The research investigates whether financial evaluations in electricity system models can adequately represent investment decisions and analyse power generation technology change. A screening of 51 existing models found that 31 utilised cost minimisation, 16 profit maximisation, and 3 growth rate approaches. A statistical analysis of investment metric values from 1980-2013 for the UK, NL, and DE found that positive threshold financial evaluations coincide with 70%+ of historic capacity investments. A separate empirical model validation of this period, where 64 model variants were tested using the TEMOA optimization model, established that profit seeking gives the best matching result to historical outcomes. Divergence of modelled results and history does occur in two ways due to non-financial factors. First, the impact of political-economy support or exclusion of technologies. Second, constraints to technology scaling limiting the speed of build-out, due to factors including public perception, land availability, and manufacturing/installation scale limitations. Interviews with electricity sector experts established that financial evaluation is the primary means for narrowing down of technology options, after which non-financial factors are considered. If electricity system models are to be employed for testing policy decision impacts on technology selection, investment, and scaling, the incorporation of non-financial factors is essential.