mutual trading strategy between customers and power...
TRANSCRIPT
1
Mutual trading strategy between customers and
power generations based on load consuming patterns
Junyong Liu, Youbo Liu
Sichuan University
2
Ⅱ Problem Analysis
Ⅲ
Research BackgroundⅠ
Methodology
Ⅳ
Outline
Trading Platform Design
V Software Development
Reviews on the development of electricity markets and trading strategy
Problem analysis of direct electricity purchase by large consumers
Novel trading strategy for direct electricity purchase based on data analytics
Trading platform for direct electricity purchase markets
Visual monitoring system for electricity markets
3
Research Backgroundd
Reviews on the Development of Electricity Markets
Electricity
Markets
Direct Electricity Purchase
of Large Consumers
Advanced
Development
Worldwide
Electricity market reform
Competitive market in generation side
Competitive market in retail side
Fair opening of power grid under
government supervision
Inter regional market transactions in
Australia Settlement residues auction
Nordic power system transactions
Nord Pool
Establish laws and regulations
Empower customers to choose
their power suppliers in
accordance with the voltage level
and power capacity
Establish independent
transmission and distribution price
mechanism
Establish surplus power handling
mechanism
Development
in China
Electricity market reform
Competitive market in generation side
Establish electric power trading center
In process of establishing competitive
market in retail side (No.9 Electricity
market reform document in 2015)
Pilot projects including
Price mechanism
Cross subsidy
Auxiliary service
Transaction scheduling
Trading strategy
4
Direct Electricity Trading Strategies
Large electricity customers and power generation companies meetdirectly and complete the transaction through bilateral consultation. Itis the most commonly used model in pilot projects in China.
• Bilateral negotiation transaction model
Large electricity customers and power generation companies proceedto bidding transaction in power trading center. It is applicable tofacilitate the formation of transactions in a short period of time.
• Centralized bidding transaction model
• Centralized matchmaking transaction model
Large electricity customers and power generation companies proceedto transaction in power trading center based on the trading electricitycurves. It is applicable for the situation which causes the minimalimpacts on the original scheduling.
Research Backgroundd
5
Problem Analysis
Theoretical Research
Most of the existing literatures in the field of direct electricity trading markets can be
concluded into 3 categories
• In centralized matchmaking transaction model, optimization model is established
based on the principle of “high-low matching”.
Shortcoming: It is hard to establish a stable market between multiple individuals
due to the frequent matchmaking process. The complexity of power system
operation is also increased.
• In bilateral negotiation transaction model, game theory is applied to analyze the
behavior characteristics of different trading individuals.
Shortcoming: It is hard to obtain complete information between each other so that
the system overall efficiency is hard to be optimized.
• Real option theory is used to establish the direct electricity trading model as well
as calculate the trading price.
Shortcoming: It has high requirement of electricity markets maturity. Thus, it is
limited in practical applications especially in China.
6
Problem Analysis
Practical situation
Transition of market profits after No.17 electricity supervision document in China
• Transaction can be directly proceeded between large
electricity customers and power generation companies.
• Large customers ask for lower electricity price and
reducing the obligations of purchasing cross subsidies.
Increase the operating
costs and risks of
electrical company
Information asymmetry
Electric
Company
Power
Generation
Company
Large
Customer
Electric
Company
Power
Generation
Company
Large
Customer
？ ？ Difficult to optimal
allocate system
electricity
Shortcoming of current trading model• Opaque price
• Low market transaction efficiency
• Imperfect competition
……
7
Methodology
A novel direct electricity trading strategy for large customers is
proposed —— Principal-Agent Transaction Model
Common Agent
(Electric Company)
Principal A
(Large customer)
Principal B
(Large customer)
Principal C
(Large customer)
……
Datasets
Large customers
• Commission
• Electricity purchase prize
• Reliability requirement
……
Electric company
• Network structure
• System Operation
parameters
• Congestion condition
……
Generation company
• Commission
• Electricity selling prize
• Auxiliary service capability
……
Data Analysis
Technologies
• Data distortion
correction
• Data forecasting
• Data clustering
• Data pattern
matching
……
Achieve a win-win situation. Large customers want to ensure high electricity reliability
and reduce electricity purchase prize.
Electric company want to bring the direct purchase
electricity into the overall power network optimization to
make the system more stable, secure and efficient.
8
Reward
Mission
Large
Customers
Electric
Company
Submit
Data analysis
Operation
Security check
Price
Electricity
Reliability
requirement
Balance
CompleteClear-price
Trading electricity
Operation
condition
Optimal allocation of
system overall electricity
Balance
Incentive
Rewards and
punishments
based on the
transaction
Ele
ctri
city
Co
nsu
min
g
Sch
eme
Form
ula
tion
Real time
scheduling
Information from
generation company
Methodology
9
Load Consuming Pattern Matching Technique
• First, the power generation output curve and large customer load curve is analyzed.
