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XI’AN JIAOTONG UNIVERSITY Wang Xiuli Xian Jiaotong University Chicago, USA July 2017 Renewable Energy Trading in Cross-Region Power Market of China
XI’AN JIAOTONG
UNIVERSITY
Wang XiuliXian Jiaotong University
Chicago, USA
July 2017
Renewable Energy Trading in
Cross-Region Power Market of China
Outline
Back Ground of Power Market in China1
Cross-regional Power Trading Method Considering Network Problems
2
Conclusion and Outlook3
1
Low Carbonized Goal of China
On November 22th, 2009, State Council of China announced
that, “By 2020, China’s carbon dioxide emission for per unit of
GDP decreases by 40%-45%, compared to that in 2005.”
2020 Goal
Medium Term Goal
Carbon dioxide emission reaches its peak around 2030 and as
early as possible, decreases by 60%-65%, compared to that in
2005.
It is not only the inherent requirement of sustainable
development of China, but also the international obligations
and responsibilities of developing countries to promote clean
energy transformation and low-carbon development.
Chinese Attitude
2
Electricity production is accounting for 40%~50% of total emissions.
By 2030, the electricity energy , twice as 2016, will reach to 11000 TWh.
The CO2 emission will reach peak in 2030.
Carbon emission in power industry needs slow growth and fast decline.
Carb
on E
mis
sio
n A
mount
（100 m
illio
n tons）
3
Low Carbonized Goal of China
Energy TypeGeneration
Capacity /TWProportion
Generated Energy / TWh
Proportion
Thermal 1.054 64.0% 4288.6 71.60%
Hydro 0.332 20.2% 1180.7 19.71%
Nuclear 0.034 2.10% 213.2 3.56%
Wind 0.149 9.10% 241.0 4.02%
Photovoltaic 0.077 4.7% 66.2 1.11%
Total 1.646 5989.7
The power energy produced by thermal power still covers 71.60% while the
renewable energy only occupies 5.12%. But the total curtailed energy of wind
and photovoltaic power are 49.7 and 7.42 TWh respectively.
How to Curb the Mega Clean Energy Waste?
Power Generation of China in 2016
4
With the rapid growth of the installed capacity, the renewable energy
consumption problem has become increasingly prominent.
In China 2016, the overall curtailed ratio of wind and photovoltaic power
reached 17% and 9.6% respectively. Some provinces in the northwest have
witnessed extremely serious curtailment, such as
Challenges of Renewable Energy Consumption in China
Energy Type Curtailed Ratio Curtailed Energy/TWh
GansuWind 43% 10.4
photovoltaic 31% 2.6
XinjiangWind 38% 13.7
photovoltaic 32% 3.1
The renewable energy just covers a relatively low generation share(~5%),
but suffers from very serious curtailment.
Why ? 5
Wind Energy Distribution Hydropower Plants Distribution
The main load is distributed in the east and south part. The inverse distribution is among the reasons for the curtailment.
So long-distance transmission/large-scale consumption of renewable
energy is the inevitable choice to achieve low-carbon emission target.
Challenges of Renewable Energy Consumption in China
Renewable energy in
northwest
Hydro energy in southwest
6
There are 7 regional grids in China. Ultra-high Voltage AC/DC Grids over
regions have been developed.
The bulk transmission system lays a good basis for the cross-regional
consumption of renewable energy.
Challenges of Renewable Energy Consumption in China
7
Current status and bottlenecks:
I. Power source structure is unreasonable, and the system is
lack of the ability for peak load regulation.
II. The consumption of renewable energy by cross-regional
transactions is still inadequate.
III. The current market mechanism limits the renewable energy
consumption in whole country.
Challenges of Renewable Energy Consumption in China
We need to send energy from west to east
for wide consumption.
8
The publication of “Opinions on further deepening of the power industry reform ” 2015
marked the start of a new round of power system reform. Series of related
documents play an active role.
