Renewable energy support policy evaluation: The role of long-term uncertainty in market modelling

Scott, I.J.; Botterud, A.; Carvalho, P.M.S.; Silva, C.A.S.

Informações chave

Autores:

Scott, I.J. (Ian James Scott); Botterud, A.; Carvalho, P.M.S. (Pedro Manuel Santos de Carvalho); Silva, C.A.S. (Carlos Augusto Santos Silva)

Publicado em

15 de novembro de 2020

Resumo

Policy makers are tasked with selecting, designing, and implementing policies to support the transition to a sustainable power system. As part of the task, they often turn to models to quantify and compare the options available to them. In this work, we investigate the importance of the approach to representing long-term uncertainty in the modelling used to evaluate different decarbonisation or renewable support policies. We compare six different policies options; a cap on CO2 emissions (as with a cap and trade scheme), a CO2 price, a renewable capacity target, a green certificates scheme, a renewable generation subsidy, and a renewable capital grant. In a case study of a small power system, we find that using common modelling approaches that attempt to capture uncertainty as multiple different independent scenarios (such as scenario analysis or Monte-Carlo simulation) perform poorly at representing the reaction of a competitive electricity market as measured by a stochastic optimisation model. We find that a policy maker using this approach to make decisions could set a policy 55% more restrictive than required to meet their target. Further, we find that a deterministic model that ignores uncertainty can underestimate carbon abatement costs by up to 86%. Incorporating uncertainty as individual scenarios only slightly improves this result and biases the estimated costs between price and quantity based approaches. Modelling scenarios individually underestimates the cost of quantity targeting policies by up to 66% and overestimates the cost of price based policies by up to 4%. Finally, we find that incorporating uncertainty as individual scenarios results in wind being selected as the most cost efficient technology to respond to decarbonisation policies, on average. However, when considering that decision makers must invest under uncertainty, solar capacity is preferable. The results provide a strong case for the use of the stochastic optimisation approach to incorporating long-term uncertainty into electricity market modelling for renewable energy support policy analysis, despite the additional computational burden.