3.6 Distributed Energy Resources
3.6.1 Scope and Structure
The electric sector model is designed to represent investment and dispatch in a competitive wholesale market in equilibrium. In contrast, distributed energy resources (DER) compete in the retail market, which is linked to wholesale markets but operates differently. These differences motivate the representation of DER adoption in a separate module within the integrated US-REGEN model.
The DER retail market penetration model simulates potential adoption of distributed technologies over time given assumptions are the rate structures faced by residential and commercial consumers. At present, the model represents rooftop solar PV adoption only and assumes the current rate environment by default where installing behind-the-meter generation offsets retail purchases at flat volumetric rates. DER investments are considered from the perspective of retail customers using price outputs from electric sector runs. As described below, DER adoption is characterized as a gradual diffusion toward a potential adoption rate based on payback period calculations with heterogeneity across customers. The deterministic model examines fundamental DER economic and behavioral drivers at a high level and includes variation in the value of DER to customers based on state-level retail prices, available incentives, resource quality, attitude toward self-generation, and time preference.
3.6.2 Model Specification, Data, and Assumptions
The model begins with estimates of the total technical potential for rooftop solar PV capacity from NREL (2016) and solar resource profiles from the MERRA-2 reanalysis dataset as described in Section 2.4.1.2. The initial economic potential is then calculated based on current market conditions, which helps to calibrate the model to historical uptake data. For each structural class, the model determines an average payback period for future vintages based on projections of retail prices from the electric sector model, capital costs of new systems, and applicable incentives (e.g., investment tax credits). These discounted cash-flow calculations are performed across structural classes to evaluate the variation in economic competitiveness of DER across heterogeneous customers. Structural classes are based on buildings data from the end-use model, which includes exogenous projections for the number of housing units and commercial square footage by building vintage, building size, and model region over time. Maximum technical potential is allocated based on the existing housing stock and commercial floor space and is assumed to scale over time with net additions. Policies currently represented in the model include the federal investment tax credit and California's residential rooftop PV mandates.
Cost markups for behind-the-meter solar PV for residential and commercial installations are based on NREL's 2018 Annual Technology Baseline (ATB) Mid scenario, which are 82% and 35% respectively in 2030.[1] Markups for new construction vis-à-vis retrofits are based on values reported by Lawrence Berkeley National Laboratory (2018). Capital costs include a $250/kWAC hookup charge.
After the cost and value of rooftop installations are used to calculate the payback period from residential and commercial consumer perspectives, the potential DER adoption rate (i.e., economic potential as a fraction of total potential) is calculated as an exponential function of the payback period. The function is calibrated so that there is 10% adoption with a 12-year payback period and 100% adoption with immediate payback. Curves are calculated across model regions accounting for variation of payback periods around the average. Similar to other sectors in the end-use model, the DER retail market model calculates potential adoption rates based on economic conditions in each period but assumes that actual deployment is based on a lagged diffusion process. The model converges from the initial potential rate toward the calculated rate with a 0.5 step length per period to avoid unrealistic increases for specific regions and calculates new additions based on convergence toward potential adoption at 6% per year for residential buildings and 3% per year for commercial buildings.[2]
Capacity from the DER retail market model and other end-use model outputs are sent to the electric sector model. The updated retail electricity prices from the electric sector model are then used as inputs for the next iteration of the DER retail market model. The models iterate until prices converge.
Available at: https://atb-archive.nrel.gov/electricity/2018/index.htmlopen in new window. ↩︎
The commercial diffusion rate is lower than the residential rate to reflect additional heterogeneity, market barriers, and prevalence of non-owner occupied buildings. ↩︎