#Electric-Fuels Supply Model

#Overview

The US-REGEN electric-fuels supply model combines a detailed dispatch and capacity expansion model of the US electric system with a parallel representation of non-electric fuel supply and conversion. In the electric sector, it includes a partially disaggregated representation of both existing generation unit capacity and the hourly profiles of load, wind speed, and solar flux. These details allow the model to explicitly evaluate dispatch decisions (when and for how long installed capacity operates) as distinct from capacity decisions (new investment, retrofit, or retirement).

Several unique features of the electric sector make the treatment of such details essential to accurately model these decisions and the impact of new policies:

• The "shape" or hourly profile of end-use demand and variable resource availability is crucial for appropriately characterizing the operational patterns and profitability or value of different types of capacity.

• These patterns and hence the value of generating assets are also dependent on the mix of installed capacity in a region (and in neighboring regions).

• Capital investments in generating capacity tend to be long-lived, creating a strong link between dispatch and investment decisions across time periods.

The fuels model component of US-REGEN describes the supply and conversion of primary energy into delivered fuels to meet end-use service demands. It balances endogenous fuel demands for electric generation with exogenous demands from the end-use sectors (i.e. buildings, transportation, and non-energy-related industry) and optimizes the supply of those fuels subject to constraints on upstream fossil and bioenergy resources and, if applicable, policy targets for economy-wide emissions. It includes several technologies for conversion, blending, and synthesis of fuels, including petroleum refining, biomass to liquids or gas, hydrogen and ammonia production, fuel synthesis, and blending of renewable natural gas and hydrogen into existing pipeline infrastructure. It also models the transport and storage of captured carbon and includes direct air capture technologies as well as natural climate solutions as potential carbon sinks.

Additional details and assumptions about the representation of technologies for electricity generation, storage, and fuel supply and conversion can be found in the respective sections under Resource and Technology Assumptions.

The electric-fuels model is formulated as an optimization over several time periods minimizing the costs incurred by electricity and fuel producers subject to the constraint of meeting electricity and fuel demands. The decision variables include both levels of capacity by region and technology type and the dispatch of these "blocks" of capacity across a range of "segments" that represent the intra-annual profile of load and variable resource availability. Each segment is a block of time that is "representative" of anywhere from one hour to over two hundred hours out of the 8760 hours in a given year. The hours represented by one segment are usually not contiguous. In addition, power may flow between adjacent regions during each segment subject to available bilateral transfer capacity.

The costs incurred by producers include variable costs that scale with dispatch (mainly fuel and variable operating and maintenance (VOM) costs), fixed operating and maintenance (FOM) costs that scale with installed capacity, and investment costs associated with new capacity additions (of generation, storage, inter-region transmission, and fuel production). The optimization considers the time paths of each of these variables and their associated costs simultaneously, subject to a discount rate reflecting the opportunity cost of capital, even though the costs themselves are incurred on very different schedules.^[1]

Electricity and fuel demands in any given iteration of the electric-fuels model are treated as fixed exogenous quantities. Typically, the demand profile is specified as an output of the end-use model, though the electric-fuels supply model can be run with any exogenous demand input (e.g. historical or observed data). The electric-fuels model outputs electricity and non-electric fuel prices that serve as an input to endogenous demand decisions in the end-use model. Demand elasticity is simulated by allowing the price output of the electric-fuels supply model and the quantity output of the end-use model to converge over multiple iterations, which provides a richer representation of the responsiveness of demand to prices than a simple elasticity. Moreover, this interaction enables hourly load shapes to be updated for each model year to reflect changes in the patterns of energy demand over time.

In the electric sector, the optimization requires that demand is met in every segment, and that available local firm capacity satisfies a planning reserve margin constraint, which simulates the clearance of both an energy market and a capacity market. That is, by requiring that sufficient electricity be produced in each segment to meet the prescribed load, this constraint also stipulates indirectly that sufficient investment in capacity occur such that electricity for the prescribed load in the "peak segment" will be available for dispatch, plus a reserve margin. As will be discussed in more detail below, this stipulation applies even with large deployment of variable renewable capacity which is known to have low coincidence with peak demand, such as wind.

