Search

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics. [ABSTRACT FROM AUTHOR]

Distribution system is known with high power losses due to his topology and its composition. Introduction of shunt capacitor into the system can brings about power loss minimizations, voltage profile and stability index improvement and loss cost reduction. In order to achieve this, shunt capacitor optimal location and sizing must be ensured as wrong location may result in unwanted effect on the system. This paper aims to minimize the total power loss and cost and also to enhance the voltage profile and stability index of distribution systems by optimal allocation of shunt capacitor, using whale optimization algorithm (WOA). The technique was validated on IEEE 33-bus system and also applied to a practical Nigerian Dada 46-bus network. The results proved that the technique proposed reduced the total power losses and cost. Also, voltage profile and voltage stability index were significantly improved.

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage. Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks. The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper. The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system. Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS). In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation. Also, considering the effect of uncertainty sources complicate the optimization problem. Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem. The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms. According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 25.5 and 5% compared to baseline values. After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values. [ABSTRACT FROM AUTHOR]

The overall efficacy of power distribution is reduced through power loss in distribution systems owing to branch resistance. For providing quality power to customers, power losses must be diminished, and voltages must be enhanced. This paper introduces a novel improvement for the traditional Salps Swarm Algorithm (SSA) called Enhance SSA (ESSA) for selecting Optimal Shunt Capacitor Ratings and their Locations (OSCRL). ESSA is viewed as an enhanced version of SSA with the aim of improving the SSA's poor performance. A multi-objective function was designed in this work for reducing real power loss and Total System Cost (TSC), as well as, improving feeder voltage profile and maximizing Net Savings (NSs) per year under security restrictions. For validating and evaluating the proposed ESSA technique, a practical 33-bus local Iraqi and standard IEEE 69-bus Radial Distribution Systems (RDSs) were used as test systems through utilizing MATLAB. The experimental results attained by the proposed ESSA are compared with other approaches currently available in the literature for addressing OSCRL problem. The experimental results prove the efficacy and supremacy of the ESSA to other literature algorithms with respect to diminishing power loss as well as TSC, reinforce bus voltage profile and increase NSs per year for multiple RDSs. [ABSTRACT FROM AUTHOR]

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem. [ABSTRACT FROM AUTHOR]

Installing capacitors to correct the power factor at particular locations is one way to enhance power system reliability. This paper offers a new formulation to address the issue of optimal placing capacitors. The proposed formulation considers reliability impact, in addition to the transient switching events. This is reflected in the cost minimization objective function, where the reliability calculations are considered to gauge the impact when capacitors are installed in the power system. The formulated problem is solved using genetic algorithm (GA), to minimize costs of capacitor installation, energy loss and failure impact. The new formulation has been examined on a test network for evaluating how effective the proposed approach is. The solution also considers the impact of uncertainties as well as future growth in the system, to ensure that it is optimal. The results evidence that the proposed approach is robust and effective in enhancing system reliability in the face of uncertainties and for future growth. [ABSTRACT FROM AUTHOR]

Reduction in power loss while maintaining the acceptable voltage profile has become a challenge for distribution system operators due to expanded living standards. Properly sized shunt capacitors (SCs) allocated at suitable locations of the distribution system can enhance its performance by tackling the power quality issues and foster multiple technical and economic benefits. Most of the existing research work in this domain is accomplished at fixed loading conditions without incorporating purchasing, installation, operation and degradation costs of the SCs. In this work, an intelligent metaheuristic polar bear optimization algorithm (PBOA) is applied to solve the optimal capacitor placement and sizing problem in the radial distribution system (RDS) at various loading conditions. By formulating multiple objective functions for the annual operating cost (AOC), this work incorporates the cost of real power loss ( P loss ) and various costs associated with SCs. The proposed technique is tested on various IEEE standard bus systems under different loading conditions. The proposed PBOA reduces AOC and P loss of RDS while maintaining tolerable voltages at systems' buses. The reductions of 51%, 35%, 26% and 53% in P loss are observed for IEEE standard 15-, 33-, 34- and 85-bus systems, respectively, at 100% loading from their uncompensated cases. Similarly, reductions in AOCs for the same systems at 100% loading are 32%, 25%, 16% and 45%, respectively. These results prove superiority of proposed method over recent state-of-the-art algorithmic approaches. The results of this work can be useful for distribution system operators in achieving reliable and efficient operation of the power grid. [ABSTRACT FROM AUTHOR]

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine–cosine algorithm (SCA) to constitute the algorithm's main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system's total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem's search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem. [ABSTRACT FROM AUTHOR]

Optimal placement and sizing of shunt capacitors are important issues to improve voltage profile and reduce power losses in distribution networks. In this regard, dealing with stochastic nature of wind farms (WFs), the issue becomes more complex in wind‐integrated radial distribution networks. This study aims at optimal siting and sizing of shunt capacitors considering wind uncertainty. Application of probabilistic approach causes to access more real and extensive information about the network. To do so, Monte Carlo simulation (MCS) is employed to assess stochastic variation of wind farm output. As the salient feature of this research, K‐means‐based data clustering approach is utilised, in which all data‐points of the output power of WFs are bunched into desired clusters to ease the solving of simple MCS. The voltage profile and power losses are analysed to do the optimal siting and sizing of capacitors. In order to assess the effectiveness of the proposed method, results are compared with the simple MCS that proves the efficiency of the proposed method from the viewpoint of computation time. [ABSTRACT FROM AUTHOR]

