# Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Literature Survey

#### 1.2. Motivation

- Electrical loss minimization using system reconfiguration [13].
- Reduction in investment during system capacity enhancement [35],
- Improvement in bus voltage [36],
- Mitigation of greenhouse gases [37],
- Improvement in voltage stability [38],
- Enhancement in system security [42],
- Facilitate system restoration [43],
- Reduction in harmonic distortion [44],
- Optimal load management strategy [45].

#### 1.3. Contribution

- The optimal locations for SPV, WTG, and BSS is obtained by considering Index-1 and Index-2.
- Optimal sizes of SPV, WTG, and BSS are derived by employing CF-PSO technique.
- Distribution system performance, including minimization of ${I}^{2}R$ loss and minimization of deviation in bus voltages, is analyzed with and without DGs.
- Comparison of CF-PSO technique with other nature-inspired optimization techniques for achieving a sound reliability assessment.
- A brief study is done on the inclusion of uncertainties in WTG and SPV reliability data such as failure rate (${\lambda}_{p}$) and time to repair (RT).
- Reliability assessment is done by evaluating the five indices namely EENS, AENS, SAIDI, SAIFI, and ASAI for Case 1, Case 2, and Case 3; where Case 1 is for integrating WTG only, Case 2 is for WTG+SPV, and Case 3 is for WTG+SPV+BSS (adding BSS optimally).

#### 1.4. Parameters Considered for the Study

**DG siting and DG sizing**: The determination of bus voltages and the flow of powers is done by an Optimal Power Flow (OPF) method. The optimal siting of DGs is required for ELM during this power flow results. The performance of the power network is affected by an inappropriate location of DG. The IEEE 1547 standards for integration and operation of DG into EDSs are presented in [49].**Power loss**: The occurrence of Active Power Loss (APL) is greater than Reactive Power Loss (RPL) in EDS. Hence, distribution companies should reduce these losses and this can be accomplished by means of reconfiguration of feeder, capacitor allocation, high voltage distribution system, grading of the conductor, DG placement, and many other methods.**Bus voltage**: It is expected to maintain bus voltages nearly 1 pu with an angle of ${0}^{0}$. The power loss occurring in EDS during OPF creates a voltage drop at each bus of the system. Therefore, the DG integration technique is implemented for voltage profile (VP) improvement.**DG type**: The three DGs have been considered, which are categorized as WTG, SPV, and BSS. The classification of the several DG technologies is based on the generation of active power ‘P’ and reactive power ‘Q’, as illustrated in Figure 2.**Reliability**: The reliability indices considered for the distribution system reliability are as follows.- -
- Expected Energy Not Supplied (EENS); MWh per year
- -
- Average Energy Not Supplied (AENS); MWh per customer per year
- -
- System Average Interruption Duration Index (SAIDI); hour per customer per year
- -
- System Average Interruption Frequency Index (SAIFI); failure per customer per year
- -
- Average System Availability Index (ASAI); pu

## 2. Problem Formulation, Objective Function (OF), and Methodology

#### 2.1. Optimal Location

#### 2.2. Power Balance

#### 2.3. Objective Function (OF)

#### 2.3.1. Active Power Loss (APL)

#### 2.3.2. Reactive Power Loss (RPL)

#### 2.3.3. Reliability Indices

#### 2.4. Constraints

#### 2.4.1. Equality Constraints

#### 2.4.2. Inequality Constraints

**A. Power flow**

**B. DG capacity**

**C. Bus voltage**

**D. Bus voltage**

#### 2.5. Constriction Factor-Based PSO (CF-PSO) Technique

## 3. Reliability Assessment of Distribution System

#### 3.1. Reliability Parameters at Load Point ‘p’

#### 3.2. System-Based Indices

#### 3.2.1. Load-Oriented Indices

#### 3.2.2. Customer Oriented Indices

## 4. Modeling of WTG, SPV, and BSS

#### 4.1. Wind Turbine Generator

#### 4.2. Solar Photovoltaic

#### 4.3. Battery Storage System

## 5. Results and Discussion

**Step****1:**- Optimal siting(s) and sizing(s) of WTG, SPV, and BSS are evaluated considering electrical loss minimization (ELM). The technical ratings of WTG, SPV, and BSS have been illustrated in Table 4, Table 5 and Table 6, respectively. The BSS is assumed to be fully charged and produces its rated output power.
**Step****2:**- APL, RPL, and bus voltages are obtained by integrating WTG, WTG+SPV, and WTG+SPV+BSS (referred as Case 1, Case 2, and Case 3, respectively) in the EDS to analyze the results obtained in Step 1.
**Step****3:**- Reliability indices are estimated for EDS considering two different WTG and SPV reliability data, including ${\lambda}_{p}$ and RT (for Scenario 1 to Scenario 6).
**Step****4:**- Furthermore, the reliability improvement is analyzed by adding BSS (considering 100% reliable) to the EDS in the presence of WTG and SPV. All related reliability data used are mentioned in Table A2 of the Appendix A.

