(N-1) contingency planning in radial distribution networks using genetic algorithms

Abstract

N-1) contingency planning has been object of study and branches. Distribution networks in Energy Australia's conin the area of distribution networks for several decades. Energy cession area operate a radial topology. Such radial networks, as distribution companies have to reconnect areas affected by the name says, have no cycles and each load is served by only an outage within a very short time, and observe operational one feeder. When designing a distribution network, though, constraints, to avoid the possibility of severe financial penalties by regulatory bodies. Distribution networks are often operated companies create robustness by adding excess connectivity, with a radial topology, but, ideally, should have more than one allowing power to follow different paths to reach the same route to deliver energy to any node of the network. Switches in customer, if needed. Together with the excess connectivity, the network are opened to create the radial topology used in switches are positioned in strategic points of the network and normal operation, and, in the case of an outage, alternate routes can be set either as closed or opened. Specific switching conare activated by opening or closing switches located at specific points of the network. Given an outage situation (in our case figurations will create the radial topology of the power flow, represented by the disconnection of a single branch), the choice assigning each load to a generator or feeder. Redundancy in of which switches should change their state is a combinatorial the distribution network is particularly critical in situations of optimisation problem, with a search space of 2 k , where k is the (n-1) contingency, in which a cable becomes faulty, affecting number of switches. Because of the exponential complexity, exact the supply for all customers located after it. In this case, an methods are prohibitively time-consuming. This work presents a genetic algorithm that provides a rapid answer to network alternate path to supply power to the affected area can be managers in terms of a switching strategy to reconnect the activated by a series of switching states changes. affected area. The method takes into account the radial topology The problem of finding alternate routes for the power of the power flow and the operational limits of voltage and cable supply, given an outage scenario, is very complex and has been load. Computational tests were conducted on a real network with studied for several decades. Alternate routes are determined 96 buses and 16 switches, located within the operational area of Energy Australia. This paper describes the genetic algorithm in by searching the solution space of switches states. That is, detail, presents thorough computational tests, and a complete if there are k switches present in the network, the search contingency plan for the test network.

Contingency analysis for gas transport networks with hydrogen injection

“…Other approaches are discussed as well. For instance, Mendes et al (2010) solves contingency problems in distribution networks, which are radial due to the current switching configuration, by means of an optimization method for a reconfiguration via switching operations. Their number is only restricted by the network.…”

Ubicación Óptima de Reconectadores en Redes de Distribución con Generación Distribuida Utilizando Algoritmos Genéticos e Índices de Calidad de Servicio

“…En [8] se consideran los costos y variaciones de voltaje asociados con interrupciones permanentes dentro de la función objetivo del problema de optimización desarrollado. Por otra parte, en [9] se investiga la reconfiguración optima de una red de distribución mediante la manipulación de dispositivos operativos, que reconectan secciones de la red de distribución en el menor tiempo posible.…”

Distributed Integrated Synthetic Adaptive Multi-Objective Reactive Power Optimization

“…Considering that the influence of distributed generation joining the distribution network is complex and multi-layered, some mathematical models of multi-objective optimization has been proposed. Taking Reference [16] and Reference [17] as examples, the optimization objectives are to reduce the system power consumption, improve the node voltage level and increase the voltage stability margin of the distribution network. After assigning a set of weights to several functions to be optimized and transforming them into a single objective function, the GA algorithm and PSO algorithm are used to solve the problem, respectively.…”