In this brief, the analysis of mixed-element structures formed with shunt capacitors separated by commensurate transmission lines is performed first time in the literature. First, a low-pass lumped-element ladder network is considered. Then the series inductors are replaced with commensurate transmission lines. As a result, a practically important mixed-element structure is obtained. Then the description of the structure by means of two frequency variables (one for shunt capacitors and one for transmission lines) is detailed: Explicit expressions for the coefficients of the descriptive two-variable polynomials in terms of the coefficients of the single variable boundary polynomials are derived for various numbers of elements, which are obtained first time in the literature. Finally, a mixed-element broadband matching network is designed to illustrate the usage of the obtained expressions. If it is preferred not to have shunt capacitors, they can be replaced with open-ended stubs via Richard’s transformation. So the resultant circuit is extremely suitable for microstrip fabrication. [ABSTRACT FROM AUTHOR]

• Roof top photovoltaic power is estimated from solar insolation models. • Gravitational search algorithm is used for optimum DG sizing and allocation. • Practical 52 bus distribution system is chosen for case study. • Performance analysis is carried out for summer, rainy and winter seasons. • The CO 2 emission reduction is estimated based on substation power reduction. In this paper a Gravitational Search Algorithm (GSA) based multiobjective approach is proposed for optimum sizing and allocation of distributed generations (DGs) and shunt capacitors (SCs) for distribution systems integrated with roof top photo voltaic systems (PV). The capacity of roof top Solar PV systems is designed based on the basic energy consumption requirements of a two-bed room apartment. A normalized multiobjective function is formulated considering the objectives of minimizing substation real power, reactive power, real power loss minimization and voltage profile improvement. The optimum DGs and SCs for three seasons summer, rainy and winter are obtained using GSA by minimizing the multiobjective function to reduce the burden on the substation and simultaneously improving the distribution system performance. A practical 52-bus, 11 kV rural radial distribution system is used to demonstrate the simulation results to show the advantage of the proposed methodology in improving the distribution system performance. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine−cosine algorithm (SCA) to constitute the algorithm’s main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system’s total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem’s search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem.

Power systems are becoming more complex day by day due to stressed situation in a distribution system, augmented growth in population and high demands of electrical power. In India, the distribution losses are around 20-25%. Distributed Generation (DG) units are broadly used to reduce the power losses in distribution system. To optimise the benefits of DGs, it is essential to determine its appropriate size and location. A new mathematical expression, power voltage sensitivity constant, has been formulated to determine its size and location. In this paper, two types of DG units (PV module and shunt capacitor) are employed for power loss reduction. The size of DG units (PV module) has been restricted to 50% of total system load in the paper. The proposed approach is tested on standard IEEE 69 bus distribution system and 130 bus real distribution system of Jamwa Ramgarh village, Jaipur, India. The results obtained from standard systems are compared with latest optimisation techniques to show the effectiveness and robustness of the proposed approach. The results of real systems are also encouraging. [ABSTRACT FROM AUTHOR]

Technologies such as the integration of distributed generations (DGs) and shunt capacitors (SCs) are key for realising smart distribution systems[AQ ID=Q2]. These technologies may be coordinated together to get better solutions so that distribution systems can achieve optimum performance. This study proposes a long‐term distribution system planning methodology to determine the optimal sizing and siting of SCs and mix of dispatchable and intermittent DGs. More practical formulations are suggested while considering realities of modern distribution systems with reference to the stochastic nature of load demand and power generation among distribution buses on account of diversity, variability and uncertainty. The long‐term costs in the proposed planning model include several techno‐economic and social objectives pertaining to investment, operation and maintenance of these distributed resources and the revenue generated by sale of electricity to customers and the utility grid. The proposed planning model is more practical as it fully considers the system uncertainties and variability in load and power generation which are efficiently handled by introducing deterministic self‐adaptive uncertainty model. The case study on a standard test distribution system being modified by wind turbines, photovoltaic generators and microturbines demonstrates the effectiveness of the proposed methodology. The results of study are investigated and presented. [ABSTRACT FROM AUTHOR]

This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation. [ABSTRACT FROM AUTHOR]

