Topic:"Cost Considerations" in M08059

the low gas price sensitivity, Scenario 13 (high wind, but with reference level capacity crediting) is more expensive (+ 2.3 percent NPVRR) under the low gas scenario 1. For base level 4 $90 million is the difference between the NPVRR for...

AI summary The text compares NPVRR differences across scenarios (13, 2, 4LG) under varying gas prices and DSM load levels. Scenario 13 shows higher costs under low gas prices but lower costs under high gas prices. Scenario 2 involves different timelines for retiring coal units and building new resources. A correction is noted for a double-counting error in the Draft Report's NPVRR analysis.

enously by Synapse—fixed O&M, sustaining capital costs, new build costs, incremental demand‐side management (DSM) costs, New Brunswick transmission costs, and Nova Scotia Maritime Link fixed payments. Fixed O&M is calculated by multiplying...

AI summary The text outlines cost components for energy planning, including fixed O&M, sustaining capital, new build, DSM, and transmission costs. It details calculation methods for each, such as annualizing capital costs and using nameplate capacity. Modeling scenarios in Plexos are discussed, focusing on capacity expansion, dispatch, and unit commitment under various sensitivities.

Wind Scen./ Sust CapCre NB Sens. # Sensitivity Name Load Capital dit Trans Comment Low Battery Cost Sensitivities 1‐LB Ref Low Battery Cost Ref Ref Ref No Med DSM High Wind Cap Cred 8‐LB Low Batt Cost Med DSM Ref High No 13‐LB Ref NB Trans...

AI summary The text presents sensitivity analyses for energy planning scenarios, including low battery costs, gas price variations, and forced retirements of power generation assets (Tufts Cove 3, Trenton 5). It also references updates to cost and availability models in energy planning tools like Plexos.

economic life of the units, the capital cost (in $2017/kW), and a weighted average cost of capital for NSPI (7 percent). Our modeling results include new build costs by unit and across each scenario. Table 5. New Build Resource Costs Resou...

AI summary The text provides details on new build resource costs for various energy generation technologies, including gas combined cycle, gas combustion turbine, wind, and battery storage, with specific costs, escalation rates, and economic lifespans. The data includes updated costs from NSPI and sensitivity analyses.

Build Costs Annualized fixed costs Per assumptions memo (see Table 5 above). Sustaining Capital Costs Annualized fixed costs Per assumptions memo (see Table 6 above). Plexos Production Cost ‐ Fuel, VO&M, FO&M, Startup, and Net Interchange...

AI summary The text discusses the composition of revenue requirements, highlighting that a majority of costs come from fixed and variable operating costs, fuel costs, and Maritime Link Surplus and New Brunswick net imports purchase costs as modeled in Plexos. Maritime Link costs are estimated to account for 14-16% of the total NPVRR and are based on the Maritime Link Interim Cost Assessment case.

requirements to allow for the inclusion of more wind energy on the system. Since this increase in interconnection capacity would extend back into New Brunswick considerably (we assumed a total cost 27 First‐year DSM costs for 2017 were est...

AI summary The text discusses the cost-sharing arrangement for interconnection reinforcement to New Brunswick, estimating first-year DSM costs for 2017 and new build costs for various energy resources. It references modeling by Synapse Energy Economics, Inc. and the use of Plexos for unit commitment and dispatch costs.

nergy Economics, Inc. NSPI Thermal Generation Utilization and Optimization M08059 52 Final Report 3.6. Sensitivities Low Battery Costs Synapse executed four low battery cost sensitivity runs. The results are presented in Figure 6a and Tabl...

AI summary The analysis explores the impact of varying battery costs and gas prices on energy generation strategies. Four low battery cost scenarios and seven gas price scenarios were modeled, considering system optimization, economies of scale, and the role of battery storage in providing regulation and capacity services.

e estimates. The ratio was also applied to the market costs of New Brunswick imports, and Surplus energy from Newfoundland, as those costs are generally tied to the price of natural gas in the region. The DSM cost accounting and the report...

