Flood Risk Management and Adaptation Actions for Taipower Transmission System

Transcription

1 Flood Risk Management and Adaptation Actions for Taipower Transmission System by WANG, King-Min *Chung-Hua Institution for Economic Research/Energy and Environmental Research Center, Taiwan, Taipei, 106. The Fifth Congress of the East Asian Association of Environmental and Resource Economics August 5-7, 2015 Academia Sinica, Taipei, Taiwan ABSTRACT Rising global mean temperature has been accompanied by changes in the extreme weather and climate variability. The implementation of adaptation action plans in various sectors is currently one of the top priorities in Taiwanese governmental policy agenda against climate change impacts. The electric sector plays the key role of climate adaptation in the energy industry. Risk assessment of impact of climate change on Taiwan electric system identifies that the extreme rainfall events from both typhoon and heavy downpour are the major risks. How to cope with the possible impacts of these extreme events and prepare appropriate adaptation is a necessary and urgent task for the utility company. The paper identifies and analyses the risk of the extreme rainfall events and formulates appropriate actions for flood risk management adaptation for the sector. We adopt the framework of decision making under risk and uncertainty and carry out a stochastic cost and benefit analysis for the flood risk management in the electric sector. Current and future Impacts of weather and climate change on power sectors are prioritized by a multi-criteria analysis to assess the vulnerability of the power system. Appropriate adaptation actions for the electricity sector are presented based on the cost and benefit analysis. Finally, we mainstream our flood adaptation actions into the risk management scheme of the utility company, Taipower. Our study can contribute significantly to the risk reduction of the flood impact of climate change and to the design of adaptation actions for the Taiwan electric sector. Keywords: global warming; multi-criteria analysis; stochastic cost and benefit analysis; mainstreaming. 1

2 1. Introduction The unprecedented advances in energy technology since the Industrial Revolution have led to fundamental changes to human civilization. Human society in general and the industrial sector in particular play two roles in anthropogenic climate change. On the one hand, the increasing use of fossil fuels and its accompanying large emissions of greenhouse gases (GHGs) are the root cause of climate change. On the other hand, the impact effects of climate change and the resulting economic and ecological losses are borne by human society. The electric sector in Taiwan is exposed to two types of climatic risks: first is the risk of operating in a low-carbon world which is responding to the need for climate change mitigations; the second is the risk associated with adapting to a world directly affected by climate change. The vulnerability of different systems varies widely. In this paper we discuss the impacts of and adaptation to the extreme events of climate changes in the power sector of Taiwan. It is clear that global warming has already become an irreversible trend (IPCC, 2013). Taiwan Government has already initiated a national adaptation policy making process and the implementation of adaptation action plans is one of the highest- priority tasks towards the prevention of the negative and harmful climate change impacts (CEPD, 2011). As the dominant energy producer in Taiwan, Taiwan Power Company (Taipower) plays the most important role of energy supply in the energy system. Deciding how to cope with the possible impacts of climate change and preparing appropriate adaptation plans is a necessary and urgent task for the utilities at the present. Taiwan is located in the western Pacific, a region that experiences a high frequency of typhoon, earthquakes and other severe natural disasters. With 73% of Taiwan s population exposed to three or more severe hazards a year and nearly 99% exposed to at least two a year, Taiwan is situated in a high risk region of the world. As the global climate exhibits increasing anomalies and a rising frequency of extreme disasters, the resulting human and economic losses continue to grow. Statistics also suggest that rainfall intensity in Taiwan has become stronger, with heavier rainfall and droughts according to the report by National Science and Technology Center for Disaster Reduction (NSTCDR, 2011). To face the challenges posed by climate and environmental change, we need to make adaptation decisions based on sound knowledge of the information about climate change in Taiwan. To design a proper adaptation policy needs to understand the characteristics of decision making process under climate change impacts. Since the climate system is dynamic and stochastic in nature a sound decision making process must deal with such nature (UNDP, 2004; Willows and Connell, 2003; UKCIP). Apart from dynamic and stochastic characteristics, the climate system is also 2