Dispatch in the dynamic electric model is by increasing order of marginal generation cost; i.e. units with the lowest variable costs per MWh are dispatched first. This omits unit commitment constraints, due to both computation constraints on including integer constraints in a linear optimization, and to unit aggregation rendering constraints such as ramp rates less meaningful. A unit commitment variant of the US-REGEN electric model can be used to explore the impact of these constraints in more detail for a single year, using the capacity mix from a dynamic electric model scenario with identical assumptions. This variant is described in the Unit-Commitment Model section, and in more detail in EPRI publication 3002004748open in new window (EPRI, 2015).

#Solution Characteristics

US-REGEN's electric-fuels supply model solution characterizes a profile of the energy system over time. By default, outputs include the model years 2015 to 2050, and the key solution variables of the model are

• capacity levels of electricity generation, storage, inter-region transmission, and fuel transformation, which reflect new and retrofit investments and retirements;
• generation (i.e. dispatch) by technology and segment and fuel production and conversion;
• inter-regional power flows by segment; and
• the price of electricity and non-electric fuels.

From those, other outputs can be derived, including fuel consumption and emissions of CO₂ and other pollutants. The solution variables satisfy the optimality conditions; that is, they represent the values that minimize net present value of costs subject to constraints.

#Optimality Conditions

The optimization formulation ensures that the key outcomes in any optimal solution will satisfy certain conditions. Equivalently, these optimality conditions describe a long-run competitive market equilibrium.

Dispatch Order: Within each segment, units will be dispatched in increasing order of marginal generation cost. Otherwise, producers' cost could be reduced (total surplus increased) by replacing the highest cost dispatched unit (i.e. marginal unit) with a less costly unit that had been bypassed in the dispatch order.

Complementary Slackness of Trade: Within each segment, if the marginal unit in one region has a higher dispatch cost than the marginal unit in an adjacent region, transmission from the adjacent region must be at its upper bound (i.e. transmission capacity must be fully utilized).^[2] Otherwise, costs could be reduced by replacing the marginal unit with electricity imported from the adjacent region. Similarly, whenever the marginal generation cost is equal in adjacent regions (or more precisely, whenever the difference is less than the loss adjustment), transmission between regions during that segment must be strictly less than the upper bound.

Profitability of Investment: For any investment in capacity, the marginal unit added will have the present value of its costs (initial capital and operating costs over the lifetime) less than or equal to the present value of revenues (quantity generated in each segment multiplied by the segment price). This condition applies to both new additions and retrofit investments, and an analogous condition applies to new additions of transmission capacity (where the definition of revenues is related to the marginal value of transmission rather than the electricity price itself).^[3] Otherwise, total surplus could be increased by dropping the investment in the marginal unit and its operation and foregoing the consumer benefit associated with the energy it produced, which at the margin is equal to its revenues. Further, if the present value of revenues is strictly greater than the present value of costs for the marginal investment in some technology, then there must be a constraint on that technology and investment must be equal to the upper bound. Otherwise, surplus could be increased by substituting investment of the positive-profit technology for some other investment with marginal revenue equal to marginal cost (i.e. zero net-profit).

#Price of Electricity

The electric-fuels supply model reports the price of electricity for each region and time step at both the wholesale and the retail level. The wholesale price is related to the generation component of the retail price, which reflects energy and capacity costs of providing wholesale electricity subject to policy constraints imposed on the generation mix. This price is an output of the supply model's cost optimization. The remainder of the retail price reflects average transmission and distribution (T&D) costs, as well as general administration costs, and is estimated ex post of the model solve, based on observed rates across sectors and regions and assumed future trends. Note that the wholesale or generation price also includes the market for inter-regional transmission capacity additions, which are endogenous to the electric model's optimization. However, a significant share of transmission costs is associated with intra-region capacity additions and maintenance, which are not explicitly captured within the optimization. These costs are included in the exogenously calculated T&D component of the retail price, which varies by end-use sector. The retail price is constructed as the sum of the generation and T&D components and is used as an input to the end-use model. Figure 1 summarizes how electricity prices are calculated in US-REGEN.