AI summary The document discusses updates to DSM cost accounting, incorporating Efficiency One's preliminary estimates of incremental DSM savings. It notes that the first-year costs for these savings are around 60 cents per kWh, but adjusted downward due to expected lower-cost efficiency spending in Nova Scotia. Total DSM costs for medium scenarios are increased to ~$410 million (NPV RR).

of these load scenarios will be accounted for outside of PLEXOS and will be based on a to-be- determined utility cost per MWh saved (energy) and utility cost per kW of firm peak reduction. We have not estimated these costs at this point in...

AI summary The document outlines that load scenarios outside PLEXOS will use utility costs per MWh saved and per kW of firm peak reduction. These costs have not yet been estimated but will be determined with stakeholder feedback as part of the project.

Appendix 5.6 REDACTED CONFIDENTIAL Memorandum TO: M08059 GENERATION UTILIZATION AND OPTIMIZATION STAKEHOLDERS FROM: BOB FAGAN, RACHEL WILSON, DAVID WHITE – SYNAPSE ENERGY ECONOMICS DATE: OCTOBER 16, 2017 RE: KEY INPUT ASSUMPTIONS AND MODEL...

AI summary This memo outlines Synapse Energy Economics' modeling plan for the Generation Utilization and Optimization study, seeking stakeholder input on assumptions related to energy storage, wind/solar costs, and demand-side management (DSM) scenarios. The analysis compares long-term costs of retaining NSPI’s thermal fleet versus alternative resource mixes, though it explicitly states it is not an integrated resource plan (IRP).

of these load scenarios will be accounted for outside of PLEXOS and will be based on a to-be- determined utility cost per MWh saved (energy) and utility cost per kW of firm peak reduction. We have not estimated these costs at this point in...

AI summary The text discusses load scenarios outside PLEXOS modeling, referencing future determination of utility costs per MWh saved and per kW of firm peak reduction. Synapse Energy Economics, Inc. acknowledges these costs have not yet been estimated but will be addressed with stakeholder input.

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.

are 50% higher than those costs. Synapse will extrapolate these annual costs for all remaining years of the study period beyond the values available from NSPI in the 2017 outlook report. Key Sources 1. NSPI, 2017 10-Year System Outlook, 20...

AI summary Synapse Energy Economics extrapolates annual generation costs beyond NSPI's 2017 outlook, using higher cost assumptions than NSPI's reported values. The analysis relies on Lazard's energy/storage cost data, EIA gas price projections, and other technical reports for modeling inputs.

ge options “are not cost competitive” compared to traditional solutions.6 However, this conclusion was based on an assessment of the Levelized Electricity Cost to Grid,7 and did not capture the value analysis (e.g., during the 2020s). An i...

AI summary The text discusses the cost competitiveness of renewable energy options, referencing the GE wind integration study and Lazard's storage analysis. It notes that the GE study is outdated and that synthetic inertia could replace traditional support. An iterative modeling approach could reduce costs in future IRPs but wasn't used due to time constraints.

zed‐cost‐of‐ storage‐v20.pdf, and the Executive Summary is also available at https://www.lazard.com/media/438041/lazard‐ lcos‐20‐executive‐summary.pdf. 6 Response to NS UARB IR‐37, M07745. 7 Ibid. 5 associated with using storage resources...

AI summary The text critiques the use of levelized cost of energy (LCOE) for evaluating energy storage, arguing it misrepresents storage's value by ignoring capacity and flexibility. It references studies from EPRI and Lazard, noting significant cost declines in lithium-ion technologies for storage applications.

7009 7885 8761 Hours per Year Load Greater than Indicated Value Source: NSPI OASIS, Hourly Load Data, 2016. 10 Based on 600 MW (20% ‐ 12%). 7 In the most recent load forecast, NSPI projects increasing peak load. In response to the Board’s...

