BPA Power Studies
EES Consulting has helped many public agency customers in their review of BPA power and transmission rates. The uncertainty and potential magnitude of variations in monthly power supply costs under BPA’s tiered rate methodology is an issue that many utilities are carefully reviewing. This review typically includes the development of an economic load forecasting model based on the utility’s historical loads, followed by a determination of how large load variations, and resulting cost variations, can be, given the typical fluctuations in weather severity. Armed with this information, utilities can then determine what level of cash reserves are needed and whether or not changes in retail rates are needed to address the large swings in monthly power supply costs.
Washington Public Agencies Group (WPAG)
Prior to BPA’s implementation of tiered rates, EES Consulting provided power supply cost forecasting models to each of the 23 Western Public Agency Group utilities. The models were designed to assist the utilities in their budget preparations going into BPA fiscal year 2012 (October 2011). The models were also used to illustrate projected changes in cash flow due to the significant change in BPA’s wholesale rate design. The models were used to prepare power point presentations for utility boards to educate the boards on the changes in BPA’s rate structure, the impacts on utilities’ cash flow and the potential impact on utilities’ retail rates. The models and presentations also provided utility boards and staff with projected cash reserve targets given the increased volatility in power supply costs that accompanied BPA’s tiered rate methodology. EES Consulting assisted WPAG utilities in evaluating whether or not the TRM wholesale rate design should be carried over into retail rate setting.
City of McMinnville
Prior to BPA’s implementation of tiered rates, EES Consulting provided McMinnville with a customized power supply cost forecasting model. The model included all of BPA’s new rate components including the load shaping, load shaping true-up, demand and tier 2 rates. The model also included McMinnville’s owned resources generating capabilities and projected costs. An analysis of the allocation of power supply costs between McMinnville’s Industrial and non-Industrial customers was included. The analysis provided McMinnville decision-makers with information regarding the risks of power supply cost fluctuations and a projection for reserves required to manage such risk.
