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A New Short-Term Planning Strategy for Multi-Objective Distribution Network Reconfiguration and Optimal DG Insertion

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Abstract

This paper suggests a new short-term planning strategy to maximize the benefits of simultaneous distribution network reconfiguration and distributed generation integration by considering the variations in DG outputs and the system load during the planning period. The objective functions to optimize are active power losses, green house emissions and operation costs, while satisfying all operational and topological constraints. A Pareto-optimality-based method is suggested to solve this combinatorial problem providing Pareto optimal solutions where utilities can select a final solution. The effectiveness of the proposed planning strategy is investigated through simulation tests performed on a standard distribution network test system. Fuzzy decision making is utilized to identify the best solutions among Pareto ones. These optimal solutions give significant economic, technical and environmental enhancements of the distribution network.

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Abbreviations

A :

Wind turbine swept area

\(C_\mathrm{p} \) :

Wind turbine power coefficient

\(C_{{\mathrm{invest}_\mathrm{PV}}}\) :

Installation cost of solar DG per installed kW

\(C_{{\mathrm{invest}_\mathrm{wind}}}\) :

Installation cost of wind turbine DG per installed kW

\(C_{{\mathrm{invest}_\mathrm{PV}}}\) :

Annual maintenance cost of solar DG per installed kW

\(C_{{\mathrm{invest}_\mathrm{wind}}}\) :

Annual maintenance cost of wind turbine DG per installed kW

\(\mathrm{CO}_2 \) :

Carbon dioxide gas

CO:

Carbon monoxide gas

\(\mathrm{Em}_\mathrm{i} \) :

Intensity of emission of \(i\mathrm{th}\) greenhouse gas

FF:

Fill factor of PV module

\(I_\mathrm{PV} \) :

Current output of PV module

\(I_\mathrm{MPPT} \) :

Current maximum power point of PV module

\(I_\mathrm{sc} \) :

Short-circuit current of PV module

\((I_{b})_\mathrm{h} \) :

Current of line b for hour h

\(I_{b_{\max }}\) :

Current maximum limit in branch b

\(\mathrm{loc}_i \) :

Location of \(i\mathrm{th}\) DG

\(n_\mathrm{PV} \) :

Number of PV modules

\(N_b \) :

Number of lines of distribution network

\(N_\mathrm{p} \) :

Number of pollutant gas types

\(N_\mathrm{c} \) :

Number of customer nodes

\(N_\mathrm{DG} \) :

Number of installed DG

n :

Number of nodes in distribution network

\(n_\mathrm{s} \) :

Number of source nodes in distribution network

\(N_\mathrm{open} \) :

Number of opened branches in distribution network

\(\mathrm{NO}_x \) :

Nitrogen oxide gas

\(\mathrm{OF}_i\) :

\(i\mathrm{th}\)objective function

\(\mathrm{OF}_i^{\min } \) :

Minimum value of \(i\mathrm{th}\) objective function

\(\mathrm{OF}_i^{\max } \) :

Maximum value of \(i\mathrm{th}\) objective function

p :

Number of Pareto optimal solutions

\(P_\mathrm{solar} \) :

Active power generated by solar PV DG

\(P_\mathrm{WT} \) :

Active power generated by wind turbine DG

\(P_\mathrm{rate} \) :

Nominal power generation of wind turbine

\(P_\mathrm{loss} \) :

Total active power loss of distribution network

\((P_\mathrm{loss} )_\mathrm{h} \) :

Active power loss of distribution network in hour h

\((P_\mathrm{L} )_\mathrm{h} \) :

Total active load for hour h

\((P_{\mathrm{DG}_{i} } )_\mathrm{h} \) :

Active power output of \(\mathrm{DG}_i \) for hour h

\((P_\mathrm{sub} )_\mathrm{h} \) :

Active power output of a substation for hour h

\(P_\mathrm{L} \) :

Total active load demand of distribution network

\(P_{\mathrm{DG}_i } \) :

Size of \(i\mathrm{th}\) installed DG

\(P_\mathrm{PV} \) :

Size of installed solar PV DG

\(P_\mathrm{wind} \) :

Size of installed wind turbine DG

\(s_\mathrm{i} \) :

Index of \(i\mathrm{th}\) opened branch

\(\mathrm{SO}_2 \) :

Sulfur dioxide gas

\(T_\mathrm{V} \) :

Voltage–temperature coefficient of PV module

\(T_\mathrm{I} \) :

Current–temperature coefficient of PV module

\(T_\mathrm{PV} \) :

Temperature of PV module

\(T_\mathrm{emp} \) :

Ambient temperature

\(T_\mathrm{rate} \) :

Rate operating temperature of PV module.

