Hedging the risk of increased emissions in long term energy planning

Abstract

The feasibility of meeting emission targets is often evaluated using long range planning optimization models in which the targets are incorporated into the system constraints. These models typically provide one ‘optimal’ solution that considers only a deterministic representative value of emissions for each technology and do not consider the risk of exceeding expected emissions for a given optimal solution. Since actual emissions for any given technology are uncertain, implementation of such an optimal solution carries inherent risk that emissions will exceed the given target. In this paper, we implement a stochastic risk structure into the OSeMOSYS optimization model to incorporate uncertainty related to the emissions of electricity generation technologies. For a given risk premium, defined as the additional amount that society is willing to pay to reduce the risk of exceeding the cost optimal system emissions, we determine the generation technology mix that has the lowest risk of exceeding this baseline. We focus on emissions risk since the literature on emissions risk is sparse while the literature on other risks such as policy risks, financial risks and technological risks is extensive.We apply the model to a case study of a primarily fossil based jurisdiction and find that, when risk is incorporated, solar and wind technologies are built out seven and five years earlier respectively and that carbon free technologies such as coal with carbon capture and storage (CCS) become effective alternatives in the energy mix when compared to the ‘optimal’ solution without consideration of risk, though this does not include the risk of carbon leakage from CCS technologies. If nuclear is included as a generation option, we find that nuclear provides an effective risk hedge against exceeding emissions.