#Wholesale Price

The wholesale price is a marginal price that corresponds to the dual variable associated with the market clearance condition (i.e. supply = demand) that is enforced in every segment, region, and time period. At optimality, a dual variable (or shadow price) is equal to the amount by which the objective function could be increased (resp. decreased) if the associated constraint were relaxed (resp. tightened) by one unit. That is, the model's reported price in each segment corresponds to the marginal cost of supplying an additional MWh at that time in that region. Note that the objective function also includes costs for new inter-regional transmission as well as imputed rents accruing to existing inter-regional transmission, hence these components are included in the wholesale price. For many infra-marginal (i.e. non-peak) segments, the shadow price corresponds to the "dispatch cost" (fuel and variable O&M cost) of the marginal generating unit in the region (or in neighboring region plus the imputed transmission cost). However, in certain segments, supplying the last MWh actually requires the addition (or retention) of a unit of capacity (either in the form of new investment or deferred retirement).^[4] For these segments, the shadow price will include all or a portion of the cost of that marginal capacity addition/retention. Additionally, the supplemental constraint requiring sufficient local firm capacity plus a reserve margin will bind in at least one such segment. The shadow price of this constraint is added to the shadow price on the market clearance condition to form the full wholesale price in peak segments. The result is that a small number of segments across regions and time periods will have very high prices, several orders of magnitude higher than the dispatch cost, and thus the annual average price across all segments reflects the full long-run marginal cost (including both fixed and variable components) of wholesale electricity supply (including inter-regional transmission).

#Retail Price

The generation component of the retail price is calculated directly as the annual average wholesale price (which includes energy and capacity), weighted across segments by delivered load, plus additional compliance costs for renewable or clean energy portfolio standards if applicable (varies by region and scenario). This is analogous to the average price a load-serving entity (LSE) would pay to provide power to its retail customers. In terms of calibration, it may not be the case that the average wholesale price calculated by the model for the base year coincides with the observed generation component of the retail rate (as reported by EIA). Such a gap may exist because of legacy investments or out-of-the-money contracts. The model includes an adjustment factor for observed base year discrepancies but assume that this factor is reduced to zero over time.

The T&D component of the retail price is estimated based on observed T&D costs in the base year, adjusted in future years by projected structural changes from the end-use model. Base year costs and prices are derived from EIA Form 861 and FERC Form 1 data, which vary by end-use sector and state. For model projection years, the average T&D cost per delivered MWh in each sector and region is scaled from the base year level based on a combination of metrics that varies for each model region, including sector-specific sales, peak net load, and distributed resource adoption. While a more detailed assessment would consider a range of other factors relevant for T&D expenditures, this approximation is intended to roughly capture the additional costs of T&D infrastructure upgrades that may accompany system changes. We continue to refine this calculation to reflect potential impacts of changing patterns of load and generation resources on T&D system requirements and costs.

#Auxiliary Markets

US-REGEN includes auxiliary markets for capacity reserves, and for spinning reserves. US-REGEN does not currently represent other auxiliary markets such as black start, regulation, or other reliability services.

The optimization algorithm is designed to ensure that sufficient reserves and capacity are built to cover any event occurring within the model's time horizon. However, many investment decisions are made to hedge against the possibility of a stochastic shock to the system. Such shocks are covered, in practice, by the imposition of a reserve margin. By default, US-REGEN adds a reserve margin to all regions equal to 12% above peak residual load. The peak residual load is calculated as the greatest hourly demand net of the intermittent renewable generation within that hour. The residual peak hour is often distinct from the absolute peak hour, which in some cases coincides with high renewable generation. Dispatchable technologies located within the region contribute their full nameplate capacity to the reserve margin, while variable resources, such as wind and solar PV, contribute their modeled output in the hour with the peak residual load. Rooftop PV does not contribute to the reserve requirement. Imports or import capacity do not contribute to the reserve margin by default. In practice, the model increases capacity of flexible generation such as natural gas relative to scenarios without a reserve margin.

US-REGEN also has a representation of spinning reserve markets. Because this is computationally taxing, it is not deployed by default; only where spinning reserve revenues may be significant for new generation capacity investment decisions.