AI summary NSPI's load forecast projects increasing peak demand, citing Synapse's report as justification. However, Synapse recommends refining peak load calculations due to historically flat historical peaks. The text argues for urgent analysis of alternatives to coal plant investments, emphasizing a portfolio approach over single substitutions to ensure cost-effective, reliable service.

LAZARD’S LEVELIZED COST OF STORAGE ANALYSIS 2.0 KEY FINDINGS Lazard has published its second Levelized Cost of Storage Analysis (“LCOS 2.0”), 1 an in-depth study that compares the costs of various energy storage technologies for particular...

AI summary Lazard's LCOS 2.0 analysis highlights that while certain energy storage technologies are becoming more viable for specialized grid applications, they remain uncompetitive for large-scale renewable energy transitions. Cost reductions are expected due to scaling, standardization, and technological advancements, driven by regulatory innovations and grid modernization needs.

hnologies are increasingly attractive for a number of specialized power grid uses, but none are yet cost-competitive for the transformational scenarios envisioned by certain renewable energy advocates • Although energy storage technology h...

AI summary Energy storage technologies are not yet cost-competitive for large-scale renewable energy transformation scenarios but show value in grid strengthening (e.g., frequency regulation) and reducing commercial/industrial demand charges through demand response programs.

er sources of value for commercial and industrial energy users through reducing utility bills (e.g., lowering demand charges) and/or participating in demand response programs • Today, energy storage appears most economically viable in use...

AI summary The text highlights energy storage's economic viability in use cases requiring power capacity and flexibility (e.g., frequency regulation, demand charge management) rather than energy density or duration. It notes that increasing discharge duration is more costly than enhancing peak output, which explains why transformational use cases like full grid defection remain economically unattractive due to higher energy duration requirements.

ented uses. The LCOS analysis identifies 10 “use cases,” and assigns detailed operational parameters to each. This methodology enables meaningful comparisons of storage technologies within use cases. 1 • The preceding observation highlight...

AI summary The LCOS analysis evaluates energy storage across 10 use cases, emphasizing the role of regulatory frameworks and market structures in determining economic viability. Unlike generation, storage systems can perform multiple functions, with their returns depending on incentives and regional power market dynamics.

tal cost declines of ~40% over the next five years, while flow and lead batteries are expected by some to experience roughly comparable five year battery capital cost declines 2 • Further, our current analysis indicates that the midpoint l...

AI summary The text discusses rapid declines in lithium-ion battery levelized costs (~12-24% since last year) and projected 40% reductions over five years, driven by manufacturing improvements. It highlights that battery-centric use cases will see faster cost declines and potential displacement of gas-fired peaking generation by energy storage technologies.

-Benefit Analysis 5 5 © 2016 Electric Power Research Institute, Inc. All rights reserved. Description of Contents Introduction This cost summary was developed to support utility resource planners in their understanding of energy storage co...

AI summary This document provides a cost summary for energy storage technologies, emphasizing the need to consider both power and energy ratings in cost evaluations. It outlines methodology and results, including 2017 reference year data for lithium-ion systems.

ions for the various technologies and an example of a detailed breakdown of lithium ion system costs that were also collected in this study. The costs are based on a 2017 reference year for Cost Metrics installation. Future cost projection...

AI summary The text discusses a 2017 study on energy storage costs, focusing on lithium-ion systems and cost metrics. It notes that future cost projections beyond 2017 were excluded, but EPRI research suggests battery costs will decline. The document outlines cost-benefit analysis and methodology for evaluating storage technologies.

Introduction  To support depth of analysis, technology scope was limited.  Technologies were prioritized based on the following criteria Methodology – Maturity of technology and derivative products Cost – Substantial recent changes in co...

AI summary The analysis evaluates energy storage technologies (CAES, Pumped Hydro, Lead Acid, Lithium Ion, NaS, Flywheel) based on maturity and cost changes. The scope aligns with EPRI Technology Assessment Guides but excludes contingencies, focusing on cost metrics and benefit analysis for selected technologies.

were not included in this study. 9 © 2016 Electric Power Research Institute, Inc. All rights reserved. Data Collection Approach and Process Introduction  Initial cost ranges developed by EPRI project experience and previous studies. Range...

