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Environmentally Sustainable Planning of Large Wind Farms for Wind Power Integration
Ke Meng | Research Academic
Centre for Intelligent Electricity Networks
The University of Newcastle
Background
Wind Energy
• Wind energy, one of the fastest growing renewable energy sources for power generation, now meets a significant percentage of electrical demand worldwide.
• Along with the introduction of various emission reduction schemes, the cumulative installed wind capacity has increased markedly since the last decade around the world.
• Governments and organizations are promoting the construction of large wind farms, encouraging power companies and utilities with generous subsidies and with regulatory support.
• The ever-increasing level of wind energy with uncertainty and variability brings potential risk to power system operations.
• Furthermore, after direct participating in real-time electricity market, the high volatility of electricity price will further intensify the risk of wind energy.
2
Background
Wind Energy in Australia
• Australian continent boasts some of the best wind resources in the world, primarily located along coastal regions and extended hundreds of kilometres to inland.
• The Australian government acknowledges the importance of renewable energy sources and has issued a series of national policies to promote research, development, commercialization of renewable energy projects, and to improve transition of latest research results into industrial applications.
• These policies, particularly the emission trading scheme and the renewable energy target are expected to underpin solid progress of wind energy industry in Australia.
• Furthermore, some power utilities have started grid upgrade for renewable energy integration. An essential part of the upgrade work is to strengthen or replace the existing facilities to accommodate the increasing wind power generation.
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4
Background
Source: http://www.renewablessa.sa.gov.au/investor-information/resources/
Background
Selected Research Fields in Wind Power • Wind resource assessment
• Wind farm planning
1. Potential location selection;
2. Environmental impacts assessment;
3. Turbine model selection;
4. Wake effects modelling;
5. Micro-siting optimization;
6. Electrical layout optimization.
• Wind farm dispatch
1. Wind power forecast;
2. Flexible operational planning framework;
3. Wind farm dispatch considering carbon tax;
5
Background
Selected Research Fields in Wind Power (Cont.) 4. Coordinate dispatch with energy storage system;
5. Optimal allocation of energy storage system;
6. Operational risk mitigation with insurance strategy.
• Power system security
1. Low-voltage ride-through (LVRT) capability;
2. Dynamic security assessment;
3. Sub-synchronous resonance.
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Wind Resource Assessment
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Wind Forecasting and Wind Farm Planning Software
OptiWind
9
Network Communicator
Client Pool Core Calculation Library
Client1 Client2
Client3 Client4
Prediction Algorithms
Planning Algorithms
Statistics Service
Thread Pool
Prediction Threads
Planning Thread
Other Threads
Request Handler
Dispatch
Request Queue
Request Request Request
Network Communicator
Functional Modules
Prediction Service
Dynamical Display Threads
Prediction Threads
Planning Thread
User Interface
Wind Farm Planning Service
NWP Assessment Service
Statistics Service
Wind Forecasting and Wind Farm Planning Software
OptiWind
Wind Resource Assessment
