# SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

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## Abstract

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## 1. Introduction

- (a)
- Injects only real power to the system. Examples are solar photovoltaic and fuel cells.
- (b)
- Injects only reactive power to the system. Examples are synchronous condensers.
- (c)
- Injects both real and reactive powers (P and Q) into the system. Examples are synchronous generators, i.e., steam turbines and cogeneration.
- (d)
- Injects real power but absorbs reactive power. Examples are synchronous condensers.

## 2. Related Work

## 3. Problem Formulation and Proposed Solution

#### 3.1. Mathematical Model

#### 3.1.1. Inequality Constraints

_{DG}), apparent power (S

_{DG}), active power (P

_{DG}) and voltages ($V$

_{DG}) as represented in (8)–(11).

#### 3.1.2. Equality Constraints

_{I}and θ

_{J}are the voltage angles of bus $I$ and $J$, respectively, as depicted in Figure 1. ${g}_{IJ}$ and ${b}_{IJ}$ are the real and imaginary parts of admittance between bus$I$ and $J$. The values of $P$, $Q$ and $V$ in the main function are computed by the utilization of power flow algorithm for radial distribution networks that conduct forward and backward sweeps to calculate the branch current and node voltages in the distribution system.

#### 3.2. Proposed SPSO Algorithm

_{D}= [S

_{D1}, S

_{D2},…, S

_{DN}]

_{D1}is the position of particles for dimension one, S

_{D}

_{2}is the position of particles for dimension two, similarly, the further positions of particles up to S

_{DN}represents the positions for remaining dimensions. In a distributed network, the number of loops formed equals the number of dimensions by closing all the tie-switches. Branches of a loop have constituted the search space of a dimension. However, the branches of the network which do not belong to any loop are not in any search space. Therefore, these branches are not considered in the optimization process. The SPSO can be applied to find the optimal solution after specifying the number of dimensions and the search space for each dimension. $DN$ denotes total selected random positions in dimension D and ${S}_{D}$ reflects its collection in dimension $D$. S

_{D}selects the best position from the different position vectors from S

_{D}

_{1}to S

_{DN}. In SPSO, the fitness function nature of the conventional PSO is replaced with logistic sigmoid function sig (${V}_{iD}^{K+1}$) because respective $D$ dimensions taken from $DN$ positions are mapped with ${S}_{D}$. So, the position of respective swarms in dimension $D$ is altered from being a point in real value to selected space. The logistic sigmoid function is given by (18).

_{min}represents the minimum possible velocity and V

_{max}maximum velocity. For each iteration velocity of swarms updated avoids invariability in the velocity of ith swarms, velocity is updated by (22).

## 4. Case Study

_{min}= 0.4, W

_{max}= 0.9 are the inertia weights. SPSO simulation is performed using MATLAB R2018Ra installed on a personnel computer with Intel Core i5-4200u processor, 1.60 GHz, 4GB RAM, x64-bit based Windows platform. The results obtained through MATLAB are also verified through ETAP.

## 5. Simulation Results and Discussion

- Evaluation of objective parameters without DGs
- Evaluation of objective parameters with DGs through SPSO
- Evaluation of objective parameters with DGs through ETAP
- Comparison of proposed SPSO outcomes with other approaches

