Multi-objective Distribution Network Reconfiguration Research Articles

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

Multi-objective Distribution Network Reconfiguration Optimization Considering Space Allocation of Electric Vehicles

This paper explores the space allocation method and fuzzy C cluster processing method of electric vehicles, establishes a multi-objective distribution network model, solves the calculation process of the model, and verifies the feasibility of this research through simulation analysis method, thus providing help for orderly charging of electric vehicles and reducing line loss.

A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals

With the increased distributed generation (DG) and the combination of residential, commercial, and industrial loads connected to the distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and complex loads. In this paper, with the aim of minimizing network loss, load balance, and maximum voltage deviation, a multiobjective reconfiguration model of the distribution network is established under the condition of satisfying network constraints. Moreover, a new social beetle swarm optimization algorithm (SBSO) considering two social behaviors is adopted to solve the complex problem according to the characteristics of the distribution network reconfiguration (DNRC). Based on the SBSO algorithm, grey target decision-making (GTDM) strategy is used to choose the best beetle in the process of solving the multiobjective problem. Additionally, The grey relation projection (GRP) method is used to divide the time period of DNRC according to the change of DG and loads in a day, in order to reduce the number of switching operations. Finally, the effectiveness of the proposed multiobjective model and algorithm are verified on the standard IEEE-33 system and IEEE-69 system.

An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

With the increased distributed generation (DG) and electric vehicle (EV) load penetration in distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and EV loads. In this article, the uncertainties of wind power, photovoltaics, conventional loads, and EV loads are considered. Photovoltaics and conventional loads are related to solar radiation, and they are subtracted to form the net load. Then, the Wasserstein distance is used to divide the scene, and K-means clustering is used to reduce the scene, so the reconstruction analysis is carried out in the limited typical scene. In addition, to minimize network loss, load balance and maximum voltage deviation, a multiobjective game reconstruction model of the distribution network is established under the condition of satisfying network constraints. Moreover, the social beetle swarm optimization algorithm considering two social behaviors is adopted to solve the complex problem. Finally, the simulation results are verified on the standard IEEE-33 system and IEEE-118 system. The results show that the proposed strategy and algorithm can effectively reduce the network loss, improve the node voltage level and ensure that the load is not overloaded.

A Chaos Disturbed Beetle Antennae Search Algorithm for a Multiobjective Distribution Network Reconfiguration Considering the Variation of Load and DG

As the distributed generation (DG) in a power supply and the user load demand constantly change in an actual distribution network, multiobjective optimal network reconfiguration considering variations in load and DG has become a major concern, which is important and required to make system operations safe and economical. The aim is to minimize the sum of the active power loss, the sum of the load balancing index and the sum of the maximum node voltage deviation index simultaneously during the reconfiguration period. Here, this article proposes a new Chaos Disturbed Beetle Antennae Search (CDBAS) algorithm to reduce the computational time and solve the multiobjective optimal problem of network reconfiguration. To adopt the Chaos Disturbed Beetle Antennae Search algorithm for solving this multiobjective problem, grey target decision-making technology is used to rank the beetles. Additionally, to the enhance the system static voltage stability and voltage quality, a grey target decision-making model is established to achieve a layer relationship between each index and the switching operation index. The plausibility and effectiveness of the presented methodology is verified on the modified IEEE 33, 69 and 118-Bus Test Radial Distribution Network. Finally, compared with other research methods in the literature, the CDBAS algorithm outperforms other algorithms and produces a quality decision solution.

Multi-objective distribution network reconfiguration optimization problem

This paper dealt with the optimal reconfiguration of a power distribution network where a genetic algorithm (GA) based on graph theory was considered. This paper addressed this issue as a multi-criteria one with low investment cost and reduced active power losses objectives. The topological and voltage stability constraints were taken into account in this study, and each objective was firstly optimized individually. The Pareto-optimality principle for the criteria trade-off was then presented. In order to calculate the active power losses, a new load-flow approach was implemented. The genetic algorithm was tested on IEEE 10-bus system. Results show that the proposed load-flow algorithm was an efficient tool to obtain an accurate calculus for the system power losses. Efficiency and capability of the GA to achieve good solutions in a conspicuously short time are also discussed, and all the simulation results explained that the two objectives mentioned above are conflicting with each other.

Fuzzy Satisfied Multiobjective Distribution Network Reconfiguration: an Application of Adaptive Weighted Improved Discrete Particle Swarm Optimization

In this paper, a multiobjective framework for distribution network reconfiguration (DNR) is developed in the fuzzy domain to minimize power loss and improve load balancing. Since it is a nonlinear and combinatorial optimization problem a new fangled adaptive weighted improved discrete particle swarm optimization (AWIDPSO) algorithm is exercised. In this formulation each objective is normalized using fuzzy membership function, these are combined with the appropriate weight factor and maximized during the course of the iteration. In order to obtain high-quality solutions within lesser executing time, the constraint violations are handled perfectly so that intern produces a stable convergence characteristic. In this study, a 33-bus system is analyzed for optimum reconfiguration using the developed framework. Comparison of the simulated results with the results of well known prudent optimization technique confirms the applicability of the AWIDPSO algorithm for DNR problem.

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even non-commensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multi-objective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.

Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

With ever increasing deployment of automation and communication systems in smart grids, distribution network reconfiguration is becoming a viable solution for improving the operation of power grids. A novel hybrid optimization algorithm is proposed in this paper that determines Pareto frontiers, as the candidate solutions, for multiobjective distribution network reconfiguration problem. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance with shuffled frog leaping algorithm (SFLA) to recognize optimal nondominated solutions identified by SFLA. The local search step of SFLA is also customized for power systems application so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure, which significantly increases the convergence speed of the algorithm. Moreover, an adaptive reliability-based frog encoding is introduced that supervises the algorithm to concentrate on more reliable network topologies. The performance of the proposed method is demonstrated on a 136-bus electricity distribution network.

Adaptive multi‐objective distribution network reconfiguration using multi‐objective discrete particles swarm optimisation algorithm and graph theory

This study proposes a Pareto-based multi-objective distribution network reconfiguration (DNRC) method using discrete particle swarm optimisation algorithm. The objectives are minimisation of power loss, the number of switching operations and deviations of bus voltages from their rated values subjected to system constraints. Probabilistic heuristics and graph theory techniques are employed to improve the stochastic random search of the algorithm self-adaptively during the optimisation process. An external archive is used to store non-dominated solutions. The archive is updated iteratively based on the Pareto-dominance concept to guide the search towards the Pareto optimal set. The method is implemented on the IEEE 33-bus and IEEE 70-bus radial distribution systems, simulations are carried out and results are compared with other available approaches in the literature. To assess the performance of the proposed method, a quantitative performance assessment is done using several performance metrics. The obtained results demonstrate the effectiveness of the proposed method in solving multi-objective DNRC problems by obtaining a Pareto front with great diversity, high quality and proper distribution of non-dominated solutions in the objective space.