Statistics Analysis (Wind Speed)
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1 2 3 4 5 6 7 8 9 10 11 12
15
913
1721
3
3.5
4
4.5
5
5.5
6
Month
Month vs. Hour of Wind Speed
Hour
Win
d S
peed
(m/s
)
2001 2002 2003 2004 2005 2006 2007 2008 20094.24.44.64.8
Seasonal-mean Variability of Wind Speed
Spr
ing
2001 2002 2003 2004 2005 2006 2007 2008 20093.63.8
44.2
Sum
mer
2001 2002 2003 2004 2005 2006 2007 2008 20094
4.5
5
Aut
umn
2001 2002 2003 2004 2005 2006 2007 2008 2009
5
5.5
6
Year
Win
ter
Wind Resource Assessment
Statistics Analysis (Wind Speed)
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0 5 10 15 200
5
10
January
Wind Speed (m/s)
Freq
uenc
y (%
)
0 5 10 150
5
10
15
February
Wind Speed (m/s)
Freq
uenc
y (%
)
0 5 10 150
5
10
15
March
Wind Speed (m/s)
Freq
uenc
y (%
)
0 5 100
5
10
15
April
Wind Speed (m/s)
Freq
uenc
y (%
)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Wind Speed (m/s)
Freq
uenc
y (A
tlas
Sec
tor:
0o -36
0o )
Weibull Fitting: y=(a/b)*(x/b)(a-1)*exp(-(x/b)a) --- a = 1.89, b = 4.60 (m/s)
Wind DistributionWeibull Fitting
Wind Resource Assessment
Statistics Analysis (Wind Direction)
January July 12
10%
20%
30%
40%
W E
S
N
0 - 22 - 44 - 66 - 88 - 1010 - 1212 - 1414 - 1616 - 18
5%
10%
15%
20%
W E
S
N
0 - 55 - 1010 - 1515 - 2020 - 2525 - 30
Wind Farm Planning
Wind Farm Planning
Power Output Estimation
• Wind Turbine Selection
Up to 20 kinds of wind turbines;
Power output ranges from 800KW to 3.6MW;
Power output curve;
• Surface Roughness Selection
Up to 17 kinds of surface roughness;
Roughness ranges from 0.0001 (water area) to 1 (city);
• Hub Height Speed Estimation
Observation station altitude, measuring height, hub height;
Air density, wind speed, surface roughness;
• Mean Power Output Calculation
14
Wind Farm Planning
15
Index Turbine Model Diameter (m) Hub Height (m) 1 Nordex N50 (800KW) 50 50 2 Nordex N60 (1300KW) 60 50 3 Vestas V52 (850KW) 52 55 4 Vestas V66 (1650KW) 66 67 5 Vestas V66 (1750KW) 66 67 6 Vestas V66 (2000KW) 66 67 7 Vestas V80 (1800KW) 80 67 8 Vestas V80 (2000KW) 80 67 9 Vestas V80 (2000KW Offshore) 80 67
10 Vestas V90 (1800KW) 90 80 11 Vestas V90 (2000KW) 90 80 12 Vestas V90 (3000KW) 90 80 13 Vestas V112 (3000KW Offshore) 112 100 14 SWT-2.3-82 (2300KW) 82.4 80 15 SWT-2.3-93 (2300KW) 93 80 16 SWT-3.6-107 (3600KW) 107 80 17 Sinovel-30-90 (3000KW) 90 80/90 18 Sinovel-30-100 (3000KW) 100 80/90/100/110 19 Sinovel-30-105 (3000KW) 105 80/90/100/110 20 Sinovel-30-113 (3000KW) 113 90/100/110
Wind Farm Planning
16
z0 [m] Terrain Surface Characteristics 1.00 City 0.80 Forest 0.50 Suburbs 0.40 --- 0.30 Shelter belts 0.20 Many trees and/or bushes 0.10 Farmland with closed appearance 0.05 Farmland with open appearance 0.03 Farmland with very few buildings/trees 0.02 Airport areas with buildings and trees 0.01 Airport runway areas
0.008 Mown grass 0.005 Bare soil (smooth) 0.001 Snow surfaces (smooth)
0.0003 Sand surfaces (smooth) 0.0002 --- 0.0001 Water areas (lakes, fjords, open sea)
Wind Farm Planning
Wake Effect Impacts
• Low capacity
• Limited wind farm site
• Wake losses (reduction of wind speed and increase of turbulence downwind of a turbine)
• Intermittent and stochastic nature
17
Wind Farm Planning
Wake Effect Modeling
• Wake loss is an important factor in the design of wind turbine micro-siting optimization.
• Normally, when a uniform incoming wind encounters a wind turbine, a linearly expanding wake behind the turbine occurs.
• In a wind farm, the turbines can be affected by the wakes of several turbines located upstream.
18
Y-ax
is
X-axis
T1
T2
T3
O
A
E
F
B
CD
θ
G
H
Wind Farm Planning
Wake Effect Modeling (Cont.)