#### 5.1. Evaluation of Objective Parameters without DGS

#### 5.2. Evaluation of Objective Parameters with DGs through SPSO

#### 5.3. Evaluation of Objective Parameters with DGs through ETAP

#### 5.4. Comparison of Proposed SPSO Outcomes with Other Approaches

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Popa, G.N.; Iagăr, A.; Diniș, C.M. Considerations on Current and Voltage Unbalance of Nonlinear Loads in Residential and Educational Sectors. Energies
**2021**, 14, 102. [Google Scholar] [CrossRef] - Bayatloo, F.; Bozorgi-Amiri, A. A novel optimization model for dynamic power grid design and expansion planning considering renewable resources. J. Clean. Prod.
**2019**, 229, 1319–1334. [Google Scholar] [CrossRef] - Ameli, A.; Bahrami, S.; Khazaeli, F.; Haghifam, M. A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner’s and Distribution Company’s Viewpoints. IEEE Trans. Power Deliv.
**2014**, 29, 1831–1840. [Google Scholar] [CrossRef] - Rizwan, M.; Hong, L.; Waseem, M.; Shu, W. Sustainable protection coordination in presence of distributed generation with distributed network. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12217. [Google Scholar] [CrossRef] - Gampa, S.R.; Das, D. Optimum placement and sizing of DGs considering average hourly variations of load. Int. J. Electr. Power Energy Syst.
**2015**, 66, 25–40. [Google Scholar] [CrossRef] - Hu, X.; Zhou, H.; Liu, Z.; Yu, X.; Li, C. Hierarchical Distributed Scheme for Demand Estimation and Power Reallocation in a Future Power Grid. IEEE Trans. Ind. Inform.
**2017**, 13, 2279–2290. [Google Scholar] [CrossRef] - Khalid, M.; Akram, U.; Shafiq, S. Optimal Planning of Multiple Distributed Generating Units and Storage in Active Distribution Networks. IEEE Access
**2018**, 6, 55234–55244. [Google Scholar] [CrossRef] - El-Fergany, A. Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int. J. Electr. Power Energy Syst.
**2015**, 64, 1197–1205. [Google Scholar] [CrossRef] - Alshehri, J.; Khalid, M. Power quality improvement in microgrids under critical disturbances using an intelligent decoupled control strategy based on battery energy storage system. IEEE Access
**2019**, 7, 147314–147326. [Google Scholar] [CrossRef] - Yin, F.; Hajjiah, A.; Jermsittiparsert, K.; Al-Sumaiti, A.S.; Elsayed, S.K.; Ghoneim, S.S.M.; Mohamed, M.A. A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids. IEEE Access
**2021**, 9, 40797–40810. [Google Scholar] [CrossRef] - Paiva, S.; Ahad, M.A.; Tripathi, G.; Feroz, N.; Casalino, G. Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges. Sensors
**2021**, 21, 2143. [Google Scholar] [CrossRef] - Ahad, M.A.; Biswas, R. Request-based, secured and energy-efficient (RBSEE) architecture for handling IoT big data. J. Inf. Sci.
**2019**, 45, 227–238. [Google Scholar] [CrossRef] - Iqbal, M.M.; Sajjad, I.A.; Manan, A.; Waseem, M.; Ali, A.; Sohail, A. Towards an Optimal Residential Home Energy Management in Presence of PV Generation, Energy Storage and Home to Grid Energy Exchange Framework. In Proceedings of the 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 29–30 January 2020; pp. 1–7. [Google Scholar]
- Grubic, T.; Varga, L.; Hu, Y.; Tewari, A. Micro-generation technologies and consumption of resources: A complex systems’ exploration. J. Clean. Prod.
