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An effective method to solve the problem of electric distribution network reconfiguration considering distributed generations for energy loss reduction

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Abstract

This paper proposes an effective network reconfiguration (NR) method in the presence of distributed generations (DGs) for energy loss. The proposed method uses average load and average power of DGs instead of the load and DGs’ generation curves. For finding the optimal network configuration, pathfinder algorithm (PFA) is used to solve the NR problem. The effectiveness of the proposed method has been validated on two distribution network systems without and with DGs placement. The obtained results show that the proposed method has a good ability to determine the optimal configuration similar to the method based on the graphs of loads and DGs with much shorter calculated time and PFA can reach optimal solution with a much higher success rate and better obtained solution compared with particle swarm optimization and sunflower optimization algorithms. As a result, the proposed method is an effective and reliable method for solving the NR problem for energy loss reduction considering time-varying condition of loads and DGs.

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Abbreviations

C :

Cost of energy loss

n :

Number of times changing switches

T i :

Period of operating by network configuration i

C a :

Energy price

C b :

Price of changing switches

ΔP i :

Power loss of network configuration i

ΔA :

Energy loss

X :

Set of tie switch positions

M :

Number of sub-intervals of period T

t m :

Sub-interval m

R i :

Resistance of branch i

V i :

Ending voltage of branch i

P i + jQ i :

Complex power flow on branch i

Nbr:

Number of branches

Nb:

Number of buses

Ndg:

Number of DGs

P DG,j :

Active power of DG j

Q DG,j :

Reactive power of DG j

P k + jQ k :

Optimal transfer power

P i,m + jQ i,m :

Complex power in branch i in sub-interval tm

P l,m + jQ l,m :

Complex power at node l in sub-interval tm

\(\bar{P}_{l} + j\bar{Q}_{l}\) :

Average complex power of load l in period T

\(\bar{P}_{i} + j\bar{Q}_{i}\) :

Average complex power on branch i in period T

\(V_{i}\) :

Ending voltage of branch i

V min :

Minimum limit of voltage

V max :

Maximum limit of voltage

\(I_{i}^{\hbox{max} }\) :

Maximum current limit of branch i

\(\bar{V}_{{{\text{rate}},j}}\) :

Voltage of node j at the average load condition

\(\bar{I}_{{{\text{rate}},i}}\) :

Current on branch i at the average load condition

\(\beta_{1} ,\beta_{2}\) :

Penalty factors

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Authors and Affiliations

  1. Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Thuan Thanh Nguyen & Long Thanh Duong

  2. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thang Trung Nguyen

  3. HCMC University of Technology and Education, Ho Chi Minh City, Vietnam

    Viet Anh Truong

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  1. Thuan Thanh Nguyen
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Correspondence to Thang Trung Nguyen.

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Appendix

Appendix

See Tables 7, 8, 9, 10, 11 and 12.

Table 7 The proportion of three types load in each bus in 18-bus network
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Table 8 Hourly load distribution for three types of different loads in 18-bus network
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Table 9 Time-varying output of the PV DG in a typical day
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Table 10 The proportion of three types load in each bus in 33-node network
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Table 11 Hourly load distribution for three types of different loads in 33-node network
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Table 12 Time-varying output of wind turbine DG in a typical day
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Nguyen, T.T., Nguyen, T.T., Duong, L.T. et al. An effective method to solve the problem of electric distribution network reconfiguration considering distributed generations for energy loss reduction. Neural Comput & Applic 33, 1625–1641 (2021). https://doi.org/10.1007/s00521-020-05092-2

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  • DOI: https://doi.org/10.1007/s00521-020-05092-2

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