Topic:"Electric Resource Assessment Model" in M12451

uildings, Tanks and Stacks) 573 Cable Tray and Conduit 574 Control Cable Systems 575 Power Cable Systems 58 ELECTRICAL CONTROL AND COMMUNICATION 4130 Included in 2020: New fire detection and suppression system has been installed (lnergen s...

AI summary The text lists various electrical systems and components, including fire detection, data acquisition systems, and communication systems, along with associated costs. It also mentions the installation of new fire suppression systems and vibration monitoring.

15 Revised Table 18 ‐ E3 PRM Study (Section Resource Nameplate Capacity Net Capacity ELCC % Coal 1081 976 90% HFO/Gas 318 232 73% Gas CTs 144 133 93% LFO CTs 231 178 77% Biomass 43 41 95% Hydro 374 355 95% Wind 596 113 19% Maritime Link Ba...

AI summary The table presents the ELCC percentages for various energy resources in Nova Scotia, including coal, gas, hydro, and wind. The Maritime Link base energy imports have an ELCC of 95%, which was adjusted by NS Power in 2022 due to high forced outage rates associated with the Labrador Island Link.

5 Figure 22: Commercial Space Heating Saturation Comparison 6 7 For the 2024 forecast, energy and peak values predicted by the SAE models are higher than the E3 8 hybrid scenario models where some of the heating requirements are handled by...

AI summary The 2024 forecast indicates that energy and peak values predicted by SAE models are higher than those of the E3 hybrid scenario models, which incorporate secondary non-electric heat sources. Figure 23 compares NS Power models with E3 projections and shows adjustments to energy and peak forecast values for the hybrid scenario.

1.1 Table A1: Energy Requirement – 2025 NS Power Forecast Energy Forecast Year Residential Sector Growth Commercial Sector Growth Industrial Sector Growth Municipal and Other Growth Losses Total Energy Growth GWh % GWh % GWh % GWh % GWh GW...

AI summary The table presents energy requirement forecasts for various sectors in Nova Scotia from 2015 to 2035, including residential, commercial, industrial, municipal, and other sectors. It shows growth percentages for each sector and total energy consumption, providing a comprehensive energy forecast for the region.

Peak Forecast (Accrued Classes) The long-term system peak forecast for the accrued classes is derived through a monthly peak linear regression model that relates monthly peak demand (excluding large customer contribution) to heating, cooli...

AI summary The document explains a method for forecasting long-term system peak demand using a linear regression model that incorporates heating, cooling, base load requirements, and average daily wind. The model normalizes heating and cooling loads for the number of days in a month and interacts them with peak-day weather conditions to estimate peak demand.