3 changing with time and, therefore, it is not a stationary or at an equilibrium state. Decision making process must be able to handle such challenging difficulties. Since the timeframe, magnitude and location of climate change impacts are unknown and full of stochastic nature, a proper adaptation policy must be derived from a risk management of a dynamic and stochastic climate system, i.e. decision making under uncertainty. Also due to the time varying nature of climate change we need adaptive thinking and learning to handle and update the new and uncertain future information in to our decision making process. Since a proper adaptation policy is kind of a risk management, in the empirical analysis the Risk Matrix method is commonly used to identify the risk level of particular climate variability and its impact to the natural, social and/or economic system (Defra, 2012; ESMAP, 2009). The loss level of a potential climate impact is determined by the hazard exposure and the related vulnerability of a system. Risk Matrix method usually adopts the consequence magnitude and its occurrence likelihood as two dimensions of the climate change impact to draw the matrix. Only for those risks identified and evaluated as very high level will be dealt with in high priority. Adaptation design depends upon the information about the risk and uncertainty. The type of uncertainty in the future events may range from perfect certainty, risk probability, fully reducible uncertainty, partially reducible uncertainty to total complete uncertainty according to their risk identification result (Lo, A. W. and Mueller, M.T., 2010). Many different methods and optimization algorithms can be applied to derive the better and appropriate adaptation decisions. It is up to the decision maker to determine which method to use and subject to the constraint of data availability. Since the adaptation to climate change is a relatively newly evolved management science, many data and knowledge are unavailable or unknown at the present but decisions have to be made now qualitative multi-criteria analyses are also commonly adopted by many empirical studies (Defra, 2012; Stefan Truck, et. al., 2013). In the future, when data are available adaptive stochastic optimal control will be a better quantitative method to derive better adaptation policy. The paper identifies and analyses the risk of the extreme rainfall events and formulates appropriate actions for flood risk management adaptation for the electric sector. We adopt the framework of decision making under risk and uncertainty and carry out a stochastic cost and benefit analysis for the flood risk management. Current and future Impacts of weather and climate change on power sectors are prioritized by a multi-criteria analysis to assess the vulnerability of the power system. Appropriate adaptation actions against flood risk for the electric sector are presented. Finally, we mainstream our flood adaptation actions into the risk management of the utility company, Taipower. Our study indicates the adaptation actions proposed can significantly reduce the flood risk of climate change impact. The results emanating from this study can be served as the 3

4 example for adaptation decision making and risk management for the Taiwan electric sector. 2. Risk Management Framework for Climate Change Adaptation 2.1 Risk Management Framework Before adopting any climate change adaptation policy and measures, a strategic direction based on holistic risk management plan should be formulated. First, how and what the impact of climate changes result in potential risks and business opportunities should be determined, and weighed into electricity management and operation decisions, including assessment of risks pertaining to climate change, formulation of response strategies, and assessment of their long term cost and benefits. From the perspective of electricity management and operation, any issues pertaining to climate change which can seriously affect the electricity supply system and threaten business development objectives should be incorporated into corporate strategy assessment and followed up for potential risk and impact. Taipower s current corporate risk management and control framework addresses risks caused by changes in its internal and external operational environment such as natural, social, international, political and economical conditions. Its core procedures are divided into seven elements, namely risk management and operation system, risk identification, risk analysis, risk evaluation, risk management, communication and coordination, monitoring and review. Figure 1 shows the relationship among these elements. In the risk management and operation system, a cycle of continuous improvement comprising many mutually influencing factors is emphasized, with particular focus on a rolling method of review and dynamic management. However, Taipower s current risk management framework only addresses general internal and external risk in its operational environment, and does not address the full risks of climate change impact. Therefore it is necessary for Taipower to incorporate climate change risk management into its risk management operation system to successfully manage the impact of climate change. 4

5 Monitoring and review Risk assessment Communication and coordination Risk management operating system Risk identification --what? --how, why, where, when? Risk analysis --the current existing options --the occurring probability --the impact severity Risk evaluation Determine risk level and priority Are they high risk level? no yes Risk management --list feasible options --assess the impact and select economic feasible options --plan and implement the options Figure 1: Taipower company risk management framework Source: Taipower (2012), Risk Management Scheme Since climate change includes short-term weather changes and mid and long-term climate variability, the nature of their risks differ from the general risks in business operation. The climate change risk management operation system in Fig. 1 cannot comprehensively illustrate the impact of climate change and the planning process for adaptation strategies, and fail to demonstrate the dynamic and stochastic nature in the circular decision-making process of adaption options. Figure 2 shows the climate change risk management and adaptation decision framework currently adopted by most countries (Defra, 2012; ESMAP, 2009; Willows, R. and Connell, R., 2003). Except for differences in presentation and focus, the principle in this framework is generally consistent to the 5