**2020**, 247, 119091. [Google Scholar] [CrossRef] - Rizwan, M.; Hong, L.; Waseem, M.; Sharaf, M.; Shafiq, M. A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. Appl. Sci.
**2020**, 10, 6318. [Google Scholar] [CrossRef] - Waseem, M.; Sajjad, I.A.; Napoli, R.; Chicco, G. Seasonal Effect on the Flexibility Assessment of Electrical Demand. In Proceedings of the 2018 53rd International Universities Power Engineering Conference (UPEC), Glasgow, UK, 4–7 September 2018; pp. 1–6. [Google Scholar]
- Waseem, M.; Sajjad, I.A.; Haroon, S.S.; Amin, S.; Farooq, H.; Martirano, L.; Napoli, R. Electrical Demand and its Flexibility in Different Energy Sectors. Electr. Power Compon. Syst.
**2020**, 48, 1339–1361. [Google Scholar] [CrossRef] - Ma, M.; Zhang, C.; Liu, X.; Chen, H. Distributed Model Predictive Load Frequency Control of the Multi-Area Power System After Deregulation. IEEE Trans. Ind. Electron.
**2017**, 64, 5129–5139. [Google Scholar] [CrossRef] - Mohandas, N.; Balamurugan, R.; Lakshminarasimman, L. Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos. Int. J. Electr. Power Energy Syst.
**2015**, 66, 41–52. [Google Scholar] [CrossRef] - Waseem, M.; Lin, Z.; Liu, S.; Zhang, Z.; Aziz, T.; Khan, D. Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources. Appl. Energy
**2021**, 290, 116761. [Google Scholar] [CrossRef] - Suchitra, D.; Jegatheesan, R.; Deepika, T.J. Optimal design of hybrid power generation system and its integration in the distribution network. Int. J. Electr. Power Energy Syst.
**2016**, 82, 136–149. [Google Scholar] [CrossRef] - Mohamed, M.A.; Abdullah, H.M.; El-Meligy, M.A.; Sharaf, M.; Soliman, A.T.; Hajjiah, A. A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles. Int. J. Electr. Power Energy Syst.
**2021**, 129, 106845. [Google Scholar] [CrossRef] - Jain, S.; Kalambe, S.; Agnihotri, G.; Mishra, A. Distributed generation deployment: State-of-the-art of distribution system planning in sustainable era. Renew. Sustain. Energy Rev.
**2017**, 77, 363–385. [Google Scholar] [CrossRef] - Farh, H.M.H.; Al-Shaalan, A.M.; Eltamaly, A.M.; Al-Shamma’A, A.A. A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation. IEEE Access
**2020**, 8, 27807–27820. [Google Scholar] [CrossRef] - Fayyaz, S.; Sattar, M.K.; Waseem, M.; Ashraf, M.U.; Ahmad, A.; Hussain, H.A.; Alsubhi, K. Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm. IEEE Access
**2021**, 9, 56152–56167. [Google Scholar] [CrossRef] - Viral, R.; Khatod, D.K. An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization. Int. J. Electr. Power Energy Syst.
**2015**, 67, 191–201. [Google Scholar] [CrossRef] - Bazmi, A.A.; Zahedi, G.; Hashim, H. Design of decentralized biopower generation and distribution system for developing countries. J. Clean. Prod.
**2015**, 86, 209–220. [Google Scholar] [CrossRef] - Hung, D.Q.; Mithulananthan, N.; Lee, K.Y. Determining PV Penetration for Distribution Systems With Time-Varying Load Models. IEEE Trans. Power Syst.
**2014**, 29, 3048–3057. [Google Scholar] [CrossRef] - Khan, H.; Choudhry, M.A. Implementation of Distributed Generation (IDG) algorithm for performance enhancement of distribution feeder under extreme load growth. Int. J. Electr. Power Energy Syst.
**2010**, 32, 985–997. [Google Scholar] [CrossRef] - Chiradeja, P.; Ramakumar, R. An approach to quantify the technical benefits of distributed generation. IEEE Trans. Energy Convers.
**2004**, 19, 764–773. [Google Scholar] [CrossRef] - Waseem, M.; Lin, Z.; Lin, Z.; Liu, S. Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort. Electr. Power Syst. Res.
**2020**, 187, 106477. [Google Scholar] [CrossRef] - Abdmouleh, Z.; Gastli, A.; Ben-Brahim, L.; Haouari, M.; Al-Emadi, N.A. Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renew. Energy
**2017**, 113, 266–280. [Google Scholar] [CrossRef] - Aziz, T.; Lin, Z.; Waseem, M.; Liu, S. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. Int. Trans. Electr. Energy Syst.
**2021**, 31, e12704. [Google Scholar] [CrossRef] - Ganguly, S.; Samajpati, D. Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm. IEEE Trans. Sustain. Energy
**2015**, 6, 688–697. [Google Scholar] [CrossRef] - Vatani, M.; Alkaran, D.S.; Sanjari, M.J.; Gharehpetian, G.B. Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods. IET Gener. Transm. Distrib.
**2016**, 10, 66–72. [Google Scholar] [CrossRef] - Sheng, W.; Liu, K.-Y.; Liu, Y.; Meng, X.; Li, Y. Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II. IEEE Trans. Power Deliv.
**2014**, 30, 569–578. [Google Scholar] [CrossRef] - Azeem, O.; Ali, M.; Abbas, G.; Uzair, M.; Qahmash, A.; Algarni, A.; Hussain, M.R. A Comprehensive Review on Integration Challenges, Optimization Techniques and Control Strategies of Hybrid AC/DC Microgrid. Appl. Sci.
**2021**, 11, 6242. [Google Scholar] [CrossRef] - Zhou, Z.; Li, F.; Abawajy, J.H.; Gao, C. Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres. IEEE Access
**2020**, 8, 55872–55880. [Google Scholar] [CrossRef] - Rosa, W.d.; Gerez, C.; Belati, E. Optimal Distributed Generation Allocating Using Particle Swarm Optimization and Linearized AC Load Flow. IEEE Latin Am. Trans.
**2018**, 16, 2665–2670. [Google Scholar] [CrossRef] - Abbas, G.; Gu, J.; Farooq, U.; Asad, M.U.; El-Hawary, M. Solution of an economic dispatch problem through particle swarm optimization: A detailed survey-part I. IEEE Access
**2017**, 5, 15105–15141. [Google Scholar] [CrossRef] - Moradi, M.H.; Abedini, M.; Hosseinian, S.M. A Combination of Evolutionary Algorithm and Game Theory for Optimal Location and Operation of DG from DG Owner Standpoints. IEEE Trans. Smart Grid
**2016**, 7, 608–616. [Google Scholar] [CrossRef] - Barati, F.; Jadid, S.; Zangeneh, A. A new approach for DG planning at the viewpoint of the independent DG investor, a case study of Iran. Int. Trans. Electr. Energy Syst.
**2017**, 27, e2319. [Google Scholar] [CrossRef] - Singh, B.; Mishra, D.K. A survey on enhancement of power system performances by optimally placed DG in distribution networks. Energy Rep.
**2018**, 4, 129–158. [Google Scholar] [CrossRef] - Gomez-Gonzalez, M.; López, A.; Jurado, F. Optimization of distributed generation systems using a new discrete PSO and OPF. Electr. Power Syst. Res.
**2012**, 84, 174–180. [Google Scholar] [CrossRef] - Pesaran, H.A.M.; Huy, P.D.; Ramachandaramurthy, V.K. A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms. Renew. Sustain. Energy Rev.
**2017**, 75, 293–312. [Google Scholar] [CrossRef] - Theo, W.L.; Lim, J.S.; Ho, W.S.; Hashim, H.; Lee, C.T. Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renew. Sustain. Energy Rev.
**2017**, 67, 531–573. [Google Scholar] [CrossRef] - IEEE. IEEE Recommended Practice for Interconnecting Distributed Resources with Electric Power Systems Distribution Secondary Networks. In IEEE Std 1547.6-2011; The IEEE Standards Association: Piscataway, NJ, USA, 2011; pp. 1–38. [Google Scholar] [CrossRef]
- Waseem, M.; Kouser, F.; Waqas, A.B.; Imran, N.; Hameed, S.; Faheem, Z.B.; Liaqat, R.; Shabbir, U. CSOA-Based Residential Energy Management System in Smart Grid Considering DGs for Demand Response. In Proceedings of the 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), Islamabad, Pakistan, 20–21 May 2021; pp. 1–6. [Google Scholar]
- Garg, H. A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput.
**2016**, 274, 292–305. [Google Scholar] [CrossRef]