• There are three different mutual effects, fully effect, no effect, and partial effect.
19
Wind Farm Planning
Wake Effect Modeling (Cont.)
• Fitness Function
Maximize mean power output;
Or minimize mean power losses.
• Challenges
Kinds of constraints;
Mixed with trigonometric function;
Mixed with definite integration;
Difficult to be solved by conventional approaches (linear programming, nonlinear programming, Quadratic programming).
20
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000
8.13%
5.66% 5.29% 4.26%
Wind Farm Planning
21
Mean Power Output = 8.1751 MW Mean Power Losses = 0.4628 MW
Mean Power Output = 8.2034 MW Mean Power Losses = 0.4345 MW
Mean Power Output = 8.2852 MW Mean Power Losses = 0.3527 MW
Mean Power Output = 7.8700 MW Mean Power Losses = 0.7679 MW
Mean Power Output = 7.9883 MW Mean Power Losses = 0.6496 MW
Mean Power Output = 8.0725 MW Mean Power Losses = 0.5654 MW 9.76% 7.00%
Wind Farm Planning
Micro-siting Optimization (Cont.)
• Aside from the two constraints discussed above, the following criteria should be considered:
Wind predictability: Improper site selection may magnify the intense of wind power fluctuations, leading to inaccurate wind resource prediction, ending up with being penalized by electricity market;
Site accessibility: Improper site selection may cause extra costs for equipment transportation, installation, and maintenance;
Terrain complexity: Overlooking may cause extra costs relative to more transmission lines and towers to support stronger mechanical stress;
Flora and Fauna: Violation may cause damage to species in imminent danger;
Obstacles and infrastructures: Improper design may cause extra costs related to signalization equipment (aviation) or obstacle avoiding (heritage, transportation, rivers, telecommunications, etc.).
22
Wind Farm Planning
Micro-siting Optimization (Cont.)
23
Multi-criteria Assessment Framework for Turbine Micrositing Optimization
Layer III: Economic Analysis
Layer IV: Optimal Plan Selection
Layer I: Technical Constraints
Layer II: Environmental Constraints
Wind Farm Planning
Electrical Layout Optimization
• Electrical layout design is an engineering task that optimizes the equipment installation and maintenance costs subject to geographic, environmental, social, and legal constraints.
• The planner decides the path and areas crossed by the facilities taking existing constraints into account:
For small wind farms, the electrical layout problem can be solved by hand, or by using exhaust search techniques that try all possible cable types and layouts.
However, this problem quickly becomes infeasible, due to the increasing number of wind turbines. The cost of a cable connecting one turbine to another depends on the type of cable used, and the terrain where it crossed.
• We developed an efficient optimal electrical layout design approach for large-scale offshore wind farms to minimize the capital cost, power loss cost, and network maintenance cost, taking into account the constraints of wind turbines, electrical cables, substations.
24
Wind Farm Planning
Electrical Layout Optimization (Cont.)
• The topography of terrain is modelled using a grid of units, where each unit represent a certain type of terrain and will be assigned a weight (cost coefficient) by decision makers according to its characteristic.
25
2
3
4
0.00 1000.00 2000.00 3000.00 4000.00Scale in Map Units
Parallel True to Scale: -11.50
1 inch on map = 1000.00 map units
1 1.00 to 2.00
2 2.00 to 3.00
3 3.00 to 4.00
4 4.00 to 4.00
5
2
2
2
3
3
1
4
2
3
3
1
3
4
2
3
2
2
1 1
1
4
4
2
2
3
3
4
1
2
1
4
4
5
31
5 5.00 to 6.00
4
Wind Farm Planning
Electrical Layout Optimization (Cont.)
