap/NB Trans Ref High Ref Yes No 20 Change Case Med DSM/HighSusCap/NB Trans Med DSM High Ref Yes No 21 Change Case High DSM/HighSusCap/NB Trans High DSM High Ref Yes No 22 Change Case Ref/NB Trans/HighWindCapCr...

AI summary The document outlines changes to input assumptions in a regulatory proceeding, adjusting the 'High Sustaining Capital' costs from a 50% to 25% increase over NSPI’s 2017 estimates. Scenarios involving demand-side management (DSM), sustainable capacity (SusCap), and New Brunswick Transmission (NB Trans) are presented.

mization M08059 A.5.4-1 Appendix Table 1: Capacity Factors – Thermal Fleet Scenario 1, Reference Case

AI summary The text references Appendix Table 1, which details 'Capacity Factors – Thermal Fleet' under Scenario 1, Reference Case. It includes a matter number (M08059) and formatting markers, but no substantive content or analysis is provided in the excerpt.

NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-2 Appendix Table 2: Capacity Factors – Thermal Fleet Scenario 2, Medium DSM

AI summary The document discusses NSPI's thermal generation utilization and optimization under 'Scenario 2, Medium DSM,' focusing on capacity factors for the thermal fleet. It references Appendix Table 2, which likely details capacity factor data for different thermal generation scenarios.

- - - - - - - - - - - - - - - - - - - - - - - - - New CT Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-3 Appendix Table 3: Capacity Factors – Thermal Fleet Scenario 4, High Sustaining Capi...

AI summary The document references an analysis by Synapse Energy Economics, Inc. on NSPI's thermal generation capacity factors under 'Scenario 4, High Sustaining Capital Costs' as part of proceeding M08059. Appendix Table 3 details capacity factors for the thermal fleet, focusing on capital cost implications.

omics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-3 Appendix Table 3: Capacity Factors – Thermal Fleet Scenario 4, High Sustaining Capital Costs

AI summary The document references NSPI's thermal generation utilization and optimization under 'Scenario 4, High Sustaining Capital Costs,' with Appendix Table 3 detailing capacity factors for the thermal fleet. The context implies analysis of generation efficiency and capital cost implications.

- - - 1% 1% 1% 5% 0% 1% 1% 1% 2% 1% 1% 1% 1% 3% 3% 8% 1% 6% 4% 3% 11% 11% New CT Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-4 Appendix Table 4: Capacity Factors – Thermal Fleet Scenario...

AI summary The text references Appendix Table 4 from Synapse Energy Economics, Inc.'s analysis of NSPI's thermal generation utilization and optimization under Scenario 5 (Medium DSM, High Sustaining Capital Cost). The table presents capacity factors for thermal fleet operations, though specific data values are not detailed in the excerpt.

- - - - - - - - - - - - - - - - - - - - - - - - - New CT Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-5 Appendix Table 5: Capacity Factors – Thermal Fleet Scenario 7, High Wind Capacity C...

AI summary The document, titled 'NSPI Thermal Generation Utilization and Optimization' (M08059), includes Appendix Table 5 analyzing capacity factors for the thermal fleet under Scenario 7, which involves high wind capacity credit.

Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-5 Appendix Table 5: Capacity Factors – Thermal Fleet Scenario 7, High Wind Capacity Credit

AI summary The document presents an appendix table analyzing thermal fleet capacity factors under Scenario 7, which involves high wind capacity credit. It references NSPI's thermal generation optimization and a matter number (M08059) related to the analysis.

- - - - - - - - - - - - - - - - - - - - - - - - - New CT Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-6 Appendix Table 6: Capacity Factors – Thermal Fleet Scenario 8, Medium DSM, High Win...