AI summary The text outlines EPRI's methodology for estimating energy storage costs, including initial ranges refined by industry input and detailed lithium-ion cost breakdowns for three use cases. Pumped Hydro and CAES costs are based on EPRI studies and internal validations.

d on similar-sized projects that have been built recently or are under current development, in order to access more up-to-date data than are available at present for other technologies. Also, Cost-Benefit sizes help to provide some compara...

AI summary The document summarizes 2017 energy storage installed costs, noting declining trends for battery-based technologies like lithium-ion. It emphasizes overnight installed cost assumptions and references EPRI as the copyright holder.

nd assumes overnight installed costs.  Suppliers and publicly available studies indicate continuing trend of cost decline for battery- based storage technologies, particularly lithium-ion. Cost-Benefit  Installed costs exclude land costs...

AI summary The text discusses energy storage installed costs in 2017, noting declining battery costs (especially lithium-ion) and clarifying that installed costs exclude land, ownership, and contingency expenses. It details cost breakdowns for lithium-ion systems, including ESS products and site integration components.

systems, pre-engineered racks and containers – Site and Grid Integration encompasses remaining costs such as grid integration equipment, installation, engineering and Results Cost project management – Fees, General and Administrative (G&A)...

AI summary The text outlines cost metrics for energy storage systems, including project management fees, general and administrative (G&A) costs assumed at 10% of total project cost, and references to balance of plant costs and system size examples (e.g., 20MW, 4-hour systems). It cites the Technology Assessment Guide and a full report for detailed cost breakdowns.

Analysis 17 © 2016 Electric Power Research Institute, Inc. All rights reserved. Metrics (Continued) Introduction Installed Cost ($) includes all equipment, delivery, installation, interconnection and step-up transformers. This metric assum...

AI summary The text defines 'Installed Cost' as covering equipment, delivery, and installation but excluding land and planning costs. It explains 'Levelized Cost of Capacity' as a lifecycle metric for generators, including variable off-peak charging costs for storage, while excluding operational benefits. Both metrics focus on capital and lifecycle costs without accounting for long-term income or replacement expenses.

charging costs which may change over time. Consistent with usage when applied to conventional generation, this metric does not incorporate any benefits or revenue obtained from storage operation. Results Cost Present Value of Life-Cycle Co...

AI summary The text discusses cost metrics for energy storage and electricity generation, including Present Value of Life-Cycle Costs, Levelized Cost of Electricity (LCOE), and Total Cost of Energy Storage (TCOES). These metrics account for fixed and variable costs over time, including installation, operations, and replacement, but exclude benefits from storage operations.

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.

Cost Cost ? Net Benefit Cost Net Cost Net Cost Results Cost Instead of full cost of resources… …Compare net cost of resources Cost Metrics • When analyzing the economics of an energy storage plant, it is important to understand not only th...

AI summary The text emphasizes the need for revised economic analysis frameworks for energy storage, highlighting that traditional cost metrics (e.g., upfront installed costs) inadequately capture storage's value. It stresses the importance of evaluating net market value, operational costs during charging/discharging, and grid services like displacing high-cost generation with low-cost electricity.

Cost-Benefit relatively low-cost (i.e., off-peak) electricity, displacing higher-cost (i.e. on-peak) generation, Analysis providing ancillary services, and supporting grid resiliency and security. Cost of new capacity is full installed cos...

AI summary The text discusses the cost-benefit analysis of energy storage, highlighting its ability to utilize low-cost off-peak electricity, displace higher-cost on-peak generation, and provide grid resiliency. It references EPRI resources like the StorageVET™ tool and cost-effectiveness studies for California.

Analysis 21 © 2016 Electric Power Research Institute, Inc. All rights reserved. Full Report Forthcoming Energy Storage Cost Summary for Utility Planning. EPRI, Palo Alto, CA, 2016. 3002008876. For more information visit www.epri.com 22 © 2...

