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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.

With the growing scale of power grids, integrated power grids often contain multiple areas. When the control centre of each regional grid conducts an assessment of local voltage stability, the calculation is always based on the local regional power grid model. However, less consideration is given to a detailed model of the entire network, which may lead to a large calculation error. Under the premise of ensuring the data and information security of Supervisory Control and Data Acquisition between different regional power grid operation control centres, the purpose of this paper is to reduce calculation error simply by using the data of a local power network.

According to the calculation methodology of “decomposition and coordination” and the power balance equation of an interconnected power grid, an improved radial equivalent independent (REI) equivalent method, which can reflect the dynamic characteristics of interconnected power grids to a certain extent, is proposed in this paper. A mathematical model of multi-area-grid L indicator synergic computing is derived as well.

With the calculation of Institute of Electrical and Electronics Engineers (IEEE) standard grids and an actual grid model, it is proven that the method proposed in this paper can significantly improve the accuracy of the regional power grid L indicator calculation and achieve the synergic computing of a multi-area power system L indicator, without an increase in data interaction among the regional power grids.

The indicator of voltage stability among multi-area was obtained by using the improved REI equivalent method with the change of the load participation factor. Particularly, the coordinated calculation method can be implemented on a local power grid without knowledge of all the parameters of its interconnection, which can avoid possible leakage of confidential data and information of the system owners.

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

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.

Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation.

In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation.

Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.

For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem.

The earlier methods are more resilient to improvements such as load shift and path change. This results in problems such as a voltage drop and a high reactive flux. In addition, due to the delay, congestion or interruption of the transmission, the system cannot receive all phasor measurement unit (PMU) measurements at the relevant time as well as the presence of noise in the received data.

With the development of wide area measurement system technologies, it seems to be possible to track voltage stability online via time-stamped PMUs. As the voltage instability causes a voltage decomposition, voltage instability is one of the most important problems when monitoring the power supply.

This harmonic distortion significantly decreases the data quality in the grid. As a result, instability ascertainment based on PMU has been suggested as a method for detecting voltage instability in power systems monitored with PMU. In addition, a technique called instability amendment via load dropping has been proposed to keep the device from collapsing due to voltage failure.

To improve the power output, the power prominence melioration technique was developed. This proposed system has been implemented in MATLAB Simulink and compared with the recent researches.

The purpose of this study is to obtain the optimal frequency for low-frequency transmission lines while minimizing losses and maintaining the voltage stability of low-frequency systems. This study also emphasizes a reduction in calculations based on mathematical approaches.

Telegrapher’s method has been used to reduce large calculations in low-frequency high-voltage alternating current (LF-HVac) lines. The static compensator (STATCOM) has been used to maintain voltage stability. For optimal frequency selection, a modified Jaya algorithm (MJAYA) for optimal load flow analysis was implemented.

The MJAYA algorithm performed better than other conventional algorithms and determined the optimum frequency selection while minimizing losses. Voltage stability was also achieved with the proposed optimal load flow (OLF), and statistical analysis showed that the proposed OLF reduces the frequency deviation and standard error of the LF-HVac lines.

The optimal frequency for LF-HVac lines has been achieved, Telegrapher’s method has been used in OLF, and STATCOM has been used in LF-HVac transmission lines.

The purpose of this paper is to present an improved approach to reactive power planning in electric power systems (EPS). It is based on minimization of a transmission network's active power losses. Several operating conditions have to be fulfilled to ensure stable operation of an EPS with minimal power losses. Some new limitations such as voltage instability detection and generator capability curve limit have been added to the existing method in order to improve the reliability of reactive power planning. The proposed method was tested on a model of the Slovenian power system. The results show the achievement of significant reduction in active power losses, while maintaining adequate EPS security.

Optimal voltage profile has to be found in order to determine minimal possible active power losses of EPS. The objective function, used to find the optimal voltage profile, has integer and floating point variables and is non‐differentiable with several local minima. Additionally, to ensure secure operation of EPS, several equality and inequality boundaries and limitations have to be applied. Differential evolution (DE) was used to solve the optimization problem.

Corresponding reactive power planning can significantly reduce active power losses in EPS. However, such planning can affect the security of EPS, therefore, several additional constrains have to be considered. The presented constrains considerably improve the operational security of EPS.

DE was used to solve the minimization problem. Although this method has proven to be fast and reliable, it is theoretically possible that the obtained solution is not global minimum.

Novel approach to voltage security constrained reactive power planning with additional nonlinear constrains, such as generator capability curves and voltage instability detection.

This research studies the impact of introducing distributed generators (DGs) into a distribution network. The aim of this paper is to optimally site DGs based on economic, environmental and reliability indices are presented.

The considered network was modelled by using the network’s line parameters and capacity of the load bus with the help of Power System Analysis Toolbox. The location of the DG is based on voltage stability index and power loss reduction index. The DG energy sources considered are the diesel generator, solar photo-voltaic (PV) and wind generator, and the objectives were to minimize cumulative cost while maximizing reliability of the network. The Advanced Interactive Multidimensional Modelling System was used for the mathematical modelling.

The obtained results in the cases of introducing renewable energy into a network improves network performance. The benefits of renewable energy on the distribution network measured in terms of electricity production cost, gas emission cost, fuel cost and value of energy not supplied were positive. The research also showed that the total benefit of renewable energy reduces as the price of the renewable generators increases.

This paper introduces a new approach to determining the optimal location of DG for reducing line losses and improved voltage profile. A new cost modelling function based on external grid power transfer cost, technical losses and cost because of the various energies source is also introduced.

In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness.

This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections.

Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models.

This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.