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Community Choice Aggregation Feasibility Analysis Alameda County <br />June, 2016 3 MRW & Associates, LLC <br />Figure 4: CCA Load Forecast by Class, 2017-203011 <br /> <br /> <br />To estimate the CCA’s peak demand in 2014, MRW multiplied the load forecast for each customer class by the PG&E’s 2014 hourly ratio of peak demand to load for that customer <br />class.12 MRW extended the peak demand forecast to 2030 using the same growth rates used for <br />the load forecast. (Peak demand is the maximum amount of power the CCA would use at any <br />time dureingt the year. It is measured in megawatts (MW). It is important because a CCA must <br />have enough power plants on (or contracted with) at all times to meet the peak demand.) This forecast is summarized in Figure 5. <br /> <br />11 Load forecasted assumes 85% participation. <br />12 Data obtained from PG&E’s dynamic load profiles for Public, Industrial, Commercial and Residential customers (https://www.pge.com/nots/rates/tariffs/energy_use_prices.shtml) and static load profiles for Pumping and <br />Streetlight customers (https://www.pge.com/nots/rates/2016_static.shtml#topic2).