TODAY’S STUDY: SOLAR INDUSTRY GETS CRUCIAL INSIGHTS INTO RATE DESIGN
Net Metering and Market Feedback Loops: Exploring the Impact of Retail Rate Design on Distributed PV Deployment
Naïm R. Darghouth, Ryan Wiser, Galen Barbose, Andrew Mills, July 2015 (Lawrence Berkley National Laboratory)
The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where timevarying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from -14% to -61%, depending on the design). Moving towards time-varying rates, on the other hand, may accelerate nearand medium-term deployment (through 2030), but is found to slow adoption in the longer term (-22% in 2050).
Deployment of distributed solar photovoltaics (PV) has expanded rapidly in the United States, growing by over 400% since 2010 in terms of total installed capacity and averaging 40% yearover-year growth in capacity additions (GTM and SEIA 2015). This rapid growth has been fueled by a combination of steeply declining costs, the advent of innovative financing options, and supportive public policies at the federal, state, and local levels. Key among the supportive policies has been net energy metering (or simply net metering or NEM), which typically compensates each unit of PV generation at the customer’s prevailing retail electricity rate. Net metering allows homes and businesses with onsite PV systems to offset their electricity consumption regardless of the temporal match between PV production and electricity consumption. As state incentive programs and federal tax credits are phased out, net metering has become increasingly pivotal to the underlying customer economics of distributed PV.
The rapid growth of net-metered PV has provoked concerns about the financial impacts on utilities and ratepayers (Accenture 2014, Kind 2013, Brown and Lund 2013, Eid et al. 2014). Central to these concerns is the contention that net metering at the full retail electricity price allows PV customers to avoid paying their full share of fixed utility infrastructure costs, thus requiring the utility to raise retail prices, including for non-PV customers, to recover those costs in full (Borlick and Wood 2014). Compounding that concern is the possibility of the feedback effect where increased retail electricity prices accelerate distributed PV adoption, resulting in even higher prices as fixed utility infrastructure costs are spread over an ever-diminishing base of electricity sales (Cai et al. 2013, Costello and Hemphill 2014, Felder and Athawale 2014, Graffy and Kihm 2014).
A wide array of corrective measures – ranging from incremental changes to utility rate design to fundamental changes to utility business and regulatory models – has been suggested to address concerns about under-recovery of fixed costs associated with distributed PV and other demandside resources (Bird et al. 2013, Fox-Penner 2010, Harvey and Aggarwal 2013, Jenkins and Perez-Arriaga 2014, Lehr 2013, SEPA and EPRI 2012, McConnell et al. 2015). Proposals to modify rate designs for PV customers come in many varieties (Faruqui and Hledik 2015, Linvill et al. 2013, Glick et al. 2014). Frequently they entail reallocating a portion of cost recovery from per-kilowatt-hour volumetric charges to fixed customer charges and/or per-kilowatt demand charges (NC Clean Energy Technology Center 2015), while other proposals involve replacing net metering with alternate mechanisms that compensate PV customers for all or some PV generation at a price different than the retail electricity rate (e.g., using a feed-in tariff or valueof-solar tariff; Blackburn et al. 2014).
Decision-making on these issues, however, is hampered by several key informational gaps. Fundamentally, significant disagreement exists about whether, or the extent to which, netmetered PV under existing rate designs causes retail electricity rates to increase. One aspect of that disagreement revolves around the question of feedback effects: Does distributed PV lead to ever-spiraling rate increases as each successive rate increase further accelerates PV adoption? Prior studies of this issue have generally remained conceptual and hypothetical; few have sought to quantitatively examine the magnitude or likelihood of effects, with the notable exceptions of Cai et al. (2013), Chew et al. (2012), and Costello and Hemphill (2014). Furthermore, analyses and discussions of retail rate feedback effects have focused only on the possible positive feedback associated with under-recovery of fixed costs. A separate – and potentially offsetting – feedback may occur when increasing PV penetration causes a shift in the temporal profile of wholesale electricity prices (see Table 1). Numerous studies have demonstrated that the capacity value and wholesale market value of PV erode as penetrations increase (Mills and Wiser 2013, Hirth 2013, Gilmore et al. 2015), and Darghouth et al. (2014) explored the implications of this effect for time-based retail rates and the customer-economics of PV systems. No studies to our knowledge, however, have estimated the impact of this effect on the deployment of distributed PV or contrasted it with the fixed-cost feedback mechanism that is the focus of current broader literature.
Key informational gaps also exist with respect to the effect of rate-design changes on PV deployment. Studies have focused on the impacts of retail rate structure on the customer economics of PV (Mills et al. 2008, Darghouth et al. 2011, Ong et al. 2010, Ong et al. 2012) but generally have not translated those findings into deployment effects. Where deployment effects have been explored (e.g., Drury et al. 2013), analyses have considered a relatively narrow range of retail rate structures and have not accounted for the two possible feedback effects between PV deployment and retail electricity prices noted above. Understanding these deployment impacts will be critical for regulators and other decision makers as they consider potential changes to retail rates – whether to mitigate adverse financial impacts from distributed PV or for other reasons – given the continued role that PV may play in advancing energy and environmental policy objectives and customer choice.