AI summary Analyzes capacity factors for NSPI's thermal fleet under Scenario 8, incorporating Medium DSM and High Wind Capacity Credit. Focuses on thermal generation utilization optimization as part of regulatory proceeding M08059.

nc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-6 Appendix Table 6: Capacity Factors – Thermal Fleet Scenario 8, Medium DSM, High Wind Capacity Credit

AI summary The document references NSPI's thermal generation utilization and optimization under Scenario 8, which includes Medium DSM and High Wind Capacity Credit. Appendix Table 6 details capacity factors for the thermal fleet, indicating analysis of generation efficiency under varying demand-side management and renewable energy scenarios.

e 3 14 - Tufts 38% 31% 30% 33% 24% 38% 35% 49% 47% 43% 45% 54% 49% 46% 46% 37% 52% 45% 55% 41% 58% 46% 49% 37% 48% Cove 4 15 - Tufts 45% 35% 34% 32% 22% 41% 31% 47% 51% 40% 47% 53% 51% 44% 50% 37% 52% 48% 56% 40% 57% 45% 48% 42% 51% Cove 5...

AI summary The text presents capacity factor data for NSPI's thermal generation fleet from Synapse Energy Economics, Inc., part of a regulatory proceeding (M08059) under Scenario 13 involving NB Transmission and additional wind resources.

eration Utilization and Optimization M08059 A.5.4-7 Appendix Table 7: Capacity Factors – Thermal Fleet Scenario 13, NB Transmission, Additional Wind

AI summary The document references a regulatory proceeding (M08059) discussing capacity factors for Nova Scotia's thermal fleet under Scenario 13, which includes NB Transmission and additional wind energy considerations. The appendix table (A.5.4-7) is part of an analysis on utilization and optimization.

- - - - - - - - - - - - - - - - - - - - - - - - - New CT Synapse Energy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 A.5.4-8 Appendix Table 8: Capacity Factors – Thermal Fleet Scenario 14, Medium DMS, NB Tran...

AI summary The document references an analysis of thermal generation capacity factors under Scenario 14, Medium DMS, NB Transmission, conducted by Synapse Energy Economics, Inc. for NSPI's Thermal Generation Utilization and Optimization (M08059). Appendix Table 8 details capacity factors for the thermal fleet.

30% 16% 12% 10% 14% 20% 17% 21% 13% 27% 23% 30% 21% 27% 18% 26% 26% 28% Cove 5 16 - Tufts 23% 15% 18% 16% 17% 16% 7% 14% 7% 5% 5% 6% 9% 8% 10% 6% 13% 12% 13% 11% 13% 9% 12% 13% 16% Cove 6 - - - - - - - - - - - - - - - - - - - - - - - - - N...

AI summary The text presents capacity factor data for thermal generation scenarios, including a reference to 'NSPI Thermal Generation Utilization and Optimization M08059' and a table titled 'Appendix Table 9: Capacity Factors – Thermal Fleet' under Scenario 16 involving NB Transmission and 600 MW Wind. Synapse Energy Economics, Inc. is cited as the author.

zation and Optimization M08059 A.5.4-9 Appendix Table 9: Capacity Factors – Thermal Fleet Scenario 16, NB Transmission, High Wind Capacity Credit, 600 MW Wind

AI summary The text references Appendix Table 9, which details capacity factors for a thermal fleet under Scenario 16 involving NB Transmission, High Wind Capacity Credit, and 600 MW Wind. This scenario likely evaluates the integration of wind energy into the grid and its impact on thermal generation capacity.

Capacity Factors - Thermal Units 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 34% 41% 30% 23% 21% 37% 44% 32% 28% 29% 29% 30% 20% 25% 20% 20% 21% 15% 22% 18% 1...

AI summary The table presents capacity factors for various units (e.g., Lingan 1, Lingan 2, Trenton 5/6) from 2018 to 2042, showing significant variability across units and years. For example, Lingan 2 maintains high capacity factors (40–80%), while Trenton 5/6 shows declining factors (down to 3–5% by 2042).

Capacity Factors - Thermal Units 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 27% 42% 31% 20% 25% 46% 21% - - - - - - - - - - - - - - - - - - 01 - Lingan 1 20%...

AI summary The text presents a table showing capacity factors for various thermal units from 2018 to 2042. The table includes percentages for different units such as Lingan 1, Lingan 2, Lingan 3, Point Aconi, Point Tupper, Trenton 5, and Trenton 6. The data indicates varying levels of capacity factors over time for each unit.

