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topics:risk [2026/07/30 14:54] – o.sachstopics:risk [2026/08/04 14:56] (current) – o.sachs
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 ===== Why this matters ===== ===== Why this matters =====
  
-Power grids are facing numerous challenges, driven by increasing complexity, higher energy demand, and a growing number of hazards/threats ((Zio, E., Aven, T. (2011). Uncertainties in smart grids behavior an modeling: What are the risks and vulnerabilities? How to analyze them?. //Energy Policy//, 39, 6308-6320.)) . 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.+Power grids are facing numerous challenges, driven by increasing complexity, higher energy demand, and a growing number of natural hazards.((Zio, E., Aven, T. (2011). Uncertainties in smart grids behavior an modeling: What are the risks and vulnerabilities? How to analyze them?. //Energy Policy//, 39, 6308-6320.))((Charpentier, A. (2008). Insurability of Climate Risks. //The Geneva Papers//, 33, 91-109.))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. In addition to that it raises questions about insurability and the possible rise of costs for maintaining, financing, expanding power grids. 
  
 <WRAP callout> <WRAP callout>
-[To be drafted]+The evolving risks facing energy grids raise new questions regarding the maintenance, modernization, insurability, financeability of grids and calls for innovative efforts like smart grids
 </WRAP> </WRAP>
  
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 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. ((Renn, O. (1998). Three decades of risk research: Accomplishments and new challenges. //Jounal of Risk Research//, 1(1), 49-71.))  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. ((Renn, O. (1998). Three decades of risk research: Accomplishments and new challenges. //Jounal of Risk Research//, 1(1), 49-71.)) 
  
 +By distributing control capabilities to each component in the electrical grid and enabling the exchange of information between components, the grid gains a higher degree of control and flexibility
 +This allows for better risk management and higher resilience in the event of sudden peak loads or other stressors.
 +However, even though Smart Grids are intended to reduce risks to power grids, their own future is exposed to significant uncertainties. Thus, the development and expansion of these systems depend on a number of factors that make quantitative risk calculations difficult: the magnitude of the required investment, uncertainty regarding technological developments, unpredictable global energy demand trends, and shifting energy and environmental policies.((Zio, E., Aven, T. (2011). Uncertainties in smart grids behavior an modeling: What are the risks and vulnerabilities? How to analyze them?. //Energy Policy//, 39, 6308-6320.))
  
 +- Risk distrubution on different actors. How wo mitigate Risk on electrical Grids (See Zio & Aven, 2011) 
  
 +- why does the fact, that there are so many ucertaincies the insurability difficult?
  
  
-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.  ((Funtowicz, S. O., Ravetz, J. R. (1993). Science for the post-normal age. //Futures//, 25, 7, 739-755. [[https://doi.org/10.1016/0016-3287(93)90022-L|https://doi.org/10.1016/0016-3287(93)90022-L]]))+- Similar to Resiliance, how to mitigate risk, Risk manegement (how would that look like in the case of Critical infrastructure like power grids)  
  
-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. +- Risk analysis (also Zio & Aven, 2011)
-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. ((Funtowicz, S. O., Ravetz, J. R. (1993). Science for the post-normal age. //Futures//, 25, 7, 739-755. [[https://doi.org/10.1016/0016-3287(93)90022-L|https://doi.org/10.1016/0016-3287(93)90022-L]]))+
  
 +- Still something missing about Insurance and hedging 
  
-<WRAP tablecap> +- Systemic risk and risk precived by individuals (see Renn, 1998) 
-**Table 1.** Problem-solving strategies.\\ +
-//Source: Funtowicz & Ravetz (1993) // +
-</WRAP>+
  
-<WRAP group> 
-<WRAP center column full> 
  
-^ Problem solving strategy ^ Goals ^ Description ^  
-|<WRAP #eef6ff> **Post-normal science** </WRAP>|<WRAP #eef6ff> Issue-driven </WRAP>|<WRAP #eef6ff> High system uncertainties and decision stakes, disputed values, urgent action is required (e.g., climate change). </WRAP>| 
-| **Professional consultancy** | Client-serving | Moderate system uncertainty and decision stakes. | 
-| **Applied science** | Mission-oriented | Low system uncertainties and decision stakes. | 
  
-</WRAP> 
-</WRAP> 
  
-<WRAP figure> 
-{{ :topics:risk_problem-solving_strategies.png?nolink&500 |}} 
  
-<WRAP figurecap> 
-**Figure 1.** Problem solving strategies according to Funtowicz and Ravetz.\\ 
-//Source: Funtowicz and Ravetz (1993)// 
-</WRAP> 
  
 ===== Perspectives ===== ===== Perspectives =====
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 <WRAP perspectives> <WRAP perspectives>
 ==== Actors and stakeholders ==== ==== Actors and stakeholders ====
 + - New Risks like the rise in the Frequency of natural disasters due to climate change raises questions about the need to insure critical infrastructure aswell as the potential rise of the cost of the insurence. Furthermore the rising need to ensure and the rise in costs of insurence, will have consequences for future investments for the expansion and modernisation of critical infrastructure as well as consequences on the cost for the end user
 ==== Technologies and infrastructure ==== ==== Technologies and infrastructure ====
 +- The growing implimentation of renuable Energy sources like solar and wind poses new challenges for power grids. 
 +- Smart meter, Flexibility of energy use. 
 +- rising Complexity 
 +- Interconnection of different parts of critical infrastructure (See Zio & Aven, 2011)
 +==== Institutional structures ====
 +- how can the state help with financing policy, lowering the Risk and therefore rise financibility of critical infrastructure
  
-==== Institutional structures ==== 
  
 </WRAP> </WRAP>