Long-term renewable electricity planning using a multistage stochastic optimization with nested decomposition

Abstract

Renewable energy plays a key role in mitigating climate change and promoting the energy transition. In this context, we address two crucial consequences for planning the electricity transition: (a) substantially more complex uncertainties in variable renewable energy and (b) a requirement to co-ordinate extensive transmission investment with the newly located generating facilities. These features combine to present a major modelling challenge both for countries that have a liberalized electricity market, where long terms plans are needed to support subsidy policies, as well as for those countries which have retained central planning. This paper develops a new multistage stochastic mixed-integer model which uses a new decomposition algorithm based on stochastic dual dynamic integer programming, with a two-phase acceleration method. The scalability of the approach is demonstrated by application to China’s electric power requirements and it performs well in terms of computational tractability and policy insights.