\(V_i \) :

Voltage value in bus i

\(V_{\max } \) :

Maximum acceptable limit of bus voltage

\(V_{\min } \) :

Minimum acceptable limit of bus voltage

\(V_\mathrm{PV} \) :

Voltage output of PV module

\(V_\mathrm{MPPT} \) :

Voltage maximum power point of PV module

\(V_\mathrm{sc} \) :

Open-circuit voltage of PV module

\(V_\mathrm{out} \) :

Cut-out speed of wind turbine

\(V_\mathrm{in} \) :

Cut-in speed of wind turbine

\(V_\mathrm{rate} \) :

Speed rate of wind turbine

\(x^{k^{*}}\) :

Best compromise solution

\(\rho \) :

Density of air

\(\delta _\mathrm{b} \) :

Binary variable corresponding to state of line b

\(\tau _i^k \) :

Membership function of \(k\mathrm{th}\) solution

\(\tau _{k^{*}} \) :

Membership function of best compromise solution

References

  1. El-Khattam, W.; Salama, M.M.A.: Distributed generation technologies, definitions and benefits. Electr. Power Syst. Res. 71(2), 119–128 (2004)

    Article  Google Scholar 

  2. Hassan, A.S.; Cipcigan, L.; Jenkins, N.: Impact of optimised distributed energy resources on local grid constraints. Energy 142, 878–895 (2018)

    Article  Google Scholar 

  3. Manditereza, P.T.; Bansal, R.: Renewable distributed generation: the hidden challenges-a review from the protection perspective. Renew. Sustain. Energy Rev. 58, 1457–1465 (2016)

    Article  Google Scholar 

  4. Colmenar-Santos, A.; Reino-Rio, C.; Borge-Diez, D.; Collado-Fernández, E.: Distributed generation: a review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks. Renew. Sustain. Energy Rev. 59, 1130–1148 (2016)

    Article  Google Scholar 

  5. Ehsan, A.; Yang, Q.: Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques. Appl. Energy 210, 44–59 (2018)

    Article  Google Scholar 

  6. Huda, A.S.N.; Živanović, R.: Large-scale integration of distributed generation into distribution networks: study objectives, review of models and computational tools. Renew. Sustain. Energy Rev. 76, 974–988 (2017)

    Article  Google Scholar 

  7. 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. 67, 531–573 (2017)

    Article  Google Scholar 

  8. Ch, Y.; Goswami, S.K.; Chatterjee, D.: Effect of network reconfiguration on power quality of distribution system. Int. J. Electr. Power Energy Syst. 83, 87–95 (2016)

    Article  Google Scholar 

  9. Merlin, A.; Back, H.: Search for a minimal-loss operating spanning tree configuration in an urban power distribution system. In: Proceedings of the 5th Power System Computation Conference, Cambridge, pp. 1–18 (1975)

  10. Hamida, I.B.; Salah, S.B.; Msahli, F.; Mimouni, M.F.: Distribution network reconfiguration using SPEA2 for power loss minimization and reliability improvement. Int. J. Energy Optim. Eng. IJEOE. 7(1), 50–65 (2018)

    Google Scholar 

  11. Mishra, S.; Das, D.; Paul, S.: A comprehensive review on power distribution network reconfiguration. Energy Syst. 8(2), 227–284 (2017)

    Article  Google Scholar 

  12. Georgilakis, P.S.; Hatziargyriou, N.D.: A review of power distribution planning in the modern power systems era: models, methods and future research. Electr. Power Syst. Res. 121, 89–100 (2015)

    Article  Google Scholar 

  13. Badran, O.; Mekhilef, S.; Mokhlis, H.; Dahalan, W.: Optimal reconfiguration of distribution system connected with distributed generations: a review of different methodologies. Renew. Sustain. Energy Rev. 73, 854–867 (2017)

    Article  Google Scholar 

  14. Taher, S.A.T.; Karimi, M.H.: Optimal reconfiguration and DG allocation in balanced and unbalanced distribution systems. Ain Shams Eng. J. 5(3), 735–749 (2014)

    Article  Google Scholar 

  15. Dahalan, W.M.; Mokhlis, H.; Ahmad, R.; Bakar, A.A.; Musirin, I.: Simultaneous network reconfiguration and DG sizing using evolutionary programming and genetic algorithm to minimize power losses. Arab. J. Sci. Eng. 39(8), 6327–633 (2014)

    Article  Google Scholar 

  16. Imran, A.M.; Kowsalya, M.; Kothari, D.P.: A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks. Int. J. Electr. Power Energy Syst. 63, 461–472 (2014)