Promote the reform of transmission/distribution prices
Promote the construction of power markets
Build and regulate the power exchange center
Deregulate the generation schedule
Promote the reform of electricity retail market
Strengthen the management of coal-fired captive power plants
New Round of Power Industry Reform
The renewable energy consumption will be promoted by the market
methods especially by the cross-regional transactions.
9
2017-
Started at 2002
Separate generation
and grid companies.
transmission and
distribution price
reform not included
2015-20172002-2015
New round of power
system reform
restarted since 2015
Establish provincial
transmission and
distribution pricing
system to customers.
Beijing and Guangzhou
power exchange
centers are erected.
More than 6000 power
retail companies are
funded.
There is no unified
transmission price of
regional power
wheeling.
Reform process of power industry
Reform process in China
10
电源类型完备
Transmission and Distribution Pricing
Method in China
Permitted income Average price =
electric power transmitted and distributed by the power grid
Calculation Method of Average Transmission and Distribution Price of
Provincial Power Grid
11
Transmission
and distribution
price
transmission and distribution price
of different voltage levels
transmission and distribution price
of different User categories
Stamp method
Apportioned To
Transmission/distribution price for big industries
Transmission/distribution price for other users
12
Transmission and Distribution Pricing
Method in China
Examples in some provinces（￥/kWh）
Cost results of the studied cases
ProvinceBig industry Others
1-10kV 35kV 110kV 220kV <1kV 10kV 35kV
Beijing Power Grid
(North)0.1956 0.1751 0.1508 0.1493 0.4674 0.4505 0.4263
Shenzhen Power
Grid (South)0.1794 0.1354 0.0679 0.0537 0.1794 0.1354
Anhui power grid
(East)0.1784 0.1634 0.1484 0.1384 0.3932 0.3782 0.3632
Shaanxi Power
Grid (West)0.1484 0.1284 0.1084 0.1034 0.3917 0.3717 0.3517
Hubei power grid
(Middle)0.1329 0.1131 0.0950 0.0760 0.4862 0.4662 0.4462
• Provincial price characteristics: west less than east; traditional load area
significantly higher than the power output area.
• The big industry price should pay extra capacity fee.
Reform of Transmission and
Distribution Price in China
13
Outline
Back Ground of Power Market in China1
Cross-regional Power Trading Method Considering Network Problems
2
Conclusion and Outlook3
14
Cross-region Renewable Energy Trading
Cross-regional transaction model to optimize allocation
of resources
The large-scale
transmission line
Clean energy is
in the west of China
Main load is in the south
east
The energy resources and demand
distribution is not balanced. The
cross-provincial/regional transactions
are in great need for large-scale
renewable resource allocation.
The network structure in China is very
complex. There are many optional
paths for power transactions.
The key problem is how to optimize
the transaction path, price the energy
transmission and finally promote the
large scale renewable energy
consumption.
15
Operation of Beijing/Guangzhou Power Exchange Center
Mid/Long-term transactions based on bidding and matchmaking is adopted.
The energy transaction is greatly promoted especially the renewable energy
transaction. Taking the Beijing Ex. (2016) as an example,
The main defect is that transaction path capacity limits, transfer cost and
transmission loss are not included.
We put forward a method to determine the optimal transaction paths
between multi-buyer and multi-seller, considering the transfer cost,
loss, and capacity limits, to make the clearing match results more
feasible and efficient.
Types Amount (TWh) Increase Rate
Energy 774.4 7.7%
Renewable Energy 362.8 21.9%
16
Model and Methodology
Basic Models in the Cross-regional transaction
Cross-regional transaction path
model considering under
several objectives
Cross-regional path
transaction model
considering fixed clean
energy paths
The risk analysis of clean
energy
Risk: The
bias
punitive
measures
Mid/Long-term
contracts
market Graph theory and network
flow model
The bidding strategies
BuyerSeller
The bidding price Demand side bidding
Centralized matching model
under different objectives
Modeling
Theoretical Analysis
Bidding strategies
Real time
balancing
market
17
Model and Methodology
Step 1 Optimization model of cross-provincial/ regional power transaction
To promote the wider cross-regional renewable consumption via market methods
provide the transaction path and amount considering transfer losses and cost
Step2 Cross-regional power generation right transaction
The generation right is the contracted electricity allocated tothermal units with priority.