6 current risk management framework adopted by Taipower in Figure 1. As with most international case examples, this paper adopts Fig. 2 as a framework for Taipower s management system for coping with climate change impact under risk and uncertainty. Such a recursive framework of circular adaption decision-making is a prevalent risk management system in dealing with climate risks and uncertainty. It was first formulated and recommended by the United Nations Development Program (UNDP, 2004), and later was formally developed by the University of Cambridge s United Kingdom Climate Impacts Programme (Brown, A., Gawith, M., Lonsdale, K. and Pringle, P., 2011), it became widely used by different countries (as reported by the ESMAP, 2009, World Bank). It has become Taiwan s guide for adapting to climate change and official guiding framework for decision-making in strategy planning (CEPD, 2011). In addition to providing central government departments in systematic evaluation of climate change impacts and developing national adaptation policy cross sectors, it is also adopted by county and city governments and many industries. This framework is divided into seven rolling circular steps. Figure 2:Adaptive decision making framework for adaptation to the climate change First, as the decision maker, the company must clearly define the scope and object of adaptation, which may be the electricity supply system or the company as a whole. It could also be a part of the electricity supply system or certain business operations such as generation, transmission, and distribution or retail sales. The vulnerability and adaptation needs of various 6

7 company departments are then evaluated and determined. Second, adaptation goals and decision criteria for future adaptation policy are established. Third, risk evaluation, including risk identification, risk analysis and risk metric determination is conducted. A total risk assessment of the climate change impact includes the assessment of both current vulnerability and future climate risks. Data for current vulnerability and risk assessments can be collected from the electricity supply system and the company historical damaging records, and the vulnerability can be assessed by drawing on latest climate change trends and historical disaster experiences. Potential risks and vulnerability can be predicted using scenario analysis, and the future development scenario of climate and socioeconomic environment can be respectively constructed to assess the potential risk of each sector needing adaptation. Finally, adaptation obstacles confronting the electricity supply system and the company should be clarified. Fourth, studies and literature review on climate adaptation options of power companies and energy sectors in other countries should be collated, and adaptation strategies that are consistent with our electricity supply system should be identified and listed. In addition, the impact of delay or non-preparation of any adaptation measure by the company should also be evaluated. Fifth, adaptation options are evaluated against decision criteria set up in step 2 to determine the implementation priority. Sixth is the decision-making stage. The preferential risk options of decision-makers are reviewed, adaptation options are assessed for accurate reflection of problems, and logical methods (such as algorithms, stochastic optimal control method, weighted scoring method, cost-benefit analysis and multi-criteria analysis) are applied to derive the appropriate adaptation options. Last, the appropriate adaptation options are systematically integrated into the short, mid and long-term strategic corporate planning of Taipower. Seventh, the best adaptation option selected is implemented. To achieve the dynamic cyclical effect of the adaptation policy planning, the company decision-makers must evaluate and monitor the expected benefits and performance of the adaptation strategy adopted. In addition, attention should be paid to new information gathered and performance should be re-evaluated, and adaptation strategies corrected when necessary. The risk-based adaptation decision-making framework is a process in the rolling circle model of decision-making. If any step in the implementation affects the next step, the process can be backtracked to the previous step for necessary correction. The process is then continued until all seven steps are completed. Such a rolling recursion process results in incorporation of new and updated climate parameters, information, adaptation knowledge and adaptation technologies, and adjustments in adaptation options are made accordingly. In the face of climate change risks and uncertainty, this recursive decision-making framework for climate change adaptation is currently one of the most appropriate methods. It allows for yearly adjustment 7

8 of adaptation strategies based on the impact consequences of climate change and climate risks as well as the updated new information and adaptation performance. 2.2 Climate change risk assessment methods To evaluate Taipower s climate change risks, the potential climate change risks confronting Taipower must be fully understood and identified. These include what climate change risk could occur, how, why, where and when it could occur, and the scope and impact of such a risk. Based on Taiwan s current and potential changes in future climate parameters and data collected from relevant international literature, the types of climate change risks and potential impact consequences confronting Taipower are listed. The types of climate risk are categorized according to the main climate change parameters. Based on four key important parameters of climate change, namely Taiwan s rainfall, temperature, sea levels and extreme weather events such as typhoons and heat waves, 51 potential climate change risks confronting Taipower are delineated for impact assessment. To help understand the scope and consequence of these major climate change risks, these 51 risk items are divided into seven clusters, namely nuclear and thermal generation, hydropower generation, renewable energy, electricity transmission and distribution system, fuel supply, electricity demand, and business operation management. In terms of risk evaluation, multi-criteria analysis of risks of climate change impact was conducted according to the research methods of UK Climate Change Risk Assessment (CCRA) (Defra, 2012) and the World Bank (ESMAP, 2009) to assess and screen for key high risk items requiring high priority management. 2.3 Results of Taipower climate change risk assessment Taipower climate change risk screening and assessment was conducted using a five-level scoring system where the impact of risks was assessed according to three criteria: scale of impact, occurrence possibility (likelihood) and urgency. Among these criteria, the scale of impact is measured according to Taipower s standard for consequence severity (as shown in Table 1), which is divided into five impact categories: fatality and safety, financial loss, energy security, social perception and reputation. Scoring rule is based on Taipower s existing 5-level scoring system. In each criterion, impact is categorized according to severity magnitude, with very high impact being a score of 5 points (Magnitude 5); medium high impact (Magnitude 4), 4 points; medium impact (Magnitude 3), 3 points; medium low impact (Magnitude 2), 2 points; and low impact (Magnitude 1), 1 point. The greater the magnitude of a consequence, the higher its score is, and the more severe its impact. Conversely, the lower the magnitude of consequence, the lower its score is, and the less 8