Our research builds on the aforementioned literature and addresses critical informational gaps for decision makers by modeling customer adoption of distributed PV under a range of rate designs. The analysis leverages the National Renewable Energy Laboratory (NREL) Solar Deployment System (SolarDS) model, which simulates PV adoption by residential and commercial customers within each U.S. state through 2050 and has been used widely for scenario analysis of future PV-adoption trends (Denholm et al. 2009). We build on prior applications of this tool (e.g., Drury et al. 2013) by incorporating the two key feedback mechanisms between PV adoption and retail electricity prices mentioned previously: (a) increases in average retail rates required to ensure utility fixed-cost recovery and (b) changes in the timing of peak-to-off-peak periods under time-varying rate structures (see Table 1). In doing so, we show whether and under what conditions retail rate changes caused by distributed PV might accelerate or decelerate future PV deployment. Given these feedback dynamics, we then consider deployment trends under a range of possible changes to retail rate design and netmetering rules, including widespread adoption of fixed customer charges, flat vs. time-varying energy charges, feed-in tariffs, and “partial” net metering (whereby PV generation exported to the grid is compensated at an avoided-cost rate). Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures, but that concerns over feedback effects may be somewhat overstated as the two feedback mechanisms operate in opposing directions…
Discussion and Conclusions
There has been significant recent interest in issues related to fixed-cost recovery with increasing distributed PV deployment, and concerns about the “utility death spiral” (Costello and Hemphill 2014, Felder and Athawale 2014, Cory and Aznar 2014, Blackburn et al. 2014, Satchwell et al. 2015). Some observers express concern that increases in net-metered PV adoption may threaten utility profitability, in part owing to a positive feedback loop: as PV deployment occurs, electricity rates increase because utilities must recover the same fixed costs over lower sales, making net-metered PV even more attractive for consumers, and accelerating PV deployment even further. Though our results do not speak comprehensively to the fixed-cost recovery issue or to the impact of PV on utility profitability, they do show that concerns about feedback effects—at least on a national basis—may be somewhat overstated, and that actual feedback effects are quite nuanced.
Our analysis suggests little change in national PV deployment due to rate feedback under our reference scenario, which includes customers on time-varying rates (mostly in the commercial sector) and flat rates (mostly in the residential sector). This is because there are, in fact, two feedback effects of relevance—one related to fixed-cost recovery and the other related to time-varying retail rates—and these two feedbacks operate in opposing directions. The fixed-cost feedback effect is found to increase cumulative national PV deployment in 2050 by 8%. But the feedback associated with time-varying rates reduces cumulative PV deployment by 5%. Current regulatory and academic discussions that focus solely on the fixed-cost recovery feedback therefore miss an important and opposing feedback mechanism that can offset the issue of concern.
Notwithstanding these aggregate national results, the net impact of the two feedback mechanisms can vary substantially across customer segments. In general, the prevalence of flat, volumetric electric rates among the residential customer class ensures a net positive feedback effect with increasing PV deployment in most cases (increasing cumulative national residential PV deployment in 2050 by 9%). In contrast, the prevalence of time-differentiated rates among commercial customers leads to a net negative feedback effect (decreasing cumulative national commercial PV deployment in 2050 by 15%). The net effect of these feedback mechanisms also varies across states, depending on the types of rates offered, the level of those rates, and PV deployment levels. Given these differences, the total feedback effect considering both residential and commercial customers is found to be –6% to +5% in the vast majority of states, and –1% in the median case. Thus, in most states, the feedbacks operate in the opposite direction of the expressed concern and, even where in the positive direction, are rarely particularly large.
Accounting for these feedback effects, we find that retail rate design and PV compensation mechanisms can have a dramatic impact on the projected level of PV deployment. For example, wider adoption of time-varying rates is found to increase PV deployment in the medium term but reduce deployment in the longer term, relative to the reference scenario based on current rate offerings; the changing pattern of deployment over time, relative to the reference case, is due to the decreasing energy and capacity value of PV with penetration, and the impacts of those trends on time-varying retail rates. The directional impact of feed-in tariffs or value-ofsolar rates, on the other hand, depends entirely on the level of the tariff that is offered in comparison to prevailing retail electricity rates. In part to address concerns about the fixed-cost feedback effect (and in part to address many other concerns), a number of utilities have proposed increased fixed customer charges, especially for the residential sector, and/or a phase-out of net energy metering. Though a variety of considerations must come into play when contemplating such changes, our analysis suggests that a natural outcome of these changes would be a substantial reduction in the future deployment of distributed PV: we estimate that cumulative national PV deployment in 2050 could be ~14% lower with a $10/month residential fixed charge, ~61% lower with a $50/month residential fixed charge, and ~31% lower with “partial” net metering. Regulators would need to weigh these impacts with many other considerations when considering changes to underlying rate designs and PV compensation mechanisms.