Capacity Factors - Thermal Units 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 26% 41% 31% 21% 23% 48% 19% - - - - - - - - - - - - - - - - - - 01 - Lingan 1 19%...

AI summary The document presents capacity factors for various thermal units from 2018 to 2042, detailing performance metrics for different power generation facilities over time.

s of resources. Our aim is to provide a modeling analysis that shows the costs of retention versus the costs of alternative resource plans that use a different mix of resources to meet capacity needs. The analysis proceeds in an electric s...

AI summary The analysis compares the costs of retaining coal resources versus alternative resource plans, considering declining renewable energy costs, increasing coal costs, infrastructure changes like the Maritime Link, and carbon emission reductions. It evaluates economic viability of coal amid aging plants and uncertain gas prices.

ns, throughout the entire period (2018-2042). Modeling Plan Synapse Energy Economics, Inc. Modeling Plan and Key Input Assumptions 1 Appendix 5.6 REDACTED The table below lists the initial set of scenarios that we will run using the PLEXOS...

AI summary Synapse Energy Economics, Inc. outlines a modeling plan using PLEXOS LT and ST modules to analyze 30 scenarios over 2018–2042, focusing on capacity expansion, input assumptions, and production cost analysis. The plan includes reference scenarios, sensitivity cases, and temporal granularity for production cost assessments.

Scenario Sust Wind HardCode Notation Scenario Name Load Capital CapCredit NB Trans RetirePath 1 NSPI Reference Ref Ref Ref Ref No No 2 Change Case Med DSM Med DSM Ref Ref No No 3 Change Case High DSM High DSM Ref Ref No No 4 Change Case Re...

AI summary The table presents multiple scenarios (Ref, Med DSM, High DSM, etc.) with parameters including load, capital, capacity credits, and transmission. It compares different cases involving demand-side management, capacity credits, and transmission planning under various conditions.

Ref Ref Ref Yes Yes 26 Change Case Med DSM/RetirePath1 Med DSM Ref Ref Yes Yes 27 Change Case High DSM/RetirePath1 High DSM Ref Ref Yes Yes 28 Change Case Ref/RetirePath2 Ref Ref Ref Yes Yes 29 Change Case Med DSM/RetirePath2 Med DSM Ref R...

AI summary The document outlines modeling parameters for different scenarios involving demand-side management (DSM) and wind capacity contributions. It references Synapse Energy Economics, Inc.'s Modeling Plan and includes sensitivity cases for load forecasts, capital costs, and wind capacity. Key variables include reference values and alternative scenarios such as 'High DSM' and '25% All Wind (GE Study)'.

course of the modeling. The PLEXOS modeling can proceed without having a firm estimate for these costs; the envelope of quantity reductions is the key input assumption. As noted, the mid DSM scenario includes ramping up the current energy...

AI summary The analysis explores mid and high DSM scenarios, emphasizing energy efficiency and demand response impacts on peak load reduction. Capital cost assumptions, wind capacity contributions, and sensitivity analyses are discussed to evaluate thermal unit retention economics and capacity expansion paths.

which use 0% capacity contribution for ERIS resources, and 17% for NRIS resources), and using 20% of the installed capacity of all wind as a capacity contribution for alternative scenarios. These values could also be tested as a sensitivit...

AI summary The document outlines capacity contribution assumptions for ERIS and NRIS resources, explores scenarios with a second 345 kV tie to New Brunswick, and evaluates aggressive retirement paths for coal and Tufts Cove 1 plant. Key variables include resource costs, fuel/O&M costs, and import costs, with sensitivity analyses suggested for capacity contribution values.

1,919 96 508 1,823 33 10,958 9,937 9,690 2032 2,357 153 16 220 2,138 443 31 1,914 96 539 1,819 32 10,956 9,863 9,640 2033 2,383 153 16 235 2,148 474 31 1,910 95 569 1,814 31 10,954 9,789 9,595 2034 2,410 153 16 251 2,159 505 31 1,905 95 60...