9 severe its impact. Table 1:Scale for Assessing Severity of Consequence Magnitude Fatality & safety Financial loss Energy security Social perception 5 Extreme 4 Major 3 Moderate 2 Minor 1 Limited Multiple fatalities >2 people Single fatality; Multiple Injuries or disability >NT$5B >NT$1B Load shedding; lost load >900MW Load shedding; lost load >500MW Single fatality >NT$0.5B Load shedding; lost load <500MW Serious injury >NT$50M An adverse event that can be absorbed with some management effort Minor injury <NT$50M Impact can be absorbed through normal activity Source: Taipower (2013), Risk Management Scheme. Big scale National protests Community protest to the central government Localized protects Localized temporary protects and complaints Minor protests and complaints Reputation/ Political context Negative international media coverage Negative national media coverage with long term impact on public opinion Negative national media coverage Negative regional media coverage Negative local media coverage The criteria for the likelihood of a climate risk event is also scored according to Taipower s 5-level possibility criteria, which uses the likelihood of an event or the frequency/possibility of an event as benchmark scoring. The higher the likelihood and frequency of a risk event, the higher its score is. Conversely, the lower the likelihood of a risk event is, the lower its score. The criteria for climate risk urgency is assessed according to when adaptation decisions are required to manage the impact of climate change and strengthen adaptation capability: before the year 2020 (5 scoring points for extreme urgency), before 2035 (4 points for high urgency), before 2050 (3 points for moderate urgency), or adaptation decisions are not required until 2065 (2 points), or adaptation decisions are not required until 2080 (1 point). Scoring for urgency criteria is also based on the severity of barrier and obstacle to adaptation in the company, which is divided into five levels, from severe barrier to low barrier or no barrier. The more severe the barrier, the greater its magnitude and score are. Conversely, the lower the magnitude is, the lower the score. Last, screening score for major climate change risks is calculated using the following formula: 100 In this formula, the maximum score is 100 and the minimum score is 3.7. The threshold score for key high-risk items is 35, indicating priority management needed. Using expert judgment through a questionnaire survey, we evaluated each item on the climate risk list. The results were organized and four major risk impact items were delineated. In 9

10 descending order, impact of flood and mudslides on electricity transmission and distribution facilities due to extreme typhoon and storm surge, increased demand for air-conditioning due to heat wave and high temperature, impact of extreme climate and strong wind on electricity transmission, distribution and dispatch, and impact of heavy downpour or extreme typhoon on reservoirs and dams constitute the four major risk impact items. In addition, extreme climate also results in fluctuation and hikes in international fuel price, and storm surge combined with rising sea level floods on coastal electricity infrastructures. Although these two impact risk items did not meet the criteria for major risk scoring threshold, their likelihood of occurring and scale of impact are high, and should also be included in the major risk impact list, as shown in Table 2. This paper conducted a climate change adaptation cost-benefit analysis on electricity transmission and distribution facilities most at risk of being impacted by flood and mudslide caused by extreme typhoon or storm surge. Ranking Table 2: Result of risk evaluation of climate change impact on Taipower system Major key risk Flood risk from typhoon and heavy downpour on transmission and the distribution system Outage risk from heat wave impact on electricity peak demand and power system operation High winds risk from storm/typhoon impact on electricity transmission and distribution system Impact of heavy downpour and extreme typhoon on reservoirs and dams Rocket rise of world fuel price from extreme climate events Flood risk from sea-level rise and tidal wave on electricity infrastructure Risk score Scale of impact Magnitude Likelihood Urgency Research Methods 3.1 Cost-benefit analysis (CBA) of Adaptations The cost-benefit analysis mainly assessed the expected long-term cost and benefit of various adaptation strategies. The adaptation cost estimate mainly covered all the adaptation expenses for the lifespan years of adaptation options while the benefit covered the expected reduction in economic, social and environmental losses caused by climate impact as a result of implementing adaptation options. The annual expected loss is the product of the likelihood of a hazard and degree of consequential loss due to the hazard impact, and calculated using the following formula (ECA, 2009): 10

