Chun-Chiao Yeh National Taipei University, Republic of China (Taiwan) Chien-Ming Lee National Taipei University, Republic of China (Taiwan) 2015/8/6 Taipei City, Taiwan Applications of Stochastic Control for Sustainable Energy Planning in Taiwan 1

Outline  Introduction Background, Research motivation  Literature review  Theory Model  Evaluations results CCUS Business model innovation Incentive policy evaluation Investment decision for the firm  Conclusions 2

Background 3 Fukushima nuclear disaster ( ) New Energy Policy in Taiwan ( ) Ensure nuclear safety, reduce verification, create green low- carbon environment, and gradually move towards a nuclear- free homeland The existing No. 1, 2 and 3 nuclear plants will be decommissioned when their lifespan is up, without us taking steps to extend their time of service. The No. 4 plant that is being built will only begin operations after we ensure safety.

Research motivation 4

Literature reveiw 5 CCS investment paradox High cost, immature technology (Jaffe and Stavins,1994; Tonn and Martin, 2000) Electricity penalty (Szolgayova et al., 2008) Environmental impact (GCCSI, 2013) Real options Investment uncertainty (Dixit and Pindyck, 1994) Green energy investment (Fuss et al., 2009; Sudduqyu and Fleten, 2010) Carbon price uncertainty (Szolgayova et al., 2008; Abadie and Chamorro, 2008)

6 Literature review Possible gateways within a CCS policy framework Source ： IEA(2012)

Literature review qualitative research for incentive policies Mandate Subsidy on Investment Production subsidy Eeffectiveness Rapid deployment when timing is appropriate Help financing by debtFaster pace of deployment Static efficiency Cost inefficiency by forcing deployment Policy cost control Output performance based Informational asymmetry NoYes Risk with credibility of public commitment NoLowYes Who pays Electricity consumers 1. Public budget 2. Electricity consumers 1. Electricity consumers(FIT) 2. Public budget Dynamic Learning cost decrease by rapid deployment. Variety Source ： Finon (2010)

Theory Model 8 Carbon price (P ) follows Geometric Brownian Motion (GBM) : Growth rate parameter : volatility parameter : standard wiener process CCS parameter data Zero Emission Platform (2011) Szolgayova et al. (2008) Luis and Chamorro (2008)

Investment decision for two stage projects under carbon price uncertainty 9 ： critical carbon ： sunk cost ： operating cost ： Value of projects ： option value of projects ： defer value Investment model for 1 st stage Critical Price in first stage 1,666 (NT dollars/ton) Critical Price in second stage 1,578 (NT dollars/ton) price Investment model for 2 nd stage

Business model innovation for CCUS 10 Areas Building Blocks Contents Offer Value propositions Carbon credits, CO 2 utilization, Low carbon electricity fee and products, Reduce CO 2 emission, CCUS technology Customers Customer segments Low carbon products, Carbon credits demand and supply, CO 2 demander Channels CO 2 pipes, Electricity network, Transaction platform of carbon credits Customer Relationships Public engagement, Merge platform Infrastructure Key resources Governmental incentives Key activity CCS technology Key partnerships CCUS industry hub Financial viability Cost StructureElectricity penalty Revenue streamPricing for products

CCUS business model simulation 11 Sell carbon credits Government subsidy Low carbon electricity fee CO 2 Utilization 1 st Scenario 1 st stage ○○ XX 2 nd stage ○○ XX 2 nd Scenario 1 st stage ○○ X X 2 nd stage ○ X ○ X 3 rd Scenario 1 st stage ○○ X X 2 nd stage ○ X X ○ 4 th Scenario 1 st stage ○ X ○ X 2 nd stage ○ X ○ ○ 1 st stage provide 12% and 2 nd stage provide 8% electricity demand, ○ ： adapted, X ： not adapted

1 st Scenario carbon credits and government subsidy 12 2 nd defer value w/o capital grant 2 nd operation grant 5% 1 st defer value w/o capital grant 1 st operation grant 5% 1 st capital grant 59% (million NTD) (NTD/ton) The firm gets revenue from selling carbon credits and receives the subsidy from government to fill the gap of cost. To compare the incentive policy, operation grant is better than capital grant for lowering the critical value of carbon price.

2 nd Scenario carbon credits and low carbon electricity fee and w/o government subsidy 13 Low carbon electricity fee decreases as carbon price increases. The firm should adapt low carbon electricity fee strategy while carbon price is higher. The variation of low carbon electricity fee Lowest carbon price in 2 nd stage (NTD/ton) Low carbon electricity fee (NTD/ kw/h)

3 rd Scenario 1 st stage w/ carbon credits and government subsidy 2 nd stage w/ carbon credits and CO 2 utilization 14 The variation of CO 2 price Critical Carbon price (NTD/ton) Storage ratio Lowest Carbon price (NTD/ton) Utilization ratio Critical CO 2 price (NTD/ton) % N/A 70%30% % %100% % 518 0N/A 70%30% % 852 0%100% % 251 0N/A 70%30%474 50% 385 0%100%318 CO 2 price increases as carbon price increases. The firm should adapt CO 2 utilization strategy while carbon price is lower.

15 carbon price in 1 st stage (NTD/ton) Low carbon electricity fee (NTD/ kw/h) Critical Carbon price (NTD/ton) Storage ratio Lowest Carbon price Utilization ratio Critical CO 2 price Low carbon electricity fee % N/A %30% % %100% % 518 0N/A %30% % %100% % 251 0N/A %30% % %100% th Scenario 1 st stage w/ carbon credits and low carbon electricity fee 2 nd stage same and w/ CO 2 utilization added The variation of low carbon electricity fee in 1 st stage The variation of low carbon electricity fee and CO 2 price in 2 nd stage

Conclusion 16 We provide effective incentive policy, reasonable operation price signals under carbon price uncertainty to reduce the “investment paradox”. The firm can have better investment decision and business strategy due to the critical price signals. We will include fuel price uncertainty in this model to have more precisely evaluation 。 It would be better if we can consider the benefit of energy security and health improvement due to adapt CCS technology in the future reserch.