• Constraints:
Active power flow constraints:
Reactive power flow constraints:
Capability limits of cables:
Bus voltage limits:
Radial constraints:
26
( ) ( )1
cos sinN
gi i j ij i j ij i j
jP VV G Bδ δ δ δ
=
= − + − ∑
( ) ( )1
sin cosN
gi i j ij i j ij i j
jQ VV G Bδ δ δ δ
=
= − − − ∑
max ,ijS S i N≤ ∀ ∈
min max
min max
,,
i
i
V V V i Ni Nδ δ δ
≤ ≤ ∀ ∈
≤ ≤ ∀ ∈
1,1,
N
iji i j
x j N= ≠
≤ ∀ ∈∑
START
Input Data,Initialize Parameters
Group WTs UsingFCM Clustering & BIP
method
Solve Master Problem(Cable Layout Scheme)
Feasible Solution?
Solve Slave Problem(Operation Constraint)
Feasible Solution?
Add a Benders Cutto Master Problem
Y
N
N
Y
Converged
YEND
(Optimal Solution)
END (Infeasible Solution)
N
Benders Decomposition
Wind Farm Planning
27
-2000 -1600 -1200 -800 -400 0 400 800 1200 1600 2000-2000
-1600
-1200
-800
-400
0
400
800
1200
1600
2000Electrical Layout Design for Large-scale Offshore Wind Farms
-2000 -1600 -1200 -800 -400 0 400 800 1200 1600 2000-2000
-1600
-1200
-800
-400
0
400
800
1200
1600
2000
Electrical Layout Optimization (Cont.)
Only considering cable cost Considering cable cost, power
loss cost, and maintenance cost
Wind Farm Dispatch
Wind Farm Dispatch
29
Wind Power Forecast
• Long-term Forecast
Numerical Weather Prediction (NWP) model;
• Short-term Forecast
K-nearest Neighbor (KNN);
Generalized Autoregressive Conditional Heteroskedasticity (GARCH);
Wavelet Decomposition (WAD);
RBF Neural Network (RBFNN);
Support Vector Machine (SVM);
Relevance Vector Machine (RVM).
Wind Farm Dispatch
30
Wind Power Forecast (Cont.)
Historical Measurements
Real-time Measurements
Numerical Weather Prediction Model
OptiWind Forecast System(Statistical, Physical, Combination Models)
Wind Profile Forecasts
Wind Farm Dispatch
31
Flexible Operational Planning Framework
Forecasted Result Sf
Step 3: Unit Commitment & Economic Load Dispatch
Step 4: Flexible Operational Plan Selection
Step 1: Wind Power Forecasting
Product NS Candidate
Operational Plans
Start
End
Æ Historical Wind DataÆ Numerical Weather Prediction DataÆ Wind Farm Details
Æ System DemandÆ Power Network TopologyÆ Parameters of Electric Equipment
Product NS Scenarios
Step 2: Scenario Production
...
...
O1 O2 O3 ONS
S2:p2 S3:p3 SNS:pNSS1:p1
Æ Probability Density Function
Æ The Change Time-point : tc
Part BChange Process
tc+Ti,xy
...
NT
...
...
Part A Part C
tc
...
...
...
...
, , 1i xy open iT T = +
The generation of the unit i in operational plan Ox
The change processThe generation of the unit i in operational plan Oy
, , 1i xy close iT T = +
Part BChange Process
tC+Ti,xy
...
NT
...
...
Part A Part C
tC
...
...
...
The generation of the unit i in operational planning Ox
The change processThe generation of the unit i in operational planning Oy
Wind Farm Dispatch
32
Flexible Operational Planning Framework (Cont.)
Operational Plan
Combined Operational
Plan
Total Cost of Combined
Operational Plan ($) Wind Changes at
05:00
Total Cost of Combined
Operational Plan ($) Wind Changes at
13:00
A
A→A 523,126.07 523,126.07
A→B 523,463.95 522,940.60
A→C 517,618.45 517,837.46
B
B→A 518,368.36 516,225.70
B→B 519,678.92 519,678.92
B→C 513,347.13 512,756.43
C
C→A 518,758.95 515,873.99
C→B 519,583.22 517,507.86
C→C 514,224.09 514,224.09
Least Adaption Cost 517,255.16 515,869.17
Optimal Operational Plan B C
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
5
10
15
20
25
30
35
40
45
50
55
60
Time (Hour)
Win
d P
ower
Out
put (
MW
)
Scenario AScenario BScenario C
...