#### 5.1. DG Location and DG Rating

#### 5.2. APL, RPL, and Bus Voltages

#### 5.3. Reliability Assessment

- Scenario 1: 0.2 f/yr and 12 h (as provided in Table A2 of Appendix A.2)
- Scenario 2: 0.4 f/yr and 12 h
- Scenario 3: 0.6 f/yr and 12 h
- Scenario 4: 0.2 f/yr and 24 h
- Scenario 5: 0.2 f/yr and 48 h
- Scenario 6: No failure

- Circuit breakers, distribution lines, and potential transformers are available throughout with 100% reliability.
- The ${\lambda}_{p}$ and RT of DG, Buses, feeders, and substations are given in Table A2.
- RT for each distribution branch = 10 h.

#### 5.3.1. Effect on Load-Oriented Indices

#### 5.3.2. Effect on System-Oriented Indices

## 6. Conclusions and Scope for Future Work

- Reliability assessment of larger EDS.
- Inclusion of reliability data of subsystems.
- System reconfiguration.
- Considering CO${}_{2}$ emission.
- Economical aspects related to the system’s reliability, including net present value, Levelized cost of energy, and many other aspects.
- Security.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AENS | Average Energy Not Supplied (MWh per customer per year) |

APL | Active Power Loss (MW) |

ASAI | Average Service Availability Index (pu) |

BSS | Battery Storage System |

CF-PSO | Constriction Factor-based Particle Swarm Optimization |

DG | Distributed Generation |

DGen | Diesel Generator |

EDS | Electrical Distribution System |

EENS | Expected Energy Not Supplied (MWh per year) |

EIR | Energy index of reliability (pu) |

ELM | Electrical Loss Minimization |

ENS | Energy Not Supplied (MWh) |

GA | Genetic Algorithm |

GE | General Electric |

IEEE | Institution of Electrical and Electronics Engineers |

LOLE | Loss of Load Expectation (hour) |

LOLP | Loss of Load Probability (pu) |

MOGA | Multi-objective Genetic Algorithm |

MW | Megawatt |

OPF | Optimal Power Flow |

pf | Power Factor (pu) |

RA | Reliability Assessment |

RDS | Radial Distribution System |

RES | Renewable Energy Source |

RPL | Reactive Power Loss (MW) |

SAIDI | System Average Interruption Duration Interruption (hour per customer per year) |

SAIFI | System Average Interruption Frequency Interruption (failure per customer per year) |