AI summary The text presents numerical data spanning years 2032–2042, including values with associated growth rates (CAGR '18-'27: 1.1% and 0.5%). The data likely represents projections or metrics related to energy, costs, or infrastructure, though thematic context is absent due to the lack of explanatory text.

Table 4. Other Variable Values, Performance, Escalation, Comments, and Sensitivity Options Perform Real Cost Comments 2017 Value -ance Escalation Possible Sensitivity Other Variables Source – Costs ($CA) Rate Cases Ave. value peaker replac...

AI summary The table outlines variable costs and sensitivity analyses for battery storage and gas prices. Battery costs (LCOS) for peaker replacement are $541/kWh with a -9% annual escalation, sourced from Lazard 2.0. Gas prices are listed as flat, sourced from NSPI 2017, with sensitivity scenarios including lower escalation rates and duration assumptions.

flat. Gas LBL, Lazard similar costs. 40% Estimated cost trend. CF, Lower/higher installed Wind Costs – installed NSPI 2017 $2,000/kW -2.0%/year Estimated performance best Utility costs, performance available sites. Recent cost Scale declin...

AI summary The text discusses wind energy installation costs ($2,000/kW with a -2%/year decline), NB Transmission costs ($400 million for a 345 kV line), and capacity costs tied to the NE Market (FCM=$6.61/kW). It highlights declining wind costs and high transmission expenditures.

0%/year – partial build or for NB Transm. Costs -month $5.30/kW-mo. $US Transmission different cost allocation Imports via ML: NE Market FCM=$6.61/kW NE FCM 2020 clearing price Synapse: Recent FCM 0%/year Negotiated pricing - NFL Capacity...

AI summary The text outlines transmission costs, Fuel Cost Mechanism (FCM) rates, solar PV costs from Lazard and LBL reports, and coal/oil prices from NSPI 2017. It references $5.30/kW-mo for transmission, $2.75/watt for solar PV, and declining fuel caps under regulations.

See NSPI 2017 Declining cap per regulations 2030-2042 Steeper Cap Carbon / CO2 emissions NSPI 2017 10-Yr. System through 2030, and continuing Decline (5%/year) Outlook p. 34 @ ~2.5%/yr thereafter. See NSPI 2017 Declining cap per regs. thru...

AI summary The text outlines NSPI's 2017 carbon and pollutant emission caps, detailing declining limits for CO2 through 2030 and beyond, with specific reductions for SO2, NOx, and Hg. It references gas-fired combined cycle (CC) and combustion turbine (CT) costs, noting capital expenditures and operational metrics from Table 5.

MW RICE Fixed and Variable O&M Sustaining capital costs pick up NSPI 2017 Varies 0%/year Costs related cost increases Based on Confidential Landsvirkjun Power/Verkís Report, Wreck Cove Pumped Stor. NSPI 2017 0%/year Dec. 2012. Table 1, pag...

AI summary The document outlines cost analyses for fossil fuel generation, including natural gas, coal, and heavy oil prices, with data from NSPI and Synapse Energy Economics. It references a 25-year capacity expansion model (2018-2042) and specific years for production cost runs.

(377.1 MW) plus 30 MW increment at Mersey. Assume 10 MW increment at Wreck Cove (Source: NSPI). • All current NSPI renewables (wind, biomass) remain in place at existing MW levels. Synapse Energy Economics, Inc. M08059 Generation Utilizati...

AI summary The text outlines NSPI's renewable energy assumptions, including wind capacity increments and existing facilities, along with modeling constraints from Synapse Energy Economics, Inc. It also details sustaining capital cost scenarios and references key sources.

$ 505,625 $ 8,912,500 TUC3 $ 824,375 $ 793,125 $ 3,280,625 $ 868,125 $ 1,255,625 $ 524,375 $ 768,125 $ 543,125 $ 868,125 $ 505,625 $ 10,231,250 TUC6 $ 1,822,500 $ 1,997,500 $ 4,847,500 $ 2,297,500 $ 4,747,500 $ 1,822,500 $ 2,197,500 $ 1,89...