From Node–Node | Resistance (Ω) | Segment Length (km) | Inductive Reactance (Ω) | Impedance (Ω) | Bus Load (kVA) |
---|---|---|---|---|---|

0–1 | 0.040 | 0.12 | 0.045 | 0.067 | 45 |

1–2 | 0.079 | 0.235 | 0.088 | 0.119 | 60 |

2–3 | 0.040 | 0.12 | 0.045 | 0.061 | 50 |

3–4 | 0.205 | 0.609 | 0.230 | 0.308 | 50 |

4–5 | 0.061 | 0.183 | 0.069 | 0.092 | 75 |

5–6 | 0.033 | 0.098 | 0.037 | 0.049 | 2705 |

6–7 | 0.279 | 0.83 | 0.313 | 0.419 | 250 |

7–8 | 0.077 | 0.229 | 0.087 | 0.116 | 125 |

8–9 | 0.092 | 0.275 | 0.104 | 0.139 | 2100 |

9–10 | 0.139 | 0.414 | 0.157 | 0.209 | 175 |

10–11 | 0.157 | 0.466 | 0.177 | 0.236 | 125 |

11–12 | 0.140 | 0.417 | 0.158 | 0.211 | 100 |

12–13 | 0.031 | 0.091 | 0.034 | 0.046 | 300 |

13–14 | 0.095 | 0.283 | 0.107 | 0.143 | 250 |

14–15 | 0.034 | 0.101 | 0.039 | 0.051 | 50 |

15–16 | 0.016 | 0.049 | 0.018 | 0.024 | 1000 |

16–17 | 0.075 | 0.223 | 0.084 | 0.113 | 375 |

17–18 | 0.007 | 0.002 | 0.008 | 0.001 | 250 |

18–19 | 0.129 | 0.384 | 0.145 | 0.194 | 100 |

19–20 | 0.177 | 0.528 | 0.199 | 0.267 | 300 |

20–21 | 0.146 | 0.436 | 0.165 | 0.221 | 325 |

21–22 | 0.528 | 1.573 | 0.594 | 0.795 | 225 |

22–23 | 0.245 | 0.732 | 0.277 | 0.370 | 300 |

23–24 | 0.206 | 0.613 | 0.232 | 0.310 | 75 |

24–25 | 0.031 | 0.091 | 0.034 | 0.046 | 105 |

25–26 | 0.398 | 1.182 | 0.447 | 0.597 | 400 |

26–27 | 0.072 | 0.214 | 0.081 | 0.108 | 975 |

27–28 | 0.135 | 0.402 | 0.152 | 0.203 | 950 |

28–29 | 0.030 | 0.09 | 0.034 | 0.045 | 700 |

29–30 | 0.108 | 0.32 | 0.121 | 0.161 | 75 |

30–31 | 0.139 | 0.412 | 0.1557 | 0.208 | 225 |

31–32 | 0.031 | 0.091 | 0.0344 | 0.046 | 125 |

32–33 | 0.108 | 0.32 | 0.121 | 0.162 | 325 |

33–34 | 0.163 | 0.488 | 0.1845 | 0.247 | 25 |

34–35 | 0.043 | 0.127 | 0.048 | 0.064 | 200 |

35–36 | 0.246 | 0.732 | 0.277 | 0.370 | 425 |

36–37 | 0.278 | 0.828 | 0.313 | 0.418 | 100 |

37–38 | 0.492 | 1.464 | 0.553 | 0.740 | 125 |

38–39 | 0.134 | 0.399 | 0.151 | 0.202 | 450 |

39–40 | 0.061 | 0.183 | 0.069 | 0.093 | 125 |

Sr. No | From Node–Node | Segment Currents (A) | Segment Voltage Drops (V) | Node Voltage at Receiving End (p.u.) | Power Loss (kW) |
---|---|---|---|---|---|