A two‐layer control method is proposed for voltage and reactive power control in harmonic polluted distribution network with penetration of photovoltaic (PV) systems. Optimal scheduling of load tap changer and shunt capacitors for minimising energy losses and improving the power quality simultaneously are performed using P‐particle swarm optimisation (PSO) optimisation method. Here, the minimising cost of real power losses and improving the power quality criteria have been pursued as the goals of an optimisation problem. Considering load and PVs output forecast, the first‐layer control determines the optimal reactive power and control settings for all mechanical controllers. Hourly errors of load and power forecasts and mechanical control setting of the first layer are used to estimate optimised reactive power of PV in order to achieve maximum voltage regulation, reduce network losses and total harmonic distortion (THD). These data are trained a neural network (NN) to estimate optimised PV reactive power. This NN in the second layer is used to optimise the online reactive power setting based on online PV power. For more practical applications of the proposed method, simulation is carried out in a large distorted 37‐bus distribution system. The algorithm will increase use of renewable energies, reduce voltage fluctuations, THD, and wear and tear of mechanical equipment. [ABSTRACT FROM AUTHOR]

This article presents the significance of efficient hybrid heuristic search algorithm(HS-PABC) based on Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) in the context of performance enhancement of distribution network through simultaneous network reconfiguration along with optimal allocation and sizing of distributed generators and shunt capacitors. The premature and slow convergence over multi model fitness landscape is the main limitation in standard HSA. In the proposed hybrid algorithm the harmony memory vector of HSA is intelligently enhanced through PABC algorithm during the optimization process to reach the optimal solution within the search space. In hybrid approach, the exploration ability of HSA and the exploitation ability of PABC algorithm are integrated to blend the potency of both algorithms. The box plot and Wilcoxon rank sum tests are used to show the quality of the solution obtained by hybrid HS-PABC with respect to HSA.The computational results prove the integrated approach of the network reconfiguration problem along with optimal placement and sizing of DG units and shunt capacitors as an efficient approach with respect to power loss reduction and voltage profile enhancement. The results obtained on 69 and 118 node network by hybrid HS-PABC method and the standard HSA reveals the effeciency of the proposed approach which guarantees to achieve global optimal solution with less iteration. [ABSTRACT FROM AUTHOR]

A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. A multi-objective function index (MOFI) approach is used for assuring the power quality (PQ) through enhancing the voltage level in addition to minimizing the power losses of the system and the whole operating cost of the grid. The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance. The obtained results are compared with the Grasshopper Optimization Algorithm (GOA), and the hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (PSOGSA). The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs’ and SCBs’ locations and capacities simultaneously. An Egyptian practical case study at different load levels via different scenarios including the control operation within 24 h is considered.

For compensating reactive power, shunt capacitors are often installed in electrical distribution networks. Consequently, in such systems, power loss reduces, voltage profile improves and feeder capacity releases. However, finding optimal size and location of capacitors in distribution networks is a complex combinatorial optimisation problem. In such problem, an objective function which is usually defined based on power losses and capacitor installation costs should be minimised subject to operational limitations. In this study, a newly developed metaheuristic technique, named crow search algorithm (CSA), is proposed for finding the optimal placement of the capacitors in a distribution network. CSA is a population‐based technique inspired by the greedy behaviour of crows in finding better food sources. The main reasons of using CSA are its easy implementation, few parameters to adjust, fast convergence speed and high efficiency. In the case studies, simulated results indicate that the proposed CSA produces more accurate results than the other studied search methods. [ABSTRACT FROM AUTHOR]

In this work a combination of fuzzy multiobjective and genetic algorithm (GA) based approach is proposed for optimal shunt capacitor placement to improve the substation power factor near unity, reduce the real power loss, and reduce the burden on the substation and to improve the voltage profile of the distribution network. In order to obtain best nodes for capacitor placement, a sensitivity index based on real power loss reduction and voltage profile improvement is considered. In the present work, an attempt is made to make reactive current component drawn by distribution network through substation is nearly zero such that power factor at the substation will be near unity. A fuzzy multiobjective function is formed considering substation reactive current component reduction, real power loss reduction, branch current constraint limit, minimum and maximum voltage limit satisfaction. The fuzzy multiobjective function is maximized using GA for obtaining the optimum sizing of fixed and switched shunt capacitors. Simulation results are shown to demonstrate the advantage of the proposed method compared to optimal shunt capacitor placement based on annual energy savings method. [ABSTRACT FROM AUTHOR]

Maximum available power (MAP) is an index for analyzing power‐voltage stability in high‐voltage direct current (HVdc) converters. Earlier work on calculating MAP for voltage sourced converters (VSC) was not consistent with the methods used for line commutated converters (LCC). The Thévenin voltage source used in VSC‐HVdc analysis was set to 1.0 pu, but for LCC‐HVdc, it was calculated so as to provide rated conditions at the converter busbar. This study attempts to rationalize the approaches used for VSC‐HVdc and LCC‐HVdc calculations by making them mutually consistent. However, as the VSC can operate at much lower short‐circuit ratio (SCR) than can the LCC, the Thévenin equivalent voltage at low SCRs can be significantly larger than 1.0 pu. This would require an unreasonably large tap changer setting. As an alternative, the paper recommends that for calculating MAP for a VSC, an ac network equivalent be used where some reactive power is locally supplied by shunt capacitors. This renders a Thévenin voltage close to 1.0 pu. This study also considers the impact of the VSC's internal voltage limits on the MAP. The key results are validated using electromagnetic transients simulation. [ABSTRACT FROM AUTHOR]