The trade is an effective measure to optimize electric powerdispatching and promote clearing energy utilization.
Replace the thermal energy by the renewable energy via the generation right transaction according to double-win principle.
18
Model and Methodology
Network flow optimization theory
Consider the network topology and branch
transmission capacity directly
Meet the Kirchhoff current law.
The bidirectional arc represents the transmission
channel, the transmission capacity and other
parameters on the correspondingarc are
represented by 𝑐𝑖𝑗 , 𝑓𝑖𝑗
Equivalence network in network flow model
purchase province G, demand province L, the transit
province A
transmission channel is simplified as the arc
the virtual node t connected to all the demand nodes
and the virtual node s connected to all the supply
nodes
19
s t
Buyer biding
Model and Methodology
Step 1 Cross-provincial/regional power transaction model
Seller biding
Losses Cost
Objective function:
• Channel capacity constraint
Constraints:
• Node flow balance constraint
• Forward and backward flow utilization hours constraint
O—Sellers Set
D—Buyers Set
max𝐸 =
(𝑜,𝑑)∈𝑍
𝑥𝑖𝑗𝑜𝑑𝑏𝑖𝑗𝑜𝑑 𝑏𝑖𝑗
𝑜𝑑 =
−𝑝𝑜 𝑗 = 𝑜, 𝑖 = 𝑠, 𝑜 ∈ 𝑂𝑝𝑑 𝑖 = 𝑑, 𝑗 = 𝑡, 𝑑 ∈ 𝐷−𝑢𝑖𝑗𝑝𝑗 − 𝑘𝑖𝑗𝑝𝑗 𝑜𝑡ℎ𝑒𝑟
Transmission Cost
20
Step 2 - Power generation right transaction
The profits from power
generation right are shared by
both new and old generators.
Objective function:
• L is all the original trading contract set ; K is the set for new generators
• Pk is the power generation right price purchased by new power generation k
Transaction type is power generation right, and the two traders are both generators
It is conducive to the consumption of clean energy and the resources allocation
, ,
max ijl ijl ijlk ijlk
i j i jl L l L
k K
E x b y q
,
,
k ol
ijl dl
ij k k
p p i s j ol
q p i dl j t
u p k p other
,
,
ol
ijl dl
ij dl k
p i s j ol
b p i dl j t
u p k p other
Model and Methodology
21
• Contract capacity
• Channel capacity
• Nodal balance of net flow
Constraints:
ijl ijlk l ijl ijlk l
j t j t i s i nsi dl i dl j ol j ol
k K k K
x y Ds x y Os l L
, , ,0
(or )or
ij ij
ijl ijlk ij ij
l L l L ijk K
v t i j s t nsx y c c
d i s ns j t
, ,
0 ,
0 ,
ijl jil
j j
ijlk jilk
j k j k
x x i s t l L k K
y y i ns t l L k K
• Forward and backward flow utilization hours
, , ,r r
ijl ijlk ijl ijlk
ij
l L l L l L l Lij ij ij ij
x y x yt i j s t ns
v v v v
Model and Methodology
22
Case Study
BUS23XIN JIANG
BUS22NING XIA
BUS20GAN SU
BUS21QING HAI
BUS19SHAAN XI
BUS14SI
CHUAN
BUS12HE NAN
BUS15CHONG QING
BUS10HU BEI
BUS11HU NAN
BUS13JIANG XI
BUS1JING-JIN-TANG
BUS3SHAN XI
BUS2HE BEI
BUS4SHANDONG
BUS18LIAO NING
BUS17LIAO NING
BUS16HEI LONG JIANG
BUS6JIANG SU
BUS8AN HUI
BUS9FU JIAN
BUS7ZHE JIANG
BUS5SHANG HAI
• Here we take the 23-node
simplified large-scale China
grid as a topology.