AI summary The 2014 Integrated Resource Plan (IRP) Action Plan's eight action items influence NSPI's thermal fleet economics by affecting peak demand and capacity contributions, directly impacting resource adequacy requirements and alternative capacity resource availability.

opportunities. 7. Obtain DSM resource commitments consistent with the IRP analysis. 8. Evaluate options for Mersey Development, and the potential to add 30 MW of capacity to the system. In addition to these IRP action items, continuing red...

AI summary The text outlines IRP action items, including evaluating Mersey Development and assessing the economic viability of retaining coal units versus renewable resources. It criticizes NSPI's lack of rigorous analysis on sustaining coal fleet investments and notes the 2014 IRP's incomplete examination of coal retention's economic optimality.

nvolved longer retention of coal units” (4/13/2017 presentation, slide 27). However, the 2014 IRP did not rigorously examine whether or not longer retention of the coal fleet was economically optimal. Instead, the 2014 IRP analyses include...

AI summary The 2014 Integrated Resource Plan (IRP) by Nova Scotia Power Inc. (NSPI) inadequately evaluated the economic optimality of retaining coal units, as it failed to account for surplus capacity differences between resource plans. The analysis used sustaining capital cost assumptions without adjusting for surplus capacity retirement, leading to unsupported conclusions about coal retention.

‐stated at the 4/13/2017 technical conference, is not well‐supported. It is not at all clear that lengthy retention of the coal units was the most economic option arising from the 2014 IRP analysis.2 2 A truer optimization, given the model...

AI summary The text critiques the 2014 Integrated Resource Plan (IRP) analysis for not adequately considering alternative capacity options to coal units, emphasizing the need for iterative planning and demand-side management (DSM) strategies to achieve cost-effective surplus capacity.

NSPI’s projected requirements. Any analysis must first rigorously explore the cost and capability of demand side options, and accurately represent their attributes in any capacity expansion exercise. 11 NSPI response to NS UARB IR‐2 (b‐c)....

AI summary NSPI emphasizes rigorous analysis of demand-side options for capacity expansion, critiques its own example of substituting a combustion turbine for a coal plant as oversimplified, and stresses the need for updated assumptions in supply-side alternatives. The analysis must address ramping requirements and wind resource integration.

Cost-Benefit project, and has units $/kWh or $/MWh delivered. “Present value” means that costs accrued and energy Analysis delivered in future years are “discounted” into present day equivalent values. LCOE is a simple and attractive metri...

AI summary Discusses Levelized Cost of Electricity (LCOE), cost-benefit analysis, net cost considerations, and comparison of capacity costs between benchmark generators and energy storage, referencing the Electric Power Research Institute (EPRI). Highlights present value calculations and energy storage cost evaluations.

es with respect to environmental concerns in both the short and long term, and technological developments with respect to renewables and storage. This option typically takes about a year to complete. With respect to the second option, this...

AI summary The text outlines two options for addressing environmental and technological considerations: a comprehensive one-year plan and a six-month screening exercise to evaluate alternatives to NSPI’s coal plant operations. The author recommends the latter, suggesting it could identify more cost-effective solutions or necessitate adjustments to NSPI’s plan or further IRP work.

Proposed Terms of Reference Plexos Optimization Analysis Cost-Effectiveness of Retaining NSPI Thermal Resources Through and Possibly Beyond 2030 Synapse Energy Economics July 7, 2017 Introduction The Nova Scotia Utility and Review Board’s...

AI summary The Nova Scotia Utility and Review Board requested Synapse Energy Economics to analyze the cost-effectiveness of retaining NSPI’s thermal resources through 2030, considering capital expenditures, storage options, wind capacity, and using the Plexos modeling system. The focus is on optimizing thermal fleet utilization for Nova Scotia ratepayers.

s modeling system to assess optimal use of NSPI’s thermal fleet, with data files and technical information concerning system stability and operating constraints provided to Synapse by NSPI. Objective The main objective of the analysis will...

