@misc{21948,
  keywords = {electricity markets and policy group, energy analysis and environmental impacts department},
  author = {Curt D Puckett and Timothy P Hennessy},
  title = {Deemed Savings Estimates for Legacy Air Conditioning and Water Heating Direct Load Control Programs in PJM Region},
  abstract = {<p>During 2005 and 2006, the PJM Interconnection (PJM) Load Analysis Subcommittee (LAS) examined ways to reduce the costs and improve the effectiveness of its existing measurement and verification (M&amp;V) protocols for Direct Load Control (DLC) programs. The current M&amp;V protocol requires that a PURPA-compliant Load Research study be conducted every five years for each Load-Serving Entity (LSE). The current M&amp;V protocol is expensive to implement and administer particularly for mature load control programs, some of which are marginally cost-effective. There was growing evidence that some LSEs were mothballing or dropping their DLC programs in lieu of incurring the expense associated with the M&amp;V. This project had several objectives:</p><ol><li>examine the potential for developing deemed savings estimates acceptable to PJM for legacy air conditioning and water heating DLC programs, and</li><li>explore the development of a collaborative, regional, consensus-based approach for conducting monitoring and verification of load reductions for emerging load management technologies for customers that do not have interval metering capability.</li></ol><p>The deemed savings estimates presented in this study are based on historical end-use metered data available across several jurisdictions. Air conditioning end-use metered data were received from Baltimore Gas and Electric (BGE), FirstEnergy (FE), and Public Service Electric and Gas (PSE&amp;G). Water heating end-use metered data were provided by Baltimore Gas and Electric. Duty cycle models were constructed to examine a wide range of potential switch cycling strategies (27%, 43%, 50%, 67%, 75%, 87% and 100%).Customer segmentation based on air conditioning size (e.g., connected load or seasonal usage) can also be accommodated with the model set. Next, the estimates of the customer's demand saving were mapped to their appropriate weather stations. Finally, regression analysis was conducted to predict the demand savings estimates from weighted temperature humidity indices. The demand savings predictions were tabularized for use by the participating utilities.</p>},
  year = {2007},
  pages = {101},
  month = {04/2007},
  publisher = {LBNL},
  address = {Berkeley},
}