Linearized hybrid stochastic/robust scheduling of active distribution networks encompassing PVs

Arash Baharvandi; Jamshid Aghaei; Ahmad Nikoobakht; Taher Niknam; Vahid Vahidinasab; Damian Giaouris; Phil Taylor

doi:10.1109/TSG.2019.2922355

Linearized hybrid stochastic/robust scheduling of active distribution networks encompassing PVs

Arash Baharvandi
, Jamshid Aghaei^*
, Ahmad Nikoobakht
, Taher Niknam
, Vahid Vahidinasab
, Damian Giaouris
, Phil Taylor

^*Corresponding author for this work

Senior Team

Research output: Contribution to journal › Article (Academic Journal) › peer-review

47 Citations (Scopus)

Abstract

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

Original language	English
Article number	8740940
Pages (from-to)	357-367
Number of pages	11
Journal	IEEE Transactions on Smart Grid
Volume	11
Issue number	1
DOIs	https://doi.org/10.1109/TSG.2019.2922355
Publication status	Published - Jan 2020

Bibliographical note

Funding Information:
Manuscript received December 30, 2018; revised April 13, 2019; accepted May 30, 2019. Date of publication June 19, 2019; date of current version December 23, 2019. This work was supported in part by the InteGRIDy Project through European Union’s H2020 Research and Innovation Programme under Grant 731268. Paper no. TSG-01956-2018. (Corresponding author: Jamshid Aghaei.) A. Baharvandi, J. Aghaei, and T. Niknam are with the Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 8643278943, Iran (e-mail: [email protected]; [email protected]; [email protected]).

Publisher Copyright:
© 2010-2012 IEEE.

Keywords

beta distribution
bounded symmetric optimization
Distributed generations
mixed integer linear programming (MILP)
normal distribution
robust optimization
stochastic optimization

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title = "Linearized hybrid stochastic/robust scheduling of active distribution networks encompassing PVs",

abstract = "This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.",

keywords = "beta distribution, bounded symmetric optimization, Distributed generations, mixed integer linear programming (MILP), normal distribution, robust optimization, stochastic optimization",

author = "Arash Baharvandi and Jamshid Aghaei and Ahmad Nikoobakht and Taher Niknam and Vahid Vahidinasab and Damian Giaouris and Phil Taylor",

note = "Funding Information: Manuscript received December 30, 2018; revised April 13, 2019; accepted May 30, 2019. Date of publication June 19, 2019; date of current version December 23, 2019. This work was supported in part by the InteGRIDy Project through European Union{\textquoteright}s H2020 Research and Innovation Programme under Grant 731268. Paper no. TSG-01956-2018. (Corresponding author: Jamshid Aghaei.) A. Baharvandi, J. Aghaei, and T. Niknam are with the Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 8643278943, Iran (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2010-2012 IEEE.",

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AU - Baharvandi, Arash

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AU - Nikoobakht, Ahmad

AU - Niknam, Taher

AU - Vahidinasab, Vahid

AU - Giaouris, Damian

AU - Taylor, Phil

N1 - Funding Information: Manuscript received December 30, 2018; revised April 13, 2019; accepted May 30, 2019. Date of publication June 19, 2019; date of current version December 23, 2019. This work was supported in part by the InteGRIDy Project through European Union’s H2020 Research and Innovation Programme under Grant 731268. Paper no. TSG-01956-2018. (Corresponding author: Jamshid Aghaei.) A. Baharvandi, J. Aghaei, and T. Niknam are with the Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 8643278943, Iran (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: © 2010-2012 IEEE.

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N2 - This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

AB - This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

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KW - bounded symmetric optimization

KW - Distributed generations

KW - mixed integer linear programming (MILP)

KW - normal distribution

KW - robust optimization

KW - stochastic optimization

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ER -

Linearized hybrid stochastic/robust scheduling of active distribution networks encompassing PVs

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Keywords

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