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Modified Shuffled Frog Leaping Optimization Algorithm Based Distributed Generation Rescheduling for Loss Minimization

  • L. D. Arya
  • Atul Koshti
Original Contribution
  • 44 Downloads

Abstract

This paper investigates the Distributed Generation (DG) capacity optimization at location based on the incremental voltage sensitivity criteria for sub-transmission network. The Modified Shuffled Frog Leaping optimization Algorithm (MSFLA) has been used to optimize the DG capacity. Induction generator model of DG (wind based generating units) has been considered for study. Standard test system IEEE-30 bus has been considered for the above study. The obtained results are also validated by shuffled frog leaping algorithm and modified version of bare bones particle swarm optimization (BBExp). The performance of MSFLA has been found more efficient than the other two algorithms for real power loss minimization problem.

Keywords

Distributed generation Modified shuffled frog leaping optimization algorithm Modified version of bare bones particle swarm optimization Voltage sensitivity Sub-transmission network 

List of Symbols

\(J_{P\theta } ,J_{PV} ,J_{Q\theta } ,J_{QV}\)

Sub Jacobian matrices

\(\left[ S \right]\)

Sensitivity matrix

\(\Delta P\), \(\Delta Q\)

Real and reactive power injection change vectors

\(\Delta \theta\), \(\Delta V\)

Increment vectors of angles and voltages

\(P_{dg,i}\)

Generated real power of ith induction generator

\(Q_{dg,i}\)

Reactive power drawn by ith induction generator

\(\Delta V_{n}\)

Incremental voltage of nth bus

\(\Delta P_{dg,i}\)

Change in real power generation of ith DG

\(\Delta Q_{dg,i}\)

Change in reactive power consumed of ith DG

\(P_{L}\)

Total real power loss of sub-transmission system

\(P_{Loss,k}\)

Real power loss of kth line

\(f_{k}\)

kth MW line flow

\(\overline{f}_{k}\)

kth MW line flow limit

\(\bar{P}_{dg,i}\)

Maximum DG capacity available at ith bus

NL

Number of transmission lines.

p

Population size

M

Number of memeplexes

rand, randj, U[0,1]

Random digits in the range [0,1]

Xi

ith Position Vector in MSFLA

\(x_{ij}\)

jth value of ith position vector

\(x_{j - \hbox{min} } ,x_{j - \hbox{max} }\)

Minimum and maximum value of jth value of ith position vector

\(X_{b}\)

Best particle for mth memeplex in population

\(X_{w}\)

Worst particle for mth memeplex in population

\(X_{g}\)

Global best in population

c

Search acceleration factor

it

Iteration

itmax

Maximum number of iterations

\(\rho\)

Modification in worst frog vector

NDG

Number of DG units

NR

Number of run

\(\overline{{P_{L} }}\)

Average value of PL in NR run

\(Max \, [P_{{L\text{ - min,1}}} \text{,}P_{{L - \text{min,2}}} { \ldots }P_{{L - \text{min,}NR}} ]\)

Worst minimum value of PL in NR run

\(Min \, [P_{{L\text{ - min,1}}} \text{,}P_{{L - \text{min,2}}} { \ldots }P_{{L - \text{min,}NR}} ]\)

Best value of PL in NR run

\(\sigma\)

Standard deviation of PL for NR number of run

\(s\)

Standard deviation of mean in NR run

\(F\)

Frequency of convergence in NR run

\(n_{b}\)

Number of values of objective function above average value in NR run

\(J( \cdot )\)