SPR | Surface Plasmon Resonance |

SPV | Solar photovoltaic |

WTG | Wind Turbine Generator |

VP | Voltage Profile |

## Appendix A

#### Appendix A.1

#### Appendix A.2

**Table A1.**Load distribution for 33 bus [52].

Type of Load | |||||
---|---|---|---|---|---|

Bus Number (or Load Point) | Number of Loads | Mixed | Same Type of Loads | ||

2–5 | 148 | Industrial (I) | C | I | R |

6–9 | 10 | Commercial (C) | “ | “ | “ |

11, 12 | 132 | “ | “ | “ | “ |

13–15 | 110 | Residential (R) | “ | “ | “ |

16 | 2 | “ | “ | “ | “ |

17–20 | 118 | “ | “ | “ | “ |

21–26 | 126 | “ | “ | “ | “ |

27–31 | 108 | “ | “ | “ | “ |

32, 33 | 58 | “ | “ | “ | “ |

**Table A2.**Reliability data for 33 bus [52].

Reliability Data for All Loads, Feeders, etc. | ||
---|---|---|

Bus, Feeder, etc. | ${\mathit{\lambda}}_{\mathit{p}}$ (f/yr) | RT (h) |

Load@4 | 0.321 | 11.04 |

Load@(5, 7–12, 29, | 0.301 | 11.44 |

30, 14, 16, 18–22, 25–28) | ||

Load@(13, 15) | 0.314 | 11.17 |

Load@(17, 23, 24) | 0.208 | 1.75 |

Load@(31–33) | 0.327 | 10.96 |

substation | 0.1 | 5 |

feeder (2, 3, 6) | 0.2 | 3 |

DG | 0.2 | 12 |

**Table A3.**Cost per kilowatt for Reliability worth estimation [78].

Type of Load | Interruption Duration (minutes) | Cost ($/kW) |
---|---|---|

1 | 0.38 | |

20 | 2.97 | |

Commercial | 60 | 8.55 |

240 | 31.32 | |

480 | 83.01 | |

1 | 1.63 | |

20 | 3.87 | |

Industrial | 60 | 9.09 |

240 | 25.16 | |

480 | 55.81 | |

1 | 0 | |

20 | 0.09 | |

Residential | 60 | 0.48 |

240 | 4.91 | |

480 | 15.69 |

## References

- Raju, K.; Madurai Elavarasan, R.; Mihet-Popa, L. An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization. Energies
**2020**, 13, 3063. [Google Scholar] - Elavarasan, R.M. The motivation for renewable energy and its comparison with other energy sources: A review. Eur. J. Sustain. Dev. Res.
**2019**, 3, em0076. [Google Scholar] [CrossRef] - Kumar, N.M.; Chopra, S.S.; Chand, A.A.; Elavarasan, R.M.; Shafiullah, G. Hybrid renewable energy microgrid for a residential community: A techno-economic and environmental perspective in the context of the SDG7. Sustainability
**2020**, 12, 3944. [Google Scholar] [CrossRef] - Elavarasan, R.M.; Shafiullah, G.; Padmanaban, S.; Kumar, N.M.; Annam, A.; Vetrichelvan, A.M.; Mihet-Popa, L.; Holm-Nielsen, J.B. A comprehensive review on renewable energy development, Challenges, and policies of leading Indian states with an international perspective. IEEE Access
**2020**, 8, 74432–74457. [Google Scholar] [CrossRef] - Elavarasan, R.M.; Shafiullah, G.; Manoj Kumar, N.; Padmanaban, S. A State-of-the-Art review on the drive of renewables in Gujarat, State of India: Present situation, barriers and future initiatives. Energies
**2020**, 13, 40. [Google Scholar] [CrossRef][Green Version] - Madurai Elavarasan, R.; Selvamanohar, L.; Raju, K.; Vijayaraghavan, R.R.; Subburaj, R.; Nurunnabi, M.; Khan, I.A.; Afridhis, S.; Hariharan, A.; Pugazhendhi, R. A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India. Sustainability
**2020**, 12, 6596. [Google Scholar] [CrossRef] - Tan, X.; Li, Q.; Wang, H. Advances and trends of energy storage technology in microgrid. Int. J. Electr. Power Energy Syst.
**2013**, 44, 179–191. [Google Scholar] [CrossRef] - Elavarasan, R.M.; Afridhis, S.; Vijayaraghavan, R.R.; Subramaniam, U.; Nurunnabi, M. SWOT analysis: A framework for comprehensive evaluation of drivers and barriers for renewable energy development in significant countries. Energy Rep.
**2020**, 6, 1838–1864. [Google Scholar] - Elavarasan, R.M. Comprehensive review on India’s growth in renewable energy technologies in comparison with other prominent renewable energy based countries. J. Sol. Energy Eng.
**2020**, 142, 030801. [Google Scholar] [CrossRef] - Sharma, S.; Bhattacharjee, S.; Bhattacharya, A. Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. IET Gener. Transm. Distrib.
**2016**, 10, 625–637. [Google Scholar] [CrossRef] - Energy Storage System: Roadmap for India 2019–32. 2019. Available online: http://www.indiasmartgrid.org/reports/ISGF_Report_Energy_Storage_System_RoadmapforIndia_2019to2032_11July2019_Draft.pdf (accessed on 29 September 2020).
- Hassan, T.; Abbassi, R.; Jerbi, H.; Mehmood, K.; Tahir, M.F.; Cheema, K.M.; Elavarasan, R.M.; Ali, F.; Khan, I.A. A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller. Energies