#Design of Aggregated Segments

In order to solve the model as an inter-temporal optimization over several time steps, it is necessary to reduce the number of intra-annual segments over which dispatch is resolved. US-REGEN uses a novel approach for selecting and appropriately weighting a subset of representative hours. In choosing a subset of weighted hours from the 8,760 hours of the year, the goal is to maintain the important characteristics of the disaggregated data so that model outcomes in the reduced form version are as close as possible to the hypothetical outcome using the full data.^[5] These characteristics include:

• The area under the load duration curve (i.e. total annual load, for each region)
• The shape of the load duration curve (for each region)
• The capacity factors of new wind and solar capacity (for each region and class)
• The shape of wind and solar output relative to load (for each region), in particular the extremes of the joint distribution (e.g. hours when load is high but wind/solar output are low)

If the problem were simply to capture the load profile, only a handful of segments, perhaps a peak, shoulder, and base load, would be necessary. This level of aggregation is often employed by models to approximate a load shape. However, wind and solar are more variable, ranging from near 0% to near 100%, and considering all three together extends the variability to multiple dimensions. Furthermore, the set of representative hours chosen should apply across all regions, so that synchronicity of inter-regional transmission is preserved. This necessarily adds additional hours to the set; while the distributions in adjacent regions may have many similarities, conditions in Florida will bear little resemblance to those at the same hour in the Pacific Northwest.

Given these complexities, trade-offs among the various criteria are inevitable, and the value of a systematic approach is strongly indicated. We have developed a novel heuristic approach that relies on a simpler integer formulation to identify a set of hours that at a minimum covers the extremes of the joint distribution in each region, combined with a clustering algorithm to capture the interior of the joint distribution of load-wind-solar. Including the extremes ensures that, on the one hand, capacity values are not over-estimated (e.g. the moment of highest load and lowest wind/solar output is represented), and on the other that abundance of wind/solar output relative to load (potentially forcing transmission, storage, or curtailment) is included. After the hours are chosen, the selected hours are weighted so as to minimize error in total load and average annual capacity factor across all regions and classes. Finally, a representation of linkages between time periods is employed to compress chronology and maintain key economic characteristics of energy storage and other resources.

We provide an abbreviated description of this algorithm below and refer the reader to Blanford et al. (2018)open in new window for more detail on the temporal aggregation approach; Merrick, Bistline, and Blanford (2024) on the energy storage and chronology representation; and Bistline (2021) on numerical comparisons of power system decarbonization decisions under alternate temporal aggregation approaches.

#Choosing Extreme Hours

In each region, we consider three synchronous hourly time series corresponding to load (as a percentage of peak), and wind and solar output (as a percentage of the annual maximum, weighted average across available classes). These hourly values can be plotted in three-dimensional space. These are the red markers in Figure 2, which gives the example of Texas. The extremes of interest are the eight vertices of the space spanned by the hourly data, as well the vertices of the one- and two-dimensional projections of this space, which may or may not coincide. These vertices are the hours identified as those closest (in the conventional Cartesian sense) to the actual vertices of the unit cube (or line or plane in the projection spaces). For example, the hour closest to the unit cube vertex (1,0,0) (with co-ordinates referring to load, wind, and solar respectively) will not literally have values (1,0,0) unless the hour with peak load also happened to have zero output of both wind and solar. Instead, we identify the hour whose values are closest to this point, which might be, for example (0.9, 0.09, 0) at 7:00 p.m. (local time). In this case, the vertex hour would capture the moment when load is still high after a hot summer day, wind has not picked up, and the sun has set. It is crucial to the analysis for the model with aggregated hours to know that such moments exist.

The essential principle behind the extreme hour selection algorithm is to identify the minimum number of hours such that at least one hour is selected with sufficient proximity to each vertex in each region. If we define "sufficient proximity" as "exactly equal," we would use the set of vertex hours themselves. However, some of the vertex hours turn out to be vertices in more than one region, and other hours close to the vertex could be used to represent extreme conditions in multiple regions. Thus, if we allow a selected hour to qualify as a vertex if it is within some small distance from the actual vertex, the number of representative extreme hours can be reduced. The bubbles in Figure 2 are centered on the identified vertices for Texas and extend five percentage points in each dimension. When we define "sufficient proximity" to each vertex according to these tolerances, the minimum number of "extreme-spanning" hours turns out to be roughly 100.^[6]

