How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

Abstract

Many studies have demonstrated the feasibility of fully renewable power systems. Yet the future costs of key technologies are highly uncertain, and little is known about the robustness of a renewable power system to these uncertainties. To analyze it, we build 315 cost scenarios by varying the costs of key technologies and we model the optimal renewable power system for France, simultaneously optimizing investment and dispatch. We add to the literature by studying a consecutive 18-years weather period; by testing all combinations of technology costs rather than changing them one-at-a-time; and by calculating the regret from optimizing the energy mix on the basis of cost assumptions that do not materialize.Our results indicate that the cost of a 100% system is not that sensitive to uncertainty. Admittedly, the optimal energy mix is highly sensitive to cost assumptions: across our scenarios, the installed capacity in PV, onshore wind and power-togas varies by a factor of 5, batteries and offshore wind even more. However, in every scenario the total production and storage cost is similar to, or lower than the current cost. This indicates that renewable technologies will become by and large substitutable. Moreover, even if the energy mix is optimized based on cost assumptions which turn out to be wrong, the extra cost is low: 4% in average and less than 9% in 95% of the scenarios.

Broad ranges of investment configurations for renewable power systems, robust to cost uncertainty and near-optimality

“… 9 , 36 When uncertainty has been considered in the literature, cost assumptions have commonly been modeled to vary between and depending on the technology’s maturity. 3 , 21 , 36 , 37 , 38 In this study, we consider uncertainty regarding the annuities of onshore wind, offshore wind, solar PV, battery and hydrogen storage systems. The latter comprises the cost of electrolysis, cavern storage, and fuel cells.…”

“…But independent uniform distributions are the most prevalent assumption. 3 , 12 , 36 , 37 , 39 , 40 , 41 , 42 This approach is backed by the maximum entropy approach, 3 which states that given the persistent lack of knowledge about the distribution the independent uniform distribution, that makes fewest assumptions, is most appropriate. Although the assumed independence may neglect synergies between technologies, for example, between offshore and onshore wind turbine development, we follow the literature by assuming that the cost are independent and uniformly distributed within the ranges specified in Table 1 .…”

Variable time-step: A method for improving computational tractability for energy system models with long-term storage

“…An analysis on the full discharge time of the pumped hydro storage (mid-term storage) technology is necessary to define the longest period. According to Shirizadeh et al (2019) discharge time of PHS plants rarely exceeds four days, therefore, we consider the longest period to be 96 hours. This analysis for the case studied by Shirizadeh et al (2019) is presented in appendix 1.…”

“…The next step is to group the remaining hours in a coherent way. We have prepared a daily power production and consumption profile by taking an average over each day of the 18-year period for which results have been previously calculated (Shirizadeh et al 2019). Using the power production profiles and focusing on the dispatchable technologies, we can divide the typical day into four time-steps:…”

“…The main difficulty is choosing the 'most important' hours, hence in this article we propose a method to achieve this and we test the performance of this 'variable time-step' approach in terms of model accuracy and calculation time. We apply the method to two national energy system models, EOLES_elecRES (Shirizadeh et al, 2019) and DIETER (Zerrahn and Schill, 2015), which have previously been implemented at an hourly resolution.…”