418 research outputs found
Dispatching Stochastic Heterogeneous Resources Accounting for Grid and Battery Losses
We compute an optimal day-ahead dispatch plan for distribution networks with stochastic resources and batteries, while accounting for grid and battery losses. We formulate and solve a scenario-based AC Optimal Power Flow (OPF), which is by construction non-convex. We explain why the existing relaxation methods do not apply and we propose a novel iterative scheme, Corrected DistFlow (CoDistFlow), to solve the scenario-based AC OPF problem in radial networks. It uses a modified branch flow model for radial networks with angle relaxation that accounts for line shunt capacitances. At each step, it solves a convex problem based on a modified DistFlow OPF with correction terms for line losses and node voltages. Then, it updates the correction terms using the results of a full load flow. We prove that under a mild condition, a fixed point of CoDistFlow provides an exact solution to the full AC power flow equations. We propose treating battery losses similarly to grid losses by using a single-port electrical equivalent instead of battery efficiencies. We evaluate the performance of the proposed scheme in a simple and real electrical networks. We conclude that grid and battery losses affect the feasibility of the day-ahead dispatch plan and show how CoDistFlow can handle them correctly
Convex Relaxation of Optimal Power Flow, Part II: Exactness
This tutorial summarizes recent advances in the convex relaxation of the
optimal power flow (OPF) problem, focusing on structural properties rather than
algorithms. Part I presents two power flow models, formulates OPF and their
relaxations in each model, and proves equivalence relations among them. Part II
presents sufficient conditions under which the convex relaxations are exact.Comment: Citation: IEEE Transactions on Control of Network Systems, June 2014.
This is an extended version with Appendex VI that proves the main results in
this tutoria
Equivalent relaxations of optimal power flow
Several convex relaxations of the optimal power flow (OPF) problem have
recently been developed using both bus injection models and branch flow models.
In this paper, we prove relations among three convex relaxations: a
semidefinite relaxation that computes a full matrix, a chordal relaxation based
on a chordal extension of the network graph, and a second-order cone relaxation
that computes the smallest partial matrix. We prove a bijection between the
feasible sets of the OPF in the bus injection model and the branch flow model,
establishing the equivalence of these two models and their second-order cone
relaxations. Our results imply that, for radial networks, all these relaxations
are equivalent and one should always solve the second-order cone relaxation.
For mesh networks, the semidefinite relaxation is tighter than the second-order
cone relaxation but requires a heavier computational effort, and the chordal
relaxation strikes a good balance. Simulations are used to illustrate these
results.Comment: 12 pages, 7 figure
Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence
This tutorial summarizes recent advances in the convex relaxation of the
optimal power flow (OPF) problem, focusing on structural properties rather than
algorithms. Part I presents two power flow models, formulates OPF and their
relaxations in each model, and proves equivalence relations among them. Part II
presents sufficient conditions under which the convex relaxations are exact.Comment: Citation: IEEE Transactions on Control of Network Systems,
15(1):15-27, March 2014. This is an extended version with Appendices VIII and
IX that provide some mathematical preliminaries and proofs of the main
result
AC OPF in Radial Distribution Networks - Parts I,II
The optimal power-flow problem (OPF) has played a key role in the planning
and operation of power systems. Due to the non-linear nature of the AC
power-flow equations, the OPF problem is known to be non-convex, therefore hard
to solve. Most proposed methods for solving the OPF rely on approximations that
render the problem convex, but that may yield inexact solutions. Recently,
Farivar and Low proposed a method that is claimed to be exact for radial
distribution systems, despite no apparent approximations. In our work, we show
that it is, in fact, not exact. On one hand, there is a misinterpretation of
the physical network model related to the ampacity constraint of the lines'
current flows. On the other hand, the proof of the exactness of the proposed
relaxation requires unrealistic assumptions related to the unboundedness of
specific control variables. We also show that the extension of this approach to
account for exact line models might provide physically infeasible solutions.
Recently, several contributions have proposed OPF algorithms that rely on the
use of the alternating-direction method of multipliers (ADMM). However, as we
show in this work, there are cases for which the ADMM-based solution of the
non-relaxed OPF problem fails to converge. To overcome the aforementioned
limitations, we propose an algorithm for the solution of a non-approximated,
non-convex OPF problem in radial distribution systems that is based on the
method of multipliers, and on a primal decomposition of the OPF. This work is
divided in two parts. In Part I, we specifically discuss the limitations of BFM
and ADMM to solve the OPF problem. In Part II, we provide a centralized version
and a distributed asynchronous version of the proposed OPF algorithm and we
evaluate its performances using both small-scale electrical networks, as well
as a modified IEEE 13-node test feeder
Convex Relaxations and Linear Approximation for Optimal Power Flow in Multiphase Radial Networks
Distribution networks are usually multiphase and radial. To facilitate power
flow computation and optimization, two semidefinite programming (SDP)
relaxations of the optimal power flow problem and a linear approximation of the
power flow are proposed. We prove that the first SDP relaxation is exact if and
only if the second one is exact. Case studies show that the second SDP
relaxation is numerically exact and that the linear approximation obtains
voltages within 0.0016 per unit of their true values for the IEEE 13, 34, 37,
123-bus networks and a real-world 2065-bus network.Comment: 9 pages, 2 figures, 3 tables, accepted by Power System Computational
Conferenc
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