Applications of Stochastic Control for
Sustainable Energy Planning in Taiwan
Chun-ChiaoYeh
National Taipei University, Republic of China (Taiwan)
Chien-Ming Lee
National Taipei University, Republic of China (Taiwan)
2015/8/6 Taipei City, 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
 Fukushima nuclear disaster (2011.3.11)
 New Energy Policy in Taiwan (2011.11.3)
Ensure nuclear safety, reduce verification, create green lowcarbon environment, and gradually move towards a nuclearfree 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.
3
Research motivation
4
Literature reveiw
 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)
5
Literature review
Possible gateways within a CCS policy framework
Source：IEA(2012)
6
Literature review
qualitative research for incentive policies
Subsidy on
Investment
Production subsidy
Help financing by debt
Faster pace of deployment
Policy cost control
Output performance
based
No
Yes
Yes
No
Low
Yes
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
Variety
Mandate
Eeffectiveness
Static efficiency
Informational
asymmetry
Risk with
credibility of
public
commitment
Source：Finon (2010)
Rapid deployment
when timing is
appropriate
Cost inefficiency by
forcing deployment
Theory Model
 Carbon price (P ) follows Geometric Brownian
Motion (GBM)


dz
dP  Pdt   Pdz
: Growth rate parameter
: volatility parameter
: standard wiener process
 CCS parameter data
 Zero Emission Platform (2011)
 Szolgayova et al. (2008)
8
 Luis and Chamorro (2008)
Investment decision for two stage projects
under carbon price uncertainty
Investment model
for 1st stage
Investment model
for 2nd stage
1   2 B2 P1 
 1  1
1   2 B2 P
C

 1  1
 1   I 2   0

r

C

 1   I 2  I1   0

r

*
*
*
S1 P1  F1 P1  V P1  I 2  I1  0
* 2
price
   
 
C ：operating cost
I1 , I 2：sunk cost
P1* , P2*：critical carbon
P1*


P2*
* 2
2
      
V P , V P ：Value of projects
F P , F P ：option value of projects
S P , S P ：defer value
S 2 P2*  F2 P2*  V P2*  I 2  0
*
1
*
1 1
1
*
1
*
2
2
*
2
2
*
2
Critical Price in first stage 1,666 (NT dollars/ton)
Critical Price in second stage 1,578 (NT dollars/ton)
9
Business model innovation for CCUS
Areas
Building
Blocks
Contents
Offer
Value
propositions
Carbon credits, CO2 utilization,
Low carbon electricity fee and products,
Reduce CO2 emission, CCUS technology
Customer
segments
Customers
Channels
Customer
Relationships
Key resources
Infrastructure
10
Low carbon products, Carbon credits
demand and supply, CO2 demander
CO2 pipes, Electricity network,
Transaction platform of carbon credits
Public engagement, Merge platform
Governmental incentives
Key activity
CCS technology
Key partnerships
CCUS industry hub
Financial
Cost Structure
Electricity penalty
viability
Revenue stream
Pricing for products
CCUS business model simulation
Sell
carbon
credits
1st stage
1st
Scenario 2nd stage
11
Low
Government
carbon
CO2
electricity Utilization
subsidy
fee
X
X
○
○
X
X
○
○
1st stage
X
X
2nd
○
○
Scenario 2nd stage
X
X
○
○
1st stage
X
X
3rd
○
○
Scenario 2nd stage
X
X
○
○
1st stage
X
X
4th
○
○
Scenario 2nd stage
X
○
○
○
1st stage provide 12% and 2nd stage provide 8% electricity
demand, ○：adapted , X：not adapted
1st Scenario
carbon credits and government subsidy
 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.
S1(P), S2(P) (million NTD)
0.16
P2*(1578)
P2*_OG(1504)
0.14
0.12
P1*(1666)
P1*_OG(1590)
P1*_CG(1617)
0.10
0.08
0.06
0.04
0.02
0.00
1300
1400
1500
1600
1700
P (NTD/ton)
S1(P) 1st
S1_OG(P)
12
S1_CG(P)
defer value w/o capital grant
1st operation grant 5%
1st capital grant 59%
2nd defer value w/o capital grant
S2_OG(P) 2nd operation grant 5%
S2(P)
2nd Scenario
carbon credits and low carbon electricity fee
and w/o government subsidy
 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 2nd stage
251
518
1071
(NTD/ton)
Low carbon electricity fee (NTD/ kw/h)
13
0.93
0.72
0.27
3rd Scenario
1st stage w/ carbon credits and government subsidy
2nd stage w/ carbon credits and CO2 utilization
 CO2 price increases as carbon price increases.
 The firm should adapt CO2 utilization strategy while carbon price
is lower.
Critical
Carbon price
(NTD/ton)
1456
685
14
318
The variation of CO2 price
Storage
ratio
100%
70%
50%
0%
100%
70%
50%
0%
100%
70%
50%
0%
Lowest
Carbon price
(NTD/ton)
1071
518
251
Utilization
ratio
Critical CO2 price
(NTD/ton)
0
30%
50%
100%
0
30%
50%
100%
0
30%
50%
100%
N/A
2354
1841
1456
N/A
1075
852
685
N/A
474
385
318
4th Scenario
1st stage w/ carbon credits and low carbon electricity fee
2nd stage same and w/ CO2 utilization added
The variation of low carbon
electricity fee in 1st stage
carbon price in
1st stage
(NTD/ton)
Low carbon
electricity fee
(NTD/ kw/h)
361
745 1540
0.90 0.65 0.09
The variation of low carbon
electricity fee and CO2 price in 2nd stage
Critical
Carbon
price
Storage
Lowest
Utilization
(NTD/ton)
ratio
Carbon
price
ratio
Critical
CO2 price
Low carbon
electricity
fee
0
30%
50%
100%
0
30%
50%
100%
0
30%
50%
100%
N/A
2354
1841
1456
N/A
1075
852
685
N/A
474
385
318
0.27
0.21
0.18
0.09
0.72
0.69
0.68
0.64
0.93
0.92
0.91
0.90
1456
685
318
15
100%
70%
50%
0%
100%
70%
50%
0%
100%
70%
50%
0%
1071
518
251
Conclusion
 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.
16