Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events

TL;DR

The paper tackles how to plan energy transitions in the face of rare but impactful unexpected events. It proposes a two-stage framework that first uses perfect foresight to identify dealbreakers and then applies myopic foresight to test the robustness of early decisions, demonstrated on Belgium’s energy system. The Belgium case identifies the loss of electrofuel imports by 2050 as the main dealbreaker, while aggressively accelerating renewable deployment yields the most robust policy. The method is implemented in EnergyScope Pathway with Sobol-based space exploration, and results are open-source for broader adoption, enabling policymakers to pinpoint vulnerabilities and design robust, region-transferrable energy transition strategies.

Abstract

Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.

Figures (23)

• Figure 1: Scenarios are generated by exploring the unexpected-events space and evaluated using an energy transition pathway optimization model with perfect foresight. This ideal setting optimizes the energy transition for each scenario starting with full knowledge on the future. In other words, if the pathway optimization model fails to find a solution, the scenario will inevitably lead to a failed transition. By labeling the scenarios based on success or failure, a classification tree can derive the infeasibility conditions. The $n-d$ feasible scenarios are then reassessed with limited foresight---a more realistic setting in which scenarios are revealed in 5-year intervals, and decisions are made without knowing future events. Thus, scenarios that are feasible with perfect foresight might become infeasible with limited foresight due to an early-stage decision that could hinder the ability to address unforeseen unexpected events that arise later. By starting from specific energy policy decisions (e.g., accelerated deployment of renewables, early phase-out of nuclear power), these scenarios are evaluated to determine how many unexpected-event scenarios are managed and the associated costs. This approach allows policymakers to assess the performance of energy policy decisions in relation to unexpected events.
• Figure 2: Classification tree on the unexpected-event scenarios---labeled as either leading to a successful or failed energy transition under perfect foresight---to discover the infeasibility conditions for the energy transition. The classification tree, post-pruned to remove branches not leading to a failed energy transition, shows that the import of electrofuels in 2050 is a key enabler: if no electrofuels are imported in 2050, the energy transition will fail. In scenarios where the electrofuel imports are low in 2050, the transition fails if the final energy demand rises. Even when the electrofuel supply is high in 2050, the transition cannot be realized if none is available in 2045, combined with a phase out of nuclear power and a high rise of final energy demand by then. The shade indicates the share of failed energy transition scenarios at each node, with each split leading to a higher proportion in the right node.
• Figure 3: Heatmap illustrating the impact of electrofuel import availability in 2045 and 2050 on the likelihood of a failed energy transition. The risk of failure is almost certain with no electrofuels available by 2050. As import availability increases, the failure risk declines sharply, becoming minimal when availability exceeds 40%. This trend remains consistent across all 2045 import levels, except when imports are zero, in which case the failure risk remains high at all 2050 availability levels. The cells are interpolated using a quadratic method to create a smooth transition between values.
• Figure 4: The cumulative transition cost curves for different energy policy decisions taken in 2030 show that decisions accelerating the transition offer better robustness against unexpected events compared to the myopic baseline. Delaying investments in renewables increases vulnerability to unexpected events and makes them more costly to manage. Specifically, the policy decision to phase out nuclear power by 2030 significantly reduces robustness against unexpected events, handling only 42% of the evaluated scenarios. The hydrogen route, which involves substantial deployment of hydrogen production and conversion technologies by 2030, is the most costly among all evaluated decisions and is less robust against unexpected events than the baseline decision due to greater reliance on electrofuel production and imports later in the transition. The perfect foresight approach (grey curve) represents the theoretical lower bound for these curves, as perfect foresight allows managing unexpected events at the lowest possible cost. The colored lines represent the cumulative cost curves, where the costs for managing unexpected-event scenarios remain within a reasonable range. The grey continuation of these curves illustrates how the costs further evolve at unreasonable rates.
• Figure S1: The classification tree depicting the primary branches leading to a successful or failed energy transition under perfect foresight for eleven leaves. The Gini index measures the impurity or disorder within the node, with lower values indicating purer (more homogeneous) nodes. "samples" indicates the proportion of unexpected-event scenarios that end up in each node, while "value" represent the share of those scenarios classified as success or failure. The class of each node corresponds to the label (success or failure) that is most prevalent within that node. Nodes are color-coded: blue for failure and orange for success. The color intensity reflects the proportion of scenarios, with deeper blue indicating a higher number of failed scenarios, and deeper orange indicating a higher number of successful scenarios. Related to Figure 2.