Primal-Dual Interior-Point Technique for Optimisation of 330kV Power System on One Variable

Abstract

The work deal on a method for optimisation of 330KV power system load flow which excels other existing methods.This method is called, PRIMAL-DUAL INTERIOR-POINT TECHNIQUE for solving optimal load flow problem. As problems of load-shedding, power outages and system losses have been cause for worries, especially among the developing nations such as Nigeria, hence need for a load flow solution technique, which, this work addresses. Optimisation is achieving maximum of required and minimum of un-required and it is obtained mathematically by differentiating the objective function with respect to the control variable(s) and equating the resulting expression(s) to zero. In 330KV Power System, optimization is maximisation of real power injection, voltage magnitude and cost effectiveness, while minimization of reactive power injection, power loss, critical clearing time of fault conditions and time of load flow simulation.. This work developed a mathematical model that solves load flow problems by engaging non-negative PRIMAL variables, “S” and “z” into the inequality constraint of the load flow problems in other to transform it to equality constraint(s). Another non-negative DUAL variables “” and “v” are incorporated together with Lagrangian multiplier “?” to solve optimisation. While solving optimisation Barrier Parameter “” which ensures feasible point(s) exist(s) within the feasible region (INTERIOR POINT). Damping factor or step length parameter “?”, in conjunction with Safety factor “” (which improves convergence and keeps the non-negative variables strictly positive) are employed to achieve result. The key-words which are capitalized joined to give this work its name, the PRIMAL-DUAL INTERIOR-POINT. The initial feasible point(s) is/are tested for convergence and where it/they fail(s), iteration starts. Variables are updated by using the computed step size and the step length parameter “?”, which thereafter, undergo another convergence test. This technique usually converges after first iteration. Primarily, this technique excels the existing methods as; it solves load flow problems with equality and inequality constraints simultaneously, it often converges after first iteration as against six or more iterations of the existing methods, its solution provides higher power generations from available capacity and minimum system loss. Example, Geregu Power Station on Bus 12 generates 0.1786p.u power from the available 0.2000p.u through the PD-IP tech. as against 0.1200p.u of the existing methods. Also it supplies 0.1750p.u with loss of 0.0036p.u as against 0.0236p.u with loss of 0.0964p.u of the existing methods. This results in 90% generation as against 60% of existing methods. Generation loss is 1.8% as against 80.3% of existing methods and availability loss of 12.5% as against 88.2% of existing. Therefore this method ensures very high system stability.

Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

“…Fossil fuels are used to run thermal power plants. The equilateral [8] connection roughly reflects the connection between gasoline expenditure ($/h) and energy output (MW) as shown in Equation 1:…”

“…Conventional techniques based on the ideas of mathematical programming can be used to handle OPF problems of various sizes. To be able to fulfill the demands of several goal functions, application kinds, and limitations, traditional classical methods are further categorized into the following categories: Methodologies such as the customized sequential quadratic programming [6], the gradient method [7], genetic algorithm method [8][9][10][11] and particle swarm optimization (PSO) [12][13][14], are some examples of the available methods. In recent years, new strategies that are backed by artificial intelligence (AI) have been made with the goal of fixing problems with analytical techniques.…”