N-1Evaluation Report
29 passages
Definitions The hourly net system requirement (MW) less all wind generation (MW) Cohorts are numbered based on the Phase they enrolled in the TVP program. For example, Cohort 4 refers to participants recruited in Phase 4 (i.e. the fourth W...
AI summary The document defines key terms and metrics related to energy consumption, rate classes, and income brackets. It outlines the TVP program's cohort structure and the definition of the Commercial rate class. It also introduces metrics such as energy consumption (kWh), power consumption (kW), and income thresholds for low-income classification.
Low-Income Medium-Income High-Income Parameters AM Peak PM Peak All Peak Hours AM Peak PM Peak All Peak Hours AM Peak PM Peak All Peak Hours Avg. Load 0.14 ± 0.15 ± 0.15 0.12 ± 0.16 ± 0.14 0.18 0.19 0.19 (kW) a Reduction 0.01 0.01 ± 0.01 0...
AI summary This table presents load reduction data for low-income, medium-income, and high-income residential TOU participants before a pilot program. It includes average load, load reduction, and relative load reduction percentages, with significance markers for statistical validity.
Participants Parameters High nest ANL Ho urs ANL Hours Coincide TOU Peak Periods Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load 0.16 ± 0.04 0.14 ± 0.13 ± 0.17 ± 0.17 ± 0.17 ± Reduction (kW) a 0.16 ± 0.04 0.03 0.02 0.03 0.03 0.02 Avg....
AI summary The table presents load reduction data from participants in a time-varying pricing program, comparing average load and reduction percentages across different participant tiers and time-of-use periods. Statistical significance is noted for some metrics, indicating potential effectiveness of the program.
3.1.4 Change in Usage Table 15 summarizes changes in daily electricity usage level for cohorts 1 to 4 and all cohorts combined during non-holiday weekdays and holiday weekends in Winter. All cohorts exhibit statistically significant reduct...
AI summary Table 15 shows a statistically significant reduction in daily electricity usage across all cohorts during non-holiday weekdays and holiday weekends in Winter. TOU participants showed an average load reduction of 0.6 kWh on holidays and weekends, indicating the effectiveness of time-of-use pricing in reducing energy consumption.
Parameters De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic Winter, Non-Holiday Weekdays a Avg. Usage Reduction(kWh/day) 0.75 ± 0.20 -0.09 ± 0.14 1.96 ± 0.10 0.15 ± 0.07 Avg. Usage – Residential TOU Part...
AI summary The table presents data on average usage reduction during different seasons and days of the week for residential TOU participants. It highlights significant reductions in winter weekdays and non-winter periods, with notable differences observed between households with higher and lower residents-per-household during winter. The data is further segmented by income level, residents-per-household, and region in Figure 21.
[Figure 22](#page-66-1) illustrates the daily load profile for residential CPP participants as well as the control group during event day and reference day with morning events only, evening events only, both morning and evening events, and...
AI summary Figure 22 compares the daily load profiles of residential CPP participants and a control group on event days, showing significant load shifts during morning, evening, and double-event periods compared to reference days. Load shifts are indicated by positive (load reduction) and negative (load increase) values.
Table 27: Change in Daily Electricity Usage (kWh/day) by Cohort during CPP Events for Residential CPP Participants Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Usage Reduction a (kWh/day) 1.6 ± 4.0 3.1 ± 6.7 1.6 ± 2.9 1.6 ± 1.5...
AI summary Table 27 presents the change in daily electricity usage (kWh/day) by cohort during Critical Peak Pricing (CPP) events for residential participants. The table shows average usage reductions, pre-pilot usage levels, and relative percentage reductions, with significance indicators for statistical relevance.
The results of the second analysis are summarized in [Table 28](#page-75-0) and show changes in daily electricity usage level for cohorts 1 to 4 and all cohorts combined during non-holiday weekdays and holiday weekends in Winter as well as...
AI summary The analysis shows that residential Critical Peak Pricing (CPP) participants significantly reduced their daily electricity usage during non-holiday weekdays in Winter. Load reductions were observed across all cohorts, though cohort 3's reduction was not statistically significant. Annual aggregated usage reductions for all CPP participants combined are estimated at 355 kWh per year.
a Participants Eco Shift Event Date Event Hour Window Is CPP Event? Is Weekend? All 04-Dec-2024 5 PM – 9 PM ✓ x CPP 15-Dec-2024 5 PM – 9 PM ✓ ✓ All 20-Dec-2024 7 AM – 11 AM ✓ x CPP 22-Dec-2024 5 PM – 9 PM ✓ ✓ All 23-Dec-2024 5 PM – 9 PM ✓...
AI summary The text presents a table listing Eco Shift events with details such as participants, dates, time windows, and whether they are CPP events or occur on weekends. It also references Table 71 and mentions 'E1 Program Balancing' in section III.2.4.