**2020**, 13, 4007. [Google Scholar] [CrossRef] - Ebrahimi, H.; Marjani, S.R.; Talavat, V. Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12244. [Google Scholar] [CrossRef] - Liu, L.; Zhang, Y.; Da, C.; Huang, Z.; Wang, M. Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi-level programming. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12366. [Google Scholar] [CrossRef] - da Silva Seta, F.; de Oliveira, L.W.; de Oliveira, E.J. Comprehensive approach for distribution system planning with uncertainties. IET Gener. Transm. Distrib.
**2019**, 13, 5467–5477. [Google Scholar] [CrossRef] - Ahmadi, M.; Lotfy, M.E.; Howlader, A.M.; Yona, A.; Senjyu, T. Centralised multi-objective integration of wind farm and battery energy storage system in real-distribution network considering environmental, technical and economic perspective. IET Gener. Transm. Distrib.
**2019**, 13, 5207–5217. [Google Scholar] [CrossRef] - Deb, G.; Chakraborty, K.; Deb, S. Spider monkey optimization technique–based allocation of distributed generation for demand side management. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12009. [Google Scholar] [CrossRef] - Dehnavi, E.; Aminifar, F.; Afsharnia, S. Congestion management through distributed generations and energy storage systems. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12018. [Google Scholar] [CrossRef] - Chedid, R.; Sawwas, A. Optimal placement and sizing of photovoltaics and battery storage in distribution networks. Energy Storage
**2019**, 1, e46. [Google Scholar] [CrossRef][Green Version] - Babu, K.B.; Maheswarapu, S. A solution to multi-objective optimal accommodation of distributed generation problem of power distribution networks: An analytical approach. Int. Trans. Electr. Energy Syst.
**2019**, 29. [Google Scholar] [CrossRef] - Hesaroor, K.; Das, D. Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12099. [Google Scholar] [CrossRef] - BiazarGhadikolaei, M.; Shahabi, M.; Barforoushi, T. Expansion planning of energy storages in microgrid under uncertainties and demand response. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12110. [Google Scholar] [CrossRef] - Amir, V.; Azimian, M.; Razavizadeh, A.S. Reliability-constrained optimal design of multicarrier microgrid. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12131. [Google Scholar] [CrossRef] - RaguRaman, L.; Ravindran, M. MFLRS-RDF technique for optimal sizing and performance analysis of HRES. Int. J. Numer. Model. Electr. Netw. Devices Fields
**2020**, 33, e2675. [Google Scholar] [CrossRef] - Gholami, M.; Zakariazadeh, A. Olympic ranking–based allocation of distributed generation units in distribution networks. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12220. [Google Scholar] [CrossRef] - Muhammad, M.A.; Mokhlis, H.; Amin, A.; Naidu, K.; Franco, J.F.; Wang, L.; Othman, M. Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm. IET Gener. Transm. Distrib.
**2019**, 13, 5071–5082. [Google Scholar] [CrossRef] - Manna, D.; Goswami, S.K. Optimum placement of distributed generation considering economics as well as operational issues. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12246. [Google Scholar] [CrossRef] - Pandey, A.K.; Kirmani, S. Optimal location and sizing of hybrid system by analytical crow search optimization algorithm. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12327. [Google Scholar] [CrossRef] - Kiani, A.T.; Nadeem, M.F.; Ahmed, A.; Khan, I.; Elavarasan, R.M.; Das, N. Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization. Energies
**2020**, 13, 4037. [Google Scholar] [CrossRef] - Kumar, S.; Saket, R.; Dheer, D.K.; Holm-Nielsen, J.; Sanjeevikumar, P. Reliability enhancement of electrical power system including impacts of renewable energy sources: A comprehensive review. IET Gener. Transm. Distrib.
**2020**, 14, 1799–1815. [Google Scholar] [CrossRef] - Samrout, M.; Yalaoui, F.; Châtelet, E.; Chebbo, N. New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization. Reliab. Eng. Syst. Saf.
**2005**, 89, 346–354. [Google Scholar] [CrossRef] - Shahzad, M.; Ahmad, I.; Gawlik, W.; Palensky, P. Load concentration factor based analytical method for optimal placement of multiple distribution generators for loss minimization and voltage profile improvement. Energies
**2016**, 9, 287. [Google Scholar] [CrossRef][Green Version] - Parihar, S.S.; Malik, N. Optimal allocation of renewable DGs in a radial distribution system based on new voltage stability index. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12295. [Google Scholar] [CrossRef] - Hassan, A.A.; Fahmy, F.H.; Nafeh, A.E.S.A.; Abu-elmagd, M.A. Hybrid genetic multi objective/fuzzy algorithm for optimal sizing and allocation of renewable DG systems. Int. Trans. Electr. Energy Syst.
