Optimal use of incentive and price based demand response to reduce costs and price volatility
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The methodology presented is a derivation of the utility owned solar electric systems model. The net present value of the system is determined by consideration of all financial benefits and costs including a specified return on investment. Life cycle costs, life cycle revenues, and residual system values are obtained. Break-even values of system parameters are estimated by setting the net present value to zero.
Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the report’s traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data. Key findings from this year’s report include the following: -Installed Prices Continued to Fall through 2017 and into 2018. National median installed prices in 2017 were $3.7 per watt (W) for residential systems (a $0.2/W or 6% decline from the prior year), $3.1/W for small non-residential systems (a $0.4/W or 11% decline), and $2.2/W (a $0.1/W or 5% decline) for large non-residential systems. Data for the first half of 2018 show an additional drop of $0.1/W for residential and small non-residential systems, and effectively no change for large non-residential systems. These recent trends are generally consistent with the pace of price declines since 2014, and mark a slowing from the years immediately preceding (2009-2013) when prices fell by roughly $1/W per year. -Installed Prices Vary Widely Across Individual Projects. Among residential systems installed in 2017, 20% were priced below $3.0/W (the 20th percentile value) while another 20% were above the 80th percentile at $4.5/W. Non-residential systems also exhibit wide spreads, albeit shifted downward, from $2.4/W to $4.1/W for small non-residential and from $1.8/W to $2.8/W for large non-residential projects. These pricing spreads have persisted over time and are both reflective of the heterogeneity in PV systems and markets, as well as suggestive of the potential for further price declines. -Pronounced Pricing Disparities Exist Across States. State-level median installed prices in 2017 ranged from $2.6/W to $4.5/W for residential systems, from $2.2/W to $4.0/W for small non-residential systems, and from $2.1/W to $2.4/W for large non-residential systems. Three of the largest state markets (California, Massachusetts, and New York) are relatively high-priced, pulling overall U.S. median prices upward. These cross-state pricing differences reflect both idiosyncratic features of particular states as well as more-fundamental differences related to market and policy conditions. -Installed Pricing Variation Also Reflects Differences in System Design, Installer, and Customer Characteristics (among other factors). Significant pricing differences were also observed across system sizes within each customer segment (with median prices ranging from $3.2/W to $4.5/W across residential systems of varying size, and from $2.1/W to $3.7/W across non-residential systems), between third-party owned (TPO) and host-owned residential systems ($3.3/W vs. $3.8/W), across residential installers ($2.1/W to $9.6/W for host-owned systems and $1.1/W to $5.1/W for TPO systems), between residential new construction and retrofit systems in California ($2.3/W vs. $3.9/W), between large non-residential systems at commercial sites and tax-exempt customer sites ($2.1/W vs. $2.6/W), and between systems with standard vs. premium efficiency modules ($3.6/W vs. $4.2/W among residential systems). A public version of the underlying dataset can be downloaded at https://emp.lbl.gov/tracking-the-sun.
The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2017, the utility-scale sector accounted for nearly 60% of all new solar capacity, and is expected to maintain its market-leading position for at least another six years. Two-thirds of all states, representing all regions of the country, are now home to one or more utility-scale solar projects. This report—the sixth in an ongoing annual series—provides in-depth data-driven analysis of the utility-scale solar project fleet in the United States. Drawing on empirical project-level data from a wide range of sources, this report analyzes technology trends, installed project prices, operating costs, capacity factors, power purchase agreement ("PPA") prices, the levelized cost of solar energy (LCOE), and the market value of solar. Given its current preeminence in the market, utility-scale PV also dominates much of this report, though data from CPV and CSP projects are also presented where appropriate. Highlights from this year's edition include: -Installation and Technology Trends: The use of solar trackers dominated 2017 installations with nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the high-insolation Southwest, the median insolation level at newly built project sites declined again in 2017. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio grew to 1.32 in 2017, allowing the inverters to operate closer to full capacity for a greater percentage of the day. -Installed Prices: Median installed PV project prices have steadily fallen by two-thirds since the 2007-2009 period, to $2.0/WAC (or $1.6/WDC) for projects completed in 2017. The lowest 20th percentile of projects within our 2017 sample were priced at or below $1.8/WAC, with the lowest-priced projects around $0.9/WAC. Overall price dispersion across the entire sample has decreased steadily every year since 2013; similarly, price variation across regions decreased in 2017. -Operation and Maintenance ("O&M") Costs: PV O&M costs were in the neighborhood of $16/kWAC-year, or $8/MWh, in 2017. These numbers include only those costs incurred to directly operate and maintain the generating plant. -Capacity Factors: The cumulative net AC capacity