Switches In Distribution Systems Research Articles

Distribution systems cost reduction based on switches upgrades: A stochastic approach

Power Distribution Systems are considered critical infrastructures, and their continuity to deliver and maintain electricity to the final customer is of extreme importance. The Optimal Switch Allocation address this topic by changing the network configuration to improve reliability, while minimizing power loss and cost. On previous researches economic constraints and dynamic failure models were considered, but neither the uncertainty in the environmental parameters nor stochastic models are applied. Additionally, the possibility to upgrade existing switches are often not considered. This work applies a Genetic Algorithm, coupled with Monte Carlo Simulation, as well as a Bayesian Inference Model, to address the probability of upgrading existing switches in a power distribution system. Economical constraints, fragility curves, variable load demand and weather scenarios are simulated. The Bayesian inference allows the model to be used as a support decision tool, when dealing with the possibility of upgrading switches in the network, especially in face of uncertainties in the distribution system. Results show that the upgrade of existing switches can achieve total cost reduction, but the uncertainties presented in the model can change the probability of upgrading an individual switch, thus sustaining the importance of the proposed approach. • A stochastic approach to deal with the upgrade of switches in a distribution system. • Monte Carlo method and genetic algorithm are used to simulate failure scenarios. • A Bayesian hierarchical approach fit the data into a final statistical model. • The final solution is the posterior probability of each switch upgrade. • The method quantifies the importance of each switch, enabling decision support.

Reconciliation of Measured and Forecast Data for Topology Identification in Distribution Systems

The status of switches in distribution systems might be changed for protection purposes or to achieve the operation objectives. However, the system topology should be known for the efficacious management and control of these systems. In most practical systems, due to the lack of enough measurements, the topology cannot be identified accurately and speedily. The forecast output can be transformed into the joint probability density function (<small>pdf</small>) of uncertain parameters, e.g., power demands. The measured data and this <small>pdf</small> are first conflated to update the joint <small>pdf</small> to best comply with the measurements. The topology identification problem, i.e., finding the probability of each topology given a set of measured values, is converted to a simpler form, i.e., finding the probability of observing these measured values in this topology. These probabilities are then calculated by tracing how the measurements affect the shape of the new consolidated <small>pdf</small> in each topology compared to the original <small>pdf</small>. The proposed technique employs as much data as available, is able to find the accurate probabilities, and is yet quite fast. The mathematical treatment of extracting such probabilities is presented and the proposed technique is validated in numerical studies in terms of accuracy, speed, and versatility.

Optimal Switch Placement in Distribution Systems: A High-Accuracy MILP Formulation

A new solution method is introduced to the problem of optimally deploying manual and automatic switches in distribution systems, where the product of two continuous variables and the inverse of a continuous variable are reformulated as a linear relation. This leads to a (mixed integer linear problem) MILP power flow formulation too. The objective function includes cost and reliability. The cost term itself includes capital investment, installation, and maintenance costs (MC) as well as customer interruption cost (CIC); while the reliability term is represented by system average interruption duration index (SAIDI). The problem is formulated as a MILP, which guarantees a global optimal solution. The effectiveness of the proposed method is validated through various case studies and sensitivity analyses on the RBTS4, followed by a comprehensive discussion and analysis of results. The proposed MILP formulation prescribes fewer switches while achieving lower SAIDI, compared to that of a previous MINLP formulation.

Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm

Achieving high-distribution reliability levels and concurrently minimizing capital costs can be considered as the main issues in distribution system optimization. Determination of the optimum number and location of switches in distribution system automation is an important issue from the reliability and economical points of view. In this paper, a novel three-state approach inspired from the discrete version of a powerful heuristic algorithm, particle swarm optimization, is developed and presented to determine the optimum number and locations of two types of switches (sectionalizers and breakers) in radial distribution systems. The novelty of the proposed algorithm is to simultaneously consider both sectionalizer and breaker switches. The feasibility of the proposed algorithm is examined by application to two distribution systems. The proposed solution approach provides a global optimal solution for the switch placement problem.

Determination of critical switches in distribution system

A methodology for determining the critical switches in a distribution system is presented. The daily load curves of various types of load customers are used in a three-phase load flow program. The program calculates the hourly current loading of distribution feeders and current flows through all line switches. The hourly optimal network configurations are solved to achieve balanced loading among feeders by minimizing the cost function of switching operations. The critical switches of the distribution system are then identified by investigating the hourly optimal switching operation patterns over a 24-hour period. A distribution system comprising nine distribution feeders in the Taiwan Power Company is selected for computer simulation. Results indicate the efficiency and effectiveness of the proposed methodology for the practical operation of distribution systems. >