The effort to integrate distributed energy resources (DERs) into a seamless instrument began nearly a decade ago. While disparate demand-side management tools existed in the early 2000’s, more recently, utilities have leveraged them as a package to address energy and carbon emission reductions. Distributed energy resources include energy efficiency (EE), demand response (DR), distributed generation (DG), energy storage, and electric vehicle charging. Any one of these technologies constitutes an entire field of innovation and adaptation. As such, evaluating the efficacy of DER technologies, both individually and as part of an integrated package, presents challenges and opportunities.
Well-established energy resources like EE have protocols for measuring the impact and cost-effectiveness of programs. For instance, years of program evaluation and methods refinement have resulted in specific algorithms and approaches to measuring program attribution (i.e., the fraction of energy savings directly attributable to an EE program). These approaches have led to critical findings in the past. For example, Opinion Dynamics has pioneered the use of multilevel modeling to identify positive, neutral, and negative savers in programs with thousands of participants, resulting in optimization and refinement of program delivery. Over time, discoveries such as these have strengthened EE technologies, resource allocation, and EE program performance.
Nascent technologies such as storage or electric vehicles do not have a rich history of measurement and verification, which leaves a broad range of energy professionals asking how to measure the impacts of these programs, and further, if it is worthwhile to do so. If the driving force behind DER is to reduce energy use and carbon emissions overall, perhaps time and money need not be spent tracing the origin of each kWh saved – a task that becomes incrementally harder with each DER component added to the mix. On the other hand, in a time of rapid growth and innovation, we see benefits to determining which technologies and programs are driving energy savings, and by doing so, developing a stronger and more informed foundation for the deployment of DERs. To forego evaluating individual DER components would be akin to treating a medical condition with five medications without ever knowing which one works—the result would be costly medical bills and compounding side effects.
To this end, we are working to develop evaluation methods in stride with our clients’ program implementation. For example, Opinion Dynamics recently contributed to the research design for PSEG Long Island’s Super Saver program, which will deploy an impressive array of DER technologies to reduce load in a capacity constrained area of Long Island. The program is one way that PSEG Long Island is advancing the New York Reforming the Energy Vision (REV) strategy. The Super Saver program will offer a mix of EE, DR and storage strategies including advanced metering, smart thermostats, energy audits, and educational materials to about 10,000 customers; all of which combined aim to allow the utility to meet rising demand without investing in costly grid upgrades. Opinion Dynamics and PSEG Long Island worked together to create a plan to quantify the demand impacts of each piece of the multi-pronged program. By participating in the research planning process before the program rolled out, we tailored evaluation strategies to meet specific program needs, and importantly, helped PSEG Long Island determine what data collection efforts would be critical to measuring program success in the future. The resulting body of work will illustrate the contribution of each DER component and bolster the implementation of a first-of-kind program for a major U.S. utility.
In New York, the speedy adoption of DER has generated questions regarding the effective and equitable distribution of new energy resources. We are working on the necessary but difficult task of developing approaches and quantitative methods to measure the incremental effects of DER technologies. In the long run this effort will result in integrated programs that deploy the most effective technologies in the communities that need them most.
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