factors of individual projects vary widely, from 14.3% to 35.2%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010-vintage to 2013-vintage projects, where they've remained fairly steady among more-recent project vintages as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. -PPA Prices and LCOE: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time, by $20-$30/MWh per year on average from 2006 through 2012, with a smaller price decline of ~$10/MWh per year evident from 2013 through 2016. Most recent PPAs in our sample—including many outside of California and the Southwest—are priced at or below $40/MWh levelized (in real 2017 dollars), with a few priced as aggressively as ~$20/MWh. The median LCOE among operational PV projects in our sample has followed PPA prices lower, suggesting a relatively competitive market for PPAs. -Solar's Wholesale Energy Market Value: Falling PPA prices have been offset to some degree by a decline in the wholesale energy market value of solar within higher-penetration markets like California, where solar earned just 79% of the average price across all hours within CAISO's real-time wholesale energy market in 2017 (down from 125% back in 2012). In other markets with less solar penetration, however, solar's hourly generation profile still earns more than the average wholesale price (e.g., 127% in ERCOT, 112% in PJM). -Solar+Storage: Adding battery storage is one way to at least partially restore the value of solar, and three recent PV+storage PPAs in Nevada (each using 4-hour batteries sized at 25% of PV nameplate capacity) suggest that the incremental PPA price adder for storage has fallen to ~$5/MWh, down from ~$15/MWh just a year ago for a similarly configured project. As PV plus battery storage becomes more cost-effective, a number of developers are regularly offering it as a viable upgrade to standalone PV. At the end of 2017, there were at least 188.5 GW of utility-scale solar power capacity within the interconnection queues across the nation. The 2017 growth within these queues is widely distributed across all regions of the country, but is most pronounced in the up-and-coming Midwest region, which accounts for 27% of the 99.2 GW of new queue capacity. The widening geographic distribution is as clear of a sign as any that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.
The market for residential solar photovoltaic (PV) systems in the United States has experienced tremendous growth over the past decade, with installed capacity more than doubling between 2014 and 2016 alone (SEIA, 2016). As the residential market continues to grow, it prompts new questions about the nature of competition between solar installers and how this competition, or lack thereof, affects the prices consumers are paying. It is often assumed that more competition leads to lower prices, but this is not universally true. For example, some studies have shown that factors such as brand loyalty could lead to a negative relationship between concentration and price in imperfectly competitive markets (Borenstein, 1985; Holmes, 1989). As such, the relationship between prices and market concentration is an open empirical question since theory could predict either a positive or negative relationship. Determining a relationship between prices and market concentration is challenging for several reasons. Most significantly, prices and market structure are simultaneously determined by each other -- the amount of competition a seller faces influences the price they can command, and prices determine a seller's market share. Previous studies have examined recent PV pricing trends over time and between markets (Davidson et al., 2015a; Davidson and Margolis 2015b; Nemet et al., 2016; Gillingham et al., 2014; Barbose and Darghouth 2015). While these studies of solar PV pricing are able to determine correlations between prices and market factors, they have not satisfactorily proven causation. Thus, to the best of our knowledge, there is little work to date that focuses on identifying the causal relationship between market structure and the prices paid by consumers. We use a unique dataset on third-party owned contract terms for the residential solar PV market in the San Diego Gas and Electricity service territory to better understand this relationship. Surprisingly, we find that firms charged higher prices in more competitive markets in our sample. The finding is robust across multiple definitions of firm concentration. There are at least two potential explanations for our findings. First, firms could be conducting entry deterrence strategies. It is possible that firms are acting in a non-competitive way and setting prices lower than they would be otherwise. Setting low prices that are below potential competitors' marginal costs could deter entrants and ensure a larger market share. Second, there could be a group of dominant firms (with a competitive fringe), and the dominant firms may occasionally engage in price wars. If this is true, prices should be lower in more concentrated markets during the price wars (Salinger, 1990). As the rooftop PV market continues to grow, market structure will remain a relevant policy issue in consideration of the potential for rooftop solar to contribute to de-carbonization efforts or other policy objectives. This paper adds to a growing emphasis on understanding supply-side factors in scaling up solar markets in the residential sector. Generally, solar markets have become more competitive over the same time period that solar technology costs decreased. While solar system hard costs have come down, our research suggests that total costs are more nuanced in early solar system TPO markets. Policymakers should consider these findings when designing markets, and have the data needed to make informed decisions.
