optimal dg with economic constraints
TRANSCRIPT
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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
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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.)
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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)
II. PROBLEM FORMULATION (CONT.)
Loss Reduction Revenue:
Energy Purchase Saving:
(19)
(20)
Where:
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(21)
(22)
(23)
(24)
II. PROBLEM FORMULATION (CONT.)
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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