11 Loss = H (climate change hazard) VA (asset value exposure to risk) V (vulnerability) After obtaining the expected cost and benefit of various adaptation options, the expected cost-benefit of various adaptation strategies was assessed by calculating the degree of loss if the climate impact can be avoided through the adaptation strategy. Usually, in comparison to a wait-and-see or a business-as-usual (BAU) strategy, the resulting loss reduction constitutes the adaptation benefit. For a positive net present value (NPV), the present value of benefit must be greater than the present value of total adaptation costs. In other words, the cost/benefit ratio of an adaptation option must be less than 1 to be economically feasible. 3.2 CATLOG spreadsheet analysis To reduce the massive amount of time needed for quantitative analysis, we used the CATLOG application software (Stefan Trück, et.al, 2013) and research method developed by the Macquarie University, Australia to quantitatively analyse the above climate change risk and economic feasibility of adaptation options. The CATLOG comprises three steps: (1) risk identification, (2) risk analysis, and (3) decision on best adaptation option for risk management. The CATLOG steps are as follow: 1) Based on expert advice or historical data, use Bayesian estimation for Poisson distribution to estimate the frequency distribution of the impact risk of the extreme typhoon. 2) Based on expert judgment on the lognormal distribution median and 95 th percentile, determine the parameters for the impact loss severity distribution. 3) Consider the decision time horizon, discount rate and economic growth rate for the simulation. 4) Calculate the discounted present value of damage and loss DPVL. 5) Use the sensitivity analysis method to calculate DPVL under varied parameter values. 6) Input the initial investment capital cost of each adaptation option and the annual operation and maintenance cost. 7) Set the loss reduction of adaptation option on the likelihood of extreme event and loss severity magnitude. 8) After setting the adaptation cost and severity reduction benefit, use simulation to calculate the expected discounted present value of loss of non-adaptation and adaptation, DPVC and DPVL* respectively. 9) Estimate and calculate the NPV, cost-benefit ratio and sensitivity analysis of the adaptation options. The NPV for implementing the adaptation project is * NPV DPVL ( DPVC DPVL ). project Based on the logic of economic rationality, the expected NPV must be greater than zero. 11

12 Following the above analysis, the priority, feasibility and appropriateness of adaptation options of the electricity supply system for responding to climate change impact can be determined. 4. Research Results and Discussion 4.1Analysis of typhoon impact The Central Weather Bureau statistics for Taiwan s losses due to meteorological disasters showed that between , typhoons resulted in the most severe losses, followed by floods and downpours (as shown in Fig. 3). Between this periods, the combination of typhoon and downpour disasters totaled 94% of disaster losses in Taiwan, with typhoon contributing to 75% of the losses, and downpour, 19%. These data indicate that the impact risks of typhoons and downpours in Taiwan are critical factors that cannot be ignored. Figure 3:Annual losses from natural disasters by various categorized hazards Source:Central Weather Bureau (2014), Typhoon Affecting Taiwan: Analysis and Forecast Aids. Currently, typhoons are graded according to wind speed. Generally, climate change mainly affects typhoon in its intensity, but based on past experience and records, typhoon induced disasters in Taiwan are mainly due to floods, mudslides and landslides caused also by storms and downpours. However, strong typhoons are not necessarily downpour type/heavy rainfall typhoons, and hence study on how climate change result in typhoon disasters should first focus on typhoons that bring heavy downpours and extreme rainfall. Figure 4 shows the frequency of typhoons with over 1000mm of rainfall over the last 60 years. Before 2000, heavy downpour typhoons occurred about 12

13 once every 3-4 years, but after 2000, the frequency increased to an average of once every 1.5 years. From , of the top 10 heavy rainfall typhoons, five occurred after 2000, indicating that in recent years, the frequency and intensity of major heavy rainfall typhoon have increased. In Taiwan, regardless of financial losses or human injuries and fatalities, heavy typhoon downpours have resulted in significant disasters. Figure 4: The frequency of typhoon events with rainfall over 1000mm Source:Central Weather Bureau (2014) Figure 5 shows the trend for maximum typhoon rainfall value for past years ( ), where the trend line for maximum typhoon rainfall value shows significant increase. The 11-year moving averages indicate that the 11-year moving average for maximum typhoon rainfall value has increased significantly. Moreover, from , the 11-year moving average for the maximum typhoon rainfall showed a decreasing trend, but after 1985, the value showed an increasing trend. Therefore, the period between 1958 and 2013 can be divided into two stages. Stage 1 ranges from and Stage 2, The 2 stages constitute the before and after periods for analyzing changes in the probability distribution for the maximum rainfall of typhoon events as a result of climate changes. 13

