We are especially grateful to Walter Short who first envisioned and developed the WinDS and ReEDS models. We also thank the NREL analysts who provided input on the technology costs, assumptions, and methodologies in ReEDS, including
Diffusion of microgeneration technologies, particularly rooftop photovoltaic (PV), represents a key option in reducing emissions in the residential sector. We use a uniquely rich dataset from the burgeoning residential PV market in Texas to study the nature of the consumer's decision-making process in the adoption of these technologies. In particular, focusing on the financial metrics and the information decision-makers use to base their decisions upon, we study how the leasing and buying models affect individual choices and, thereby, the adoption of capital-intensive energy technologies. Overall, our findings suggest that the leasing model more effectively addresses consumers' informational requirements and that, contrary to some other studies, buyers and lessees of PV do not necessarily differ significantly along socio-demographic variables. Instead, we find that the leasing model has opened up the residential PV market to a new, and potentially very large, consumer segment-those with a tight cash-flow situation.
Use of geographic information systems (GIS)Extensive use of geospatial data; all agents assigned point-location based on sector and population-weighted sampling. GIS framework permits integration and addition of disparate data sets under common framework. Default resolution at U.S. county level (3,108) and 10 agents per countysector Limited use of geographic data. Customers not assigned a pointlocation. Default resolution at substate ( 218) level. Costs of electricityBased on OpenEI Utility-Rate Database, calculates bill savings "bottoms-up" using hourly generation and consumption profiles, incorporating demand charges TOU charges, etc.Based on EIA 861 average costs of electricity by state; time-of-use charges pre-calculated using multiplier on average rates Model users can customize numerous parameters related to current and future DER performance improvements and cost reductions, customer financing structures, market projections (e.g., load and rate growth), siting criteria, and incentive and net metering policies. With these inputs, model users can investigate the effects of a diverse set of scenarios on market potential and identify the critical market factors that drive end-use demand. In this capacity, dGen can be a powerful tool for exploring pathways through which the U.S. DER market could develop and identifying the effects of various factors on DER market growth. This report documents the dGen model framework, algorithms, and underlying assumptions. This report does not present results using the model, which will be released in separate follow-on documents.
Wind power is one of the fastest growing sources of new electricity generation in the United States. Cumulative installed capacity was more than 74,000 megawatts (MW) at year-end 2015 and wind power supplied 4.7% of total 2015 U.S. electricity generation. Despite the growth of the wind power industry, the distributed wind market has remained limited. Cumulative installations of distributed wind through 2015 totaled 934 MW. This first-of-a-kind exploratory analysis characterizes the future opportunity for behind-the-meter distributed wind, serving primarily rural or suburban homes, farms, and manufacturing facilities. This work focuses only on the grid-connected, behind-the-meter subset of the broader distributed wind market. 1 We estimate this segment to be approximately half of the 934 MW of total installed distributed wind capacity at year-end 2015. Potential from other distributed wind market segments including systems installed in front of the meter (e.g., community wind) and in remote, off-grid locations is not assessed in this analysis and therefore, would be additive to results presented here. These other distributed wind market segments are not considered in this initial effort because of their relatively unique economic and market attributes. Opportunities for behind-the-meter distributed wind are considered from three perspectives: addressable resource potential, economic potential, and market potential. The first of these perspectives is intended to frame the overall scale of the opportunity 2 ; the second quantifies the potential capacity of systems that could generate a positive net present value (NPV) at a specific point in time; the third considers economics as well as consumer adoption behaviors to estimate potential deployment levels for the specific conditions assessed. For addressable resource potential, we identify a single estimate for all theoretical behind-themeter distributed wind applications. We use scenarios or an array of future conditions to more fully explore economic and market potential. Variables in our scenarios include capital and operation and maintenance costs, technology performance, the value of distributed generation, system financing and leasing costs, consumer adoption rates, and siting criteria. More details on the scenario framework including the Combined scenarios as well as explicit Low, Reference, High, and Breakthrough values are provided in Section 1.1. Consistent with prior distributed generation analyses conducted at the National Renewable Energy Laboratory and as a first assessment of the opportunity for behind-the-meter distributed wind, this work does not consider potential competition from alternative distributed-generation sources such as rooftop solar photovoltaics, assumes federal and state tax incentives and renewable portfolio standards as legislated, and may not capture all costs of integration into the distribution network. Also, consistent with prior work, net metering and siting setbacks are varied within the range of existing policies today. vi This...
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