Private vs. public value of U.S. residential battery storage operated for solar self-consumption
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Adoption of paired solar-plus-storage systems has accelerated in recent years, driven by both the demand for backup power and a desire to manage utility bills. Tradeoffs between those two uses can arise through the reserve setting on the battery storage system, which serves to maintain a minimum state of charge in case of a power interruption. Our paper applies an economic framework to evaluate this tradeoff in terms of changes in bill savings and customer reliability value across reserve levels, considering how those tradeoffs depend on the underlying electricity rate structure and levels. The analysis is based on a representative set of load profiles, solar profiles, tariff designs, and stochastic power interruption events across ten different regions in the United States. We find that the opportunity cost of holding storage capacity in reserve, in terms of foregone bill reductions, outweighs any gains in reliability value from mitigated power interruptions in the majority of customer situations. Higher storage reserve levels increase total customer value only in specific circumstances, such as for customers with inferior reliability (10x average interruptions), with a very high value of lost load ($50/kWh), and with tariff or interconnection rules that disallow grid charging. However, even this result is dampened when considering tariff designs with higher price differentials that increase the opportunity cost of holding storage in reserve (e.g. import/export or time-of-use rates). Allowing grid charging in tariffs essentially eliminates the necessity to hold any storage in reserve in all sensitivity cases explored.
We report that as of the second quarter of 2016 more than 1.1 million solar photovoltaic (PV) homes exist in the US. Capturing the value these PV systems add to home sales is therefore important. Our study enhances the PV-home-valuation literature by analyzing 22,822 home sales, of which 3951 have PV, and which span eight states during 2002–2013. We also, for the first time, compare premiums with contributory value estimates derived from the present value of saved energy costs (income approach) and, separately, the replacement cost of systems at the time of sale (cost approach) to examine market signals. We find home buyers are consistently willing to pay PV home premiums across various states, housing and PV markets, and home types; average premiums equate to approximately $4/W or $15,000 for an average-sized 3.6-kW PV system. We find that a replacement cost net of state and federal incentives is a better proxy for premiums than gross installed costs, and that the income approach is a good signal if it accounts for tiered volumetric retail rates. Finally, other results include detailed premium analyses for PV home sub-populations.
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Shifting and shedding power demand in buildings can be cost-effective techniques for grids to function reliably and for end users to earn compensation. Grid operators reimburse customers in proportion to the quantity of load shed. Simple data-driven methods are used to quantify this shed, which is the difference between a measured load during the event and modeled "baseline" that would have occurred in absence of the event. These methods have evolved over the years and in many cases have been integrated with building physics, to make them a hybrid between physics based and empirical models. However, there is no comprehensive analysis that provides guidance to building operators, grid operators and researchers in selecting appropriate models based on their specific needs and available data. Here, this work aims to fill this gap by critically assessing the performance of baseline models put forward from the year 2000 through 2023. The literature reviewed includes reports generated by grid operators, reports from national laboratories and academic journal articles. The work outlines modeling features like the inputs, training period, estimation method, adjustments to fine tune the predictions and metrics to evaluate the performance. A comprehensive list of 50 models has been provided. For each model, the study explores the applicability of the model to weather sensitive buildings, variability in the building profile, timing of the event, and whether the building reduces energy consumption before an event. The work identifies the situations in which a particular model works and draws lessons based on evidence of performance. Finally, recommendations to aid in model selection are given.
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