#Choosing Cluster Hours

For most of the year, conditions are not at any of these extremes, so using only these extreme hours tends to over-represent the tails of the load, wind, and solar distributions. Thus, in addition to the selection of extreme hours (which drive capacity requirements), US-REGEN also employs a clustering algorithm to select additional segments to ensure that the interior region of the joint distribution of load-wind-solar is adequately sampled. These additional interior hours describe shoulder and base operating conditions and better capture region-specific load duration curves, capacity factor distributions, and correlations between load and intermittent resources. Although adding these cluster hours results in longer runtimes, the model demonstrates a better fit for regional outputs like load duration curves. The model uses k-means clustering to partition the observations for regional load and capacity factors for intermittent technologies, including all resource classes, into a specified number of mutually exclusive sets ("clusters") by minimizing the within-cluster sums of point-to-centroid distances. The selected hours are the segments closest to the cluster centers ("centroids"). This partitioning method provides representative clusters that contain hours with characteristics that are as close to each other (and as far away as observations in other clusters) as possible. The blue markers on Figure 2 represent the hours chosen by the algorithm (both extreme and cluster hours) plotted for Texas.

In the 2019 version of US-REGEN, the extreme hours are chosen to achieve the specified tolerances, then sufficient clustering hours are added to total 120 representative hours for the default 16 region version of the model. For comparison, the 2016 version of EIA's National Energy Modeling System (NEMS) uses 9 hours in its electric sector expansion model. Blanford et al. (2018)open in new window provides an extended discussion and a comparison to other segment selection methods. When US-REGEN uses different aggregations of states to regions, the number of hours chosen may vary as the number of extreme hours is chosen endogenously to satisfy a given tolerance limit. Additionally, the model chooses a different set of hours for each time period when run in conjunction with the end-use model, as the load shape varies over time as the end-use mix changes.

#Weighting Chosen Hours

Once the representative hours have been chosen, they must be weighted such that the sum of weights equals 8,760. That is, for each moment described by a representative hour, in what fraction of the year do those conditions prevail? Since the conditions in a given representative hour likely vary significantly across regions and since only one set of weights can be applied, it is an over-constrained problem to select mean-preserving weights (i.e. weights such that total load and average annual capacity factor for the aggregated distribution are equal to those in the hourly distribution). Thus, the objective of the weighting procedure is to minimize the sum of squared normalized errors between the aggregated averages and the hourly averages across regions for load and each wind and solar class. To avoid numerical problems associated with very small weights, we enforce a lower bound of one (i.e. each representative hour gets a weight of at least one hour). This formulation is easily solved by non-linear optimization in GAMS with errors of 5% or less.

In summary, the first step of the aggregation heuristic is designed to ensure that the shapes of load, wind, and solar relative to each other are adequately represented, and the second ensures that magnitude of load and the wind/solar resource is not significantly altered. A sample of the results is shown for load, wind, and solar photovoltaic output in Texas in Figure 3, Figure 4, and Figure 5. In each figure, the duration curve (i.e. plot of time series values sorted in descending order) is shown in black for the hourly data (smooth curve) and in red for the aggregated representative hour segments (piecewise linear curve). In each case, while there is some deviation, the basic characteristics of the shape and the area under the curve (for which error was minimized) are preserved by the aggregation. Moreover, it can be shown that the distributions for more complex attributes such as load relative to wind are also captured well by the representative hours approach. These graphs are representative of the methodology.

#Representing Chronology

After representative hours and their weights are chosen, the model employs a novel expected value approach to represent chronological information in the optimization. Building in on the representative hour approach to aggregate periods with similar characteristics, the expected value approach calculates a synthetic mapping between states to represent reduced-form chronology in the form of a state transition matrix (i.e., probabilities of on representative hour being temporally adjacent to another), duration in each state, and weights associated with each period. Figure 6 offers a stylized illustration of how 8,760 hourly data can be grouped into representative or aggregate states, with transition probabilities derived from the hourly time-series data. An economic interpretation of transition matrix values is that each state has an “expected value” of energy storage available (based on the probability distribution across preceding states) when making dispatch decisions in the context of the intertemporal optimization. Additional detail on the expected value method, comparisons with other aggregation approaches in the literature, and key principles to evaluate aggregation methods can be found in Merrick, Bistline, and Blanford (2024).