NTtc
The state of the unit i is closed in Ox
EENS
... ...
...
The generation of the unit i in operational plan Ox
The generation of the unit i in operational plan Oy
The state of the unit i is open in Oy
t
Pi(t)
Because the unit i can not open within the limited time to change into operational
plan Oy , EENS is generated.CT
33
Wind Farm Dispatch
Wind Farm-BESS Dispatch Scheme
• Advantages
Proven and reliable (most RE systems use them);
Many solution options (Lead-acid, NaS, NiCd, NiZn, NiMH, and Li-ion).
• Disadvantages
Relatively high capital cost;
Heavy (low energy density);
Require regular maintenance;
Low charging/discharging efficiency (70-80%);
Short lifespan (5-10 yrs.).
Wind Farm Dispatch
34
Wind Farm-BESS Dispatch Scheme (Cont.)
• The benefits of this idea are, the wind power output can be controlled; the maximum capacity of battery storage system can be reduced, the optimal size can be estimated; the lifetime of energy storage device can be prolonged.
Wind power source
text
BESS
ACDC
text Grid
Pwind
PBESS
PTOTAL
Power Converter
35
• Technical Reports:
“Study on Transient Stability of HK Electric System with Offshore Wind Farm,” K. Meng, Z.Y. Dong, and K.P. Wong, Hong Kong Polytechnic University, Jul. 2011. To: Hong Kong Electric Company.
“Wind Forecast for Wind Power Generation in Hong Kong,” K. Meng, Z.Y. Dong, K.P. Wong, and F.J. Luo, Hong Kong Polytechnic University, Feb. 2011. To: Hong Kong Innovation and Technology Fund (ITF).
• Patents:
Z.Y. Dong, K.P. Wong, and K. Meng, “Wavelet analysis based wind speed prediction method and system for wind farms,” Assignee: The Hong Kong Polytechnic University, Chinese Patent 201010560929.1, Nov. 26, 2010.
Z.Y. Dong, K.P. Wong, and K. Meng, “Data-driven short-term wind speed prediction method and system for wind farms,” Assignee: The Hong Kong Polytechnic University, Chinese Patent 201010557609.0, Nov. 24, 2010.
Z.Y. Dong, K.P. Wong, and K. Meng, “Composite artificial neural networks based short-term wind speed prediction method and system for wind farms,” Assignee: The Hong Kong Polytechnic University, Chinese Patent 201010557446.6, Nov. 24, 2010.
Publications
36
• Selected Journal Papers: Y. Zheng, Z.Y. Dong, F.J. Luo, K. Meng, J. Qiu, and K.P. Wong, “Optimal allocation of energy storage system for risk mitigation of DISCOs
with high renewable penetrations,” accepted by IEEE Transactions on Power Systems, 17 Jul. 2013.
D.L. Xie, Z. Xu, L.H. Yang, J. Østergaard, Y.S. Xue, and K.P. Wong, “A Comprehensive LVRT Control Strategy for DFIG Wind Turbines with Enhanced Reactive Power Support,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3302-3310, Aug. 2013.
Y. Xu, Z.Y. Dong, Z. Xu, K. Meng, and K.P. Wong, “An intelligent dynamic security assessment framework for power systems with wind power,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 995-1003, Nov. 2012.
F. Yao, Z.Y. Dong, K. Meng, Z. Xu, H. Iu, and K.P. Wong, “Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 880-888, Nov. 2012.
L.H. Yang, Z. Xu, J. Østergaard, Z.Y. Dong, and K.P. Wong, “Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through”, IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 713-722, May 2012.
