Nonlinear Primal-dual Interior-point Method Research Articles

Performance analysis of Newton-Raphson Power flow computations based on Power and Current Mismatches

This work studies the performance of single-phase power flow computations implemented using vector-based Python scripting language. Two approaches are considered, namely the current mismatch and power mismatch based on Newton-Raphson method. Both approaches are developed using rectangular coordinates and tested through a variety of IEEE test systems modelling high voltage transmission networks. The computational burden and accuracy of both implementations are duly discussed. This work will be later incorporated into our vector-based Non-linear Primal-Dual Interior Point Method as constraints.

Multi-area Generation Dispatching Distributed Coordination and Optimization Algorithm

As the fast expansion and interconnection of power system, it makes higher requirements for Optimal Power Flow (OPF) calculation in modern power grid. A distributed coordination and optimization method is presented in this paper. Injection power equivalent of sub-system boundary bus is utilized to solve multi-area dispatching problem. Based on regional decomposition model, it decouples the integrated optimization and avoids complex computation of system parameters equivalent in conventional methods. Besides, it reduces the communication between different regions. The nonlinear primal-dual interior point method is used for optimization in each sub-system, outer coordination of boundary bus’s voltage correction is conducted to compute the optimized dispatching in the whole interconnected power system. Numerical simulations are taken in IEEE test systems. The results show that the proposed method is validated, effective and suitable for online application.

Piecewise Normalized Normal Constraint Method Applied to Minimization of Voltage Deviation and Active Power Loss in an AC–DC Hybrid Power System

In this paper, we establish a mathematical model of the multi-objective reactive power optimization (MORPO) problem in an ac–dc hybrid power system. To get an evenly distributed set of Pareto solutions of this MORPO problem throughout a whole day, we propose a piecewise normalized normal constraint (PNNC) method. Based on this method, the Pareto frontier is divided into four arcs so that the fluctuation of angle between the tangent line at each Pareto point and its sub-Utopia line is reduced. By doing so, the original MORPO problem is transformed into four sets of single objective optimization problems, which can be solved efficiently by a nonlinear primal-dual interior-point method. Performance of the PNNC method is tested on a real ac–dc interconnection system. The results demonstrate that it has the capability to generate a more evenly distributed set of Pareto solutions when compared with the normalized normal constraint (NNC) and weighted sum (WS) methods. In addition, a fuzzy-based membership value assignment method is employed to derive 96 optimal compromise solutions corresponding to 96 time periods in a whole day; the numerical outcomes show that a saving in active power loss and an improvement in the voltage profile can be gained simultaneously.

Maximum capacity model of grid-connected multi-wind farms considering static security constraints in electrical grids

An increasing interest in wind energy and the advance of related technologies have increased the connection of wind power generation into electrical grids. This paper proposes an optimization model for determining the maximum capacity of wind farms in a power system. In this model, generator power output limits, voltage limits and thermal limits of branches in the grid system were considered in order to limit the steady-state security influence of wind generators on the power system. The optimization model was solved by a nonlinear primal-dual interior-point method. An IEEE-30 bus system with two wind farms was tested through simulation studies, plus an analysis conducted to verify the effectiveness of the proposed model. The results indicated that the model is efficient and reasonable.

Optimization-based approach to path planning for closed chain robot systems

Optimization-based approach to path planning for closed chain robot systems An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.

Optimal power flow solutions under variable load conditions

This work presents a methodology to calculate a sequence of optimal power flow (OPF) solutions under variable load conditions. The aim is to obtain a set of optimal operating points in the neighborhood of the bounds of the region defined by the load flow equations and a set of operational limits. For this, an algorithm based on the continuation method and on a primal-dual interior point optimization method is proposed. Such an algorithm consists of two main steps: the predictor step, which uses a linear approximation of the Karush-Kuhn-Tucker (KKT) conditions to estimate a new operating point for an increment in the system load; and the corrector step, which calculates the optimum corresponding to the new load level via a nonlinear primal-dual interior point method. Indices for critical buses and inequality constraints are a byproduct of the methodology. In addition, sensitivity analysis is performed to calculate the amount of reactive compensation which allows for a pre-specified increase in the system load. Results for realistic test systems are presented.