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The purpose of the paper is to find the best distributed generators (DGs) location for improving reliability and reducing power loss using distribution system reconfiguration. This is implemented in the presence of the tie-switches. It proposes a search-based algorithm for the reconfiguration problem. Individual DG placement is obtained for all system configurations, and analytical hierarchy process tool is used for finding the overall best location. This is carried out for various system loadings.

This paper proposes a knowledge-based search algorithm which needs the base conditions of the distribution system. A detailed analysis is carried out for finding the best DG locations from the obtained DG placements for various system configurations. Simulations are rigorously carried out with the help of programming. Results from these simulations are further given to analytical hierarchy tool for obtaining the DGs location.

The findings of the paper are the DG placement for various system loadings and various system configurations and to obtain the best DG location for any system configuration. A search-based algorithm is designed for accomplishing it.

The proposed method identifies the placement of distributed generation at distribution systems for reliability improvement and power loss reduction which is one of the present day needs for fulfilling the raising power consumers.

This study aims to propose a sensitivity-based methodology for the optimum accommodation of distributed generation (DG) units in meshed power networks with appropriate technologies. The effect of load variation is incorporated into the proposed methodology to identify the most trusted locations for DG placement.

The effectiveness of minimizing active power losses is considered a key criterion. A priority list comprising both sensitivity indexes and realistic indicators is deduced to rank the optimum sites for the placement of DG units. A sorting index for distinguishing the suitable DG type(s) for each candidate location is organized. Three common DG types are considered in this work. The modified IEEE 30-bus meshed system is chosen to perform the proposed methodology.

Results demonstrate that the obtained priority index can be used to achieve the best real loss minimization rates. Numerous load buses can be safely excluded as candidate locations using the proposed approach. Consequently, the methodology can minimize the computational process of diagnosing the optimum sites for DG accommodation.

The findings determine that instead of installing many DG units at various locations with one DG type, a few certain load buses can be used to accommodate more than one DG type and significantly reduce losses.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption.

Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution.

In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN).

The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources.

In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

Notable energy losses and voltage deviation issues in low-voltage radial distribution systems are a major concern for power planners and utility companies because of the integration of electric vehicles (EVs). Electric vehicle charging stations (EVCSs) are the key components in the network where the EVs are equipped to energize their battery. The purpose of this paper is coordinating the EVCS and distributed generation (DG) so as to place them optimally using swarm-based elephant herding optimization techniques by considering energy losses, voltage sensitivity and branch current as key indices. The placement and sizing of the EVCS and DG were found in steps.

The IEEE 33-bus test feeder and 52-bus Indian practical radial networks were used as the test system for the network characteristic analysis. To enhance the system performance, the radial network is divided into zones for the placement of charging stations and dispersed generation units. Balanced coordination is discussed with three defined situations for the EVCS and DG.

The proposed analysis shows that DG collaboration with EVCS with suitable size and location in the network improves the performance in terms of stability and losses.

Stability and loss indices are handled with equal weight factor to find the best solution.

The proposed method is coordinating EVCS and DG in the existing system; the EV integration in the low-voltage side can be incorporated suitably. So, it has societal impact.

In this study, the proposed method shows improved results in terms EVCS and DG integration in the system with minimum losses and voltage sensitivity. The results have been compared with another population-based particle swarm optimization method (PSO). There is an improvement of 18% in terms of total power losses and 9% better result in minimum node voltage as compared to the PSO technique. Also, there is an enhancement of 33% in the defined voltage stability index which shows the proficiency of the proposed analysis.

The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.

In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.

The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.

The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.

The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS).

This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied.

The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations.

The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system.

Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (P^loss_total), reduction of voltage deviation and balancing of current through feeder sections.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

The purpose of the paper is to develop a simple, efficient and robust power flow (PF) method for ill-conditioned distribution networks (DNs).

It first formulates the PF problem as an optimization problem of minimizing the node power mismatches, while treating the corrections of node voltages as problem variables and then uses soccer game optimization (SGO), an artificial intelligent algorithm simulating the behavior of soccer game players in scoring goals, in solving the formulated PF problem.

It studies the performances of the developed method on four standard test DNs and exhibits that the method is superior in respect of accuracy, robustness and computational speed than those of existing methods.

It suggests a novel and new PF method using SGO and portrays that the proposed method is as accurate as any other PF method, robust like non-Newton type of PF methods and faster than Newton type of PF methods.

The use of power sectionalizers in electric power distribution networks as disconnecting devices for optimum network configuration is indispensable. Major reasons to use sectionalizers, here manual sectionalizers, is their lower installation and operating prices compared to other types of disconnecting devices and that most of conventional realistic electric power distribution systems are still using manual sectionalizers due to their ease of procurement. However, in case of failure for these switches, power supply interruptions are unavoidable unless optimum solutions are used for configuration (and possibly reconfiguration) of sectionalizers. Thus, in this research, binary exchange market algorithm (BEMA) as a novel evolutionary metaheuristic is used to meet the maximized customer satisfaction by optimized configuration of sectionalizers within electric power distribution networks in the presence of distributed generations (DGs). To solve the problem, BEMA is used on sectionalizing switch placement problem, which has only two open and close (0/1) states. A novel multi-objective optimization problem has been formulated as a function of two aspects, namely, improved reliability index (for customer benefit) and minimized sectionalizing switch costs (for utility benefits). Simulations are carried out in three different case studies to validate the effectiveness of the BEMA both in theory and practice: Standard IEEE 33-bus test system, practical feeder-8 of MeshkinShahr Town’s electric power distribution network in northwest of Iran; and Roy Billinton test system Bus 4 (RBTS-Bus 4). The obtained results are compared with those of the previously validated ant colony optimization (ACO) technique in RBTS-Bus 4.

The optimum configuration of sectionalizers in the presence of DGs has been formulated as a multi-objective function consisting of two conflicting objectives. First objective is to improve the power distribution network reliability indices. Second objective is to fulfill the first objective with a minimized sectionalizing switch cost. The latter is probably obtained by reducing the number of installed sectionalizers. The obtained results by BEMA have been compared with those of ACO technique.

In this paper, optimal configuration of sectionalizers has been performed based on a multiobjective function by binary exchange market algorithm. By simulations carried out on two standards and one practical test systems, the proposed algorithm effectiveness was confirmed and the obtained results were compared to ACO algorithm. Changing weighting factors shows that better satisfaction can be obtained when difference between the weighting factors is relatively greater. In other words, the reliability membership function is more than switch cost membership, and thus, if the values of two weighting factors are close enough, the satisfaction level reduces. The number of installed sectionalizing switches by BEMA and ACO techniques in different scenarios were performed.

Proposal of a novel multi-objective function for finding optimal location of sectionalizers in the presence of DGs with binary exchange market algorithm whose merit over the other heuristics is to consider all the problem specifications only in one multi-objective function. Despite previously reported works that have used various high-priced protective devices for achieving the enhanced reliability this research only utilizes inexpensive manual sectionalizers with the least possible cost in the presence of DGs. Two standard test cases IEEE 33-bus test system and RBTS-Bus 4 and one realistic test case feeder-8 of MeshkinShahr Town power distribution network in northwest of Iran are used to demonstrate the effectiveness of the proposed technique in theory and real-world applications. Thus, utilities may take the advantage of the proposed method for configuration of sectionalizers in their own local power distribution systems throughout the country.