29 research outputs found
PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations
In recent years, the power system research community has seen an explosion of
novel methods for formulating and solving power network optimization problems.
These emerging methods range from new power flow approximations, which go
beyond the traditional DC power flow by capturing reactive power, to convex
relaxations, which provide solution quality and runtime performance guarantees.
Unfortunately, the sophistication of these emerging methods often presents a
significant barrier to evaluating them on a wide variety of power system
optimization applications. To address this issue, this work proposes
PowerModels, an open-source platform for comparing power flow formulations.
From its inception, PowerModels was designed to streamline the process of
evaluating different power flow formulations on shared optimization problem
specifications. This work provides a brief introduction to the design of
PowerModels, validates its implementation, and demonstrates its effectiveness
with a proof-of-concept study analyzing five different formulations of the
Optimal Power Flow problem
Linear/Quadratic Programming-Based Optimal Power Flow using Linear Power Flow and Absolute Loss Approximations
This paper presents novel methods to approximate the nonlinear AC optimal
power flow (OPF) into tractable linear/quadratic programming (LP/QP) based OPF
problems that can be used for power system planning and operation. We derive a
linear power flow approximation and consider a convex reformulation of the
power losses in the form of absolute value functions. We show four ways how to
incorporate this approximation into LP/QP based OPF problems. In a
comprehensive case study the usefulness of our OPF methods is analyzed and
compared with an existing OPF relaxation and approximation method. As a result,
the errors on voltage magnitudes and angles are reasonable, while obtaining
near-optimal results for typical scenarios. We find that our methods reduce
significantly the computational complexity compared to the nonlinear AC-OPF
making them a good choice for planning purposes
Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation
Many autonomous microgrids have extensive penetration of distributed generation (DG). Optimal power flow (OPF) is necessary for the optimal dispatch of networked microgrids (NMGs). Existing convex relaxation methods for three-phase OPF are limited to radial networks. In light of this, we develop a semidefinite programming (SDP) convex relaxation model which can cope with meshed networks and also includes a model of three-phase DG and under-load voltage regulators with different connection types. The SDP model solves the OPF problem of multi-phase meshed network effectively, with satisfactory accuracy, as validated by real 6-bus, 9-bus, and 30-bus NMGs, and the IEEE 123-bus test cases. In the SDP model, the convex symmetric component of the three-phase DG model is demonstrated to be more accurate than a three-phase DG modelled as three single-phase DG units in three-phase unbalanced OPF. The proposed method also has higher accuracy than the existing convex relaxation methods. The resultant optimal control variables obtained from the convex relaxation optimization can be used for both final optimal dispatch strategy and initial value of the non-convex OPF to obtain the globally optimal solution efficiently