the research on optimal distributed generation placement in power distribution networks

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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