AI summary The analysis aims to evaluate the cost-effectiveness of retaining NSPI’s thermal fleet through 2030, comparing retention costs with alternatives like retirement, capacity utilization, and replacement resources. Synapse will use the Plexos modeling framework to assess optimal resource paths, considering transmission system needs and sensitivity to input assumptions.

Discussion of Critical Steps – Modeling Plan and Resource Cost and Quantity Assumptions Three critical steps include establishing a modeling plan using the Plexos tools, determining the resource cost assumptions to use as inputs to the Ple...

AI summary The document outlines three critical steps in modeling: using Plexos tools for unit commitment/dispatch and capacity expansion, incorporating load forecasts, and specifying resource costs. Resource categories include existing thermal, hydro, and wind resources, with NSPI expected to provide detailed input files. The capacity expansion module optimizes long-term resources, while unit commitment ensures cost-effective annual operations.

5 Appendix – Synapse Comments After April 2017 Technical Conference Introduction These comments are provided in response to the technical conference held at NSPI on April 13, 2017. They address a number of issues concerning the future plan...

AI summary NSPI plans to operate all thermal steam units except Lingan 2 through 2025/26, expecting continued operation through 2030. This contrasts with the 2014 IRP Action Plan, as Tufts Cove 1 (TUC1) will remain operational past 2025 due to 2016 load forecasts requiring resource adequacy. Sustaining capital expenditures for TUC1 are only planned through 2024, raising concerns about future costs.

t paths. Additional firm capacity could also be available across the Maritime Link to meet resource adequacy needs. 6 – Demand Response and DSM Resource Commitments through Energy Efficiency Programs NSPI’s need for capacity resources is p...

AI summary NSPI's capacity needs are based on peak load, which occurs infrequently during winter. Demand response mechanisms could reduce the need for 150-300 MW of capacity. The 2016 load duration curve illustrates peak load data, sourced from NSPI OASIS, with a calculation note referencing 600 MW (20% - 12%).

NSPI’s projected requirements. Any analysis must first rigorously explore the cost and capability of demand side options, and accurately represent their attributes in any capacity expansion exercise. 14 NSPI response to NS UARB IR-2 (b-c)....

AI summary NSPI argues that its example of substituting a combustion turbine for a coal plant oversimplifies capacity expansion considerations, emphasizing variable factors like capital costs and locational economics. The analysis must rigorously evaluate demand-side options and include updated assumptions for supply-side alternatives, addressing system ramping and ancillary service needs under varying renewable penetration scenarios.

ge capital injection levels (e.g., $10 million, Lingan 3, 2020; $10 million, Lingan 4, 2024; $13 million, Point Aconi, 2021; $9 million, Trenton 5, 2020; $15 million, Trenton 6, 2025). NS Power notes that the larger-than average capital in...

AI summary NS Power explains that higher capital injections for specific power plants (e.g., Lingan 3, Trenton 6) reflect major outage intervals tied to unit utilization. Synapse critiques the 2014 IRP for using a levelized approach to sustaining capital costs, ignoring year-to-year variations, and for failing to adjust surplus capacity assumptions in different plans, leading to significant disparities in outcomes.

higher than those associated with lower peak load reduction plans, because no adjustment was made in sustaining capital contribution to allow for retirement of surplus capacity. NS Power notes that the surplus capacities shown in the Figur...

AI summary NS Power argues surplus capacity may no longer exist due to corrected DSM assumptions in the 2016 Load Forecast Report and revised efficiency program forecasts. They dispute Synapse's claim of flat historical peak loads, citing a 1% annual increase in firm peak load since 2016.

ts illustrated below. Due to time limitations, we will not define scenarios for all possible permutations of input assumptions. Illustrative Matrix: Example of key parameters and range of assumptions Alternative assumptions: Set 1 Set 2 Se...