#Dynamic vs Static Modes

#Dynamic Mode and End-Effects

The main formulation of the US-REGEN electric-fuels model is an inter-temporal or dynamic optimization over several time periods spanning a multi-decadal time horizon. In this setting, intra-annual time segments are aggregated into representative hours, as outlined in Design of Aggregated Segments. Solving the model in dynamic mode allows an explicit representation of investment over time. However, this also introduces the potential for end-effects. To mitigate the distortion of a finite time-horizon on the trade-off between up-front and ongoing operational costs, the model adjusts (i.e. discounts) capital costs of new capacity additions to reflect the remaining time horizon in the optimization. The implication is that capacity investment transitions to capacity rental as time steps approach the end of the time horizon.

#Static Mode and Hourly Resolution

To enable full 8,760 hourly resolution, US-REGEN can also be run in static mode. In this mode, the inter-temporal optimization is replaced with optimization over a single year with a detailed hourly representation and rental of capacity (rather than investment and retirement over time). Instead of using aggregated representative hours, running the model in static mode optimizes dispatch for each of the full 8,760 hours. In other respects, the operation of the model remains similar. Generation units remain aggregated into capacity blocks that are dispatched together, and the capacity mix is determined endogenously based on a rental formulation for new capacity. Fuel prices and energy demand are fixed, and the model linearly optimizes to balance the load.

The static model is a useful bridge between the sub-hourly timescales that detail the daily operations of the power system and the long-run projections of the sort that US-REGEN's coupled electricity and end-use models generate. The complete hourly generation profile created by the static model helps evaluate the efficacy of the long-run dynamic model by noting the differences between complete hourly generation and demand profiles with the aggregated representative hours methodology of the dynamic model. Because of the rental formulation, there are no retirement decisions. The static model can run for any future year using capacity values from a dynamic model run as inputs. This allows the static model to test the hour-to-hour response of a projected system state output by the dynamic model, giving a more realistic assessment of the hourly load shape and generation choices for a given future state. In turn, the dynamic model informs the static model's profile of generation assets and outputs a fixed demand, assessing how the intra-yearly challenges affect the shifting equilibrium of generation and end-use demand.

One major advantage of the static model is the ability to explore storage technologies with higher fidelity. The hourly model naturally maintains chronology, which allows the model to accurately represent the generation and storage of electricity across adjacent hourly load segments. The dynamic model can only approximate chronology across its more aggregated intra-annual segments.

The static version of US-REGEN can also be configured to include the supply, transmission, and storage of natural gas. Supply is modeled at the basin level and differentiated by reservoir type. Demand for gas in the power sector is modeled at an hourly level linked to dispatch in the electric model. Demand for gas in the end-use sectors is based on a scenario from the End-Use Model. State-level pipeline and storage facility data is aggregated to the regional level and constrains the movement of gas between supply and demand with daily resolution. The inclusion of this additional structure of the gas market leads to regional and seasonal variation in the natural gas price.

#Unit-Commitment Model

The US-REGEN electric model incorporates a relatively simple model of dispatch that excludes several operational costs and constraints such as ramping and minimum load levels due to the high computational cost of including them in an inter-temporal perfect foresight model. In recognition of these limitations, a standalone unit commitment (UC) version of the US-REGEN electric model has been developed to better understand the short-run costs and engineering challenges of operating the different capacity mixes output from the US-REGEN dynamic model. This model runs separately from the full US-REGEN model and does not iterate with the end-use model.

This UC version of the model solves for most units in a region for all hours in a single year, given a fixed capacity mix. It determines commitment and dispatch states for individual units, with the objective of minimizing operating costs, while accounting for technical system constraints and chronological operations. The goal of this approach is to integrate the capacity planning perspective (i.e., examining long-run investment decisions) with a UC and economic dispatch one (i.e., understanding the short-run costs and engineering challenges of operating different capacity mixes). This framework provides a test bed for assessing flexibility needs in the context of endogenous investments and regional heterogeneity.

