Risk

Power grids are facing numerous challenges, driven by increasing complexity, higher energy demand, and a growing number of hazards/threats ¹⁾ . These changing circumstances have significant consequences. On the one hand, the risk of a grid failure is rising; on the other hand, risks are becoming increasingly difficult to calculate. This, in turn, raises questions about insurability and leads to higher costs for maintaining, financing, and expanding power grids.

Risk describes the probability of negative consequences affecting things that people value. A distinction must be made between risk assessment and risk management. Risk assessment refers to the, usually quantitative, calculation of the probability that a specific event will occur and its potential magnitude. Risk management refers to the process of dealing with risk, that is, reducing it to an acceptable level. ²⁾

Distinctions can be made between different forms of risk concepts. For instance, the classical form is technical risk, which is predominantly used by insurance companies and involves calculating the average probability of an event based on historical data. This means that the frequency with which a specific event occurred over a given period in the past is used to project the likelihood of its future occurrence.

However, this approach to risk calculation reaches its limits when historical data is lacking or unavailable, as is often the case with unprecedented environmental disasters. Furthermore, technical risk analysis fails to take into account that individuals perceive risks differently and, depending on their values, may be more willing to accept certain risks than others. Technical risk therefore represents only a fraction of comprehensive risk identification, assessment, and management. For example, psychological phenomena such as cognitive biases directly influence how people perceive risk. Consequently, individual actions are driven by perceived risk rather than by scientific, rational calculations. Therefore, these individual and personal factors must be taken into account when evaluating risk. ³⁾

Science is undergoing a profound transformation, driven by the challenges it faces within a globally industrialized world, a world fundamentally shaped by science itself. Today, scientific problem-solving increasingly operates in a “post-normal” context, where facts are uncertain, values are in dispute, stakes are high, and decisions are urgent. The COVID-19 pandemic illustrated this reality: policymakers had to act rapidly despite severe data scarcity, navigating agonizing value conflicts, such as balancing the immediate protection of the elderly against the long-term well-being of the young. Because these contemporary crises are defined by such uncertainty, they inevitably challenge the traditional authority and quality assurance of scientific conclusions. To conceptualize this shift, Funtowicz and Ravetz (1993) introduced the framework of “post-normal science” to navigate the complexities of decision-making when orthodox scientific methods reach their limits. ⁴⁾

According to Funtowicz and Ravetz, the interaction between decision-making stakes and system uncertainties determines which problem-solving strategy is appropriate. They distinguish three distinct types of science: applied science, professional consultancy, and post-normal science. Applied Science: This operates when both system uncertainties and decision-making stakes are low. Here, problems are managed using routine, standardized procedures; the uncertainties are purely technical, and the outcomes do not carry immediate, high-stakes political or societal risks. Professional Consultancy: This applies to intermediate conditions where both uncertainty and stakes are higher. A key factor here is the introduction of the client, which can create conflicting values between stakeholders and scientists. Because the findings directly influence human lives or corporate interests, risk assessments are required. Furthermore, since the focus shifts toward solving a unique, specific problem, universal reproducibility is no longer the central priority. Post-Normal Science: This strategy becomes essential when both system uncertainties and decision stakes are high, conditions common to environmental and public health crises. In this realm, urgent decisions must be made despite severe data gaps, and the conflicting values of diverse groups mean that traditional scientific consensus is impossible. ⁵⁾