Quantifying Resolution Implications for Agent-based Distributed Energy Resource Customer Adoption Models

Abstract

In this report we explore tradeoffs of top-down and bottom-up methods in their precision and computational burden using NREL's dGen model, an agent-based model of residential and non-residential distributed PV adoption. In particular, we assess the role of agent resolution in instantiating statistically-representative populations in the model--and the resulting variance of model projections at the state, sector and county levels. Running the model for a single state with fixed macroeconomic assumptions, we vary both the number of agents instantiated and the number of unique simulations. Notably, in each run we introduce sources of uncertainty in cumulative DPV adoption results during agent instantiation as we stochastically sample agent attributes from localized building characteristic and solar resource distributions. We find three key trends which guide what can be expected of results. First, precision of the model projections, as measured by the variance of the unique simulations increased as a function of the number of agents sampled, though with diminishing returns to scale. The result is expected given the Law of Large Numbers. Second, the mean or expected value of projections decreased as the number of agents instantiated increased. Empirically, aggregate DPV capacities projected by dGen are 7% lower on average at a 10-agent resolution compared to 1-agent resolutions, and up to 10% lower in the non-residential sector. We attribute this outcome to a system sizing scheme that biases lower cumulative installed capacity as agent resolution increases when roof size and load are not positively correlated. Finally, the variance of model projections differed by sector, where variance was substantially larger in the commercial sector, largely due to differences in parameterization by sector. We explain the larger variance in the non-residential sector as an outcome of the greater variance in building uses and load shapes as compared to the residential sector, and we find that a resolution on the order of 100-agents per county would be necessary to bring non-residential variance in line with residential variance at a 2-agent resolution. We propose that further research explore increasing precision and agent resolution in the non-residential sector. Moreover, given that days of intensive computing may be required to resolve a suitable estimate even at the extent of a single state within acceptable levels of precision, to promote computational efficiency we also suggest more clearly quantifying sources of variability at the county level such that resolution can by dynamically increased where county level results are least precise.