The UC model determines the startup, shutdown, and operating schedule (including unit-specific output levels) for every unit during each hour of an annual time horizon. Combining economic dispatch with UC constraints results in a mixed-integer optimization problem with the objective of minimizing total system operating costs. The four primary cost elements in this objective function are variable O&M costs, fuel costs (with output-dependent heat rates), startup costs, and shutdown costs. The model accounts for multiple constraints, including a load balance condition for each region, maximum and minimum output levels for each unit, transmission constraints, optional operating reserve requirements, startup and shutdown logic for generators, minimum up and down times, and maximum ramp rates. Fuel use characteristics and emissions are a function of unit-specific output levels.

The UC model retains individual unit detail for a majority of the fleet in the region of interest. Decision variables related to operation are indexed over the set of all units in the US-REGEN region greater than 40 MW. Since intra-regional transmission is not modeled, variable generation resources across a model region are aggregated by their capacity types and dispatched as blocks. Wind and solar technologies can be curtailed during periods of over-generation.

Given the significance of transmission and trade in influencing electricity market outcomes, a novel feature of the US-REGEN UC model is its endogenous treatment of imports and exports. Trade may be an important flexibility resource to facilitate the exchange of electricity across regions during periods of surpluses or deficits, especially as intermittent resources comprise a greater fraction of generation and regional electricity markets become more tightly integrated. However, most UC models make simplifying assumptions about imports and exports, often assuming that future trade flows will mimic historical patterns. US-REGEN's integrated perspective models many regions at once to capture the increasingly interconnected landscape for system balancing. Cross-border flows are restricted by net transfer capacities, which are influenced by transmission investments in the dynamic model. To make the UC model of the entire US computationally tractable, US-REGEN has individual unit detail in the region of interest but aggregates units into capacity blocks for all other US regions. This formulation endogenously determines price-responsive trade positions.

Full documentation for the unit commitment version of US-REGEN is maintained in EPRI publication 3002004748open in new window (EPRI, 2015).

#Policies

US-REGEN has the capability to model diverse energy, electricity, and emissions policies. By default, the electric sector model includes a suite of current policies including emissions constraints, regional greenhouse gas targets, taxes and subsidies, and state renewable portfolio standards (RPS) and clean energy standards (CES). The model has also been configured to represent a variety of other proposed policies, such as national carbon constraints, CO₂ taxes, clean energy standards, and emission intensity standards. These can be enabled or disabled, reconfigured, or replaced entirely.

#CO₂ Emissions Constraints and Carbon Pricing

The model can simulate state, regional, and national caps on CO₂ emissions as well as price-based carbon policies. These policies can cover both electric and non-electric sectors in the electric-fuels supply model and through iteration with the end-use model. The model includes economy-wide cap-and-trade systems, including California’s cap-and-trade program (with a 2045 net-zero target) and New York State’s “cap-and-invest” program. The model also includes power sector only CO₂ pricing, including the Regional Greenhouse Gas Initiative (RGGI) cap on CO₂ emissions from Northeast states and other state-based electric CO₂ caps.

The complete list of current policies changes frequently with announcements. The following list describes the implementation of recent policies, but may not reflect the full list implemented in the reference current policy scenario.

#Tax Credits and Subsidy Policies

The US-REGEN reference case includes the Inflation Reduction Act (IRA) climate and energy provisions for the electric sector, end-use sectors, and low-emitting energy supply.

Additional details on IRA incentives and US-REGEN modeling can be found in Bistline, Mehrotra, and Wolfram (2023)open in new window, and comparisons of IRA’s impacts between US-REGEN and other models is discussed in Bistline, et al. (2023)open in new window and Bistline, et al. (2024a). Key IRA provisions include:

• 45Y Clean Electricity Production Credit (IRA §13701): Projects receive up to $30/MWh for 10 years, which is technology neutral beginning in 2025 for all technologies with “emissions intensity not greater than zero.” Energy community bonuses are included as exogenous shares of qualifying technologies. Domestic content bonuses are not included in the reference case, as increases in project costs are assumed to offset the incremental value of the bonus (EPRI, 2023aopen in new window).

• 48E Clean Electricity Investment Credit (IRA §13702): Projects receive a 30% credit with labor bonus and a 10 percentage point bonus for energy communities. US-REGEN has technology-specific eligibility for production and investment tax credits and assumes that technologies select credits with higher net present values.