**2016**, 26, 2588–2617. [Google Scholar] [CrossRef] - Atteya, I.I.; Ashour, H.; Fahmi, N.; Strickland, D. Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation. CIRED Open Access Proc. J.
**2017**, 2017, 2505–2508. [Google Scholar] [CrossRef][Green Version] - Wazir, A.; Arbab, N. Analysis and optimization of IEEE 33 bus radial distributed system using optimization algorithm. JETAE J. Emerg. Trends Appl. Eng.
**2016**, 1, 2518–4059. [Google Scholar] - Paliwal, N.K.; Singh, A.K.; Singh, N.K.; Kumar, P. Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm. Int. Trans. Electr. Energy Syst.
**2019**, 29, e2685. [Google Scholar] [CrossRef][Green Version] - Saric, M.; Hivziefendic, J.; Konjic, T.; Ktena, A. Distributed generation allocation considering uncertainties. Int. Trans. Electr. Energy Syst.
**2018**, 28, e2585. [Google Scholar] [CrossRef] - Baran, M.; Wu, F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv.
**1989**, 4, 1401–1407. [Google Scholar] [CrossRef] - Chong, B.; Zhang, X.; Godfrey, K.; Yao, L.; Bazargan, M. Optimal location of unified power flow controller for congestion management. Eur. Trans. Electr. Power
**2010**, 20, 600–610. [Google Scholar] [CrossRef] - Aghajani, A.; Kazemzadeh, R.; Ebrahimi, A. Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market. Int. Trans. Electr. Energy Syst.
**2018**, 28, e2621. [Google Scholar] [CrossRef] - Charfi, S.; Atieh, A.; Chaabene, M. Optimal sizing of a hybrid solar energy system using particle swarm optimization algorithm based on cost and pollution criteria. Environ. Prog. Sustain. Energy
**2019**, 38, e13055. [Google Scholar] [CrossRef] - Wu, C.; Lou, Y.; Lou, P.; Xiao, H. DG location and capacity optimization considering several objectives with cloud theory adapted GA. Int. Trans. Electr. Energy Syst.
**2014**, 24, 1076–1088. [Google Scholar] [CrossRef] - Duong, M.Q.; Pham, T.D.; Nguyen, T.T.; Doan, A.T.; Tran, H.V. Determination of optimal location and sizing of solar photovoltaic distribution generation units in radial distribution systems. Energies
**2019**, 12, 174. [Google Scholar] [CrossRef][Green Version] - Madurai Elavarasan, R.; Ghosh, A.; K Mallick, T.; Krishnamurthy, A.; Saravanan, M. Investigations on performance enhancement measures of the bidirectional converter in PV–wind interconnected microgrid system. Energies
**2019**, 12, 2672. [Google Scholar] [CrossRef][Green Version] - Natarajan, M.; Ramadoss, B.; Lakshmanarao, L. Optimal location and sizing of MW and MVAR based DG units to improve voltage stability margin in distribution system using a chaotic artificial bee colony algorithm. Int. Trans. Electr. Energy Syst.
**2017**, 27, e2287. [Google Scholar] [CrossRef] - Nawaz, S.; Bansal, A.K.; Sharma, M.P. Allocation of DG and capacitor units for power loss reduction in radial distribution system. In Proceedings of the 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, 23–25 December 2016; pp. 1–6. [Google Scholar]
- Vita, V. Development of a decision-making algorithm for the optimum size and placement of distributed generation units in distribution networks. Energies
**2017**, 10, 1433. [Google Scholar] [CrossRef][Green Version] - Basso, T. IEEE 1547 and 2030 Standards for Distributed Energy Resources Interconnection and Interoperability with the Electricity Grid; Technical report; National Renewable Energy Lab. (NREL): Golden, CO, USA, 2014. [Google Scholar]
- Huang, W.; Zhang, N.; Yang, J.; Wang, Y.; Kang, C. Optimal configuration planning of multi-energy systems considering distributed renewable energy. IEEE Trans. Smart Grid
**2017**, 10, 1452–1464. [Google Scholar] [CrossRef] - Bahramirad, S.; Daneshi, H. Optimal Sizing of Smart Grid Storage Management System in a Microgrid. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012, Washington DC, USA, 16–20 January 2012; pp. 1–6. [Google Scholar]
- Kumar, D.; Samantaray, S.; Kamwa, I.; Sahoo, N. Reliability-constrained based optimal placement and sizing of multiple distributed generators in power distribution network using cat swarm optimization. Electr. Power Compon. Syst.
**2014**, 42, 149–164. [Google Scholar] [CrossRef] - Soliman, S.A.H.; Mantawy, A.A.H. Modern Optimization Techniques with Applications in Electric Power Systems; Springer Science & Business Media: New York, NY, USA, 2011. [Google Scholar]
- Bhumkittipich, K.; Phuangpornpitak, W. Optimal placement and sizing of distributed generation for power loss reduction using particle swarm optimization. Energy Procedia
