Residential solar adoption—main determinants and the future of small-scale photovoltaics

Abstract We present a data-driven microsimulation model analyzing residential photovoltaic adoption in Germany to address uncertainties in future adoption rates. We develop and calibrate a technology adoption model incorporating economic, financial, and behavioral factors to replicate historical adoption patterns from 2007 to 2020 and project future scenarios. The model simulates household adoption decisions based on expected profitability, environmental attitudes, financial constraints, and peer effects, while differentiating across income groups and geographical locations. Combining household survey data with installation records, we demonstrate that our model accurately reproduces historical adoption patterns. Our projections follow the sigmoid curve typical of innovation diffusion theory, with peer effects accelerating adoption as ownership increases. The baseline scenario predicts approximately 47% household saturation by 2050, with maximum adoption growth occurring around 2030, though systemic grid-level constraints may limit penetration well before reaching such levels. Alternative scenarios demonstrate the critical role of technology costs, electricity prices, and feed-in tariffs in driving adoption. We find significant disparities between income groups, with the highest income decile reaching 77% adoption by 2050 compared to only 6% for the lowest decile. This disparity highlights the regressive distribution of feed-in tariff benefits, with wealthier households receiving disproportionately larger subsidies, though historical technology diffusion patterns suggest such inequities may diminish over time. Our findings provide valuable insights for policymakers regarding grid planning, subsidy design, and the distributional implications of renewable energy policies.