Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific

**Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation.** / Rostampour, Vahab; ter Haar, Ole; Keviczky, Tamas.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific

Rostampour, V, ter Haar, O & Keviczky, T 2019, Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation. in P Palensky, M Cvetkovic & T Keviczky (eds), *Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft.* Springer, Cham, Switzerland, pp. 215-233. https://doi.org/10.1007/978-3-030-00057-8_10

Rostampour, V., ter Haar, O., & Keviczky, T. (2019). Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation. In P. Palensky, M. Cvetkovic, & T. Keviczky (Eds.), *Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft *(pp. 215-233). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-00057-8_10

Rostampour V, ter Haar O, Keviczky T. Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation. In Palensky P, Cvetkovic M, Keviczky T, editors, Intelligent Integrated Energy Systems: The PowerWeb Program at TU Delft. Cham, Switzerland: Springer. 2019. p. 215-233 https://doi.org/10.1007/978-3-030-00057-8_10

@inbook{415a4beaf4504484a393de02c56f6f2c,

title = "Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation",

abstract = "TThis work presents a solution method for a day-ahead stochastic reserve scheduling (RS) problem using an AC optimal power flow (OPF) formulation. Such a problem is known to be non-convex and in general hard to solve. Existing approaches follow either linearized (DC) power flow or iterative approximation of nonlinearities, which may lead to either infeasibility or computational intractability. In this work we present two new ideas to address this problem. We first develop an algorithm to determine the level of reserve requirements using vertex enumeration (VE) on the deviation of wind power scenarios from its forecasted value. We provide a theoretical result on the level of reliability of a solution obtained using VE. Such a solution is then incorporated in OPF-RS problem to determine up- and down-spinning reserves by distributing among generators, and relying on the structure of constraint functions with respect to the uncertain parameters. As a second contribution, we use the sparsity pattern of the power system to reduce computational time complexity. We then provide a novel recovery algorithm to find a feasible solution for the OPF-RS problem from the partial solution which is guaranteed to be rank-one. The IEEE 30 bus system is used to verify our theoretical developments together with a comparison with the DC counterpart using Monte Carlo simulations.",

author = "Vahab Rostampour and {ter Haar}, Ole and Tamas Keviczky",

year = "2019",

doi = "10.1007/978-3-030-00057-8_10",

language = "English",

isbn = "987-3-030-00056-1",

pages = "215--233",

editor = "Peter Palensky and Milos Cvetkovic and Tamas Keviczky",

booktitle = "Intelligent Integrated Energy Systems",

publisher = "Springer",

}

TY - CHAP

T1 - Computationally Tractable Reserve Scheduling for AC Power Systems with Wind Power Generation

AU - Rostampour, Vahab

AU - ter Haar, Ole

AU - Keviczky, Tamas

PY - 2019

Y1 - 2019

N2 - TThis work presents a solution method for a day-ahead stochastic reserve scheduling (RS) problem using an AC optimal power flow (OPF) formulation. Such a problem is known to be non-convex and in general hard to solve. Existing approaches follow either linearized (DC) power flow or iterative approximation of nonlinearities, which may lead to either infeasibility or computational intractability. In this work we present two new ideas to address this problem. We first develop an algorithm to determine the level of reserve requirements using vertex enumeration (VE) on the deviation of wind power scenarios from its forecasted value. We provide a theoretical result on the level of reliability of a solution obtained using VE. Such a solution is then incorporated in OPF-RS problem to determine up- and down-spinning reserves by distributing among generators, and relying on the structure of constraint functions with respect to the uncertain parameters. As a second contribution, we use the sparsity pattern of the power system to reduce computational time complexity. We then provide a novel recovery algorithm to find a feasible solution for the OPF-RS problem from the partial solution which is guaranteed to be rank-one. The IEEE 30 bus system is used to verify our theoretical developments together with a comparison with the DC counterpart using Monte Carlo simulations.

AB - TThis work presents a solution method for a day-ahead stochastic reserve scheduling (RS) problem using an AC optimal power flow (OPF) formulation. Such a problem is known to be non-convex and in general hard to solve. Existing approaches follow either linearized (DC) power flow or iterative approximation of nonlinearities, which may lead to either infeasibility or computational intractability. In this work we present two new ideas to address this problem. We first develop an algorithm to determine the level of reserve requirements using vertex enumeration (VE) on the deviation of wind power scenarios from its forecasted value. We provide a theoretical result on the level of reliability of a solution obtained using VE. Such a solution is then incorporated in OPF-RS problem to determine up- and down-spinning reserves by distributing among generators, and relying on the structure of constraint functions with respect to the uncertain parameters. As a second contribution, we use the sparsity pattern of the power system to reduce computational time complexity. We then provide a novel recovery algorithm to find a feasible solution for the OPF-RS problem from the partial solution which is guaranteed to be rank-one. The IEEE 30 bus system is used to verify our theoretical developments together with a comparison with the DC counterpart using Monte Carlo simulations.

U2 - 10.1007/978-3-030-00057-8_10

DO - 10.1007/978-3-030-00057-8_10

M3 - Chapter

SN - 987-3-030-00056-1

SP - 215

EP - 233

BT - Intelligent Integrated Energy Systems

A2 - Palensky, Peter

A2 - Cvetkovic, Milos

A2 - Keviczky, Tamas

PB - Springer

CY - Cham, Switzerland

ER -

ID: 47928569