• Each node represents a
province grid.
Nodes
Forward Transmission
Capacity
(MW)
BackwardTransmission
Capacity
(MW)
Loss Rate
Jiangsu--
Zhejiang4000 4000 0.01
Hubei--Hunan 2600 1100 0.02
⋯⋯ ⋯⋯ ⋯⋯ ⋯⋯
Shaanxi—
Henan1000 1000 0.02
Shaanxi--
Gansu2600 2000 0.02
Transmission Capacity Constraint and Loss Rate
23
Case Study
Sale NodesSale Power
(GW.h)
Price
(Yuan/MWh)Demand Nodes
Demand Power
(GW.h)
Price
(Yuan/MWh)Shanxi
(Bus-3)1931 315
Jingjintang
(Bus-1)857.6 382
Shandong
(Bus-5)2790 397
Hebei
(Bus-2)0 395
Anhui
(Bus-8)1000 398
Shanghai
(Bus-5)5989 462
Fujian
(Bus-9)127.3 422
Jiangsu
(Bus-6)4900 436
Hubei
(Bus-10)10224.6 354
Zhejiang
(Bus-7)3926.1 458
Sichuan
(Bus-14)255 288
Hunan
(Bus-11)53 371
Chongqing
(Bus-15)227 291
Henan
(Bus-12)301.8 358
Heilongjiang
(Bus-16)1435 400
Jiangxi
(Bus-13)585 391
Jilin
(Bus-17)1431.41 376
Liaoning
(Bus-18)321.2 380
Gansu
(Bus-20)220.2 277
Shaanxi
(Bus-19)34.5 297
Qinghai
(Bus-21)418.6 279
Xinjiang
(Bus-23)0 250
Ningxia
(Bus-22)117.6 268
24
Total transaction
number
Total traded power
Energy（GW.h）Total profit
（million yuan）
17 8645.9 771 The optimized trading
results and the
detailed path of each
transaction could be
achieved by the
model.
The trading can be
executed more
conveniently.
It is mid-long period
transaction ( e.g. for
one month).
Case Study
Trading results (without transmission cost)
25
Case Study
Trading results (with transmission cost)
Total transaction
number
Total traded power
energy（GW.h）Total profit（million
yuan）
11 8592.9 456 The transmission
cost is considered.
The results show
that the trading
paths become
shorter and trading
number
decreased.
The total amount
is not changed so
much.
26
Case Study
No Path Traded Amount（GWh） Region
1 Shanxi-Jingjintang 857.6 North China
2 Anhui-Jiangsu 1000 East China
3 Fujian-Zhejiang 127.3 East China
4 Hubei-Jiangxi 103 Central China
5 Sichuan-Chongqing-Hubei-Jiangxi 255 Central China
6 Chongqing-Hubei-Jiangxi 227 Central China
7 Hubei-Shanghai 2700 cross-region
8 Hubei-Jiangsu 2700 cross-region
9 Gansu-Shaanxi-Henan 220.2 cross-region
10 Ningxia-Gansu-Shaanxi-Henan 81.6 cross-region
11Shanxi-Jingjintang
-Liaoning321.2 cross-region
27
Trading results (with transmission cost k=5%)
Case Study
Impact of transmission cost on cross-regional transactions
Transmission cost is assumed as an ratio of the demanding province transmission/distribution price
The transmission cost will seriously affect the cross-regional transaction.
Unreasonable cost are not conducive to allocate the energy optimally.28
0
2
4
6
8
10
12
14
16
18
0% 5% 10% 15%
Transactions Number
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0% 5% 10% 15%
Transactions Amount(GWh)
0
100
200
300
400
500
600
700
800
0% 5% 10% 15%
Total Profit (million yuan)
Case Study
Power generation right transaction
Province Node Amount（GWh） Price（yuan/MWh)
Gansu 20 500 100
Qinghai 21 600 120
Henan 12 300 140
Xinjiang 23 700 135
Sichuan 14 1000 130
Renewable generation biding data
29
The bidding price is relatively lower to avoid the curtailment of renewable energy.
