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Arabian Journal for Science and Engineering

, Volume 44, Issue 8, pp 6813–6826 | Cite as

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

  • Imen Ben HamidaEmail author
  • Saoussen Brini Salah
  • Faouzi Msahli
  • Mohamed Faouzi Mimouni
Research Article - Electrical Engineering
  • 20 Downloads

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.

Keywords

Distribution network planning Distribution network reconfiguration Renewable distributed generation Multi-objective optimization Pareto optimality 

List of Symbols

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

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Notes

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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Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Imen Ben Hamida
    • 1
    Email author
  • Saoussen Brini Salah
    • 2
  • Faouzi Msahli
    • 3
  • Mohamed Faouzi Mimouni
    • 3
  1. 1.Department of Electrical EngineeringENISO University of SousseSousseTunisia
  2. 2.Department of Electrical EngineeringENISSfaxTunisia
  3. 3.Department of Electrical EngineeringENIMMonastirTunisia

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