1 | 0–1 | 773.649 | 46.952 | 0.995 | 182.002 |

2 | 1–2 | 771.287 | 91.667 | 0.987 | 46.972 |

3 | 2–3 | 768.138 | 46.618 | 0.983 | 134.812 |

4 | 3–4 | 765.514 | 235.778 | 0.962 | 119.912 |

5 | 4–5 | 762.889 | 70.606 | 0.955 | 35.786 |

6 | 5–6 | 758.953 | 37.616 | 0.952 | 18.967 |

7 | 6–7 | 617.953 | 259.398 | 0.928 | 106.495 |

8 | 7–8 | 604.832 | 70.049 | 0.922 | 28.147 |

9 | 8–9 | 598.271 | 83.207 | 0.914 | 33.073 |

10 | 9–10 | 488.271 | 102.233 | 0.905 | 33.163 |

11 | 10–11 | 479.085 | 112.091 | 0.895 | 35.937 |

12 | 11–12 | 472.524 | 99.654 | 0.886 | 31.284 |

13 | 12–13 | 467.276 | 21.505 | 0.884 | 6.676 |

14 | 13–14 | 451.530 | 64.625 | 0.877 | 19.386 |

15 | 14–15 | 438.408 | 22.394 | 0.876 | 6.523 |

16 | 15–16 | 435.784 | 10.799 | 0.875 | 3.126 |

17 | 16–17 | 383.298 | 43.228 | 0.871 | 28.038 |

18 | 17–18 | 363.615 | 0.368 | 0.871 | 37.450 |

19 | 18–19 | 350.494 | 68.068 | 0.865 | 15.850 |

20 | 19–20 | 345.245 | 92.192 | 0.856 | 21.146 |

21 | 20–21 | 329.499 | 72.656 | 0.849 | 15.905 |

22 | 21–22 | 312.441 | 248.559 | 0.827 | 51.595 |

23 | 22–23 | 300.632 | 111.296 | 0.817 | 22.229 |

24 | 23–24 | 284.886 | 88.321 | 0.809 | 16.716 |

25 | 24–25 | 281.496 | 12.955 | 0.808 | 2.423 |

26 | 25–26 | 275.986 | 164.982 | 0.792 | 30.250 |

27 | 26–27 | 254.992 | 27.597 | 0.790 | 24.182 |

28 | 27–28 | 203.892 | 41.453 | 0.785 | 5.615 |

29 | 28–29 | 154.029 | 7.011 | 0.786 | 47.534 |

30 | 29–30 | 117.329 | 18.988 | 0.784 | 1.480 |

31 | 30–31 | 113.393 | 23.627 | 0.781 | 1.266 |

32 | 31–32 | 101.593 | 4.675 | 0.782 | 0.934 |

33 | 32–33 | 95.032 | 15.379 | 0.780 | 0.971 |

34 | 33–34 | 78.032 | 19.258 | 0.778 | 0.998 |

35 | 34–35 | 76.719 | 4.927 | 0.778 | 8.201 |

36 | 35–36 | 66.223 | 24.516 | 0.775 | 0.069 |

37 | 36–37 | 43.915 | 18.390 | 0.774 | 0.536 |

38 | 37–38 | 38.667 | 28.629 | 0.772 | 0.137 |

39 | 38–39 | 32.107 | 6.478 | 0.770 | 0.138 |

40 | 39–40 | 8.488 | 0.785 | 0.771 | 0.004 |

Total Voltage Drop = 2520.365 V | Total Power Loss = 1175.935 kW |

Sr. No. | From Node–Node | Segment Currents (A) | Segment Voltage Drops (V) | Node Voltage at Receiving End (p.u.) | Power Loss (kW) |
---|---|---|---|---|---|