S. Taggart, G. James, ZY Dong and C. Russell, “The Future of Renewables Lined by a Transnational Asian Grid,” Proceedings of the IEEE, vol. 100, no. 2, pp. 348-359, Feb 2012.
L.H. Yang, Z. Xu, J. Østergaard, Z.Y. Dong, K.P Wong, and X.K. Ma, “Oscillatory Stability and Eigenvalue Sensitivity Analysis of A Doubly Fed Induction Generator Wind Turbine System,” IEEE Transaction on Energy Conversion, vol. 26, no. 1, pp. 328-339, Mar. 2011.
Y. Chen, Z. Xu, and J. Østergaard, “Security Assessment for Intentional Island Operation in Distribution Network with Distributed Generations,” Electric Power System Research, vol. 81, no. 9, pp. 1849-1857, Sep. 2011.
L.H. Yang, Z. Xu, J. Østergaard, Anders Foosnes, C. Andersen, and S. Holthusen, “Electric Vehicles in Power Systems with Large Penetration of Wind Power: the EDISON Programme in Denmark,” Automation Electric Power System (in Chinese), vol. 35, no.14, pp. 43-47, Jul. 2011.
L.Z. Xu, G.Y. Yang, Z. Xu, ZY Dong, J. Stergaard, and Y.J. Cao, “Composite microgrid dispatch considering wind power and CHP”, Automation of Electric Power Systems (in Chinese), v35, no. 9, pp. 53-60,66, May 2011.
Publications
37
• Selected Journal Papers (Cont.): F.X. Li, W. Qiao, H.G. Sun, H. Wan, J.H. Wang, Y. Xia, Z. Xu, and P. Zhang, “Smart Transmission Grid: Vision and
Framework,” IEEE Transactions on Smart Grid, vol. 1, no. 2, Sep. 2010.
L.H. Yang, G.Y. Yang, Z. Xu, Z.Y. Dong, K.P. Wong, and X.K. Ma, “Optimal controller design of a doubly fed induction generator wind turbine system for small signal stability enhancement,” IET Generation, Transmission & Distribution, vol. 4, no. 5, pp. 579-597, 2010.
Y. Mishra, S. Mishra, F.X. Li, Z.Y. Dong and R.C. Bansal, “Small-Signal Stability Analysis of a DFIG-Based Wind Power System Under Different Modes of Operation”, IEEE Transactions on Energy Conversion, vol 24, no. 4, pp. 972- 982, Dec 2009.
R. Bansal, V. Kumar, S. Kong, Z.Y. Dong, W. Freitas, and HD Mathur, “Three-Phase Doubly-Fed Induction Generators: An Overview”, IET Electric Power Applications, vol. 4, no. 2, pp. 75-89, 2010.
Y. Mishra, S. Mishra, M. Tripathy, N. Senroy, and Z.Y. Dong, “Improving Stability of a DFIG-Based Wind Power System With Tuned Damping Controller”, IEEE Transactions on Energy Conversion, vol. 24, no. 3, pp. 650- 660, Sep. 2009.
L.B. Shi, Z. Xu, and Z.Y. Dong, “Wind turbine model and initialization analysis with high penetration of grid-connected wind farms of DFIG type”, Automation of Electric Power systems (in Chinese), vol. 3, pp 44-48, 2008.
L.B. Shi, Z. Xu, and Z.Y. Dong, “Wind Turbine Model and Initialization Analysis with High Penetration of Grid-connected Wind Farms of DFIG Type”, Hydropower Automation and Dam Monitoring, vol. 32, no. 3, pp. 44-48, 2008.
Publications
38
• Peer Reviewed Conference Papers: F.J. Luo, Z.Y. Dong, Y.Y. Chen, K. Meng, G. Chen, H.Q. Tian, and K.P. Wong, “A novel short-term dispatch scheme for wind farm with
battery energy storage system,” PES 2013: IEEE Power Engineering Society General Meeting, Vancouver, BC, Canada, Jul. 2013.
