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SummaryThe demand for electricity is expected to continue its historical growth trend far into the future and particularly over the 20-year projection period discussed in this report. To meet this growth with traditional approaches will require added generation, transmission, and distribution, costing up to $1.4 billion/GW ($1,400/kW in year 2000 dollars) on the utility side of the meter. The amount of capacity needed in each of these categories must supply peak demand and provide a reserve margin to protect against outages and other contingencies. The "nameplate" capacity of many power system components is typically utilized for only a few hundred hours per year. Thus, traditional approaches to maintaining the adequacy of the Nation's power generation and delivery system are characterized by lower than desirable asset utilization, particularly for assets located near the end-user.Other issues are beginning to affect the utility industry's ability to supply future load growth. The disparity between current levels of investment in generation and transmission suggests a looming crisis that creates a strong element of urgency for finding alternative solutions. In addition, any solution needs to address the cycle of boom and bust that is typical of certain sectors of the electric industry and is likely to become more pronounced as deregulation takes hold across the Nation.The increased availability of energy information technologies can play an important role in addressing these issues. Historically, power supply infrastructure has been created to serve load as a passive element of the system. Today, information technology is at the point of allowing larger portions of the demand-side infrastructure to function as an integrated system element that participates in control and protection functions as well as real-time economic interaction with the grid. The collective application of these information-based technologies to the U. S. power grid is becoming known as the GridWise™ vision or concept.

Research for this report involved collaborations among several firms and major contributors, including Pacific Northwest National Laboratory (PNNL), Power Costs, Inc. (PCI), Clean Power Research (CPR), Alstom, and Duke Energy. PNNL and PCI performed the generation impact analysis with photovoltaic (PV) data simulated by CPR. Duke Energy conducted the transmission simulations, and Alstom modeled the distribution effects. PNNL verified all simulation results, performed the analyses, and compiled information from the collaborators into this report. Shuai Lu coordinated the study efforts at PNNL, and compiled the report with help from Mike Warwick. Shuai Lu also led the generation impact analysis and authored the generation section of the report, with significant contributions from Da Meng regarding ESIOS development, Ruisheng Diao and Chunlian Jin regarding reserve requirements, Forrest Chassin and Tony Nguyen regarding simulations using ESIOS and cost analyses, and Yu Zhang for graphic analyses of variability. Nader Samaan led the transmission analysis with contributions from Bharat Vyakaranam, and wrote the transmission study. Jason Fuller led the distribution analysis and wrote the distribution report. Mark Osborn and Marcelo Elizondo provided valuable comments to the draft report. The PNNL team was guided by Landis Kannberg. Buck Feng and Nate Finucane from PCI performed GenTrader simulations and contributed to the methodology development for the generation study. Ben Norris and Skip Dise of CPR were primarily responsible for providing PV data. Ethan Boardman from Alstom worked with Duke Energy to develop the solution approach for distribution modeling and championed the project internal to Alstom. Jesse Gantz from Alstom was responsible for the distribution project deliverables, performed the model enhancements, simulations runs, and validation of data for the distribution study. The study was not possible without the cooperation and individual contributions of many engineers and analysts from various departments at Duke Energy. They provided data and valuable insights throughout the study as well as a critical review of this report. In addition, the authors would like to acknowledge comments and suggestions received on the draft report from the review panel coordinated by Aidan Tuohy from the Electric Power Research Institute. The panel includes the following colleagues:

In 1990, the Washington State legislature passed a residential energy efficiency code to be effective July 1, 1992. The Bonneville Power Administration {Bonneville) supported passage and implementation of the code ~ to ensure that new electrically heated residences in the State of Washington were as energy efficient as possible. Bonneville contracted with the Washington State Energy Office {WSEO) to provide code implementation support to the building industry and code enforcement jurisdictions through the Washington State Energy Code {WSEC) program. Support under the WSEC program includes training and other activities to provide builders and building inspectors with knowledge of the energy efficiency features of the code to ensure high levels of code compliance. Participation of builders and subcontractors in training needs to be increased; a certification process might increase participation. The WSEC needs to be revised to simplify implementation and to adapt it to building code revision cycles. Bonnev1ille should review the role of energy efficiency codes as resource acquis~tion mechanisms and adopt a clear policy regarding this role. Energy efficiency should continue to be promoted to ensure market demand for energy-efficient homes and high levels of code compliance. Code compliance can be evaluated and savings projected using quantitative measures.

Assessing data quality is a fundamental task during the research process. Information derived from data of inadequate quality may lead to invalid conclusions and misinformed management decisions for healthcare organisations. To minimise such risk a data quality framework can be utilised to ensure suitability and to quality assure datasets. This current research involved a review of existing frameworks and the formation of a new framework which combines quality criteria derived from different research disciplines. The current framework is robust, it can effectively assess data quality across a range of criteria and supports researchers to formulate a decision on whether to use the dataset. Further development of the quality framework would include an emphasis on the interdependencies of quality criteria.