14 Figure 5: The annual maximum rainfall of typhoon events over period in Taiwan Source:Central Weather Bureau (2014), Taipei, TAIWAN Figure 6 is the density distribution map for maximum annual typhoon rainfall probability. The distribution pattern is more consistent with a lognormal distribution, where the lower part of the figure (Figure (b)) represents Stage 1 distribution from and Stage 2 distribution from The statistical data shows that average maximum rainfall for Stage 2 is 992mm, which is an increase of about 190mm from the Stage 1 average. The average value for whole period is 900mm, a value between that of Stage 1 and Stage 2. Of the three values, Stage 2 showed the greatest standard deviation, indicating the greatest degree of variance. In terms of kurtosis, except for Stage 1 where the kurtosis is less than 3, Stage 2 kurtosis and kurtosis for past years are greater than 3, indicating a fat-tailed extreme value. In particular, Stage 2 showed the greatest kurtosis coefficient of 6.4. In terms of skewness, all three distributions were greater than zero and skewed to the right. Stage 2 maximum rainfall typhoon events showed a very right skewed compared to Stage 1, indicating a high degree of right skewness. The distribution map shows that in Stage 2, the distribution frequency for 900mm or more rainfall typhoon events was much higher than in Stage 1 while the distribution frequency for 300mm or less rainfall is lower; indicating that maximum rainfall of typhoon events in Stage 2 is increasing significantly. 14

15 (a) (b) mean mean mean median 698 median 921 median Stdev Stdev Stdev kurtosis kurtosis kurtosis skewness skewness skewness min 172 min 336 min 172 max 2162 max 3060 max 3060 Figure 6:The probability distribution of annual maximum rainfall of typhoon events 4.2 Input Data and Adaptation Option Setting Before simulating impact loss, event frequency, severity reduction, simulation probability distribution, simulation length and adaption option investment and maintenance cost must be first established. Table 3 shows the content of base scenario simulations for the severity of losses confronting Taipower in the event of heavy downpour type typhoons. The frequency setting is estimated according to the frequency of typhoons in Taiwan with over 1000mm of rainfall during 15

16 the period after 2000, and in this region, the frequency of event is about 0.64 times per year. The upper and lower limit for this setting are based on the frequency of typhoons with over 1000mm of rainfall over the past years, and the estimated values are 2 times and 0 time per year, respectively. Due to the lack of data, estimated loss is based on expert qualitative evaluation of assessed climate change impact on Taipower. Questionnaire survey indicated that experts believe that Taipower s financial loss due to the impact of typhoon with heavy downpour is mainly of Magnitude 4, which defines the loss at $1-5 billion range. Hence, assuming that the financial loss is $2.9 billion in the event of a typhoon disaster, the estimated loss at the 95 th percentile is of Magnitude 5 at an estimated value of $7.5 billion. The loss simulation probability distribution is based on the abovementioned annual maximum typhoon downpour, and its pattern is more consistent with a lognormal distribution. Hence the disaster loss simulation is made based on a lognormal distribution. Furthermore, in adapting to various impacts, it is assumed that adaptation options can only change the distribution probability of the disaster impact severity magnitude, but cannot change the probability distribution of heavy rainfall typhoon event frequency. Table 3:Input data set for business as usual scenario (wait and see) frequency 0.64 per year, upper=2 and lower=0 Median of impact severity NT$ 2.9b 95 percentile of impact severity NT$ 7.5b Severity distribution Lognormal Simulation period 50 years Discount rate and economic growth rate 2% 3% respectively Table 4 shows the assumed data for various adaptations to downpour typhoons, including initial investment cost, annual operations and maintenance cost, and loss reduction as the result of adaptation implementation (expressed in percentage cut). Then based on the assumed data, a cost-benefit simulation is analyzed for the adaptation options in response to downpour typhoons. 16

17 Table 4: Assumed input data set for adaptation options for flood risk management Severity Initial Adaptation options reduction investment (%) unit: NT$100M O&M cost 1. Wait and see, base case scenario; Develop GIS and database for hazard maps and vulnerability assessment; Incorporate climate risk assessment and cost-benefit analyses into electric sector planning and asset design Monitoring vulnerable generation, transmission and distribution assets and designing hardening measures Review and upgrade design codes and standards for assets and infrastructure for climate resilience Flood sectionalizing and adaptive dispatch in response to flooding Harden existing infrastructure against flooding and water infiltration, such as levees, sandbags, water pumps, water gates, floodwalls, etc Develop and enhance early warning system and awareness-raising Develop and practice contingency and restoration plans Selective developing smart grid technology Invest and enhance ancillary services including demand side management, operating reserve, regulation, blackstart and voltage support, etc. 12. Develop flood protection measures and improve downpour management with other sectors and community Results and discussion Figure 7 shows the distribution of Taipower s base case scenario simulated loss in the event of downpour typhoon. Based on 10,000 simulated losses, the loss distribution map constructs the total loss probability distribution for a 50-year period, and estimates an average total loss of $82.5 billion for Taipower in the next 50 years as a result of downpour typhoons. This value is greater than the median $81.2 billion in total loss. The total loss distribution is skewed right, the estimated total loss at the 99 th percentile is $142.6 billion, and the most severe loss is about 1.7 times that of average loss. The loss distribution shows that Taipower has a total loss of $ billion for a 50-year period, with a higher likelihood of $70-90 billion in losses and a relatively low likelihood of $140 billion or more in losses. 17

