Optimal DG with Economic Constraints

8/9/2019 Optimal DG with Economic Constraints

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Wirote Buaklee1, Komsan Hongesombut2

1,2 Department of Electrical Engineering, Kasetsart University

Email:[email protected] and [email protected]

Optimal DG Placement in a Smart Distribution Grid

Considering Economic Aspects



OUTLINE

I. INTRODUCTION

II. PROBLEM FORMULATION

III. BAT ALGORITHM

IV. PROPOSE METHODOLOGY

V. NUMERICAL STUDY

VI. CONCLUSION

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I. INTRODUCTION

At present, a smart grid is being promoted in many countries:

energy independence, global warming and emergency

resilience.

DG installation in the smart distribution system is one way toachieve those problems: both positive and negative impact.

The optimal location and sizing of DG is one important issue

to maximize overall system efficiency and to ensure stable andreliable operation in parallel with the smart distribution

system.

Various optimization techniques:

Analytical method

Artificial intelligence approach: GA, SA, TS

Heuristic approach: PSO, CS, ACO

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In this paper a Bat Algorithm is proposed for the optimal DG

allocation to mitigate the energy losses and to improve the

voltage as well as the fault level of the distribution network.

The performance of the BA will be investigated by various

study cases of the simplify 9-bus distribution network of the

PEA.

To evaluate the result, the BA coded by DPL in DIgSILENTsoftware is used as a simulation tool.

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I. INTRODUCTION(CONT.)



II. PROBLEM FORMULATION

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:inimize obj f BCR PE

The objective of the placement approach is to maximize

the Benefit to Cost Ratio (BCR):

(1)

where

PE h V h S h IC h IPCC







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The inequality constraints :

min max ;i i iG G G i G P P P N

min max ;i i iG G G i GQ Q Q N

min max

;i i i DG DG DG i DG P P P N

min max ;i i i DG DG DG i DGQ Q Q N

min max ;i i i iV V V N

max ;i i i LS S N

(10)

(11)

(12)

(13)

(14)

(15)

II. PROBLEM FORMULATION (CONT.)



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The benefit and cost of DG are calculated from:

(16)

(17)

(18)


Loss Reduction Revenue:

Energy Purchase Saving:

(19)

(20)

Where:



10

(21)

(22)

(23)

(24)


Initial Installation Cost of DG:

O&M of DG:



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BA is a meta-heuristic algorithm

• Developed by Prof. Xin She Yang in 2010.

• BA is based on the echolocation behavior of bats

•

All bats use echolocation to sense distance, and they also‘know’ the difference between food/prey and background

barriers in some magical way.

• Bats fly randomly with velocity vi at position xi with a

frequency f min , varying wavelength and loudness A0 tosearch for prey. They can automatically adjust the wavelength

(or frequency) of their emitted pulses and adjust the rate of

pulse emission r ∈ [0, 1], depending on the proximity of their

target.

• Although the loudness can vary in many ways, we assume that

the loudness varies from a large (positive) A0 to a minimum

constant value Amin .

III. BAT ALGORITHM (BA)





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III. BAT ALGORITHM(CONT.)



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C. Loudness and Pulse Emission

III. BAT ALGORITHM(CONT.)



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IV. PROPOSED METHODOLOGY



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V. NUMERICAL R ESULT

Fig.1. The simplify 9-bus distribution test system



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V. NUMERICAL R ESULT

Fig. 2. Typical daily load curve



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V. NUMERICAL R ESULT(CONT.)

A. Initialize parameter

• Number of bats =25

• Loudness A0 = 1.5

• pulse rate r = 0.1

• constant α = 0.9

• constant γ = 0.1

•

Number of iteration =50• All buses have been considered as candidate location for

DG installation except “Bus 1”.

• Two scenarios have been explored:

1) w/o economic consideration2) with economic consideration

• no DG installation• one DG installation

• two DGs installation

• three DGs installation



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IV. SIMULATION R ESULT(CONT.)

A. Result of Optimal DG Placement



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IV. SIMULATION R ESULT(CONT.)

A. Result of Optimal DG Placement





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VI. CONCLUSION

• The proper DG allocation can enhance the voltage profile

and reduce energy losses while the short circuit level and

branch loading are still within the acceptable limit.• By taking economic into consideration, it can avoid the over

investment in OPDG process compare to the case of without

consideration.

• The BA is efficiently and effectively employed fordetermining the optimal site and size of DG.

• The proposed method can be applied in the planning of a

smart distribution grid connecting with renewable energy

DG such as biomass, biogas, solar and wind power.



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Optimal DG with Economic Constraints