**2013**, 34, 307–317. [Google Scholar] [CrossRef][Green Version] - Aman, M.; Jasmon, G.; Mokhlis, H.; Bakar, A. Optimal placement and sizing of a DG based on a new power stability index and line losses. Int. Trans. Electr. Energy Syst.
**2012**, 43, 1296–1304. [Google Scholar] [CrossRef] - Shukla, T.; Singh, S.; Srinivasarao, V.; Naik, K. Optimal sizing of distributed generation placed on radial distribution systems. Electr. Power Compon. Syst.
**2010**, 38, 260–274. [Google Scholar] [CrossRef] - Abdel-Aal, H.; Bassyouni, M.; Abdelkreem, M.; Abdel-Hamid, S.; Zohdy, K. Feasibility Study for a Solar-Energy Stand-Alone System:(SESAS). Smart Grid Renew. Energy
**2012**, 3, 204. [Google Scholar] [CrossRef][Green Version] - Hamouda, Y.A. Wind energy in Egypt: Economic feasibility for Cairo. Renew. Sustain. Energy Rev.
**2012**, 16, 3312–3319. [Google Scholar] [CrossRef][Green Version] - Zhao, B.; Guo, C.; Cao, Y. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst.
**2005**, 20, 1070–1078. [Google Scholar] [CrossRef] - Kusiak, A.; Zhang, Z.; Xu, G. Minimization of wind farm operational cost based on data-driven models. IEEE Trans. Sustain. Energy
**2013**, 4, 756–764. [Google Scholar] [CrossRef][Green Version] - Wang, L.; Singh, C. Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Trans. Energy Convers.
**2009**, 24, 163–172. [Google Scholar] [CrossRef] - Ramezani, M.; Haghifam, M.R.; Singh, C.; Seifi, H.; Moghaddam, M.P. Determination of capacity benefit margin in multiarea power systems using particle swarm optimization. IEEE Trans. Power Syst.
**2008**, 24, 631–641. [Google Scholar] [CrossRef] - Yang, H.; Xie, K.; Tai, H.M.; Chai, Y. Wind farm layout optimization and its application to power system reliability analysis. IEEE Trans. Power Syst.
**2015**, 31, 2135–2143. [Google Scholar] [CrossRef] - Sunil Joseph, P.; DineshBalaji, C. Transmission loss minimization using optimization technique Based on PSO. 2011 IEEE Symp. Ind. Electron. Appl.
**2013**, 1, 1–5. [Google Scholar] [CrossRef] - Clerc, M.; Kennedy, J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput.
**2002**, 6, 58–73. [Google Scholar] [CrossRef][Green Version] - El-Zonkoly, A. Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation. IET Gener. Transm. Distrib.
**2011**, 5, 760–771. [Google Scholar] [CrossRef] - Kansal, S.; Kumar, V.; Tyagi, B. Optimal placement of different type of DG sources in distribution networks. Int. J. Electr. Power Energy Syst.
**2013**, 53, 752–760. [Google Scholar] [CrossRef] - Kansal, S.; Sai, B.; Tyagi, B.; Kumar, V. Optimal placement of distributed generation in distribution networks. Int. J. Eng. Sci. Technol.
**2011**, 3, 47–55. [Google Scholar] [CrossRef] - Subcommittee, D. IEEE Guide for Electric Power Distribution Reliability Indices. Distribution
**2012**, 1997, 1–43. [Google Scholar] - Heier, S. Grid Integration of Wind Energy: Onshore and Offshore Conversion Systems; John Wiley & Sons: Sussex, UK, 2014. [Google Scholar]
- Koutroulis, E.; Kolokotsa, D.; Potirakis, A.; Kalaitzakis, K. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol. Energy
**2006**, 80, 1072–1088. [Google Scholar] [CrossRef] - van Sark, W.G. Teaching the relation between solar cell efficiency and annual energy yield. Eur. J. Phys.
**2007**, 28, 415. [Google Scholar] [CrossRef] - Singh, R.; Sharma, M.; Rawat, R.; Banerjee, C. An assessment of series resistance estimation techniques for different silicon based SPV modules. Renew. Sustain. Energy Rev.
**2018**, 98, 199–216. [Google Scholar] [CrossRef] - Yang, J.; Sun, Y.; Xu, Y. Modeling impact of environmental factors on photovoltaic array performance. Int. J. Energy Environ.
**2013**, 4, 955–968. [Google Scholar] - Gunawardana, A. Proper sizing of energy storage for grid connected photovoltaic system. Master’s Thesis, Department of Engineering Science Faculty of Engineering and Science, University of Agder, Kristiansand, Norway, 2014. [Google Scholar]
- Sultana, S.; Roy, P.K. Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Int. J. Electr. Power Energy Syst.
**2014**, 63, 534–545. [Google Scholar] [CrossRef] - Mahmoud, K.; Yorino, N.; Ahmed, A. Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans. Power Syst.
**2015**, 31, 960–969. [Google Scholar] [CrossRef] - Billington, R.; Allan, R.N. Reliability Evaluation of Power Systems; Springer: New York, NY, USA, 1996. [Google Scholar]

