Post on 02-Jan-2016
Faculty of Engineering, Kasetsart University
Mr.Wirote BuakleeAugust 24, 2013
The Research onOptimal Distributed Generation Placement in
Power Distribution Networks
Selected Journal
[1] Georgilakis, Pavlos S. and Hatziargyriou, Nikos D.“Optimal Distributed Generation Placement in PowerDistribution Networks: Models, Methods, and FutureResearch”, IEEE Transactions on Power System, page1-9,issue 99, January 2013.
[2] Sayyid Mohssen Sajjadi,Mahmoud-Reza Haghifam,Javad Salehi, “Simultaneous placement of distributedgeneration and capacitors in distribution networksconsidering voltage stability index”, International Journalof Electric Power and Energy Systems, page 366-375, issue46, 2013.
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Presentation Topics
I. Introduction
II. Mathematical Formulation
III. Methodology
IV. Future Research
V. Example of the Future Research
Simultaneous placement of distributed generation and capacitors in distribution networks considering voltage stability index
VI. Conclusion
I.Introduction
Optimal DG Placement (ODGP) can improve network performance:
Voltage profile
Reduce flows and system losses
Power quality
System reliability
It can provide DSOs, Regulators and Policy markets useful input for the incentives and regulatory measures for last 15 years
This paper will
outline and classify the previous published models and methods
suggest the future research ideas
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II. Mathematical Formulation [1]
General Problem
Optimum location and size of DG unit
Subject to network operating constraint, DG operating constraint and investment constraint
ODGP is a complex mixed integer nonlinear optimization problem
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II. Mathematical Formulation [1]
Objective
Single
• Total power loss minimization
• Energy loss minimization
• SAIDI minimization
• Cost minimization
• Voltage deviation minimization
• DG capacity maximization
• Profit maximization
• B/C ratio maximization
• Voltage limit loadability maximization
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II. Mathematical Formulation [1]
Objective
Multiple
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II. Mathematical Formulation [1]
Number of DG
Single DG
Multiple DGs
DG Variables
Location
Size
Location and Size
Type, Location and Size
Number, Location and Size
Number, Type, Location and Size
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II. Mathematical Formulation [1]
Load Variables
One-load level
Multi-load level
Time-varying
Probabilistic
Fuzzy
DG Technologies
Rotating device: Synchronous, Asynchronous
Static device: PV, Fuel Cell
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• Distributed Load
• Spot Load
• Constant power
• Voltage dependency
• Probabilistic
• Fuzzy
II. Mathematical Formulation [1]
Constraints
Power flow equality constraint
Bus voltage or voltage drop limit
Line/transformer overloading/capacity limit
Total harmonic voltage distortion
Short circuit level limit
Reliability constraint
Power generation limit
Budget limit
DG with constant power factor
DG penetration limit
Maximum number of DG
Limited bus for DG installation
Discrete size of DG unit
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III. Methodology [1]
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Analytic Methods
2/3 rule
Sequential loadflow
Numerical Methods
Gradient Search
Linear Programming (LP)
Sequential Quadratic Programming (SQP)
Nonlinear Programming (NLP)
Dynamic Programming (DP)
Ordinal Optimization (OO)
Exhaustive Search
III. Methodology [1]
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Heuristic Methods
Genetic Algorithm (GA)
Tabu Search (TS)
Particle Swarm Optimization (PSO)
Ant Colony Optimization (ACO)
Artificial Bee Colony (ABC)
Differential Evolution (DE)
Harmony Search (HS)
Practical Heuristic Algorithm
IV. Future Research [1]
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Coordinated Planning
Reconfiguration
Capacitor placement
DG placement
Substation/Feeder Expansion
Dynamic ODGP
When multiple year are considered
Uncertainties and Stochastic Optimization
Wind/Solar power generation
Fuel price
Future load growth, capital cost, market price, availability of fuel supply system
Power of plug-in EV
IV. Future Research [1]
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Active Network Management (ANM)
Communication and control
Using real-time information about operation and devices
Control both voltage and prevent overloading
ANM can reduce total costs of integrating high penetration of DG
New ODGP model with embedded ANM is required to help ensure adequate PQ with high penetration of DG
Islanded Operation
Form of microgrid->need ESS and ANM
Increase economic competitiveness and reliability
New ODGP model is needed
IV. Future Research [1]
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Ancillary Services
should be taken into account within ODGP model
Further Improvement in Methods
Parameter setting of heuristic ODGP algorithm (GA,PSO..) should be adaptively and automatically tuned in order to improve the efficiency.
V. Example of the Future Research [2]
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“Simultaneous placement of distributed generation and capacitors in distribution networks considering
voltage stability index”
Objective function: to reduce active and reactive power losses, energy losses and improve voltage profile and voltage stability.
Method: Memetic algorithm
Test System: IEEE 34-bus
V. Example of the Future Research [cont’]
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A. Load modeling
V. Example of the Future Research [cont’]
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B. Objective function
V. Example of the Future Research [cont’]
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C. Problem formulation
Capacitor installation cost
DG installation cost
V. Example of the Future Research [cont’]
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C. Problem formulation
DG Maintenance cost
V. Example of the Future Research [cont’]
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C. Problem formulation
DG Operation cost
V. Example of the Future Research [cont’]
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C. Problem formulation
Purchased active power demand from Transmission grid and
Network loss reduction due to DG installation
V. Example of the Future Research [cont’]
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C. Problem formulation
V. Example of the Future Research [cont’]
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C. Problem formulation
Reactive power loss reduction due to DG and Capacitor
V. Example of the Future Research [cont’]
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C. Problem formulation
= energy rateeC
jEL n = number of load level
Energy loss reduction due to capacitor
V. Example of the Future Research [cont’]
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C. Problem formulation
1
m
j ij
i
EL EL
2
ij ij i iEL T R I
m = number of line section
Peak power loss reduction due to capacitor
V. Example of the Future Research [cont’]
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C. Problem formulation
Voltage stability index improvement
V. Example of the Future Research [cont’]
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C. Problem formulation
V. Example of the Future Research [cont’]
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C. Problem formulation
VSI(m2)
SI
V. Example of the Future Research [cont’]
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C. Problem formulation
V. Example of the Future Research [cont’]
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D. Memetic algorithm
• Initial population formation
• Crossover operator
Stochastic number: 0-1
V. Example of the Future Research [cont’]
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D. Memetic algorithm
• Mutation operator
• Local search
V. Example of the Future Research [cont’]
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E. Case study and results
E.1 Case Study
5DGs with 250kW @ 0.9 pf.
V. Example of the Future Research [cont’]
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E. Case study and results
E.1 Case Study
V. Example of the Future Research [cont’]
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E. Case study and results
E.2 Result
V. Example of the Future Research [cont’]
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E. Case study and results
E.2 Result: Consider loss and VSI
0.93
0.64
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V. Example of the Future Research [cont’]
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E. Case study and results
E.2 Result: w/o considering VSI
0.87
0.64
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V. Example of the Future Research [cont’]
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E. Case study and results
E.2 Result: impact of VSI importance curve
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VI. Conclusions
The models, optimization methods and future research of the ODGP are presented.
The most ODGP model has the following characteristics:
Installation of multiple DGs
Design variable: Location and Size
Objective function: minimize total power loss
The most technique used in ODGP are GA and Practical HA
Future research: coordinated planning, dynamic ODGP, uncertainty and stochastic optimization, ANM and Islanded operation.
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VII. Future Works
Due to the recent “Roof top” policy of Thai government
the Active Network Management may be required in Thailand especially for PEA distribution system.
The traditional power system will become to the Active Distribution Network
These issues should be further studied.
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