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A probabilistic approach to assess the impact of wind power generation in transmission network expansion planning

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Abstract

In order to accommodate the uncertainty and variability of wind power, this paper proposes a scenario-based probabilistic model to assess the impact of intermittent wind power-based Renewable Energy Resources (RES) on the Transmission Network Expansion Planning (TNEP). The objectives comprise the evaluation of impacts considering the wind power penetration into connected and unconnected buses, as well as the optimization of reinforcements that must be made to avoid unwanted wind cutting operations and load shedding. The wind power uncertainties are represented through scenarios obtained from real historical series grouped by using the well-known k-means algorithm. The methodology performance is verified in a practical equivalent Brazilian southern system, modified to include a significant amount of wind energy. The obtained results show that the RES insertion impacts the TNEP task, changing the expansion decision.

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Abbreviations

AC :

Alternating current

DC :

Direct current

EHA :

Efficient hybrid algorithm

EPS :

Electrical power system

MBA :

Modified bat-inspired algorithm

MINLP :

Mixed-integer nonlinear programming

OBF :

Objective function

OPF :

Optimal power flow

RES :

Renewable energy sources

SC :

Simulation case

TNEP :

Transmission network expansion planning

WS :

Wind scenarios

\(\gamma _{ij}\) :

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

\(\rho _{w}\) :

Wind generation scenarios probability of occurrence

\(b_{ij}\) :

Susceptance of line ij

\(ce_{ij}\) :

Investment cost of a candidate transmission line ij ($)

\(d_{i,w}\) :

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

\(dc_i\) :

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

\(dwc_i\) :

Wind Curtailment cost at bus i ($/MW)

\(fC^{max}_{ij}\) :

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

\(fE^{max}_{ij}\) :

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

\(g_{ij}\) :

Conductance of line ij

k :

Number of clusters to the k-means metho

\(N_{S}\) :

Number of wind generation scenarios

\(pg_i^{max}\) :

Superior limit of \(pg_{i,w}\) (MW)

\(pg_i^{min}\) :

Inferior limit of \(pg_{i,w}\) (MW)

\(pw_{i,w}\) :

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

B :

Set of load buses

C :

Set of branches with candidate transmission lines

E :

Set of branches with existing transmission lines

F :

Set of branches with fictitious transmission lines

W :

Set of wind generation scenarios

w :

Index for wind generation scenario

Z :

Set of generation buses

\(Z^*\) :

Set of buses where the wind generation units were allocated

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

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

\(EP_{ij}\) :

Expansion binary 0/1 parameter for reinforcement ij

\(fC_{ij,w}\) :

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

\(fE_{ij,w}\) :

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

\(fF_{ij,w}\) :

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

\(pd_{i,w}\) :

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

\(pg_{i,w}\) :

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

\(pwc_{i,w}\) :

Wind Curtailment at bus i at wind generation scenario w (MW)

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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, Brazil

    Camile A. Moraes, Leonardo W. de Oliveira, Edimar J. de Oliveira, Daniel F. Botelho & Arthur Neves de Paula

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

    Milena F. Pinto

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  1. Camile A. Moraes
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Correspondence to Daniel F. Botelho.

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Moraes, C.A., de Oliveira, L.W., de Oliveira, E.J. et al. A probabilistic approach to assess the impact of wind power generation in transmission network expansion planning. Electr Eng 104, 1029–1040 (2022). https://doi.org/10.1007/s00202-021-01361-y

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