It is often recognized that gathering large and robust data sets is a critical component for the utility of the future to successfully integrate energy efficiency as a grid resource. However, collecting massive amounts of data without specific goals and associated methodologies will result in large, high-cost studies that are not necessarily useful for saving energy in buildings. The right data will enable utilities to implement effective Integrated Demand-Side Management (IDSM) and energy efficiency (EE) programs, forecast constrained networks with improved accuracy, and provide building performance measurement and verification (M&V) on a real-time basis. This paper answers the questions of what data is needed to accomplish these tasks, as well as how to cost-effectively gather and analyze that data. New York’s Con Edison has committed to meeting more than 50 MW of capacity requirements using targeted energy efficiency for permanent demand reductions in a network experiencing rapid growth and change. The authors of this paper and Con Edison have embarked on a pioneering metering and market characterization effort to gather a comprehensive data set including characteristics for all energy-consuming equipment and the corresponding metered load shapes at the building, end-use, and equipment levels. This data collection effort spans 150-plus small businesses and 40 multifamily buildings covering shared spaces and more than 100 dwelling units. This paper will outline a data-driven approach designing data collection efforts that gather the right data for successful implementation and defense of IDSM and energy efficiency as a resource.
Planning and delivering reliable customer-side demand reductions as a reliable grid resource requires a high resolution understanding of how customers use energy. Con Edison launched their nationally recognized Brooklyn Queens Demand Management (BQDM) program to reduce forecasted overloads of a substation serving parts of the New York City boroughs of Brooklyn and Queens. Current projections, as presented in Figure 1, illustrate that demand will exceed capacity in the 12-hour span from noon to midnight, with the greatest shortfall occurring at 9:00 p.m. in the targeted area. With a 12-hour peak, typical demand response programs will not be sufficient.
Gathering data about how customers use energy is critical for successful implementation of demand reduction solutions. What measures would work in this constrained network? What is the savings potential at each hour on a peak demand day? How can we measure reductions to a level of accuracy that will provide system planners with confidence to defer or cancel planned infrastructure upgrades? No doubt, answering these questions requires data. Yet simply gathering more data without a thoughtful approach can lead to costly efforts that do not significantly improve program delivery.
When planning a data collection effort, it is difficult to determine what data is useful. What equipment should be metered? How many meters should be installed? What should the meter sampling rate be? Trying to answer these questions without direction quickly leads to the common debate about the merits of owning your very own “Big Data.”
Through this data collection effort we were forced to ask the question: How will this data be used? We were then able to develop a process of how to apply a data-driven approach to implement successful targeted demand side management (DSM). With this process in place, it is much easier to define the scope and purpose of a data collection effort. Upon reading this paper, we hope that system planners, program managers, policy makers, and other stakeholders can use this process to initiate and design their own data collection efforts that support successful implementation of demand side reductions as a reliable grid resource.
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