• Second, the most similar pairs of “generation—load” are matched with each other.
• Third, transaction strategy is formulated with the objective of maximum the “overall
pattern matching performance” in the whole power network.
Correlation Analysis of Load/Generation Curven
i i
k 1
1r ( k )
n
o i o i
i k i ki
o i o ii k
min min | y ( k ) y ( k )| max max | y ( k ) y ( k )|( k )
| y ( k ) y ( k )| max max | y ( k ) y ( k )|
直购电厂
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 2 4 6 8 10 12 14 16 18 20 22
大用户
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 2 4 6 8 10 12 14 16 18 20 22
Output curve
of multiple
generations
Output curve
of multiple
large load
customers
0
200
400
600
800
1000
0
500
1000
1500
2000
0
500
1000
1500
Methodology
10
Load Consuming Pattern Matching Technique
Effect on system power balance
P
t
Backup
scheduling
-ΔP1
Generation
control
ΔP1
P
t
Power
network
support
Backup
scheduling
–ΔP2
Generation
control
ΔP2
P
t
Generation
Load
P
t
Generation
Load
Power
network
support
High
correlation
performance
Low
correlation
performance
Effect on economical benefits• Peak load periods ( 8:00-11:00 & 18:00-21:00)
Reduce the peak-shifting pressure of power grid when large customer load increase sharply.
• Valley load periods ( 22:00 - 6:00 )
Reduce the shutdown risks of power generation company when large customer load decrease sharply.
Methodology
11
Distortion correction of statistic load/generation data
Distortion
correction
for similar
typical days
, ,
1
1...961
N
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n
iN
x x
2 2,,
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,, 3n in i ix x
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2
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5
n i n i j
j
ix x
Distortion
correction for
similar hours in
typical days
' '
, , ,n i n i n ix x x
, 2 , 1 2 , 2 2( ) /n i n i n ix x x
Clustering of statistic load/generation data
• Mixed clustering algorithm based on K-means algorithm and self-organizing map algorithm
Input layer
Output layer
• Minimum distance connection weight
|| || || ||i a i jV r min V r
• Topology iteration
( 1) ( ) ( ) ( )( ( ) ( ))j j aj i j
r t r t t f t V t r t
• K-means clustering standard measure function based
on SOM output layer 2
1
| |i
k
i
i p C
E p m
Methodology
12
Load Consuming Pattern Matching Technique
Case study
• System power balance requirement (unit: kW)Q
RT
• System uncertainty factor (unit: RMB/kWh) F P (1 C )
• System real-time operation cost factor (unit: RMB/h) M F R
0
200
400
600
800
1000
1200
1400
1600
1800
0
200
400
600
800
1000
1200
1400
1600
1800
0 2 4 6 8 10 12 14 16 18 20 22
电厂13 大用户17
0
200
400
600
800
1000
1200
1400
1600
0
200
400
600
800
1000
1200
1400
0 2 4 6 8 10 12 14 16 18 20 22
电厂19 大用户2
• Peak load period
Case
numberR Cf F M
1 3530.6009 0.149381 0.199045 700.38244
2 -4410.66 0.712422 0.08426 -370.2144
3 -620.2191 0.673752 0.088576 -50.51114
4 -4140.724 -0.42401 0.383771 -1590.159
5 490.55969 0.914785 0.022582 10.119156
6 4140.6191 0.239723 0.19349 800.22486
7 -240.17 0.80873 0.057381 -10.3869
8 -1980.694 -0.14012 0.322654 -640.1094
9 200.38406 0.629718 0.089423 10.822806
10 2470.5013 0.80547 0.051161 120.66251
11 -170.7675 0.610513 0.088803 -10.57781
12 -690.0819 0.228474 0.188638 -130.0315
13 -1480.498 -0.3656 0.356422 -520.9279
Assessment indices
Methodology
13
Steps of Direct Electricity Purchasing Market Construction in Shanghai
Establish Regulations
One-to-one
Bilateral negotiation
Passive security checkMatchmaking Trade Platform
Many-to-many
Multiple trading periods
Generation-demand matching
Principal-Agent Platform
Many-to-many
Load pattern matching
Security check
Comprehensive assessment
Disordered bilateral
negotiation
Orderly centralized
matchmaking
Step II
Step I
Step III
Methodology
14
Complete Electricity Trading Procedure
System
scheduling
System scheduling+
direct electricity trading Pre Pair matching
Transaction
curve
Trading
Channel
Transmission
Capability
P
t
Capability
Channel
Transaction
P
t
Capability
Channel
Transaction
P
t
Capability
Channel
Transaction
Operation Security check
By sequence
Large
customers
Power
generations
Non-conflictsSerious negative
impactCurtailment needed
Direct electricity trading confirmation
+
Earn
ings
curv
e
Curtailment
(or)
+
Transaction cancel
(or)Purchase and sale to the
regular market (or)
System operation scheduling confirmation
Trading Platform Design
15
Trading Platform Design Case Study in Shanghai
Load data distortion correction and clustering
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200.1
0.15
0.2
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
5 10 15 200
0.5
1
Generation
output
Large
customer
load
5 10 15 200
50
100
150
200
5 10 15 200
50
100
150(A)
(B)
Clustering results
Input data: Statistic data of 48 large customers and 18 power generation
companies in Shanghai.