Case Study
ContractRenewable
producersPath
Trade amount
(GWh)
5 Xinjiang Xinjiang-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Jiangxi 255
6 Xinjiang Xinjiang-Gansu-Shaanxi-Henan-Hubei-Jiangxi 210.8
6 Xinjiang Xinjiang-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Jiangxi 16.2
7 Qinghai Qinghai-Gansu-Shaanxi-Sichuan-Chongqing-Hubei-Shanghai 228.8
7 Xinjiang Xinjiang-Gansu-Shaanxi-Henan-Hubei-Shanghai 216.2
7 Sichuan Sichuan-Chongqing-Hubei-Shanghai 1000
9 Henan Henan 218.4
9 Xinjiang Xinjiang-Gansu-Shaanxi-Henan 1.8
10 Henan Henan 81.6
11 Qinghai Qinghai-Gansu-Shaanxi-Henan-Shanxi-Jingjintang-Liaoning 321.2
Generation right trading results
30The renewable energy consumption is promoted which is mainly produced in west China.
Outline
Back Ground of Power Market in China1
Cross-regional Power Trading Method Considering Network Problems
2
Conclusion and Outlook3
31
Conclusion
The proposed cross-region trading method is an useful tool to country’s
Power Exchange Centre, such as Beijing and Guangzhou. It can give
concrete trading amount and flow path between supply and demand
sides. The trading can promote the clean energy transportation, help to
solve the problem of uneven energy distribution, and achieve power
supply with low carbon emission.
The proposed model gains advantages on taking the transmission loss
and cost into account. The numerical analysis has validates its
effectiveness.
From the environment and the sustainable development view, the
generation right transaction is beneficial to promote low-emission
energy to replace high-emission energy.
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Energy layouts and power flow patterns in future
According to the resource endowment, the large energy bases
in the west and northern regions should be developed to
promote renewable energy consumptions.
The construction of coal power stations in the eastern and
central regions should be strictly controlled.
Power flow pattern is west-to east and north-to-south. The
cross-region power trading and the power generation right
transaction is very important for the optimal allocation of
resources over a wider area.
The country should improve trading mechanisms in electricity
market and fully release the cross-provincial/regional
transactions.
Outlook
33
The power markets in China is far from mature and mainly for the mid/long-
term energy transactions. Even so, great challenges related to big data
technique are faced.
Power Exchange
Participant Regulator
Massive data for the
large system
Model the complex
behavior of others via
data mining
Multiple data sources
to determine
transmission prices
Extremely large system
Complex behavior
Multiple data sources
Big Data Application for Advanced Power Markets
34
Outlook
Towards a short-term or real-time power market, the difficulties and challenges will
be more complex.
We hope that large data technology will help the effective work of the electricity
market and the realization of low carbon targets.
Great Uncertainty Supporting System Quick Decision
Large scale
wind and
photovoltaic
power
precise forecast
for multiple
producers
related analysis
demanding
requirement
collect, test,
analyze,
visualize the
data
High-speed and
reliable
management
high-frequency
transaction
advanced
techniques
supporting fast
clearing
Big Data Application for Advanced Power Markets
35
Outlook
The renewable generation contributes a lot to the low carbon goal.
By the year 2020, The installed capacity of wind power and photovoltaic power
is about to reach 210 and 110 GW respectively. The following yearly pollutant
and emission reduction can be achieved equivalently.
Beneficial Result of Renewable Generation in China
Types Reduced Quantity/Tons
SO2 8 million
NOX 3 million
soot 4 million
CO2 1.2 billion
A great but challenging target!
Outlook
36
Thanks！
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