California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.
In competitive wholesale electricity markets, significant effort is devoted to designing markets that set efficient prices for maintaining supply-demand balance. One factor that can impact prices is administratively-set scarcity pricing, which sets prices to a preset level when the market is not able to meet operating reserve or energy requirements. When energy and operating reserves are co-optimized, assumptions surrounding operating reserve requirements and scarcity pricing can impact system-wide price outcomes for both operating reserves and energy. This study uses production cost modeling of an ERCOT-like system to evaluate the impact of operating reserve eligibility, scarcity pricing, and quantity rules on electricity prices, and therefore also on generator revenues. Results reveal economic and operational benefits with allowing open participation in reserve markets, as this enables greater access to the full set of capable resources at lowest cost. Furthermore, both energy and reserve prices are strongly impacted by reserve scarcity pricing events, which reveals that reserve scarcity pricing assumptions can impact price outcomes even for units not providing reserves. This study highlights the importance of operating reserve scarcity pricing rules because of the strong coupling between energy and reserve prices and because these rules serve as proxies for true price responsive demand.
This study introduces a method to systematically project retailed gasoline prices in China and provides projection results. Based on oil taxation and pricing mechanisms in China, this study establishes the statistical relationship between the retailed gasoline prices in China and international crude oil prices using error correction models (ECM), time-series models. Detailed comparison among asymmetric ECM, threshold ECM, and threshold asymmetric ECM are provided. By adopting the ECMs, the study projects the retailed gasoline prices from 2019 to 2050 based on the international crude oil prices from the Annual Energy Outlook provided by the U.S. Energy Information Administration. The results show that the asymmetric responsiveness and the threshold effect exist under current Chinese oil pricing policy with a lag of one month or even shorter in gasoline price adjustment. Moreover, the analysis informs one that, in the short-run, the retailed gasoline prices in China are more sensitive to international crude oil price increases; while in the long-run, the retailed gasoline prices are more sensitive to international crude oil price decreases. Compared with the national average gasoline price in China of 6.04 CNY/Liter in 2017, the national average gasoline price in China is projected to be about 7.64 CNY/Liter (2017 dollar) by 2050.
Prices of solar PV have dropped dramatically, by half in just the past 6 years. But looking simply at prices paid today, there is considerable heterogeneity. For systems installed in 2014, the 10-90 percentile range for the observed $/W spans nearly a factor of 2. This apparent price dispersion raises policy relevant questions, such as: why are consumers paying more than they need to? And would better-informed consumers increase the social benefits of solar PV? This paper analyzes price dispersion in U.S. residential PV installations between 2008 and 2014. Focusing on the most commonly used metric in previous studies of price dispersion, we use the quarterly coefficient of variation (CV) as our measure of price dispersion. We find higher levels of price dispersion in our data (0.22) than the average of 55 previous studies we reviewed (0.16). We also find that price dispersion has been persistent; it has remained above 0.15 since 2000 with no trend over that period. If anything, price dispersion has been increasing recently during the period for which we have complete data, 2008-14. Econometric analysis of the factors affecting price dispersion supports theories from the economic literature focusing on access to information and the costs and benefits of consumer search. Factors that increase the consumer payoffs of investing time in searching for information—system size and the value of solar— are associated with lower levels of price dispersion. Factors that reduce the costs of search—neighbors who have recently installed solar and having third-party quotes available—are also associated with less price dispersion. These results provide support for the importance of public efforts to enhance access to price information, e.g. by supporting private sector price quote providers. The results also point to the particular need for information in nascent markets for PV in which access to the experience of neighbors is not available.
Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.