AI summary The text presents a matrix of alternative assumptions for energy planning parameters, including load, capital requirements, gas prices, transmission, wind resource costs, demand response, storage, carbon costs, and balancing areas. Scenarios explore varying levels of energy efficiency, capital needs, and resource availability.

g resource adequacy requirements), or they affect the capacity contributions available from existing and potentially new alternative capacity resources. The eight action items are characterized below: 1. Optimize the level of sustaining ca...

AI summary The document outlines eight action items addressing resource adequacy, including optimizing capital expenditures, studying wind penetration impacts, regional coordination, evaluating wind resources, monitoring market opportunities, assessing demand response, securing DSM commitments, and evaluating Mersey Development. These steps aim to ensure system reliability and capacity contributions.

opportunities. 7. Obtain DSM resource commitments consistent with the IRP analysis. 8. Evaluate options for Mersey Development, and the potential to add 30 MW of capacity to the system. In addition to these IRP action items, continuing red...

AI summary The document outlines IRP action items, including DSM resource commitments and evaluating Mersey Development's 30 MW capacity. It criticizes NSPI for not rigorously analyzing the economic optimality of retaining seven coal units through 2030, citing the 2014 IRP's lack of thorough examination. Renewable energy cost reductions and traditional gas-fired resources are noted as factors affecting coal unit retention economics.

nvolved longer retention of coal units” (4/13/2017 presentation, slide 27). However, the 2014 IRP did not rigorously examine whether or not longer retention of the coal fleet was economically optimal. Instead, the 2014 IRP analyses include...

AI summary The 2014 Integrated Resource Plan (IRP) by NSPI inadequately evaluated the economic optimality of retaining coal units, failing to account for surplus capacity differences across resource plans. This oversight undermined the validity of NSPI's conclusion that prolonged coal unit retention was economically justified, as surplus capacity levels varied significantly between plans.

t paths. Additional firm capacity could also be available across the Maritime Link to meet resource adequacy needs. 6 – Demand Response and DSM Resource Commitments through Energy Efficiency Programs NSPI’s need for capacity resources is p...

AI summary NSPI's capacity needs are tied to peak load, which occurs infrequently during winter. Demand response mechanisms could reduce the need for 150–300 MW of capacity. The 2016 load duration curve illustrates that peak loads are rare, with most hours below 2,500 MW. A calculation example shows capacity needs based on 600 MW and efficiency differences.

NSPI’s projected requirements. Any analysis must first rigorously explore the cost and capability of demand side options, and accurately represent their attributes in any capacity expansion exercise. 15 NSPI response to NS UARB IR-2 (b-c)....

AI summary NSPI argues that demand-side options must be rigorously evaluated for cost and capability in capacity expansion. The Board critiques NSPI's example of substituting a combustion turbine for a coal plant as oversimplified, noting variable assumptions and unaddressed locational factors. Supply-side analysis must include updated cost/performance data and address ramping/ancillary service needs from wind and storage integration.

respecting these issues. Conclusion The Synapse Report confirms that it is cost-effective to customers to retain NS Power’s thermal fleet through 2030, and possibly beyond. As stated above, NS Power’s comments are not to be taken as an end...

AI summary The Synapse Report concludes retaining NS Power’s thermal fleet through 2030 is cost-effective. NS Power acknowledges the report addresses the Board’s original questions but disputes its assumptions and modeling. They oppose a hearing on the report, advocating instead for proceeding to the next IRP and implementing the report’s nine recommendations.

will be interpolated between years. Page 5 of 9 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED - Appendix A NSPI to Synapse GU&O Final Report Page 6 of 9 iii) NS Power notes that the detailed production cost modelling may exhibit res...

AI summary NS Power notes that detailed production cost modeling may show different plan rankings than LT optimization due to granular parameters. DSM assumptions significantly affect modeling, particularly when DSM modifies load, impacting capacity investment and NPV comparisons. The 'High DSM Case' lacks specific cost details, complicating plan comparisons.

A does not have any concerns about NSPI spending resources and time on these recommendations, but submits that they should not be given enhanced priority as a result of being referenced in the Report. The SBA also submits that it would be...

