Computationally tractable reserve scheduling for AC power systems with wind power generation

Book chapter (2019)

Authors

Vahab Rostampour Team Tamas Keviczky - Mechanical, Maritime and Materials Engineering
Ole Ter Haar Student
T. Keviczky Team Tamas Keviczky - Mechanical, Maritime and Materials Engineering
Research Group
Team Tamas Keviczky (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 Vahab Rostampour, Ole ter Haar, T. Keviczky
DOI: https://doi.org/10.1007/978-3-030-00057-8_10
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Published Date

2019

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Tamas Keviczky

Abstract

This 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.