Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood
Abstract
:1. Introduction
1.1. Background and Literature Review
- The potential of each of the RE carriers
- Identification and selection of suitable areas (site-finding)
- A revised perspective for the future of REs
- Economic justification according to various factors
- Planning, mode and capacity of investment, with prioritization of each RE
- A proper planning to develop technology
- Capacity and substitution capability
1.2. Importance and Necessity of the Research
1.3. Optimal Placement of Distributed Generators in Power Distribution Systems
1.4. Motivation and Main Contribution
1.5. Paper Structure
2. Voltage Stability and Reducing Losses in Distribution Networks
2.1. Voltage Stability
2.2. Important Benefits of Using Distributed Generatiors in Power Network
2.2.1. Power Supply
2.2.2. Reserved Power
2.2.3. Load Flow Analysis
2.2.4. Improving Power Quality and Reliability
2.2.5. Improving Voltage Profile
2.2.6. Increasing the Life of Equipment
2.2.7. Reducing Losses
2.2.8. Distributed Generation and Environmental Issues
3. Proposed Tools for DG Allocation to Reduce Losses and Increase Voltage Stability
3.1. Reasons for the Use of Evolutionary Algorithms
- Genetic Algorithms (GA) provided by Holland and studied by Goldberg
- Evolutionary Strategies (ES) presented by Rechenberg and Schwefel
- Evolutionary Planning (EP) provided by L.J. Fogel et al. modified by D.B. Fogel
3.2. Objective Function Optimizer Algorithm
3.3. Problem Modeling without Considering Greenhouse Gas Costs
3.4. Problem Modeling Considering the Cost of Greenhouse Gases
- Determining the buses that DGs are installed on; (33 variables per 33 buses);
- Determining the type of DGs that are assigned on the buses defined in item 1; (33 variables for 33 buses);
- Determining the working range of each of the DGs installed in item 2; (33 variables for 33 buses);
3.5. Optimal Placement of DGs in 33-Bus Network
- The first step is to create a 33-base model of the IEEE standard with the aid of matrixes;
- The second step is to create various constraints on the system specification in order to make the model more realistic;
- Step three is generating random responses and calculate the extent to which responses are violated from the problem space;
- Step four is applying random answers to the target function and the penalty function to calculate the cost of each response;
- Step five is comparing responses and find the lowest cost among all the search methods
- The sixth stage is displaying the output response as to which DG type with a few percent of the maximum power on which bus to use.
4. Results of Optimal DGs Placement in the IEEE 33-Buss Networks
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Power Plant Type | Hydroelectric | Sea | Biomass | Geothermal | Wind | Solar | |
---|---|---|---|---|---|---|---|
Characteristics | |||||||
Amount, Distribution, Variation, Intensity | High worldwide, seasonal, medium to low | Very high in coastal, seasonal, tidal, low | Very high worldwide, weather dependent, medium to low | Medium, cross-plate, up to 600 degrees, low average | High In coastal and mountainous shores, very variable, low average | Extremely high worldwide, daily, seasonal, low pick | |
Technology; Options | Dams and turbines with different altitude | Low temperature thermodynamic cycles, mechanical wave oscillators, tidal dams | Burning, fermenting, digestion, gas production, liquidation | Steam and double thermodynamic cycles, total current turbines, ground pressure, lava | Horizontal and vertical wind turbines, wind pumps, sail power | Thermal systems with low to high temperature, PVs, inactive systems, biological transformation | |