AI summary The SBA submits that NSPI should track investment in ACE Plans for thermal generating units and highlights three key findings: most thermal units warrant investment through 2030, increased sustaining capital costs could render some units obsolete, and alternative resource costs will impact thermal fleet economics. The SBA recommends a comprehensive IRP in 2019 instead of hearings.

Addendum to Final Report - M08059 Generation Utilization and Optimization - DSM Avoided Costs Memorandum TO: M08059 GENERATION UTILIZATION AND OPTIMIZATION STAKEHOLDERS FROM: BOB FAGAN – SYNAPSE ENERGY ECONOMICS DATE: OCTOBER 19, 2018 RE:...

AI summary This memo calculates avoided energy and capacity costs from DSM resources in Scenario 2 of the Generation Utilization and Optimization Study. It uses a differential revenue requirements approach, analyzing fixed (capacity-related) and variable (energy-related) costs between Scenario 1 (reference) and Scenario 2 (medium DSM) across 2018–2042. Results show per-MWh and per-kW avoided costs for each study year.

ction/build costs, and sustaining capital costs. Variable (or energy related) cost components include fuel, variable O&M, renewable energy provision, interchange, and startup/shutdown elements. 1 Final Report, “Nova Scotia Power Inc. Therm...

AI summary The text outlines a method for calculating avoided capacity and energy costs under different DSM scenarios, considering fixed and variable costs, emissions constraints, and transmission/distribution infrastructure impacts. It references a 2018 report (M08059) analyzing fossil-fueled thermal fleet retention and optimization.

rastructure under the medium DSM scenario. We also included a third, “Total – energy basis” computation, that provides a single stream of avoided (energy + capacity) costs, on a per MWh basis. The results reflect the build and dispatch pat...

AI summary The text analyzes avoided energy and capacity costs under the Medium DSM scenario, comparing it to the reference scenario. It highlights the impact of the Maritime Link and a new combined cycle resource on cost trends, noting that DSM displaces more expensive energy sources rather than coal. The study discusses these dynamics in the context of revenue requirements.

mmary Avoided Energy and Capacity Costs – 2018‐2042 Avoided Energy and Avoided Capacity Computation - Reference Scenario vs. Medium DSM Scenario Differential Revenue Requirements (Wholesale) Basis Variable Cost Fuel, VOM, RE, Interchange,...

AI summary The document compares energy and capacity costs under a reference scenario versus a medium Demand Side Management (DSM) scenario from 2018 to 2042. It quantifies avoided energy costs by subtracting Scenario 2 (MedDSM) costs from Scenario 1 (Ref) costs, showing cumulative savings increasing over time, with the largest differential in 2042 at $136,741,000.

9 $43.02 $85.06 $75.39 $89.05 $64.59 $66.41 $76.90 $81.64 $80.17 $91.55 $65.70 $84.47 $77.30 $89.61 $90.14 $99.94 $103.38 $102.72 Fixed Cost Components FOM, SusCap, New build 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030...

AI summary The text presents financial data comparing two capacity cost scenarios (Sc 1 and Sc 2) for Nova Scotia Power Inc. over 2018–2042, including fixed costs, avoided capacity costs, and peak load metrics. Scenario 2 incorporates MedDSM costs, showing avoided capacity costs relative to Scenario 1, with significant differences in later years.

0 2,084.0 2,083.0 2,086.0 2,096.2 2,106.5 2,116.8 2,127.2 2,137.6 2,148.1 2,158.6 2,169.2 2,179.8 2,190.4 2,201.1 2,211.9 2,222.7 2,233.7 2,244.8 Sc 2 Peak Load, MW 1,980.2 1,993.4 2,014.5 2,016.9 2,022.5 2,024.9 2,014.3 2,003.0 1,986.8 1,...

AI summary The text presents numerical data comparing peak load scenarios (Sc 1 vs. Sc 2), showing increasing peak savings deltas and fluctuating avoided capacity costs. The data highlights variations in cost per kilowatt saved, suggesting analysis of demand-side management program effectiveness and capacity planning economics.