Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm
Abstract
:1. Introduction
- (a)
- A multilevel optimization method for optimal network power expansion planning has been proposed.
- (b)
- A new version of binary dragonfly’s algorithm has been proposed.
- (c)
- Considering distributed generation as renewable energies in power expansion planning.
- (d)
- The proposed objective function involves the minimization of the cost of investment, operation, and repair, plus the cost of reliability for the development of the network.
2. Mathematical Formulation for Multiyear Network Expansion Planning
2.1. Problem Assumptions
2.2. Objective Function
2.3. Problem Constraints
2.3.1. Voltage Constraint
2.3.2. Thermal Capacity of Feeders
3. Dragonfly Optimization Algorithm
Binary Version of the Dragonfly Algorithm
4. Simulation and Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
IC | Installation capacity | EENSi | Expected energy not supplied of distribution network in year i |
OC | Operation cost | VOLL0 | Value of lost load in base year |
RC | Reliability cost | σj | Average failure rate of feeder section located before bus i |
γ(t) | Converting function | FLj | Length of feeder section located before bus i |
Dr | Discount rate (%) | SP | Set of loads which can be restored by DG |
U(t) | Unit step function | UP | Set of loads which cannot be restored by DG |
InvDG | Investment Cost of DG | TPP | Set of loads which are shed after the fault occurrence on the ith feeder section and can be restored by the upstream grid |
Kg,i | Binary variables to represent the installation decision of DG at bus i | Trs | Required time to detect the fault location and isolating in the faulted feeder section |
kr,i | Binary variables to represent the decision for reinforcement of the feeder section before bus i | Trp | Required time to repair the faulted feeder section |
FR | Reinforcement cost of the feeder section | Vj | Voltage of jth bus |
πi,ls | Electricity price for purchasing power from the upstream grid at load level ls of year i | IL | A factor that determines the DGs capacity constraints per year |
PUi,ls | Generated active power from the upstream grid at load level ls of year i | ||
Abbreviations | |||
TDls | Time duration of load level ls | MNEP | Multiyear Network Expansion Planning |
OpeDG | DGs operation and maintenance cost | DG | Distributed Generation |
PGi,j,ls | Generated active power of DG in bus j and load level ls of year i | GA | Genetic Algorithm |
Ny | Number of years of the planning period | SA | Simulated Annealing Algorithm |
Nb | Number of load buses | PSO | Particle Swarm Optimization Algorithm |
Nls | Number of load levels | TS | Tabu Search |
π0,ls | Electricity price for purchasing power from the upstream grid at load level ls of year 0 | DA | Dragonfly Algorithm |
Demandi,ls | Total load demand of distribution network at load level ls of year i |
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Number of Buses | R (ohm) | X (ohm) | P (kW) | Q (kVAR) | |
---|---|---|---|---|---|
From Bus i | To Bus j | ||||
1 | 2 | 0.0922 | 0.047 | 100 | 60 |
2 | 3 | 0.493 | 0.2511 | 90 | 40 |
3 | 4 | 0.366 | 0.1864 | 120 | 80 |
4 | 5 | 0.3811 | 0.1941 | 60 | 30 |
5 | 6 | 0.819 | 0.707 | 60 | 20 |
6 | 7 | 0.1872 | 0.6188 | 200 | 100 |
7 | 8 | 0.7114 | 0.2351 | 200 | 100 |
8 | 9 | 1.03 | 0.74 | 60 | 20 |
9 | 10 | 1.044 | 0.74 | 60 | 20 |
10 | 11 | 0.1966 | 0.065 | 45 | 30 |
11 | 12 | 0.3744 | 0.1238 | 60 | 35 |