    Article  Google Scholar 

  17. Tolabi, H.B.; Ali, M.H.; Rizwan, M.: Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzy-ACO approach. IEEE Trans. Sustain. Energy 6(1), 210–218 (2015)

    Article  Google Scholar 

  18. Badran, O.; Mokhlis, H.; Mekhilef, S.; Dahalan, W.: Multi-objective network reconfiguration with optimal DG output using meta-heuristic search algorithms. Arab. J. Sci. Eng. pp. 1–14 (2017)

  19. Ben Hamida, I.; Salah, S.B.; Msahli, F.; Mimouni, M.F.: Optimal integration of distributed generations with network reconfiguration using a pareto algorithm. Int. J. Renew. Energy Res. 8(1), 345–356 (2018)

    Google Scholar 

  20. Hamida, I.B.; Salah, S.B.; Msahli, F.; Mimouni, M.F.: Optimal network reconfiguration and renewable DGs integration considering time sequence variation of load and DGs. Renew. Energy. 121, 66–80 (2018)

    Article  Google Scholar 

  21. Farahani, V.; Vahidi, B.; Abyaneh, H.A.: Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method. IEEE Trans. Power Syst. 27(2), 587–595 (2012)

    Article  Google Scholar 

  22. Khatod, D.K.; Pant, V.; Sharma, J.: Evolutionary programming based optimal placement of renewable distributed generators. IEEE Trans. Power Syst. 28(2), 683–695 (2013)

    Article  Google Scholar 

  23. Mori, H.; Yamada, Y.: An efficient multi-objective meta-heuristic method for distribution network expansion planning. In: Power Tech, 2007 IEEE Lausanne, pp. 374–379 (2007)

  24. Divya, K.C.; Rao, P.N.: Models for wind turbine generating systems and their application in load flow studies. Electr. Power Syst. Res. 76(9), 844–856 (2006)

    Article  Google Scholar 

  25. Ohya, Y.; Karasudani, T.: A shrouded wind turbine generating high output power with wind-lens technology. Energies 3(4), 634–649 (2010)

    Article  Google Scholar 

  26. Enacheanu, F. B.: Outils d’aide à la conduite pour les opérateurs des réseaux de distribution. Institut National Polytechnique de Grenoble-INPG (2007)

  27. Liu, K.; Sheng, W.; Liu, Y.; Meng, X.; Liu, Y.: Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs. Int. J. Electr. Power Energy Syst. 69, 430–440 (2015)

    Article  Google Scholar 

  28. Rana, A.D.; Darji, J.B.; Pandya, M.: Backward/forward sweep load flow algorithm for radial distribution system. Int. J. Sci. Res. Dev. 2(1), 398–400 (2014)

    Google Scholar 

  29. Harary, F.: Graph Theory, Ser. Addison-Wesley Series in Mathematics. Addison-Wesley Pub. Co., Boston (1969)

    Google Scholar 

  30. Rao, R.S.; Ravindra, K.; Satish, K.; Narasimham, S.V.L.: Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans. Power Syst. 28(1), 317–325 (2013)

    Article  Google Scholar 

  31. Hamida, I. B.; Salah, S. B.; Msahli, F.; Mimouni, M. F.: Strength pareto evolutionary algorithm 2 for environmental/economic power dispatch. In: 7th International Conference on Modelling, Identification and Control (ICMIC), pp. 1–6 (2015)

  32. Zitzler, E.; Laumanns, M.; Thiele, L.; et al.: SPEA2: improving the strength Pareto evolutionary algorithm. Eurogen 3242, 95–100 (2001)

    Google Scholar 

  33. Roudenko, O.: Application des algorithmes évolutionnaires aux problèmes d’optimisation multi-objectif avec contraintes. Ecole Polytechnique X (2004)

  34. Baran, M.E.; Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4(2), 1401–1407 (1989)

    Article  Google Scholar 

  35. IRENA Solar Data: International Renewable Energy Agency. http://irena.masdar.ac.ae/?map=488.

  36. IRENA Wind Data. International Renewable Energy Agency. http://irena.masdar.ac.ae/?map=488.

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Acknowledgements

The authors gratefully recognize the technical and financial support of the Ministry of Higher Education and Scientific Research in Tunisia.

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Correspondence to Imen Ben Hamida.

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Ben Hamida, I., Brini Salah, S., Msahli, F. et al. A New Short-Term Planning Strategy for Multi-Objective Distribution Network Reconfiguration and Optimal DG Insertion. Arab J Sci Eng 44, 6813–6826 (2019). https://doi.org/10.1007/s13369-018-3645-9

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  • DOI: https://doi.org/10.1007/s13369-018-3645-9

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