**Figure 9.**Load-oriented indices for different DG reliability data (

**a**) EENS at different ${\lambda}_{p}$, (

**b**) EENS at different RT, (

**c**) AENS at different ${\lambda}_{p}$, and (

**d**) AENS at different RT.

**Figure 10.**System-oriented indices for different DG reliability data (

**a**) SAIDI at different ${\lambda}_{p}$, (

**b**) SAIDI at different RT, (

**c**) SAIFI at different ${\lambda}_{p}$, and (

**d**) SAIFI at different RT.

**Figure 11.**System-oriented indices for different DG reliability data (

**a**) ASAI at different ${\lambda}_{p}$, (

**b**) ASAI at different RT.

No. of Bus | Parameters Considered | |||||||
---|---|---|---|---|---|---|---|---|

Size | Location | Voltage | Loss | Reliability | Power Factor | DG | Reference | |

34, 69 | ✓ | ✓ | ✓ | ✓ | PV/WTG | [33] | ||

33, 69, 119 | ✓ | ✓ | ✓ | ✓ | PV/WTG | [34] | ||

12 | ✓ | ✓ | ✓ | DGen | [38] | |||

33, 69 | ✓ | ✓ | ✓ | ✓ | PV | [44] | ||

13 | ✓ | ✓ | ✓ | ✓ | PV/BSS | [19] | ||

33, 118 | ✓ | ✓ | ✓ | PV/WTG | [21] | |||

38 | ✓ | ✓ | PV/WTG | [27] | ||||

33, 69 | ✓ | ✓ | ✓ | ✓ | PV/ESS | [28] | ||

38, 69 | ✓ | ✓ | ✓ | GT/WTG | [46] | |||

69, 118 | ✓ | ✓ | ✓ | ✓ | DGen/Cap | [47] | ||

33 | ✓ | ✓ | ✓ | ✓ | ✓ | PV/WTG | [48] | |

33 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | SPV/WTG/BSS | Present Work |

Method | # of DG | DG Position | Total DG Size (MW) | Loss (MW) | Reference |
---|---|---|---|---|---|

MOGA | 1(SPV) | 8 | 1.6333 | 0.113 | [34] |

2(SPV) | 14, 30 | 0.8337, 0.99851 | 0.08435 | ||

1(WTG) | 8 | 1.85 | 0.08556 | ||

2(WTG) | 14, 30 | 1.1, 0.75 | 0.04791 | ||

GA | 3(SPV) | 14, 24, 28 | 0.6947, 1.1844, 1.4628 | 0.0756 | [44] |

ABC | 3(SPV) | 9, 24, 32 | 1.1372, 1.0674, 0.8031 | 0.0752 | |

PSO | 3(SPV) | 9, 24, 30 | 1.0625, 1.0447, 0.9518 | 0.0744 | |

BBO | 3(SPV) | 14, 24, 30 | 0.7539, 1.0994, 1.0714 | 0.0715 | |

CSO | 5(BSS) | 1, 4, 11, 12, 18 | 0.15, 0.4117, 0.6705, 0.1, 8.9055 | 0.02379 | [28] |

1(SPV) | 6 | 2 | 0.0908 | ||

DMA | 18 | 1 | 0.1175 | [48] | |

1(WTG) | 33 | 1.65 | 0.1068 |

Index-1 | Index-2 | ||
---|---|---|---|

Value | Bus No. | Value | Bus No. |

$1.350\times {10}^{-3}$ | 6 | $41.52\times {10}^{-3}$ | 30 |

$0.928\times {10}^{-3}$ | 29 | $16.44\times {10}^{-3}$ | 13 |

$0.867\times {10}^{-3}$ | 30 | $16.43\times {10}^{-3}$ | 24 |

$0.864\times {10}^{-3}$ | 5 | $7.36\times {10}^{-3}$ | 31 |

$0.735\times {10}^{-3}$ | 28 | $6.49\times {10}^{-3}$ | 20 |

Parameter | Rating (Unit) |
---|---|

Rated output power | 5.6 MW |

Cut-in Speed (${V}_{cin}$) | 3 m/s |

Cut-out Speed (${V}_{cout}$) | 25 m/s |

Temperature | $-20$${}^{\circ}$C to 45 ${}^{\circ}$C |

Diameter | 162 m |

Swept Area | 20612 m${}^{2}$ |

Frequency | 50/60 Hz |

Hub Height | 119 m, 125 m, 148 m, 149 m, and 166 m |

Parameter | Rating (Unit) | Parameter | Rating (Unit) |
---|---|---|---|

nominal power | 545 W | Maximum Series Fuse | 25 A |

Tolerance of Power | $\pm 3/0\%$ | Temperature | −40–85 ${}^{\circ}$C |

Efficiency | 21.1% | Power Temperature Coefficient | $-0.34\%$/${}^{\circ}$C |

Rated voltage | 46.1 V | Voltage Temperature Coefficient | $-0.28\%$/${}^{\circ}$C |

Rated current | 11.84 A | Current Temperature Coefficient | $0.06\%$/${}^{\circ}$C |

Open circuit voltage | 55.8 V | Weight | 31.5 kg |

Short circuit current | 12.62 A | Solar Cells | Mono-crystalline |

Maximum System Voltage (IEC) | 1500 V | L × B × H mm${}^{3}$ | 2362 × 1092 × 35 |