Current Status | Often commercial | In development | Some commercial, some in development | Many commercial, some in development | Many commercial, some in development | In development, some commercial | |
Coefficient, Capacity | Discontinued to base load | Discontinued to base load | Based on the need for short-term storage | High, basic load | Variable, 30–15% | No storage less than 25%, medium | |
Ways of Progression | Turbine, cost, design, information | Technologically, materials, cost per source | Technologically, agricultural management and forestry | Exploration, extraction, use of hot stones | Material, layout, installation location, information about the source | Material, cost, efficiency, information about the source | |
Specifications: Environmental Characteristics | Very clean, effect on local aquatic environment, use of the earth | Very clean, effect on local aquatic environment, effect on landscape | Clean, effects on animals and other poisonous waste plants | Clean, soluble gases, water and salt consumption | Very clean, affecting the visibility, noise, the mortality of the birds | Very clean, affecting the landscape and local air, producing Photovoltaic (PV) modules |
Number of Transmitted Buses | Number of Received Buses | Number of Transmission Lines | Ohmic Resistance | Inductive Resistance |
---|---|---|---|---|
1 | 2 | 1 | 0.0992 | 0.0447 |
2 | 3 | 1 | 0.493 | 0.2511 |
3 | 4 | 1 | 0.366 | 0.1864 |
4 | 5 | 1 | 0.3811 | 0.1941 |
5 | 6 | 1 | 0.819 | 0.707 |
6 | 7 | 1 | 0.1872 | 0.6188 |
7 | 8 | 1 | 1.7114 | 1.2351 |
8 | 9 | 1 | 1.03 | 0.74 |
9 | 10 | 1 | 1.04 | 0.74 |
10 | 11 | 1 | 0.1966 | 0.065 |
11 | 12 | 1 | 0.3744 | 0.1238 |
12 | 13 | 1 | 1.468 | 1.155 |
13 | 14 | 1 | 0.5416 | 0.7129 |
14 | 15 | 1 | 0.591 | 0.526 |
15 | 16 | 1 | 0.7463 | 0.545 |
16 | 17 | 1 | 1.289 | 1.721 |
17 | 18 | 1 | 0.732 | 0.574 |
18 | 19 | 1 | 0.164 | 0.1565 |
19 | 20 | 1 | 1.5042 | 1.3554 |
20 | 21 | 1 | 0.4095 | 0.4784 |
21 | 22 | 1 | 0.7089 | 0.9373 |
22 | 23 | 1 | 0.4512 | 0.3083 |
23 | 24 | 1 | 0.898 | 0.7091 |
24 | 25 | 1 | 0.896 | 0.7011 |
25 | 26 | 1 | 0.203 | 0.1034 |
26 | 27 | 1 | 0.2842 | 0.1447 |
27 | 28 | 1 | 1.059 | 0.9337 |
28 | 29 | 1 | 0.8042 | 0.7006 |
29 | 30 | 1 | 0.5075 | 0.2585 |
30 | 31 | 1 | 0.9744 | 0.963 |
31 | 32 | 1 | 0.3105 | 0.3619 |
32 | 33 | 1 | 0.341 | 0.5302 |
Number of Buses | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
P | 3.8 | 0.1 | 0.09 | 0.12 | 0.06 | 0.06 | 0.2 | 0.2 | 0.06 | 0.06 | 0.045 |
Q | 2.4 | 0.06 | 0.04 | 0.08 | 0.03 | 0.02 | 0.1 | 0.02 | 0.02 | 0.03 | 0.03 |
Number of Buses | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 |
P | 0.06 | 0.06 | 0.12 | 0.06 | 0.06 | 0.06 | 0.09 | 0.09 | 0.09 | 0.09 | 0.09 |
Q | 0.035 | 0.035 | 0.08 | 0.01 | 0.02 | 0.02 | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 |
Number of Buses | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 |
P | 0.09 | 0.42 | 0.42 | 0.06 | 0.06 | 0.06 | 0.12 | 0.2 | 0.15 | 0.21 | 0.06 |
Q | 0.05 | 0.2 | 0.2 | 0.025 | 0.025 | 0.02 | 0.07 | 0.6 | 0.07 | 0.1 | 0.04 |
Bus Number of DG | Type of DG | Installed Capacity (MVA) |
---|---|---|
5 | PV | 1.12 |
13 | Wind | 1.54 |
15 | Win | 7.62 |
Bus Number of DG | Type of DG | Installed Capacity (MVA) |
---|---|---|
5 | PV | 6.173 |
13 | PV | 6.204 |
19 | PV | 6.192 |
30 | Wind | 6.204 |
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Fathi, M.; Ghiasi, M. Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood. Smart Cities 2019, 2, 328-344. https://doi.org/10.3390/smartcities2020020
Fathi M, Ghiasi M. Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood. Smart Cities. 2019; 2(2):328-344. https://doi.org/10.3390/smartcities2020020
Chicago/Turabian StyleFathi, Mohammadreza, and Mohammad Ghiasi. 2019. "Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood" Smart Cities 2, no. 2: 328-344. https://doi.org/10.3390/smartcities2020020