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This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments.

This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities.

A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization.

Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated.

Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.

This research paper reports a novel genetic algorithm (GA)‐based approach for reconfiguration of radial distribution networks for real loss minimization and power quality improvement.

A fuzzy controlled GA has been used for efficient reconfiguration of radial distribution systems for loss minimization and power quality improvement. The special features of the proposed algorithm are: an improved chromosome coding/decoding for network representation so as to preserve the radial property without islanding any load after reconfiguration and an efficient convergence characteristics attributed to fuzzy controlled mutation.

The proposed network reconfiguration algorithm is very much effective in arriving at the global optimal solution (minimum loss network structure) because of efficient search of the solution space. Also, no invalid chromosomes are generated in the genetic evolution because of appropriate coding/decoding. The algorithm is found to be very much suitable for real time implementations.

This research paper provides the power distribution engineers with a computationally efficient approach for optimal operation of distribution systems.

The algorithm proposed in this paper is computationally much faster compared to most of the present day mathematical programming approaches for distribution system operation. This makes it very much attractive for online implementations in any radial distribution network.

This paper has proposed a novel chromosome coding/decoding technique for radial distribution system and a fuzzy logic‐based mutation probability controller for efficient search of global solution space to be used in GA‐based optimal operation of radial distribution systems.

To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented Kalman filter (UKF) and newly developed algorithm, termed Improved unscented Kalman Filter (IUKF) for IEEE-30, 33 and 69-bus radial distribution networks for load variations and bad data for two measurement noise scenarios, i.e. 30 and 50 per cent are shown.

State estimation (SE) plays an instrumental role in realizing smart grid features like distribution automation (DA), enhanced distribution generation (DG) penetration and demand response (DR). Implementation of DA requires robust, accurate and computationally efficient dynamic SE techniques that can capture the fast changing dynamics of distribution systems more effectively. In this paper, the UKF is improved by changing the way the state covariance matrix is calculated, to enhance its robustness and accuracy under noisy measurement conditions. UKF and proposed IUKF are compared under the cummulative effect of load variations and bad data based on various statistical metrics such as Maximum Absolute Deviation (MAD), Maximum Absolute Per cent Error (MAPE), Root Mean Square Error (RMSE) and Overall Performance Index (J) for three radial distribution networks. All the simulations are performed in MATLAB 2014b environment running on an hp core i5 laptop with 4GB memory and 2.6 GHz processor.

An Improved Unscented Kalman Filter Algorithm (IUKF) is developed for distribution network state estimation. The developed IUKF is used to predict network states (voltage magnitude and angle at all buses) and measurements (source voltage magnitude, line power flows and bus injections) in the presence of load variations and bad data. The statistical performance of the coventional UKF and the proposed IUKF is carried out for a variety of simulation scenarios for IEEE-30, 33 and 69 bus radial distribution systems. The IUKF demonstrated superiority in terms of: RMSE; MAD; MAPE; and overall performance index J for two measurement noise scenarios (30 and 50 per cent). Moreover, it is shown that for a measurement noise of 50 per cent and above, UKF fails while IUKF performs.

UKF shows degraded performance under high measurement noise and fails in some cases. The proposed IUKF is shown to outperform the UKF in all the simulated scenarios. Moreover, this work is novel and has justified improvement in the robustness of the conventional UKF algorithm.

In the vast majority of published papers, the optimal allocation of photovoltaic distributed generation (PVDG) units and reconfiguration problems are proposed along with the number of PVDG used in the simulation. However, optimisation without selecting the number of PVDG units installed in the distribution grid is insufficient to achieve a better operational performance of power systems. Moreover, multi-objective installation of PVDG units and reconfiguration aims to simultaneously relieve congestion problems, improve voltage profile and minimise the active and reactive power losses. Therefore, this paper aims to propose a new modified camel algorithm (NMCA) to solve multi-objective problems considering radial distribution system to achieve secure and stable operation of electric power system with good performance.

In this paper, the decision variables include the location and size of PVDG units with specific rang to determine the number of PVDG units needed to install and open network lines determined using NMCA based on the L_∞ technique. This also satisfies the operating and radial constraints. Furthermore, a benchmark comparison with different well known optimisation algorithms has been made to confirm the solutions. Finally, an analysis of the findings was conducted, and the feasibility of solutions was fully verified and discussed.

Two test systems – the institute of electrical and electronics engineers (IEEE) 33-bus and IEEE 69-bus, were used to examine the accuracy and effectiveness of the proposed algorithm. The findings obtained amply proved the efficiency and superiority of the NMCA algorithm over the other different optimisation algorithms.

The proposed approach is applied to solve the installation PVDG unit’s problem and reconfiguration problem in the radial distribution system, satisfying the operating and radial constraints. Also, it minimises active and reactive power losses and improves voltage profile.

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 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 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 this paper is to estimate the locational marginal price (LMP) at each distributed generation (DG) bus based on DG unit contribution in loss reduction. This LMP value can be used by distribution company (DISCO) to control private DG owners and operate network optimally in terms of active power loss.

This paper proposes proportional nucleolus game theory (PNGT)-based iterative method to compute LMP at each DG unit. In this algorithm, PNGT has been used to identify the share of each DG unit in loss reduction. New mathematical modeling has been incorporated in the proposed algorithm to compute incentives being given to each DG owner.

The findings of this paper are that the LMP and reactive power price values for each DG unit were computed by the proposed method for the first time. Network can be operated with less loss and zero DISCO’s extra benefit, which is essential in deregulated environment. Fair competition has been maintained among private DG owners using the proposed method.

PNGT has been used for the first time for computation of LMP in distribution system based on loss reduction. Incentives to each DG unit has have been computed based on financial savings of DISCO due to loss reduction. Share of active and reactive power generation of each DG unit on change in active power loss of network due to that DG unit has been computed with new mathematical modeling. The proposed method provides LMP value to each DG unit in such a way that the network will be operated with less loss.

Heuristic algorithms have been widely used in different types of optimization problems. Their unique features in terms of running time and flexibility have made them superior to deterministic algorithms. To accurately compare different heuristic algorithms in solving optimization problems, the final optimal solution needs to be known. Existing deterministic methods such as Exhaustive Search and Integer Linear Programming can provide the final global optimal solution for small-scale optimization problems. However, as the system grows the number of calculations and required memory size incredibly increases, so applying existing deterministic methods is no longer possible for medium and large-scale systems. The purpose of this paper is to introduce a novel deterministic method with short running time and small memory size requirement for optimal placement of Micro Phasor Measurement Units (µPMUs) in radial electricity distribution systems to make the system completely observable.

First, the principle of the method is explained and the observability of the system is analyzed. Then, the algorithm’s running time and memory usage when applying on some of the modified versions of the Institute of Electrical and Electronics Engineers 123-node test feeder are obtained and compared with those of its deterministic counterparts.

Because of the innovative method of step-by-step placement of µPMUs, a unique method is developed. Simulation results elucidate that the proposed method has unique features of short running time and small memory size requirements.

While the mathematical background of the observability study of electricity distribution systems is very well-presented in the referenced papers, the proposed step-by-step placement method of µPMUs, which shrinks unobservable parts of the system in each step, is not discussed yet. The presented paper is directly applicable to typical problems in the field of power systems.