AI summary The document references EPRI's 2016 energy storage cost summary report for utility planning, citing multiple EPRI publications on energy storage technologies and cost templates. It highlights EPRI's role in researching electricity generation, delivery, and use, emphasizing reliability, efficiency, and affordability.

options “are not cost competitive” compared to traditional solutions. 9 However, this conclusion was based on an assessment of the Levelized Electricity Cost to Grid, 10 and did not capture the value analysis (e.g., during the 2020s). An i...

AI summary The text discusses the cost competitiveness of energy solutions, noting that some options are not cost-effective based on Levelized Electricity Cost to Grid assessments. It highlights the potential of iterative modeling to reduce costs and references studies on synthetic inertia and energy storage. The GE 2013 wind integration study and Lazard's LCOS 2.0 analysis are cited as key resources.

d-cost-of- storage-v20.pdf, and the Executive Summary is also available at https://www.lazard.com/media/438041/lazard- lcos-20-executive-summary.pdf. 9 Response to NS UARB IR-37, M07745. 10 Ibid. A-5 associated with using storage resources...

AI summary The text critiques the use of levelized cost of energy (LCOE) for storage, arguing it misrepresents storage's value by ignoring capacity and flexibility. It highlights a Lazard study showing lithium-ion storage costs have decreased by 11-24% across use cases, with further declines expected.

~ Blackburn Law July 17, 2017 VIA EMAIL Ms. Doreen Friis Regulatory Affairs Officer/Clerk Nova Scotia Utility and Review Board 1601 Lower Water Street, 3rd Floor Halifax NS B3J 3S3 Dear Ms. Friis: Re: M08059 - Generation Utilization and Op...

AI summary The Small Business Advocate (SBA) critiques Synapse's Terms of Reference for the Thermal Fleet Study, emphasizing concerns about limited scope, undefined cost minimization, and insufficient metrics. The SBA proposes additional metrics, including revenue requirement minimization, annual electricity pricing, and environmental/emissions factors, to better align with stakeholder needs and short-term business timelines.

1. How is the potential amount of price responsive demand being determined and at what cost? 2. How does this overlap energy efficiency activities? B. Wind Generation 1. How is the maximum potential for wind generation developed? 2. Are th...

AI summary The document outlines questions about energy resource potential, including wind, solar, storage, and imports, focusing on cost, financing, and integration. It also asks about the role of behind-the-meter (BTM) systems and power purchase agreements (PPAs). The SBA acknowledges the opportunity to comment.

tinually assess. 6.5% to 10.4% of total NPVRR costs in our main scenarios. It is critical to continue to Page 5 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) June 7, 2018 D. Friis Synapse Recommendation NS Power Response / Position

AI summary The text references a percentage range (6.5% to 10.4%) of total NPVRR costs in main scenarios, alongside a Synapse Recommendation and NS Power's response. The document is redacted, with confidential information removed, and includes a date (June 7, 2018) and a name (D. Friis).

des nearly 900 MW of peak reduction (approximately the capacity of 6 coal units), but provides no specific details on the cost or deliverability of this capacity. NS Power submits that it is critical to develop and vet the assumed costs an...

AI summary Nova Scotia Power (NSP) emphasizes the need for detailed cost and deliverability data for proposed Demand Side Management (DSM) programs before evaluating resource portfolios. It also recommends including DSM and Demand Response in the LT Module and adjusting the Medium DSM case by excluding PHP Mill load from energy savings calculations.

at it would provide more up to date information as it completed this work. The most recent update to this information is provided below in Figure 4. ii) NS Power notes that some of the proposed cost escalation rates (e.g. - 9%/year for bat...

AI summary NS Power challenges proposed 9%/year battery cost escalation rates over 25 years as unrealistic, advocating for cost trend curves instead. It also critiques Lazard 2.0's Levelized Cost of Storage metric, arguing it should be replaced with 'Total Installed Cost' and operating parameters for accurate Plexos modeling.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED - Appendix A NSPI to Synapse GU&O Final Report Page 9 of 9 Figure 4: Updated Costs & Trends for New Resources Resource Type (2017) Capital Cost (2017) Fixed Percent Change ($/kW) Cost ($...

