A Two‐Stage Emergency Reconfiguration Strategy for Port Cyber‐Physical Systems During Disasters Considering the Marginal Value Quantification of Multi‐Service Information Flows

Differential evolution using ancestor tree for service restoration in power distribution systems

This paper presents an application of a modified Teaching learning based optimization (TLBO) algorithm for reconfiguration of static primary power distribution system. The objective of the proposed work is the minimization of operational cost and maximization of system availability. For feeder reconfiguration purpose the optimal possible connections between the buses are considered to be the design variables and this represents the discrete nature of problem statement. Thus, in the proposed work a bit-relocate-based TLBO algorithm is developed that can handle both continuous as well as discrete nature of the planning problem to find the optimal reconfigured route by satisfying all technical constraints. For reliability purpose a novel reliability indicator called contingency-load-loss-index is used. Proposed approach performance is assessed on the standard IEEE-33 bus test system considering real-time design practices. Furthermore, a qualitative comparison is made with other techniques, to show the efficacy of the proposed approach.

Dynamic Reconfiguration of Electrical Power Distribution Systems with Distributed Generation and Storage

A metaheuristic methodology for the reconfiguration of distribution system networks to minimize power losses is presented. It is based on a simulation of behavior in ant colonies (Ant Colony Optimization - A.C.O). This method differs from current A.C.O. algorithms in the way the agents build their paths. To illustrate the proposed method, a numerical example is worked out.

A novel application of the meta-heuristic “Particle Swarm Optimization” is implemented in this work to solve the problem of reconfiguration of power distribution systems in the presence of non-linear loads with the objective of minimizing the total harmonic distortion. The proposed methodology is based on the behavior of a flock of birds when flying in search of food. In order to guarantee the convergence when the distribution power network is radial and large, a heuristic rule is deployed to improve the methodology efficiency. The developed algorithm was applied to the IEEE 33-bus radial system. The results demonstrate the efficiency of the proposed method to find the optimal topology of the distribution system to reduce harmonic distortion.

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

A Data-Driven Methodology for Dynamic Reconfiguration of Power Distribution Systems

Minimum-loss network reconfiguration: A minimum spanning tree problem

A reliability based power distribution system reconfiguration methodology is described in this paper. Probabilistic reliability models are used in order to evaluate the reliability at the load points. An algorithm for finding the minimal cutsets is used to find the minimal set of components appearing between the feeder and any particular load point. The optimal statuses of the switches in the system are found by a binary particle swarm optimization (BPSO) based search algorithm. The proposed methodology is applied on a modified IEEE 123 node test system.

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. Besides, reclosers use to eliminate transient faults, faults isolation, network management and enhance reliability to reduce customer outages. For load uncertainty a new method based on probabilistic interval arithmetic approach is used to incorporate uncertainty in load demand that can forecast reasonably accurate operational conditions of radial system distribution (RDS) with better computational efficiency. In this paper, the optimization process is performed by considering power loss reduction along with reliability index as objective functions. Simulation results on radial 33 buses test system indicates that simultaneous optimization of these two issues has significant impact on system performance.

Decision support system for multicriteria reconfiguration of power distribution systems using CSO and efficient graph traversal and repository management techniques

A novel algorithm for the network reconfiguration of power distribution systems is presented. An optimal loss reduction is accomplished to maintain acceptable voltage at customer loads as well as to assure sufficient conductor and substation current capacity to handle load requirements. The success of the algorithm depends directly upon the straightforward and highly-efficient solution of the quadratic cost transshipment problem. The proposed algorithm completely eliminates the need for matrix operations and executes all operations directly on a graph of the distribution system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The electric power system has become a very complicated network at present because of re-structuring and the penetration of distributed generation and storage. A single fault can lead to massive cascading effects affecting power supply and power quality. An overall systematic solution for these issues could be obtained by an artificial intelligent mechanism called the multi-agent system. This paper presents a multi-agent system model for fault detection and reconfiguration based on graph theory and mathematical programming. The multi-agent models are simulated in Java Agent Development Framework and Matlab <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> and are applied to a power system model designed in the commercial software, the Distributed Engineering Workstation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">copy</sup> . The circuit that is used to model the power distribution system is a simplified model of the Circuit of the Future, developed by Southern California Edison. Possible fault cases were tested and a few critical test scenarios are presented in this paper. The results obtained were promising and were as expected.

Optimal reconfiguration of power distribution systems is a complex combinatorial optimization problem with the purpose of identifying a radial network that optimizes given objectives. In this paper, a dynamic fuzzy-based genetic algorithm is presented to find an optimal configuration for the distribution networks that minimizes the total power loss of the network. The efficiency of the proposed algorithm is demonstrated by its application on a 33-bus distribution system. The simulation results demonstrate the superior performance of the proposed method compared to the classic genetic algorithm based methods.

Distribution system reconfiguration (DSR) is a multiobjective, non-linear problem. This paper introduces a new, fast, non-dominated sorting genetic algorithm (FNSGA) for solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively short computational time. The goals of the project are to minimize real power losses, and improve the voltage profile and load balancing index with minimum switching operations. Instead of dealing with a single objective function while the others are formulated as constraints, as in the traditional methods, the FNSGA is applied to optimize all the objectives according to the operator’s wishes. Moreover, an adapted mutation operation is applied instead of a random one to speed up convergence. Radial topology is satisfied using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of each topology. The algorithm is applied to two different systems, the IEEE16 bus and the IEEE 32 bus test systems. The results show the efficiency of this algorithm as compared to other methods in terms of both achieving all the goals and minimizing the computational time with reasonable population and generation sizes.