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Wind Generation Impact in Transmission Expansion Planning

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Abstract

This research proposes a post-stage investment analysis in the transmission expansion network planning problem considering the intermittence of renewable energy sources, especially wind power sources. The main goal of this study is to analyze the voltage profiles in all the system buses and the level of electrical losses in the lines using AC flow for impact evaluation of renewable energy insertion. This research also performs an additional analysis considering wind energy insertion during the planning stage. This analysis is to demonstrate the effect in the stage of investment in transmission lines. The variability of these sources is represented through scenarios obtained by the K-means classification algorithm that allows the preservation of the correlation among generating stations. The methodology performance is tested using the IEEE 24-bus system, which is modified to include a significant share of wind energy.

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Abbreviations

E :

Set of branches with existing transmission lines

C :

Set of branches with candidate transmission lines

F :

Set of branches with fictitious transmission lines

B :

Set of load buses

Z :

Set of generation buses

\(R_{ij}\) :

Set of candidate reinforcements of branch ij

\(E_{ij}\) :

Set of existing transmission lines of branch ij

\(F_{ij}\) :

Set of fictitious transmission lines of branch ij

\(\varOmega E_i\) :

Set of existing lines connected to bus i

\(\varOmega C_i\) :

Set of candidate lines connected to bus i

k :

Index for existing or reinforcement transmission line

w :

Index for wind generation scenario

\({\hbox {pg}}_{i,w}\) :

Active power generation at bus i and wind generation scenario w (MW)

\({\hbox {pw}}_{i,w}\) :

Active wind power generation at bus i and wind generation scenario w (MW)

\({\hbox {pd}}_{i,w}\) :

Active power deficit at bus i and wind generation scenario w (MW)

\({\hbox {EP}}_{k,ij}\) :

Expansion binary 0/1 parameter for reinforcement k in branch ij

\(\theta _{ij,w}\) :

Angular difference between terminal buses i and j at wind generation scenario w

\(fE_{k,w}\) :

Active power flow (MW) of transmission line k in branch ij, at wind generation scenario w

\(fC_{k,w}\) :

Active power flow (MW) of candidate transmission line k for branch ij, at wind generation scenario w

\(fF_{k,w}\) :

Active power flow (MW) of fictitious line k for branch ij, at wind generation scenario w

\({\hbox {dc}}_i\) :

Specific deficit generation cost at bus i ($/MW)

\({\hbox {pg}}_i^{\mathrm{min}}\) :

Inferior limit of \(\hbox {pg}_{i,w}\) (MW)

\({\hbox {pg}}_i^{\mathrm{max}}\) :

Superior limit of \(\hbox {pg}_{i,w}\) (MW)

\(d_{i,w}\) :

Demand at bus i (MW) at wind generation scenario w

\(fE_k^{\mathrm{max}}\) :

Active power flow limit of an existing transmission line k (MW)

\(fC_k^{\mathrm{max}}\) :

Active power flow limit of a candidate transmission line k (MW)

\({\hbox {ce}}_k\) :

Investment cost of a candidate transmission line k ($)

\(b_k\) :

Susceptance of line k

\(\gamma _k\) :

Susceptance of fictitious line k, considered as 0.001 per unit (pu)

\(g_k\) :

Conductance of line k

K :

Number of clusters to the K-means method

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Acknowledgements

The authors would like to thank the Brazilian Research Agencies: CAPES, CNPq, FAPEMIG and INERGE for supporting this research.

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Authors and Affiliations

  1. Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil

    Camile Arêdes Moraes, Edimar José de Oliveira, Daniel Fioresi Botelho & Leonardo Willer de Oliveira

  2. Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro, Brazil

    Milena Faria Pinto

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  1. Camile Arêdes Moraes
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  2. Edimar José de Oliveira
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  3. Daniel Fioresi Botelho
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  4. Leonardo Willer de Oliveira
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Correspondence to Camile Arêdes Moraes.

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Arêdes Moraes, C., de Oliveira, E.J., Fioresi Botelho, D. et al. Wind Generation Impact in Transmission Expansion Planning. J Control Autom Electr Syst 31, 247–256 (2020). https://doi.org/10.1007/s40313-019-00547-x

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