J. Qiu, Z.Y. Dong, K. Meng, Y. Zheng, Y.Y. Chen, and H.Q. Tian, “Risk sharing strategy for minimizing imbalance costs of wind power forecast errors,” PES 2013: IEEE Power Engineering Society General Meeting, Vancouver, BC, Canada, Jul. 2013.
Y. Zheng, ZY Dong, K. Meng, F.J. Luo, H.Q. Tian and K.P. Wong, “A control strategy of battery energy storage system and allocation in distribution systems,” PES 2013: IEEE Power Engineering Society General Meeting, Vancouver, BC, Canada, Jul. 2013.
K. Meng, Z.Y. Dong, Y. Zheng, and J. Qiu, “Optimal allocation of ESS in distribution systems considering wind power uncertainties,” APSCOM2012: IET International Conference on Advances in Power System Control, Operation and Management, Hong Kong, Nov. 2012.
F. Yao, Z.Y. Dong, K. Meng, Y. Xu, H. Lu, and K.P. Wong, “Unit commitment considering probabilistic wind generation,” APSCOM2012: IET International Conference on Advances in Power System Control, Operation and Management, Hong Kong, Nov. 2012.
X.R. Li, C.W. Yu, F.J. Luo, S.Y. Ren, Z.Y. Dong, Y. Wu, K. Meng, and K.P. Wong, “Optimal decision making model for GENCO under the emission trading scheme,” PES2012: IEEE Power Engineering Society General Meeting, San Diego, USA, Jul. 2012.
F.J. Luo, Z.Y. Dong, X.R. Li, Y.Y. Chen, Y. Xu, K. Meng, and K.P. Wong, “A profit-based generation scheduling model for GENCO considering multi-markets and CO2 emission allowance,” PES2012: IEEE Power Engineering Society General Meeting, San Diego, USA, Jul. 2012.
F. Yao, K. Meng, Z.Y. Dong, Z. Xu, H. Lu, J.H. Zhao, and K.P. Wong, “Differential evolution algorithm for multi-objective economic load dispatch considering minimum emission costs,” PES2011: IEEE Power Engineering Society General Meeting, Detroit, USA, Jul. 2011.
Q.W. Wu, Z. Xu, J. Østergaard, “Grid integration issues for large scale wind power plants (WPPs), PES2010: IEEE Power Engineering Society General Meeting, Minneapolis, July2010.
D.H. Feng, Z. Xu, J Østergaard, ‘Redesign electricity market for the next generation power system of renewable energy and distributed storage technologies, PES2010: IEEE Power Engineering Society General Meeting, Minneapolis, July2010.
Publications
39
• Submitted/Under Review: J. Qiu, Z.Y. Dong, J.H. Zhao, K. Meng, and Y. Zheng, “Insurance strategy for minimizing imbalance costs of wind power in real-time
markets,” submitted to IEEE Transactions on Power Systems.
Y.Y. Chen, Z.Y. Dong, K. Meng, F.J. Luo, Z.Xu, and K.P. Wong, “Optimal electrical layout design for offshore wind farms using fuzzy c-means clustering and benders decomposition,” submitted to IEEE Transactions on Power Systems.
K. Meng, Z.Y. Dong, W.X. Guo, M.H. Chen, F.S. Wen, and Y. Xu, “A flexible daily operational planning framework with intermittent wind power,” submitted to IEEE Transactions on Power Systems.
F.J. Luo, Z.Y. Dong, Y.Y. Chen, X.R. Li, Y. Xu, K. Meng, and K.P. Wong, “Fuzzy adaptive particle swarm optimization for unit commitment,” submitted to Electric Power Systems Research.
H.M. Yang, D.Q. Zhang, K. Meng, and Z.Y. Dong, “Optimal scheduling of combined cooling heating and power system considering the risk of loss in renewable energy by extreme learning machine,” submitted to ELM2013.
Publications
Thanks! Any Questions?
Ke Meng | Research Academic
Centre for Intelligent Electricity Networks
The University of Newcastle