Attachment V: Validation of Mixed Effects Regression The NS Power commercial TVP evaluations (Phase 1 – 3)[25](#page-152-1) has historically provided statistically insignificant results. Findings such as "statistically insignificant" can b...
AI summary The NS Power commercial TVP evaluations have historically yielded statistically insignificant results due to low statistical power and methodological choices. The use of a fixed effects regression model for commercial customers, similar to residential, introduces Type II errors. In Phase 4, a mixed effects regression model was introduced to address the heterogeneity of commercial customer load profiles, improving the evaluation's accuracy.
age_153_Figure_6.jpeg) 26 Frees, E. W. (2003). Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Cambridge University Press. 125 For commercial TOU customers during Phase 4, [Figure](#page-154-0) 52(a,b) prese...
AI summary The text compares Fixed-Effect and Mixed-Effect models for analyzing load reduction in commercial TOU customers. It highlights that the Fixed-Effect model results in higher residual autocorrelation and larger median residual errors compared to the Mixed-Effect model, which shows a more normal distribution of residuals.
Residual Diagnostics for Fixed- vs. Mixed-Effect Modeling Approaches It is important to note that a Fixed-Effect model absorbs the time-invariant differences, such as Baseload heterogeneity across MURBs, by eliminating it from the analysis...
AI summary The text discusses the use of Fixed-Effect and Mixed-Effect models in analyzing the impact of Time-of-Use (TOU) pricing on load reduction and bill savings in MURBs. It highlights that Mixed-Effect models are more suitable due to baseline load differences and presents residual diagnostics comparing both models.
Purpose and Objectives - Narrative Research is conducting the survey on behalf of Nova Scotia Power. NSP was interested in collecting feedback from participants of the program (both new and ongoing participants). Research sought to achieve...
AI summary Nova Scotia Power is conducting a survey through Narrative Research to gather feedback from participants of the Time-Varying Pricing Rate Pilot. The objectives include measuring customer experiences, identifying behavior changes, assessing communication effectiveness, collecting preferences for program changes, and tracking opinion shifts over time.
Satisfaction with Communications CPP participants continue to be more satisfied with pilot communication than ToU participants, although results for both pilots are consistent with 2024 results. Satisfaction with email content, content on...
AI summary CPP participants are more satisfied with pilot communication than ToU participants, with consistent results from 2024. Satisfaction with email content, website content, and email frequency remains consistent for both groups, though CPP participants show slightly lower satisfaction with website content, indicating an opportunity for improvement.
Most Helpful Communication Reminders, emails, and time of day charts are viewed as the most helpful communication received by pilot participants. Asked about the most helpful communication they have received as part of the pilot, participa...
AI summary Participants in the pilot program found reminders, emails, and time-of-day charts to be the most helpful communication. Reminders for peak hours, emails, and clear explanations of rates and times were particularly valued. These communications helped participants manage their energy use effectively.
Awareness of when events critical peak events can happen is improving, though some participants remain unclear. CPP participants were asked when they understood peak events can be called. This year saw an increase in the number of particip...
AI summary Awareness of when critical peak events can occur is improving, with more participants understanding that these events can be called from November to March and on any day of the week. However, some participants remain unclear, particularly regarding the possibility of events on weekends and the misconception that events can occur at any time of day.
Likelihood of Leaving the CPP Pilot One in four participants are at least somewhat likely to leave the CPP pilot if peak events can be called year-round. Presented with two scenarios that may impact their participation in the program, one...
AI summary One in four CPP pilot participants are at least somewhat likely to leave the program if peak events can be called year-round. This is based on survey responses comparing scenarios with and without changes to the current limit of 18 events. A minority (8%) indicated they would leave without any changes to the event limit.
Likelihood of Installing a Smart Thermostat There is a mixed likelihood of installing a smart thermostat after joining a Time-Varying Pricing Rate pilot program. Reports of participants being likely to install a smart thermostat continue t...
AI summary The likelihood of installing a smart thermostat among participants in a Time-Varying Pricing Rate pilot program is mixed, with a growing proportion of participants unlikely to install one. The decline in interest may be due to installation challenges, prior installations, or skepticism about smart thermostats.
Cost savings is the primary reasonsthat participants installed or are interested in installing a smart thermostat. The top reasons given by participants who currently have a smart thermostat installed are to save on electricity or heating...
AI summary Participants installed or are interested in installing smart thermostats primarily for cost savings and improved heating efficiency. The majority of those likely to install a smart thermostat cite saving on electricity or heating costs as their main motivation.