18 Loss Distribution Scenario Mean 825 Median 812 Standard Deviation % of loss 1,124 95% of loss 1,219 99% of loss 1,426 Figure 7: The simulated impact results of extreme typhoon events under the wait and see scenario during a 50 years period Table 5 and Figure 8 for Taipower show the results of simulated loss and distribution following Taiwpower s adaptation to downpour typhoons. We individually simulated the loss distributions following the 11 flood adaptation options proposed by Taipower. Compared to other options, the Contingency and Restoration plans measure resulted in the greatest reduction of total loss for Taipower for the next 50 years. After adopting this option, Taipower s total downpour typhoon losses for the next 50 years is reduced to an average of $37.5 billion, and total loss at the 99th percentile is significantly reduced to $59.7, which is 2 times less than the most severe loss prior to any action. Next, the adaptation option of constructing and hardening infrastructure against flooding the total average downpour typhoon loss for Taipower for the next 50 years is reduced to $45.7 billion, and total loss at the 99th percentile significantly reduced to $73.4 billion, which is 2 times less than the most severe loss compared to non-action. The Develop GIS/hazard maps adaptation measure has the least effect on loss reduction. Implementing this measure reduced Taipower s total average downpour typhoon loss to $79.1 billion, and total loss at the 99 th percentile is reduced to $132.8 billion. Although not large, losses are reduced compared to losses prior to any action. In other words, specific and appropriate adaptation actions are far more effective than raising awareness. Raising awareness alone is inadequate for reducing disaster, and must be coupled with the use of appropriate adaptation measures. The figure also shows that the 50-year loss distribution for Contingency and restoration plans is in the $20-70 billion range, with a higher likelihood of $30-40 billion in losses and a relatively low likelihood of total loss exceeding $60 billion. Next, in the hardening and construction of infrastructure against flooding option, the 50-year total loss distribution is in the $20-80 billion range, with a higher likelihood of $35-50 billion in losses and a relatively low 18

19 likelihood of total loss exceeding $70 billion. Compared to the abovementioned, the total 50-year base case scenario loss prior to any adaptation action by Taipower is in the $ billion range. Following adaptation, the total loss distribution range is not only significantly reduced; losses in the higher likelihood ranges and the amount of extreme high losses are also significantly reduced. Evidently, if Taipower was to prepare for adaptation prior to the risk impact of downpour typhoons, the severity of losses caused by extreme typhoon risk impact can be effectively reduced. Figure 8: Simulated loss distribution with respect to different flood adaptation options adopted by Taipower 19

20 Table 5: Simulated loss distribution and statistic parameters with respect to different flood adaptation options adopted by Taipower Loss Distribution Abbreviation Flood adaptation options Mean Median Standard Deviation 90% of loss 95% of loss 99% of loss BAU Wait and see( Base Case Scenario) ,124 1,219 1,426 GIS Develop GIS/hazard maps ,077 1,162 1,328 RACBA Risk assessment and CBA MHGTD Monitoring and hardening vulnerable Gen,T&D assets ,119 C&S Upgrade design codes, standards for climate resilience ,016 1,108 1,273 FLSEC Flood sectionalizing and adaptive dispatch ,117 HDINFR Harden existing infrastructure against flooding EWS Early warning system and awareness-raising CRP Contingency and restoration plans SGRID Selective developing smart grid ANCIL Investing and enhancing ancillary services FLPM Develop flood protection measures and improve downpour management Figure 9 simulates Taipower s expected cumulative downpour typhoon losses for the next 50 years. In the BAU scenario, losses increased from $1.2 billion in the first year to $82.5 billion in the 50 th year. Among other adaptation options, contingency and restoration plans resulted in the least loss of about $57.3 billion. Initial investment cost for the smart grid option was too high, resulting in cumulative loss for the first 38 years being greater compared to the BAU scenario, and becoming less than the BAU scenario only during the latter stage. The cumulative loss of other adaptation options compared with the BAU scenario is also shown in Figure 9. 20