Parameter | Rating |
---|---|

Rated output power | 3000 kW |

Storage Capacity | 1000 kWh |

Rated output current | 2795 A |

Rated output AC voltage | 620 V |

Power factor | 0.95 Cap …0.95 Ind |

Total harmonic distortion | <3% |

Efficiency | >98% |

Type | Lithium-ion |

IGBT Switching Frequency (Converter) | 2–4 kHz |

Case 1 | Case 2 | Case 3 | |
---|---|---|---|

Location (bus no.) | 6 | 30, 13 | 30, 13, 24 |

size@upf (MW) | 2.564 | 1.148, 0.843 | 1.048, 0.801, 1.105 |

pf | No DG | Case 1 | Case 2 | Case 3 | |
---|---|---|---|---|---|

Unity | 0.11104 | 0.0727 | 0.05148 | ||

Present work | 0.85 | 0.21101 | 0.06831 | 0.04539 | 0.02795 |

0.82 | 0.06831 | 0.0444 | 0.02702 | ||

APL obtained considering power factor of all Conventional DGs | |||||

pf | No DG | Single DG | Two DG | Three DG | |

Unity | 0.11107 | 0.087172 | 0.072787 | ||

EA [77] | 0.85 | 0.211 | 0.068170 | 0.03119 | 0.01552 |

0.82 | 0.067870 | 0.03041 | 0.01514 |

Minimum Voltage (%) | RPL | |||||||
---|---|---|---|---|---|---|---|---|

pf | No DG | Case 1 | Case 2 | Case 3 | No DG | Case 1 | Case 2 | Case 3 |

Unity | 94.26 | 96.88 | 96.86 | 0.08168 | 0.05121 | 0.03848 | ||

0.85 | 90.44 | 95.74 | 98.12 | 98.15 | 0.14306 | 0.05504 | 0.03257 | 0.02185 |

0.82 | 96.0 | 98.20 | 98.22 | 0.05504 | 0.03195 | 0.02119 |

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | |
---|---|---|---|---|---|---|

No DG | 82.763 | 82.763 | 82.763 | 82.763 | 82.763 | 82.763 |

Case 1 | 65.533 | 68.465 | 73.397 | 68.465 | 78.329 | 58.601 |

Case 2 | 31.817 | 29.249 | 31.817 | 29.249 | 34.385 | 24.113 |

Case 3 | 30.135 | 27.567 | 30.135 | 27.567 | 32.703 | 22.431 |

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | |
---|---|---|---|---|---|---|

No DG | 0.0255 | 0.0255 | 0.0255 | 0.0255 | 0.0255 | 0.0255 |

Case 1 | 0.0196 | 0.0203 | 0.0226 | 0.0211 | 0.0241 | 0.0181 |

Case 2 | 0.0082 | 0.009 | 0.0098 | 0.009 | 0.0106 | 0.0074 |

Case 3 | 0.0077 | 0.0085 | 0.0093 | 0.0085 | 0.0101 | 0.0069 |

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | |
---|---|---|---|---|---|---|

No DG | 24.012 | 24.012 | 24.012 | 24.012 | 24.012 | 24.012 |

Case 1 | 18.764 | 20.085 | 21.406 | 20.085 | 22.728 | 17.442 |

Case 2 | 7.388 | 8.053 | 8.719 | 8.053 | 9.385 | 6.722 |

Case 3 | 7.201 | 7.866 | 8.532 | 7.866 | 9.198 | 6.535 |

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | |
---|---|---|---|---|---|---|

No DG | 3.179 | 3.179 | 3.179 | 3.179 | 3.179 | 3.179 |

Case 1 | 2.109 | 2.219 | 2.329 | 2.109 | 2.109 | 1.999 |

Case 2 | 0.915 | 0.970 | 1.026 | 0.915 | 0.915 | 0.859 |

Case 3 | 0.842 | 0.897 | 0.953 | 0.842 | 0.842 | 0.786 |

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 | |
---|---|---|---|---|---|---|

No DG | 0.99726 | 0.99726 | 0.99726 | 0.99726 | 0.99726 | 0.99726 |

Case 1 | 0.99786 | 0.99771 | 0.99756 | 0.99771 | 0.99741 | 0.99801 |

Case 2 | 0.99916 | 0.99908 | 0.99900 | 0.99908 | 0.99893 | 0.99923 |

Case 3 | 0.99918 | 0.99910 | 0.99903 | 0.99910 | 0.99895 | 0.99925 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kumar, S.; Sarita, K.; Vardhan, A.S.S.; Elavarasan, R.M.; Saket, R.K.; Das, N.
Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique. *Energies* **2020**, *13*, 5631.
https://doi.org/10.3390/en13215631

**AMA Style**

Kumar S, Sarita K, Vardhan ASS, Elavarasan RM, Saket RK, Das N.
Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique. *Energies*. 2020; 13(21):5631.
https://doi.org/10.3390/en13215631

**Chicago/Turabian Style**

Kumar, Sachin, Kumari Sarita, Akanksha Singh S Vardhan, Rajvikram Madurai Elavarasan, R. K. Saket, and Narottam Das.
2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique" *Energies* 13, no. 21: 5631.
https://doi.org/10.3390/en13215631