16
Trading Platform Design Case Study in Shanghai
Enumeration of “generation-load” pattern matching
Results of matching strategy 36, 37
(high correlation case)
Strategy
Number
Combination
results of
generation
Combination
results of large
customers
Correlation
Degree
(single match)
36
2/6/8 1/3/4/5/6/7/8 0.9881
1/3/5/7 2/9 0.9201
4 10 0.8896
Rest of electricity Rest of electricity 0.9494
37
2/6/8 1/3/5/6/7/8/9 0.9880
3/5/7 2/4/10 0.9600
1/4+Rest of
electricityRest of electricity 0.8996
Rest of electricity means electricity that cannot be consumed by direct electricity transaction
17
Case Study in Shanghai
Trading Platform Design
Direct electricity purchase plan list based on the optimal-matching principle
Plan
Number
Combination plan of
generations
Combination plan of
large customers
Integrated
correlation
degree (M)
24 2/6/8；1/3/5/7；4 1/3/4/5/6/8；2/7/9；10 0.87547
36 2/6/8；1/3/5/7；4 1/3/4/5/6/7/8；2/9；10 0.87435
37 2/6/8；3/5/7 1/3/5/6/7/8/9；2/4/10 0.85036
31 1/2/6/8；5/7 1/3/4/8/9/10；2/5/6/7 0.84662
26 1/2/6；5/7 1/3/4/8/9/10；2/5/6/7 0.84408
39 2/5/6；1/7 2/3/4/5/8/9/10；1/6/7 0.83977
… … … …
n
i i r
i 1
n
i r
i 1
e c e R
A
e e
n
i i
i 1
M r A
Power grid dispatching department can obtain the power balance information in each
time interval from the list above. Then, it can make scheduling plan on the basis of
system operation status. Finally, the direct electricity transaction can be confirmed.
18
Trading Platform Design
Case Study in Shanghai
Comparison of different trading strategies
Trading strategyAverage power
flow distribution
Maximum
load
Minimum
load
Complete direct purchase
electricity (MWh)
Economic profits of electric
company (104RMB)
Principal-Agent 63% 90% 1% 12890 296.13
Centralized
matchmaking57% 100% 1% 12465 304.61
Bilateral
negotiation66.5% 100% 1% 12903 258.06
In terms of electric company economic profits, the result of principal-agent model is slight less
than the centralized matchmaking model. That is because principal-agent model aims at
maximizing “pattern correlation” instead of maximizing economic income. However, the
volume of transactions is guaranteed, which has a positive impact on coordinating the profits of
the whole market.
In terms of operation security, the presented principal-agent model provided the most uniform
power flow distribution. The network is stable and secure with sufficient capacity margin.
19
Software Development
Data receiving platform for electricity markets
SCADA/EMS WARMS DMISHydroelectric
generation
dispatching system
Load
forecasting
Data integration, mining and intelligent analysis
System real-
time data
visualization
Power grid
analysis
visualization
Application of
GIS
Technology
Functional structure diagram of power grid trading visualization software
20
Software Development Software screenshot
Power Generation distribution in Shanghai Load curve in different typical days
Trade based on congestion analysis 3D map of power system stability using PMU
21
22Conclusion
Based on the problems existing in direct electricity purchase markets in China,
this research puts forward the corresponding solution and practical application,
which has been successfully applied in Shanghai pilot projects.
The main novelty of the research is proposing a new mutual trading strategy —
— principal-agent transaction strategy based on the data analysis techniques.
The most significant one, i.e. load consuming pattern matching technique, is able
to economically optimize the allocation of power resources in the whole network.
It also can reflect the real trade willingness of large customers so that the
transparency and fairness of the electricity market are guaranteed.
An active security check mechanism is considered in the electricity trading
process, which ensures the high reliability performance for both power grid and
large customers. The guarantee of secure and stable power network operation is
positive for the power markets development.
