Reducing the computational effort of stochastic multi-period DC optimal power flow with storage

Mégel, Olivier; Andersson, Göran; Mathieu, Johanna L.

doi:10.1109/pscc.2016.7541033

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In the Pure PTDF formulation no auxiliary variables are used such that the optimization problem is given by (13) subject to the constraints (16) and…”

Section: Pure Ptdf Formulationmentioning

confidence: 99%

“…When large networks are optimized over multiple representative feed-in situations, especially with discrete constraints on generation dispatch, the LOPF problems can still take a significant time to solve, despite the linearization of the problem. Approaches in the literature to reducing the computational times of LOPF problems include decomposition [11,12,13,14,15], reformulating the problem using Power Transfer Distribution Factors (PTDFs) [16,17] and a parallelizable algorithm using the primal-dual interior point method [18].…”

Section: Introductionmentioning

confidence: 99%

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Linear optimal power flow using cycle flows

Hörsch

Ronellenfitsch

Witthaut

et al. 2018

Electric Power Systems Research

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Linear optimal power flow (LOPF) algorithms use a linearization of the alternating current (AC) load flow equations to optimize generator dispatch in a network subject to the loading constraints of the network branches. Common algorithms use the voltage angles at the buses as optimization variables, but alternatives can be computationally advantageous. In this article we provide a review of existing methods and describe a new formulation that expresses the loading constraints directly in terms of the flows themselves, using a decomposition of the network graph into a spanning tree and closed cycles. We provide a comprehensive study of the computational performance of the various formulations, in settings that include computationally challenging applications such as multi-period LOPF with storage dispatch and generation capacity expansion. We show that the new formulation of the LOPF solves up to 7 times faster than the angle formulation using a commercial linear programming solver, while another existing cycle-based formulation solves up to 20 times faster, with an average speed-up of factor 3 for the standard networks considered here. If generation capacities are also optimized, the average speed-up rises to a factor of 12, reaching up to factor 213 in a particular instance. The speed-up is largest for networks with many buses and decentral generators throughout the network, which is highly relevant given the rise of distributed renewable generation and the computational challenge of operation and planning in such networks.

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“…In the Pure PTDF formulation no auxiliary variables are used such that the optimization problem is given by (13) subject to the constraints (16) and…”

Section: Pure Ptdf Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear optimal power flow using cycle flows

Hörsch

Ronellenfitsch

Witthaut

et al. 2018

Electric Power Systems Research

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show abstract

“…Introduction in [209] Lagrangian relaxation is implemented to solve the problem. A twostage approach is also presented in [210] to reduce the computational effort of stochastic multi-period DCOPF problem in which the stages are linked together using Bender Cuts.…”

Section: Chapter 5 a Computationally Efficient Approach For Solving mentioning

confidence: 99%

Computationally efficient methods for solving optimal power flow problems by exploiting supplementary information

Barzegar¹

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List of Symbols Symbol Description A i i-th submatrices of matrix A associated with clique C i. b k (t) State of Charge (SOC) of the storage unit at bus k at time step t b loss k Storage energy loss at bus k. c k0 Constant cost coefficient of OPF problem. c SOX k0 Constant SOX emission coefficient of OPF problem. c k1 Linear cost coefficient of OPF problem. c SOX k1 Linear SOX emission coefficient of OPF problem. c k2 Quadratic cost coefficient of OPF problem. c SOX k1 Quadratic SOX emission coefficient of OPF problem. C DAM (t) Day-ahead market cost at time step t. C MC (t) Market clearing prices at time step t. C p (t) Cost during the peak-periods at time step t. C b (t) Contracted storage costs (row vector) at time step t. C g (t) Cost for dispatchable generation row vector at time step t. C d (t) Cost for flexible loads row vector at time step t. d k Valve point loading effect coefficient of OPF problem. d N OX ik Constant NOX emission coefficient of OPF problem. e k Valve point loading effect coefficient of OPF problem.

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Operations research in optimal power flow: A guide to recent and emerging methodologies and applications

Skolfield

Escobedo

2022

European Journal of Operational Research

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Reducing the computational effort of stochastic multi-period DC optimal power flow with storage

Cited by 7 publications

References 15 publications

Linear optimal power flow using cycle flows

Linear optimal power flow using cycle flows

Computationally efficient methods for solving optimal power flow problems by exploiting supplementary information

Operations research in optimal power flow: A guide to recent and emerging methodologies and applications

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