Theofilos Sotiropoulos-Michalakakos

A Meta-Analysis of Low Carbon Energy Investment Metrics

Low carbon energy investment metrics are used by decision makers to make cost-effective choices geared towards mitigating climate change. Examples of such metrics are the LCOE, MAC, LACE, and EROEI. These metrics are used to compare different technology options on measures of absolute cost, emissions reduction, systems cost, and the efficiency of energy production. A key issue with these metrics is that analysts report values that rarely communicate uncertainties beyond stating averages with ranges. Decisions using average values do not consider the variability of outcomes in the real world and can lead to inconsistent results. The primary aim of this study is to demonstrate the use of Monte Carlo simulations for making decisions regarding low carbon energy investments. Monte Carlo simulations are used to develop stochastic models for the LCOE, LACE, MAC, and EROEI metrics using six electricity generation technologies. The six technologies modeled are solar PV, onshore/offshore wind, nuclear, CCS-Coal, and CCGT. The outputs assess how competitive different supply-side energy solutions are when considering the uncertainty in cost and technical performance. The outputs also describe how the technologies fare in a multi-metric analysis. In addition, mean-variance optimization is used to produce technology portfolios to determine the likelihood that the UK will meet its 2030 electricity grid decarbonization targets. The results demonstrate the highly uncertain nature of low carbon energy metrics, and provide a comprehensive comparison of technology tradeoffs. The outputs of the mean-variance optimization identify the benefits of diversified energy portfolios by measuring the trade-off between risk and cost. In addition, the analysis reveals that portfolios which achieved the UK’s 2030 average emissions target of 50 <::><::><::>2<::><::>ℎ did so with a 44% probability of exceeding it. This conclusion demonstrates the importance of incorporating uncertainty in low carbon decision making, as the risks posed by climate change are far too great to be left to chance.