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

Load flow studies on stand alone microgrid system in Ranau, Sabah

This paper presents the power flow or load flow analysis of Ranau microgrid, a standalone microgrid in the district of Ranau,West Coast Division of Sabah. Power flow for IEEE 9 bus also performed and analyzed. Power flow is define as an important tool involving numerical analysis applied to power system. Power flow uses simplified notation such as one line diagram and per-unit system focusing on voltages, voltage angles, real power and reactive power. To achieved that purpose, this research is done by analyzing the power flow analysis and calculation of all the elements in the microgrid such as generators, buses, loads, transformers, transmission lines using the Power Factory DIGSilent 14 software to calculate the power flow. After the analysis and calculations, the results were analysed and compared

Power Flow Calculations by Deterministic Methods and Artificial Intelligence Method

In this paper, we will present different methods for Power Flow Calculations. First, we will describe the deterministic methods; which are Gauss-Seidel (GS) and Newton-Raphson (NR) methods, in addition to that, we will use also a Newton based method Fast Decoupled Load Flow (FDLF). Second, we have the Artificial intelligence method Neural Network (NN). Matlab programs were developed for solving Power Flow problem using GS and NR methods and regarding the ANN, we established and trained artificial neural networks models for computing voltage magnitudes and voltage phase angles. We used these methods to solve the Power Flow problem of the Institute of Electrical and Electronics Engineers (IEEE) 14 bus system. The results that we obtained were presented in graphs at the end of the paper

Recent Advances in Computational Methods for the Power Flow Equations

The power flow equations are at the core of most of the computations for designing and operating electric power systems. The power flow equations are a system of multivariate nonlinear equations which relate the power injections and voltages in a power system. A plethora of methods have been devised to solve these equations, starting from Newton-based methods to homotopy continuation and other optimization-based methods. While many of these methods often efficiently find a high-voltage, stable solution due to its large basin of attraction, most of the methods struggle to find low-voltage solutions which play significant role in certain stability-related computations. While we do not claim to have exhausted the existing literature on all related methods, this tutorial paper introduces some of the recent advances in methods for solving power flow equations to the wider power systems community as well as bringing attention from the computational mathematics and optimization communities to the power systems problems. After briefly reviewing some of the traditional computational methods used to solve the power flow equations, we focus on three emerging methods: the numerical polynomial homotopy continuation method, Groebner basis techniques, and moment/sum-of-squares relaxations using semidefinite programming. In passing, we also emphasize the importance of an upper bound on the number of solutions of the power flow equations and review the current status of research in this direction.Comment: 13 pages, 2 figures. Submitted to the Tutorial Session at IEEE 2016 American Control Conferenc

Numerical simulation of turbulent duct flows with constant power input

The numerical simulation of a flow through a duct requires an externally specified forcing that makes the fluid flow against viscous friction. To this aim, it is customary to enforce a constant value for either the flow rate (CFR) or the pressure gradient (CPG). When comparing a laminar duct flow before and after a geometrical modification that induces a change of the viscous drag, both approaches (CFR and CPG) lead to a change of the power input across the comparison. Similarly, when carrying out the (DNS and LES) numerical simulation of unsteady turbulent flows, the power input is not constant over time. Carrying out a simulation at constant power input (CPI) is thus a further physically sound option, that becomes particularly appealing in the context of flow control, where a comparison between control-on and control-off conditions has to be made. We describe how to carry out a CPI simulation, and start with defining a new power-related Reynolds number, whose velocity scale is the bulk flow that can be attained with a given pumping power in the laminar regime. Under the CPI condition, we derive a relation that is equivalent to the Fukagata--Iwamoto--Kasagi relation valid for CFR (and to its extension valid for CPG), that presents the additional advantage of natively including the required control power. The implementation of the CPI approach is then exemplified in the standard case of a plane turbulent channel flow, and then further applied to a flow control case, where the spanwise-oscillating wall is used for skin friction drag reduction. For this low-Reynolds number flow, using 90% of the available power for the pumping system and the remaining 10% for the control system is found to be the optimum share that yields the largest increase of the flow rate above the reference case, where 100% of the power goes to the pump.Comment: Accepted for publication in J. Fluid Mec