• 45Q CO₂ Capture and Storage Credit (IRA §13104): Projects receive up to $85/t-CO₂ captured with the labor bonus. There is a 12-year eligibility for projects, which must commence construction by 2032.

• 45V Clean Hydrogen Production Credit (IRA §13204): Subsidy schedule depends on the lifecycle emissions intensity of production, up to $3/kg with 10-year eligibility (must begin construction by 2032). IRA credits for clean hydrogen include Treasury guidance with “three pillars” criteria (Blanford and Bistline, 2023open in new window).

IRA’s 45Y and 45E tax credits begin to expire in 2032 or after power sector CO₂ reaches 25% of 2022 levels, whichever is later.

#State Renewable Portfolio Standards and Clean Electricity Standards

The model includes a representation of state-level renewable portfolio standard (RPS) and clean electricity standard (CES) requirements as of mid-2024. Renewable portfolio standards are included for AZ, CA, CO, CT, DC, DE, HI, IA, IL, MA, MD, ME, MI, MN, MO, MT, NC, NH, NJ, NM, NV, NY, OH, OR, PA, RI, TX, VA, VT, WA, and WI, including solar carve-outs in AZ, CO, DC, DE, IL, MA, MD, MN, MO, NC, NJ, NM, NV, PA, and TX (based on the DSIRE Databaseopen in new window).

The renewable and clean electricity targets by state over time are aggregated to model region targets by taking the load-weighted average based on retail sales in the base year. For state-level RPS requirements, there is a minimum for non-hydro renewable generation as a percentage of retail sales based on the targets adopted by individual states within the region and the relative size of that state's load. In states such as New York where the target includes hydro, expected generation from hydro is removed and only the portion of the target likely to be satisfied by non-hydro renewables is included. The resulting RPS minimums for 2030 in each region are shown in Figure 7.

In addition to the targets themselves, the model includes other implementation details relevant to compliance with state portfolio standards. These include technology-specific crediting; restrictions on the trade of renewable energy certificates (RECs); whether RECs must be bundled (i.e. purchased along with physically delivered power from a renewable generator) or can be unbundled (i.e. purchased on a national market); whether alternative compliance payments (ACP) are allowed and at what price; and carve-outs for particular technologies. Each of these features has been incorporated into the model as specified in the various statutes.

In addition to existing state-level renewable standards, the model can simulate other regional or national standards, such as a clean electricity standard. See Santen, Young, and Blanford (2021) for a detailed analysis of CES design decisions and potential impacts on electric sector investments, generation, and emissions outcomes.

#Technology Constraints

In addition to the solar carve-outs for some states with RPS requirements, there are several state-level technology-specific policies that are represented in US-REGEN:

• Offshore wind mandates in CT, MA, MD, ME, NC, NJ, NY, RI, and VA are represented as lower bounds on installed capacity.

• Electricity storage mandates in CA, CT, IL, MA, ME, MI, NJ, NV, NY, OR, and VA are represented as lower bounds on installed capacity.

• Effective prohibition on new nuclear capacity based on NCSL data. However, when aggregating states to the standard 16 regions in US-REGEN, this constraint primarily impacts California.

#Federal Regulations

The U.S. Environmental Protection Agency (EPA) finalized GHG emissions intensity rules for new and existing fossil fuel-fired power plants under Sections 111(b) and (d) of the Clean Air Act, respectively. The rules place additional requirements on existing coal generation and new gas-fired generation. The US-REGEN reference scenario includes stylized representations of these regulations, which approximate the detailed modeling in Bistline, et al. (2024b)open in new window and Venkatesh and Bistline (2024). The representation of these regulations means that existing coal capacity can only remain online after 2032 with at least 40% co-firing with natural gas (if retiring by 2039) or 90% CCS (if retiring after 2039). New gas-fired capacity, starting in 2032, can only run higher than a 40% annual capacity factor with 90% CCS. Earlier emissions intensity standards under Section 111(b) of the Clean Air Act effectively prohibit construction of new coal-fired units without CCS.

US-REGEN also represents EPA’s Cross-State Air Pollution Rule (CSAPR), which aims to lower emissions of sulfur dioxide (SO₂) and nitrogen oxides (NO_x). The US-REGEN CSAPR formulation includes separate annual and ozone-season constraints, regional credit trading, and credit banking.