12 | 13 | 1.468 | 1.155 | 60 | 35 |
13 | 14 | 0.5416 | 0.7129 | 120 | 80 |
14 | 15 | 0.591 | 0.526 | 60 | 10 |
15 | 16 | 0.7463 | 0.545 | 60 | 20 |
16 | 17 | 1.289 | 1.721 | 60 | 20 |
17 | 18 | 0.732 | 0.574 | 90 | 40 |
2 | 19 | 0.164 | 0.1565 | 90 | 40 |
19 | 20 | 1.5042 | 1.3554 | 90 | 40 |
20 | 21 | 0.4095 | 0.4784 | 90 | 40 |
21 | 22 | 0.7089 | 0.9373 | 90 | 40 |
3 | 23 | 0.4512 | 0.3083 | 90 | 50 |
23 | 24 | 0.898 | 0.7091 | 420 | 200 |
24 | 25 | 0.896 | 0.7011 | 420 | 200 |
6 | 26 | 0.203 | 0.1034 | 60 | 25 |
26 | 27 | 0.2842 | 0.1447 | 60 | 25 |
27 | 28 | 1.059 | 0.9337 | 60 | 20 |
28 | 29 | 0.8042 | 0.7006 | 120 | 70 |
29 | 30 | 0.5075 | 0.2585 | 200 | 600 |
30 | 31 | 0.9744 | 0.963 | 150 | 70 |
31 | 32 | 0.3105 | 0.3619 | 210 | 100 |
32 | 33 | 0.341 | 0.5302 | 60 | 40 |
Parameters | Value | Unit |
---|---|---|
InvDG | 318 | $/kW |
OpeDG | 0.036 | $/kWh |
FRj | 6000 | $/km |
IL | 0.4 | - |
IR | 10 | % |
Dr | 12 | % |
4 | Years | |
Population size | 200 | - |
Max_iter | 100 | - |
Power facor | 0.9 | - |
σi | 0.2 | fr/yr/km |
Vmin | 0.95 | p.u. |
Vmax | 1.05 | p.u. |
Trs | 1 | hr |
Trp | 4 | hrs |
Load Level | Percentage of Annual Peak Load (%) | Time Duration (hrs) | Ρ0,ls ($/MWh) |
---|---|---|---|
Heavy (ls = 1) | 100 | 1500 | 70 |
Medium (ls = 2) | 70 | 5000 | 50 |
Light (ls = 3) | 50 | 2260 | 35 |
Objective function value (M$) | 4.7403 | |
Feeders’ reinforcement cost | 0.006584 | |
DG investment cost | 0.300479 | |
Purchased power cost | 2.308 | |
DG operation cost | 1.8586 | |
Reliability cost (M$) | 0.04806 | |
Optimal DG capacity and location for every year of the planning period | First year | 100 kW at bus 16 200 kW at bus 17 400 kW at bus 31 100 kW at bus 32 |
Second year | 100 kW at bus 6 400 kW at bus 11 200 kW at bus 15 100 kW at bus 20 | |
Third year | 100 kW at bus 3 100 kW at bus 26 | |
Fourth year | 100 kW at bus 22 | |
Feeders’ reinforcement pattern for every year of the planning period | First year | --- |
Second year | Feeders 1, 3, 29 | |
Third year | Feeders 5, 10, 11, 16, 26 | |
Fourth year | Feeder 32 |
EENSt (kWh/year) | Without DG | With DG | ||||||
---|---|---|---|---|---|---|---|---|
GA | PSO | TS | Proposed Method | GA | PSO | TS | Proposed Method | |
EENS1 | 19,201 | 21,251 | 29,234 | 18,191 | 17,692 | 18,490 | 28,221 | 12,847.9 |
EENS2 | 21,298 | 23,390 | 31,402 | 19,101 | 16,721 | 18,432 | 27,172 | 11,910.6 |
EENS3 | 23,295 | 24,607 | 32,521 | 20,056.1 | 16,734 | 17,456 | 26,639 | 11,724.7 |
EENS4 | 23,971 | 24,806 | 32,902 | 21,058.9 | 15,892 | 16,786 | 26,015 | 11,579.8 |
Optimization Methods | Run Time for 33-Bus Test System (s) |
---|---|
GA | 2452 |
PSO | 1126 |
TS | 1989 |
Proposed method | 1490 |
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Kakueinejad, M.H.; Heydari, A.; Askari, M.; Keynia, F. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Appl. Sci. 2020, 10, 4795. https://doi.org/10.3390/app10144795
Kakueinejad MH, Heydari A, Askari M, Keynia F. Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences. 2020; 10(14):4795. https://doi.org/10.3390/app10144795
Chicago/Turabian StyleKakueinejad, Mohammad Hossein, Azim Heydari, Mostafa Askari, and Farshid Keynia. 2020. "Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm" Applied Sciences 10, no. 14: 4795. https://doi.org/10.3390/app10144795
APA StyleKakueinejad, M. H., Heydari, A., Askari, M., & Keynia, F. (2020). Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm. Applied Sciences, 10(14), 4795. https://doi.org/10.3390/app10144795