Objective function to be optimized

References

  1. 1.
    J.A. Pecas Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, N. Jenkins, Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr. Power Syst. Res. 77(9), 1189–1203 (2007)CrossRefGoogle Scholar
  2. 2.
    M. Kolenc, I. Papič, B. Blažič, Assessment of maximum distributed generation penetration levels in low voltage networks using a probabilistic approach. Int. J. Electr. Power Energy Syst. 64, 505–515 (2015)CrossRefGoogle Scholar
  3. 3.
    A. El-Fergany, Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int. J. Electr. Power Energy Syst. 64, 1197–1205 (2015)CrossRefGoogle Scholar
  4. 4.
    N. Mohandas, R. Balamurugan, L. Lakshminarasimman, 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. 66, 41–52 (2015)CrossRefGoogle Scholar
  5. 5.
    A. Shahmohammadi, M.T. Ameli, Proper sizing and placement of distributed power generation aids the intentional islanding process. Electr. Power Syst. Res. 106, 73–85 (2014)CrossRefGoogle Scholar
  6. 6.
    A. Kechroud, P.F. Ribeiro, W.L. Kling, Distributed generation support for voltage regulation: an adaptive approach’. Electr. Power Syst. Res. 107, 213–220 (2014)CrossRefGoogle Scholar
  7. 7.
    L.D. Arya, A. Koshti, S.C. Choube, Distributed generation planning using differential evolution accounting voltage stability consideration. Int. J. Electr. Power Energy Syst. 42(1), 196–207 (2012)CrossRefGoogle Scholar
  8. 8.
    A. Koshti, L.D. Arya, S.C. Choube, Voltage stability constrained distributed generation planning using modified bare bones particle swarm optimization. J. Inst. Eng. (India) Ser. B 94(2), 123–133 (2013)CrossRefGoogle Scholar
  9. 9.
    E.S. Ali, S.M.A. Elazim, A.Y. Abdelaziz, Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renew. Energy 101, 1311–1324 (2017)CrossRefGoogle Scholar
  10. 10.
    L.W. Oliveira, T.C.J. Maria, Planning of renewable generation in distribution systems considering daily operating periods. IEEE Latin America Transactions 15(5), 901–907 (2017)CrossRefGoogle Scholar
  11. 11.
    H.A. Mahmoud Pesaran, P.D. Huy, V.K. Ramachandaramurthy, A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms. Renewable Sustainable Energy Rev 75, 293–312 (2017)CrossRefGoogle Scholar
  12. 12.
    M.A. Akbari, J. Aghaei, M. Barani, Multiobjective capacity planning of photovoltaics in smart electrical energy networks: improved normal boundary intersection method. IET Renew. Power Gener. 11(13), 1679–1687 (2017)CrossRefGoogle Scholar
  13. 13.
    Z. Abdmouleh, A. Gastli, L.B. Brahim, M. Haouari, N.A. Al-Emadi, Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renew. Energy 113, 266–280 (2017)CrossRefGoogle Scholar
  14. 14.
    H.H. Fard, A. Jalilian, A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms. Int. J. Green Energy 13(15), 1615–1625 (2016)Google Scholar
  15. 15.
    A.E. Fergany, Multi-objective allocation of multi-type distributed generators along distribution networks using backtracking search algorithm and fuzzy expert rules. Electr. Power Compon. Syst. 44(3), 252–267 (2016)CrossRefGoogle Scholar
  16. 16.
    S. Dahal, H. Salehfar, Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network. Int. J. Electr. Power Energy Syst. 77, 256–262 (2016)CrossRefGoogle Scholar
  17. 17.
    A.M. Dalavi, P.J. Pawar, T.P. Singh, Tool path planning of hole-making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm. J. Comput. Des. Eng. 3(3), 266–273 (2016)Google Scholar
  18. 18.
    M.M. Rasid, M. Junichi, T. Hirotaka, Simultaneous determination of optimal sizes and locations of distributed generation units by differential evolution, in Intelligent System Application to Power Systems (ISAP), 18th International IEEE Conference, Porto, Portugal, 11–16 Sept 2015Google Scholar
  19. 19.
    C.W. Taylor, Power System Voltage Stability (Tata McGraw-Hill, New York, 1994)Google Scholar
  20. 20.
    W. Prommee, W. Ongsakul, Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization. Eur. Trans. Electr. Power 21(1), 489–504 (2011)CrossRefGoogle Scholar
  21. 21.
    T. Venkatesan, M.Y. Sana Vullah, SFLA approach to solve PBUC problem with emission limitation. Int. J. Electr. Power Energy Syst. 46, 1–4 (2013)CrossRefGoogle Scholar
  22. 22.
    E. Elbeltagi, T. Hegazy, D. Grierson, A modified shuffled frog-leaping optimization algorithm: applications to project management. Struct. Infrastruct. Eng. 3(1), 53–60 (2007)CrossRefGoogle Scholar
  23. 23.
    L.D. Arya, A. Koshti, Anticipatory load shedding for line overload alleviation using Teaching learning based optimization (TLBO). Int. J. Electr. Power Energy Syst. 63, 862–877 (2014)CrossRefGoogle Scholar
  24. 24.
    J. Kennedy, Bare bones particle swarms, in Proceeding of the IEEE Swarm Intelligence Symposium, 2003, pp. 80–87Google Scholar
  25. 25.
    H. Zhang, D.D. Kennedy, G.P. Rangaiah, A. Bonilla-Petriciolet, Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data. Fluid Phase Equilib. 301(1), 33–45 (2011)CrossRefGoogle Scholar

Copyright information

© The Institution of Engineers (India) 2018

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentMedi-Caps UniversityIndoreIndia
  2. 2.Electrical Engineering DepartmentGokhale Education Society’s R.H. Sapat College of Engineering, Management Studies and ResearchNashikIndia

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