Transcript Document
Stockpile Strategy for China's Emergency Oil Reserve: A Dynamic Programming Approach
Yang Bai a , Carol A Dahl b , Dequn Zhou a , Peng Zhou a a
Research Centre for Soft Energy Science, College of Economics and Management, Nanjing University of Aeronautics and Astronautics
b
Mineeral and Energy Economics Program, Colorado School of Mines, Economics, College of Business Administration, King Saud University, Riyadh
Introduction
China now accounts for
12.5%
of total world oil imports China imports more than 50% of its petroleum.
World oil import structure 2010 China’s oil consumption and production, 1983-2010 consumption Oil import Net import dependency has been over 50% Production
Source: BP Statistical Review of World Energy, 2011.
Source: EIA, International Energy Statistics.
Layout of China’s SPR sites for three Phases
China aims to hold a capacity equivalent to 90 days of net petroleum import which is 440 million barrels .
The reserve facilities is expected to take 15 years in three phases, with a total investment of 100 billion RMB.
Phase I (2004 – 2009) : 102 mln barrels of storage capacity in 4 separate sites .(finished) Phase II (2009 - TBA) : 169 mln barrels of storage capacity in 8 separate storage sites .(Under construction) Phase III (finish before 2020) : 169 mln barrels of storage capacity with the number of sites still to be determined. (Planned).
Research purposes
Modeling for China’s stockpile and drawdown policy; Simulate China’s
optimal stockpile and drawdown policy
representative numerical examples.
Estimate the
influence of stockpile on oil price
.
with
Methods
To estimate the cost of stockpile, we take into account
consumer cost
,
acquisition cost
(or drawdown revenue) and
holding cost
. Basic method:
Equilibrium condition supply demand consumer cost oil price Cost function acquisition cost holding cost Stockpile for SPR Additional demand
Inventory size 169 MB 0 MB Jan.
Feb.
Mar.
Nov.
Planning stockpile period (1 year) Dec.
Month
Methods
Dynamic programming algorithm for an oil stockpile
u
1
p
1
s
1
D emand u p t t t p t
s t
1
t
( ,
t t t
1
S upply s t u t
p t
1
f t
1 (
u t
1 ,
p t
1
s r t s t
1
s T
1
T T
,
T p T T
1
u t
1
u T
Algorithm starts from T, works backward till 1
D emand
(u,p,t) :demand function.
S upply
(i,t) :supply function.
s :existing capacity (state variable).
u :stockpile acquisition size (control variable).
f (u,p,s,r) :The objective function M :planning SPR capacity.
p :oil price.
r :discount rate.
s T M
Methods
The SPR model
t
( )
t
u t
t
min
t
0,
M
V
( , )
t t
1 1
r f t
1 (
s t
1 ) ,
t
T
,...,1
f T
1 (
s t
1
V s u t
)
S
u t upply
t
0
p t
0
t
, , )
t
D emand
0,
M
.
t
t
t
u t
Initial implementation
Market state
normal state (i=0); disruption state (i=1, 2…)
Supply and demand illustration
Demand function:
D emand
Supply function:
S upply
Price assumptions
t
i
)
Q
0
p t
u t
The price growth with a certain rate in base case.
Price growth scenarios: Low growth trend; Medium growth trend; High growth trend.
Initial implementation
Parameters in the model No.
1 2 3 4 5 6 7 8 9 Parameter
M T Q 0 p 0 q ν σ g λ
Value 169 12 260 63 114 1 -0.2
1% 1.5% 2% 5% 10% Units Million barrels Months Million barrels Dollar per barrel Million barrels Dollar per barrel —— —— —— —— —— —— Description Planning stockpile capacity Stockpile period Supply amount in normal state Oil price in normal state Minimum short-run level of consumption Holding cost per unit Price elasticity Medium growth rate Normal growth rate Low growth rate 5% disruption 10% disruption
Simulation results
1. Result for base case In base case, China’s optimal acquisition size varies from 4 to 19 million barrels per month.
Fig. 1 Optimal stockpile and inventory size over time in base case
Simulation results
The acquisition of stockpile drives the price up by approximately depending on the acquisition size.
15% to 48% Fig. 2 The effect of stockpile acquisition on oil price
Simulation results
2. Results under various price assumptions The stockpile strategy could vary significantly to different price assumptions.
Fig. 3 Optimal inventory trajectories to various growth rate of oil price
Simulation results
3.Results under disruption scenarios When
5%
cutback happens,
6
million barrels of stocks are suggested to released to the market.
Fig. 4 Optimal stockpile strategy under different disruption scenarios
when
10% 10
disruption occurs, million barrels of the capacity should be released.
Simulation results
When
5%
cutback of supply happens, the oil price increases by With the drawdown of stockpile, the oil price decreases
5%
.
9%
and goes up to
$95/bbl
.
In the case of
10 10%
disruption, the price increases by 31% and surges to
$115/bbl
million barrels is released to the market, the oil price decreases significantly to . When
$78/bbl
.
Fig. 5 Oil price variation in response to disruptions
Conclusions
The optimal monthly stockpile acquisition size varies from
4 to 19 million barrels per month
. The best acquisition amount and time mainly depend on the oil price.
Stockpiling exerts an
upward pressure on price
the acquisition size. While our representative example shows the stockpile drives up the oil price by
15% to 48%.
. The impact depends on In a disruption, we find that the drawdown of stockpile
soaring price dampens the
effectively. However, the optimal drawdown should
leave price higher
than before the disruption.
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