Here, errors in forecasting load and renewable-based generation in restructured power systems mean that independent system operators (ISOs) must procure sufficient operating reserves to keep the real-time operation of the system reliable and secure. But when procured reserves turn out to be insufficient in real-time due to the lack of resource capacity or ramp capability, operators often set higher prices for reserves and energy to encourage more supply, and to motivate consumers to decrease usage or shift it to other times. This procedure, which is called scarcity or shortage pricing, is a core feature of U.S. electricity markets. It is receiving increased attention from market designers and stakeholders because scarcity will become more important for spot price formation in the future with the increased penetration of zero-marginal cost renewables, and the shrinking role of fuel costs in setting prices. Scarcity pricing is implemented in various ways by different ISOs. These differences have practical implications for the level of prices and incentives for investment, operations, and demand modification. In this paper, general approaches and specific calculation procedures for reserve and energy scarcity pricing practices and calculations across the seven ISO-based U.S. power markets are reviewed and compared. A consistent terminology is used to facilitate the comparison. Current scarcity pricing practices are grouped into three approaches: (1) imposing an adder after the spot market is run; (2) including stepwise demand curves within market clearing procedures for non-contingency reserve products (e.g., the novel flexiramp product), which tends to yield longer right tails for energy scarcity premium curves; and (3) having stepwise demand curves for traditional contingency reserve products only, which results in shorter right tails in energy scarcity curves. A generic numerical example is presented to highlight the large practical differences among the reserve scarcity pricing approaches and specific implementations. To further investigate factors that contribute the most to demand curves differences among ISOs, a sensitivity analysis is performed. This analysis shows that the largest source of differences among the curves is the scarcity prices assumed in the case of severe scarcity, while the number of steps used and whether flexiramp is considered also yields important differences in scarcity prices. As renewable penetration increases, it will become increasingly crucial to employ administrative demand curves so that spot prices more effectively motivate supply and demand adjustments exactly when and where they are needed. This study shows that the different assumptions yield very different scarcity premiums for reserves and energy, and are likely to provide divergent incentives for resources to respond to shortages. It is concluded that to promote market efficiency, a reserve shortage demand curve should have at least three features: inclusion of the marginal value of reserve products at each shortage level, consideration of the magnitude and probability of supply contingencies, and avoidance of abrupt price discontinuities that can cause excessively volatile market outcomes.
The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2016, the utility-scale sector installed more than 2.5 times as much new capacity as did the residential and commercial sectors combined, and is expected to maintain its dominant position for at least another five years. This report—the fifth edition in an ongoing annual series—provides data-driven analysis of the utility-scale solar project fleet in the United States. We analyze not just installed project prices, but also operating costs, capacity factors, and power purchase agreement ("PPA") prices from a large sample of utility-scale PV and CSP projects throughout the United States. Highlights from this year's edition include the following: Installation Trends: The use of solar tracking devices dominated 2016 installations, at nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the Southwest, the median long-term average insolation level at newly built project sites declined again in 2016. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio has stabilized in 2016 at 1.3 for both tracking and fixed-tilt projects. Installed Prices: Median installed PV project prices within a sizable sample have fallen by two-thirds since the 2007-2009 period, to $2.2/WAC (or $1.7/WDC) for projects completed in 2016. The lowest 20th percentile of projects within our 2016 sample were priced at or below $2.0/WAC, with the lowest-priced projects around $1.5/WAC. Overall price dispersion across the entire sample and across geographic regions decreased significantly in 2016. Operation and Maintenance (“O&M”) Costs: What limited empirical O&M cost data are publicly available suggest that PV O&M costs were in the neighborhood of $18/kWAC-year, or $8/MWh, in 2016. These numbers include only those costs incurred to directly operate and maintain the generating plant. Capacity Factors: The cumulative net AC capacity factors of individual PV projects range widely, from 15.4% to 35.5%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed-tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010- to 2013-vintage projects, where they’ve remained fairly steady among both 2014- and 2015-vintage projects as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. Meanwhile, several of the newer CSP projects in the United States are struggling to match long-term performance expectations. PPA Prices: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time. Most recent PPAs in our sample are priced at or below $50/MWh levelized, with a few priced as aggressively as ~$30/MWh. Though impressive in pace and scale, these falling PPA prices have been offset to some degree by declining wholesale market value within high penetration markets like California, where in 2016 a MWh of solar generation was worth just 83% of a MWh of flat, round-the-clock generation. At the end of 2016, there were at least 121.4 GW of utility-scale solar power capacity within the interconnection queues across the nation. The growth within these queues is widely distributed across all regions of the country: California and the Southeast each account for 23% of the 83.3 GW of solar that first entered the queues in 2016, followed by the Northeast (17%), the Southwest (16%), the Central region (12%), Texas (6%) and the Northwest (3%). The widening geographic distribution of solar projects is a clear sign that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.