AI summary The document includes a cost trend analysis for energy resources (e.g., solar, wind, battery storage) showing significant cost reductions by 2030, and a letter from Nova Scotia Power Inc. (NSPI) responding to Synapse Energy Economics' draft report on a Generation Utilization and Optimization study under matter M08059.

ed Partnership (PHP) and EfficiencyOne (E1). On December 29, 2017, Synapse issued its “Response to Stakeholder Comments on Key Input Assumptions and Modeling Plan for Plexos Optimization Analysis”. On March 2, 2018, Synapse circulated its...

AI summary The document outlines the Generation Utilization and Optimization (GUO) study process led by Synapse, with input from NS Power, EfficiencyOne (E1), and the ed Partnership (PHP). NS Power provides comments on Synapse's draft report, emphasizing the study's objective to assess the cost-effectiveness of retaining NSPI's thermal fleet through 2030.

and Review Board (the “Board”) and customers are best served by focusing on next steps and preparing for a future Integrated Resource Plan (“IRP”), not a hearing process surrounding the Final Report. Given the conclusions and recommendatio...

AI summary PHP argues that the Board and customers should focus on preparing for the next Integrated Resource Plan (IRP) rather than a hearing on the Final Report. PHP highlights a post-processing error in Synapse's Draft Report that double-counted Nova Scotia wind costs, significantly affecting NPVRR calculations for multiple scenarios.

rror that double counted the costs of new Nova Scotia wind, which significantly affected the NPVRR costs associated with all scenarios with increased wind levels, Scenario 13, 14, 16, 17, 25, and 26.” As active participants throughout this...

AI summary PHP highlights an error in Synapse's analysis that double-counted Nova Scotia wind costs, affecting multiple scenarios. While the error didn't alter conclusions, PHP advocates for proactive disclosure of significant changes post-technical conferences to enhance transparency and stakeholder input in future proceedings.

al Generation Utilization and Optimization Economic Analysis of Retention of Fossil-Fueled Thermal Fleet To and Beyond 2030 - M08059, Synapse Energy Economics Inc. (the "Report"), May 1, 2018, page 1. 2 may be other factors that need to be...

AI summary The SBA criticizes the overemphasis on long-term metrics like NPV RRs in Synapse's analysis, stressing that short-term electricity costs for small businesses are equally important. It cautions against prioritizing certain recommendations from the report and highlights concerns about obscured cost comparisons in the analysis.

2 relative-to-reference-case cost savings (i.e., in percentage terms) for the three lowest-cost scenarios shown – scenarios 8, 14 and 17.6 PHP notes its concern with our late-in-coming reporting of a post-processing error that altered the...

AI summary The text discusses cost savings from three low-cost scenarios (8, 14, 17) and addresses a post-processing error in NPV cost reporting. PHP criticizes the delayed error disclosure, while EfficiencyOne emphasizes continued DSM cost-effectiveness despite higher per-unit costs due to carbon cap constraints. The analysis concludes that a second thermal unit retirement before 2030 is indicated under low-cost scenarios, with anticipation of NS Power’s 2019 IRP process.

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.

3 $16,488 $39,168 $39,812 $51,786 $41,624 $46,311 $57,502 $65,666 $68,575 $84,542 $64,061 $85,919 $82,817 $100,882 $105,754 $124,228 $133,502 $136,741 11,098.1 11,098.3 11,060.6 11,043.3 10,990.6 11,155.7 11,171.1 11,046.7 11,044.2 11,068....

AI summary The text presents comparative data between two energy scenarios (Sc 1 and Sc 2) showing energy consumption in GWh, energy saved by Sc 2 DSM relative to Sc 1, and avoided energy costs per MWh saved. The data highlights DSM's impact on energy reduction and cost savings over time.