Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Tabu Search Based Algorithm for Multi-Objective Network Reconfiguration Problem

Abstract: The electric power distribution usually operates in a radial configuration, with tie switches between circuits to provide alternate feeds. The losses would be minimized if all switches were closed, but this is not done because it complicates the system’s protection against over currents. Whenever components fail, some of the switches must be operated to restore power to as many customers as possible. As loads vary with time, switch operations may reduce losses in the system. All of these are applications for reconfiguration. The reconfiguration problem is combinatorial problem, which precludes algorithms that guarantee a global optimum. Most existing reconfiguration algorithms fall into two categories. In the first, branch exchange, the system operates in a feasible radial configuration and the algorithm opens and closes candidate switches in pairs. In the second, loop cutting, the system is completely meshed and the algorithm opens candidate switches to reach a feasible radial configuration. Reconfiguration algorithms based on neural network, heuristics, genetic algorithms, and simulated annealing have also been reported, but not widely used. The objective of the paper presented in this work is to make a Tabu Search (TS) based algorithm for multi-objective programming to solve the network reconfiguration problem in a radial distribution system. Here six objectives are considered in conjunction with network constraints. The main objective of research is allocation of optimal switches to reduce the power losses of the system. It is tested for 33 bus systems. Simulation results of the case studies demonstrate the effectiveness of the solution algorithm and proved that the TS is suitable to solve this kind of problems. Key words: Combinatorial optimization; Distribution system; Energy Loss minimization; Genetic Algorithm; Simulating Annealing; Tabu searc

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks

Cyber-Physical Systems (CPS) are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging, through to cyber attacks. Indeed, the development of effective strategies to restore disrupted infrastructure systems continues to be a major challenge. Hitherto, there have been an increasing number of papers evaluating cyber-physical infrastructures, yet a comprehensive review focusing on mathematical modeling and different optimization methods is still lacking. Thus, this review paper appraises the literature on optimization techniques for CPS facing disruption, to synthesize key findings on the current methods in this domain. A total of 108 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, meta-heuristic), objective function, and network size. We also perform keyword clustering and bibliographic coupling analyses to summarize the current research trends. Future research needs in terms of the scalability of optimization algorithms are discussed. Overall, there is a need to shift towards more scalable optimization solution algorithms, empowered by data-driven methods and machine learning, to provide reliable decision-support systems for decision-makers and practitioners

Advances and applications in high-dimensional heuristic optimization

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv

Hybrid Algorithm based on Genetic Algorithm and Tabu Search for Reconfiguration Problem in Smart Grid Networks Using "R"

Reconfiguration of distribution networks aims to support the decision support, planning and/or real-time control of the operation of the electricity network. It is accomplished modifying the network structure of distribution feeders by changing the sectionalizing switches. Ensure higher levels of continuity and reliability to the electricity supply service are some of the requirements of consumers and electric power providers in the Smart Grid (SG) context. The goal of this paper is to propose a hybrid algorithm (Genetic and Tabu) for the reconfiguration problem based on " R " in order to better support the decision making process. Beyond that, " R " modeling of electricity networks improves the response time when handling issues of network reconfiguration using graph theory. The status of switches is decided according to graph theory subject to the radiality constraint of the distribution networks. The algorithm is presented and simulation results of IEEE 16-bus system, showing good results and computational efficiency

A novel strategy to restore power systems after a great blackout: The Argentinean case

This paper presents a multi-objective mathematical model to perform power restoration. It considers simultaneously the objective functions: restoration time, load shedding, and cost. New formulations are developed to improve the maneuverability of the system elements. This distinguishes the novel proposal from the rest of the one or two objective approaches. The linear nature of the formulation allows for obtaining feasible solutions within efficient times. The proposal includes in the main objective function the social view in terms of prioritize the restitution of the system for as many users as possible. It enables using this proposal to restore large scale systems. Results indicate that more equitable and faster restoration solutions can be obtained than the reported one in the mentioned case.Fil: Alvarez, Gonzalo Exequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature