A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems

Ameli, Amir; Ahmadifar, Amir; Shariatkhah, Mohammad-Hossein; Vakilian, Mehdi; Haghifam, Mahmoud‐Reza

doi:10.1016/j.ijepes.2016.09.008

Cited by 54 publications

(34 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, several works have been carried out to improve the technical conditions of electrical grids with the lowest investment possible [2]. Some of those solutions include the reconfiguration of the Distribution System (DS) [3], the installation of capacitor banks [4], implementation of solid-state synchronous compensators [5], installation of voltage regulators [6], integration of energy storage systems [7] and the installation of Distributed Generators (DGs) [8]. In particular, the integration of DGs into an electrical grid is, nowadays, strongly discussed because it enables the combination of conventional and non-conventional (renewable) energy sources.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

View full text Add to dashboard Cite

The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.Energies 2018, 11, 1018 2 of 27 generation [11,12]: reduction of power losses (due to transmission), improvement of voltage profiles and stability index, power factor enhancement, reduction of the harmonic distortion and increased line loadability, among others [13]. However, incorrect location or sizing procedures may result in voltage profiles out of conventional ranges, voltage fluctuations, line capacity violation, increased failure levels due to intermittent generation and higher costs associated to the DGs [14].Different methods have been developed to optimize the location and sizing of DGs. Those methods are aimed at reducing the computation time required and to improve the technical criteria of the grid, such as power losses, voltage profiles and power factor, among others [15,16]. For example, the work reported in [17] presents a hybrid method between the genetic algorithm proposed by Chu and Beasley (GACB) [18] and a heuristic approach to locate, select and size the feeders in a distributed generation environment. Such a strategy enables the reduction of the costs associated with the feeders (DGs) and the power losses, which is achieved by reducing the peak load using active power injection based on diesel-based DGs only. On the other hand, the work reported in [19] describes a multi-target approach based on the Particle Swarm Optimization (PSO) technique for locating the DGs, and an optimal flow analysis for sizing the DGs. That work considers several generators and different load models, thus enabling high penetration levels of distributed generation.A similar approach was presented in [20], which is a hybrid solution based on both the GACB and PSO. The main drawbacks of such a solution were the high level of power injection requested to the DGs and the lack of analysis of the computation time required by the proposed method. Another solution, based on the Loss ...

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

View full text Add to dashboard Cite

show abstract

“…Table 6 includes application of ant colony in rotating electrical machines. Ameli (2017) [80] applies the ant colony algorithm in distribution systems. The purpose of their study is to minimize the total operational cost of the grid and to reduce the costs and losses.…”

Section: Ant Colonymentioning

confidence: 99%

“…Figure 14. Results for the study by Ameli (2017) [80]. Batista (2014) [81] gives a novel solution where the permanent interior magnet motor design domain is discretized into a suitable equivalent graph representation and an ant system (AS) algorithm is employed to achieve an efficient distribution of materials into this graph.…”

Section: Ant Colonymentioning

confidence: 99%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

View full text Add to dashboard Cite

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

show abstract

“…The operating costs of the distribution network are minimized in the normal operation mode by the optimal scheduling and dispatching of controllable DGs based on the model presented in [22]. In addition, the probabilistic and intermittent characteristics of renewable DG units, including an hourly variation of the demand and power market prices in the power system, have been considered within the scope of our optimal scheduling framework [23][24][25]. Thus, the optimal operational scheduling problem, which is already a non-linear, combinatorial, and NP-hard optimization problem, becomes a more complex problem [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

2019

View full text Add to dashboard Cite

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

show abstract

A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems

Cited by 54 publications

References 30 publications

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

Contact Info

Product

Resources

About