1 | 0–1 | 188.749 | 11.455 | 0.998 | 10.856 |

2 | 1–2 | 186.387 | 22.152 | 0.996 | 2.748 |

3 | 2–3 | 183.238 | 11.120 | 0.995 | 17.783 |

4 | 3–4 | 180.614 | 55.629 | 0.990 | 6.689 |

5 | 4–5 | 177.989 | 16.473 | 0.989 | 1.953 |

6 | 5–6 | 174.053 | 8.626 | 0.988 | 0.999 |

7 | 6–7 | 33.053 | 13.874 | 0.987 | 0.308 |

8 | 7–8 | 19.931 | 2.308 | 0.987 | 0.031 |

9 | 8–9 | 13.370 | 1.859 | 0.987 | 0.053 |

10 | 9–10 | 96.629 | 20.232 | 0.985 | 0.921 |

11 | 10–11 | 105.814 | 24.938 | 0.983 | 3.977 |

12 | 11–12 | 112.375 | 23.699 | 0.981 | 1.763 |

13 | 12–13 | 117.624 | 5.413 | 0.981 | 0.421 |

14 | 13–14 | 133.037 | 19.088 | 0.978 | 1.686 |

15 | 14–15 | 146.491 | 7.482 | 0.978 | 7.189 |

16 | 15–16 | 149.116 | 3.695 | 0.977 | 0.365 |

17 | 16–17 | 105.598 | 11.909 | 0.998 | 2.176 |

18 | 17–18 | 85.916 | 0.086 | 0.998 | 2.149 |

19 | 18–19 | 72.794 | 14.137 | 0.997 | 0.706 |

20 | 19–20 | 67.545 | 18.036 | 0.996 | 0.838 |

21 | 20–21 | 51.799 | 11.422 | 0.994 | 0.411 |

22 | 21–22 | 34.742 | 27.638 | 0.992 | 0.682 |

23 | 22–23 | 22.932 | 8.489 | 0.993 | 0.259 |

24 | 23–24 | 7.186 | 2.227 | 0.992 | 0.028 |

25 | 24–25 | 3.796 | 0.174 | 0.992 | 0.008 |

26 | 25–26 | 1.713 | 1.024 | 0.991 | 0.001 |

27 | 26–27 | 22.709 | 2.457 | 0.991 | 0.172 |

28 | 27–28 | 73.809 | 15.006 | 0.989 | 0.712 |

29 | 28–29 | 123.671 | 5.629 | 0.989 | 7.553 |

30 | 29–30 | 117.329 | 18.988 | 0.998 | 4.233 |

31 | 30–31 | 113.393 | 23.627 | 0.996 | 6.408 |

32 | 31–32 | 101.592 | 4.675 | 0.998 | 0.934 |

33 | 32–33 | 95.032 | 15.379 | 0.994 | 0.971 |

34 | 33–34 | 78.032 | 19.258 | 0.992 | 0.998 |

35 | 34–35 | 76.719 | 4.927 | 0.992 | 5.847 |

36 | 35–36 | 66.222 | 24.516 | 0.989 | 0.069 |

37 | 36–37 | 43.916 | 18.390 | 0.988 | 0.536 |

38 | 37–38 | 38.667 | 28.629 | 0.986 | 0.137 |

39 | 38–39 | 32.106 | 6.478 | 0.985 | 0.138 |

40 | 39–40 | 8.488 | 0.785 | 0.984 | 0.004 |

Total Voltage Drop = 531.929 V | Total Power Loss = 92.44 kW |

Sr. No. | From Node–Node | Segment Currents (A) | Segment Voltage Drops (V) | Node Voltage at Receiving End (p.u.) | Power Loss (kW) |
---|---|---|---|---|---|

1 | 0–1 | 188.949 | 11.467 | 0.996 | 10.856 |

2 | 1–2 | 186.587 | 22.176 | 0.995 | 2.748 |

3 | 2–3 | 183.438 | 11.132 | 0.990 | 17.783 |

4 | 3–4 | 180.813 | 55.690 | 0.989 | 6.689 |

5 | 4–5 | 178.189 | 16.491 | 0.988 | 1.952 |

6 | 5–6 | 174.253 | 8.636 | 0.987 | 0.999 |

7 | 6–7 | 33.253 | 13.958 | 0.987 | 0.308 |

8 | 7–8 | 20.131 | 2.331 | 0.986 | 0.032 |

9 | 8–9 | 13.570 | 1.887 | 0.985 | 0.053 |

10 | 9–10 | 96.429 | 20.190 | 0.982 | 0.922 |

11 | 10–11 | 105.614 | 24.890 | 0.980 | 3.977 |

12 | 11–12 | 112.175 | 23.657 | 0.980 | 1.763 |

13 | 12–13 | 117.423 | 5.404 | 0.978 | 0.422 |

14 | 13–14 | 133.169 | 19.060 | 0.977 | 1.686 |

15 | 14–15 | 146.291 | 7.472 | 0.977 | 7.189 |

16 | 15–16 | 148.915 | 3.690 | 0.998 | 0.365 |

17 | 16–17 | 104.998 | 11.841 | 0.998 | 2.104 |

18 | 17–18 | 85.315 | 0.862 | 0.997 | 2.061 |

19 | 18–19 | 72.194 | 14.020 | 0.996 | 0.673 |

20 | 19–20 | 66.945 | 17.876 | 0.995 | 0.796 |

21 | 20–21 | 51.199 | 11.289 | 0.992 | 0.384 |

22 | 21–22 | 34.141 | 27.160 | 0.992 | 0.616 |

23 | 22–23 | 22.331 | 8.267 | 0.991 | 0.223 |

24 | 23–24 | 6.586 | 2.041 | 0.992 | 0.018 |

25 | 24–25 | 3.196 | 0.147 | 0.991 | 0.312 |

26 | 25–26 | 2.313 | 1.383 | 0.991 | 0.021 |

27 | 26–27 | 23.308 | 2.522 | 0.988 | 0.202 |

28 | 27–28 | 74.408 | 15.128 | 0.989 | 0.747 |

29 | 28–29 | 124.270 | 5.656 | 0.998 | 7.776 |

30 | 29–30 | 117.329 | 18.988 | 0.996 | 4.233 |

31 | 30–31 | 113.392 | 23.627 | 0.996 | 6.408 |

32 | 31–32 | 101.592 | 4.675 | 0.994 | 0.935 |

33 | 32–33 | 95.032 | 15.379 | 0.993 | 0.972 |

34 | 33–34 | 78.032 | 19.258 | 0.992 | 0.998 |

35 | 34–35 | 76.719 | 4.927 | 0.989 | 5.847 |

36 | 35–36 | 66.222 | 24.516 | 0.988 | 0.069 |

37 | 36–37 | 43.915 | 18.390 | 0.985 | 0.536 |

38 | 37–38 | 38.667 | 28.629 | 0.985 | 0.137 |

39 | 38–39 | 32.106 | 6.478 | 0.985 | 0.138 |

40 | 39–40 | 8.487 | 0.785 | 0.997 | 0.443 |

Total Voltage Drop = 550.975 | Total Power Loss = 93.996 kW |

Total Voltage Drops (V) | Total Power Loss (kW) | Reduction in Voltage Drops | Reduction in Power Loss | |
---|---|---|---|---|

Without DGs | 2520.365 | 1175.935 | ||

With DGs through SPSO | 531.929 | 92.44 | 78.89% | 92.139% |

With DGs through ETAP | 550.975 | 93.996 | 78.13% | 92.006% |

Approach | Optimal Size (kVA)/Location (Bus No). | Active Power Loss P _{L} (kW) | P_{L} Reduction (%) | Reactive Power Loss Q_{L} (kVAR) | Q_{L} Reduction (%) | V_{min}/(Bus No). | Execution Time (s) |
---|---|---|---|---|---|---|---|

Base Case | 1175.935 | 987.159 | |||||

ABC [19] | 5125/19 3503/30 | 105.54 | 91.025 | 119.054 | 87.8905 | 0.987/15 | 145.78 |

CSOA [48] | 5518/17 5128/30 | 95.80 | 91.853 | 112.572 | 88.59637 | 0.989/15 | 224.76 |

BPOA [25] | 5274/17 4804/30 | 97.10 | 91.743 | 117.847 | 88.0572 | 0.983/15 | 103.98 |

PSO-GA [49] | 5880/17 4864/30 | 101.15 | 91.398 | 115.594 | 88.290 | 0.981/15 | 92.08 |

DPSO [44] | 5516/17 5126/30 | 98.208 | 91.649 | 128.077 | 86.955 | 0.979/15 | 82.78 |

Proposed SPSO | 5802/17 5232/30 | 94.044 | 91.969 | 109.615 | 88.896 | 0.991/15 | 40.06 |

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## Share and Cite

**MDPI and ACS Style**

Rizwan, M.; Waseem, M.; Liaqat, R.; Sajjad, I.A.; Dampage, U.; Salmen, S.H.; Obaid, S.A.; Mohamed, M.A.; Annuk, A.
SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities. *Electronics* **2021**, *10*, 2542.
https://doi.org/10.3390/electronics10202542

**AMA Style**

Rizwan M, Waseem M, Liaqat R, Sajjad IA, Dampage U, Salmen SH, Obaid SA, Mohamed MA, Annuk A.
SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities. *Electronics*. 2021; 10(20):2542.
https://doi.org/10.3390/electronics10202542

**Chicago/Turabian Style**

Rizwan, Mian, Muhammad Waseem, Rehan Liaqat, Intisar Ali Sajjad, Udaya Dampage, Saleh H. Salmen, Sami Al Obaid, Mohamed A. Mohamed, and Andres Annuk.
2021. "SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities" *Electronics* 10, no. 20: 2542.
https://doi.org/10.3390/electronics10202542