- Direct Mail had the longest lifespan and still brings in web traffic to date. Residential YEAR 1 YEAR 2 YEAR 3 YEAR 4 Email YOY % Emails Recipients 43,280 19,489 112,089 355,035 217% ↑ Email Opens 21,778 10,855 71,011 215133 203% ↑ Open...
AI summary The document presents data on the performance of various marketing channels for a residential program, showing significant growth in email recipients, opens, and clicks over the years, along with metrics for web traffic, paid search, and direct mail. The data highlights the effectiveness of different channels in achieving enrollment targets for Time-of-Use (TOU) and Critical Peak Pricing (CPP) programs.
Pilot Enrollment Active Participants Tariff End of Phase 2 Winter Beginning of Phase 3 Winter End of Phase 3 Winter Participants Who Completed Year 3 Residential TOU 891 2,396 2,241 93.5% Residential CPP 373 950 922 97.1% The percentage of...
AI summary The pilot enrollment data shows that the percentage of participants exiting the Residential CPP tariff is slightly lower compared to the Residential TOU tariff, with 97.1% of Residential CPP participants completing Year 3 versus 93.5% for Residential TOU.
Likelihood of Installing Smart Thermostat Among customers without automated heating, one-third express a high likelihood of installing a smart thermostat in the next three years. Although a minority, this result suggests that smart thermos...
AI summary Among customers without automated heating, one-third are likely to install a smart thermostat within three years. Higher likelihood is seen in non-B2B businesses, small businesses, newer buildings, and those on rate code 11. However, small sample sizes in some subgroups require caution in interpreting these differences.
Electric Devices Computer servers, ventilation, coolers/freezers, and exterior lighting are the most common major equipment business customers pay for at their business location. Business customer were also asked the type of equipment they...
AI summary The text discusses the types of electric devices used by business customers in their primary business locations, highlighting computer servers, ventilation, coolers/freezers, and exterior lighting as the most common. Businesses with heat pumps and those with more employees, larger square footage, and on rate 11 are more likely to use these devices.
Usage Among participants who completed the online survey, electricity is the most common energy source used for their home heat, as used by three-quarters, while oil is used by four in ten and wood by one-quarter. In terms of temperature c...
AI summary The survey highlights that electricity is the most common home heating source, with three-quarters of participants using it. Smart thermostats are less common, with only one in ten customers having one. Customers with electric heat primarily install smart thermostats to save on heating costs and improve efficiency. Most customers do not have smart appliances, and electric water heaters are more common than heat pump water heaters.
- Updated Year 4 Results, see table below: Results (previously shared) Year 4 Year 4 Results (updated) Enrolment Target 500 TOU / 500 CPP 1,000 TOU / 1,000 CPP Applications Received 0 TOU / 26 CPP 0 TOU / 26 CPP Pilot Enrolment 54 TOU / 53...
AI summary The updated Year 4 results show that the enrolment target for the Time of Use (TOU) and Critical Peak Pricing (CPP) programs has doubled to 1,000 each, but the percentage of the target achieved has decreased from 11% to 5% for both programs. Applications received remain unchanged at 0 TOU and 26 CPP.
Tactics and Schedule - Email October 22 – December 13 - Introduced Retargeting and Segmentation/Personalization - Paid Channels October 21 November 30, Extended until December 2 - Direct Mail Postcard - Digital Ads Social, Google/Bing Disp...
AI summary The document outlines a marketing and engagement strategy for a program, including email campaigns, paid and owned media channels, internal communications, earned media efforts, and customer onboarding and retention initiatives. The plan spans from October to December and includes tactics like retargeting, direct mail, digital ads, and web content updates.
• What we are proposing: - Leverage NS Power Commercial Advisors as they are the trusted partners, who know the correct decision makers. - Allowing NS Power Commercial Advisors to begin conversations now and build out application informati...
AI summary The proposal outlines strategies to engage larger commercial customers through NS Power Commercial Advisors, leveraging trusted partnerships, targeted outreach, and digital marketing to promote TVP Rates and increase participation.
the limited success of its "customized recruitment approach" when it uses it again in year 5? Will more resources be required to do this? cannot be the same as residential. To elaborate, what is most successful for residential (email) is n...
AI summary The document discusses the challenges of recruiting SMB customers for TVP programs, noting that face-to-face interactions are more effective than email campaigns. It highlights the high cost of acquisition and the potential need for additional resources to support recruitment efforts.
Stakeholder Comment NS Power Response segmentation by income due to the difficulty of knowing the income levels for control group customers and already large error bars. However, we submit that measuring load response by segmenting custome...
AI summary The discussion focuses on the segmentation of domestic TVP participants by income levels to better understand load response differences. Synapse recommends using tertiles instead of two groups to reveal clearer contrasts, while NS Power acknowledges the current grouping may obscure differences and agrees to explore splitting income into thirds in Year 4.