21 Figure 9: Simulated cumulative loss with respect to different flood adaptation options adopted by Taipower Figure 10 shows the outcome of the economic analysis of the expected cost-benefit of various adaptation options for downpour typhoons. The analysis outcome of each adaptation option is represented by 3 statistical values, namely benefit (reduction in impact severity of BAU scenario), adaptation option cost, and expected net present value (net benefit) of the adaptation option. The Contingency and restoration plans investment has the highest net present value, followed by Hardening infrastructure against flooding, Risk assessment and CBA for feasibility. Develop flood protection measures and improving downpour management showed a relatively high net present value, while Develop GIS/hazard maps showed the lowest value. 21

22 Figure 10: Simulated cost and benefits with respect to different flood adaptation options adopted by Taipower Figure 11 shows the outcome of the economic analysis of the total cost-benefit of adaptation options for downpour typhoons at the 99th percentile. The figure can be interpreted as the adaptation option cost-benefit for the worst case of downpour typhoon impact. The analysis outcome for each adaptation option is represented by 3 statistical values, namely, benefit, adaptation option cost, and expected net present value of the adaptation option. The ranking for the net present value of adaptation options is consistent with the average climate impact scenario. The top four options are Contingency and restoration plans Constructing and hardening infrastructure against flooding, Risk assessment and CBA for feasibility of adaptation measures, and Develop flood protection measures and improving downpour management, respectively, and the lowest option is Develop GIS/hazard maps. Among the adaptation options, Upgrade asset design codes, standards for climate resilience has a lower investment cost, and therefore the lowest cost-benefit ratio and the most cost effective, followed by Flood sectionalizing and adaptive dispatch. Although Develop GIS/hazard maps does not have a high investment cost, its benefit is low, and only a little higher than its investment cost. Hence it has the lowest cost-benefit ratio and the lowest investment net present value. 22

23 Figure 11: Simulated cost and benefits with respect to different flood adaptation options adopted by Taipower under worst scenario 5. Conclusions and Recommendations Current stochastic cost/benefit analysis of economic feasibility for assessing flood management options addresses the climate uncertainty only. The method to identify the costs and benefits is limited to the calculation of cash flow in each time period arising from the adaptation options. The cyclic dynamic characteristics of interactions among the climate, adaptation options and the electricity supply and demand system are neglected in the calculation. Actually, the state of the electricity supply and demand system will be affected by both the adaptation options adopted and the uncertain climate changes occurred each year. After assessing the initial state of the electric system at the beginning of each period, the decision maker will in turn determine the feasible adaptation options which will again affect the system for the next decision period. Therefore, the better holistic economic method to derive adaptation options is of recursive and adaptive nature. Current stochastic costs and benefits calculation is of static in nature and omit the imputed value of the state which will be affected by the adaptation options adopted in each year. The future further study of the economics of adaptation should focus on the adaptive and dynamic nature of the system and its related risk management decision making. The climate change adaptations against flood are mainstreamed into the company s development strategy, which comprises 2 steps: (1) adjust the direction of existing policies and measures, and (2) compensate for policy gaps in existing measures. In terms of the impact of natural disaster on electricity transmission and distribution, Taipower s 12 existing electricity 23

24 transmission measures for risk management and 3 measures for electricity distribution are corresponded to the study s risk adaptation options for impact of risk on electricity transmission and distribution facility due to downpour typhoons, rainstorm floods or mudslides, and comparison showed that Taipower s existing measures are fully within the range of risk adaptation options of this study. However, the adaptation options listed below are not included in the policy gap compensation, and therefore new risk management measures should be added. To reduce the impact of floods on electricity transmission and distribution facilities, the following 8 preventative and response measures to compensate for policy gaps are recommended: Construct hazard maps to identify, and circumvent high vulnerability areas in the electricity system by planning reinforcement or relocation of a new facility; Closely monitor high vulnerable T & D assets, and implement preventive and reinforcement measures (e.g. reinforce grid tower pillars and protect slopes); Construct and harden infrastructure against flooding, such as coastal barriers, dams, dikes, water gates, pumps, sandbags and walls; Strengthen and improve early landslide warning systems, enhance disaster risk awareness and improve weather warning systems; Selectively develop a smart grid system; Invest in distributed generation, such as a micro-grid system, PV, combined heat and power (CHP), electric vehicle (EV), fuel cells and small wind turbines; Speed up service restoration via pre-connections for mobile substations; Improve backup generation for major critical customers. Finally, in severity modelling in this study, Bayesian estimation was not adopted due to lack of observable data of damages. Further study may adopt Bayesian estimation in severity modelling when data are available. 24

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