Bus Radial Distribution System Research Articles

Atom Search Optimization Algorithm for Allocating Distributed Generators in Radial Distribution Systems

In recent years the use of renewable energy sources (RES) by many power grid companies worldwide has increased significantly. The trend towards RES use is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. This paper introduces a hybrid approach to find the optimal location and size of distributed generations (DG) in the radial distribution system (RDS). The proposed approach is based on the atom search optimization (ASO) technique to calculate the optimal allocation of DGs and power loss sensitivity (PLS) index to obtain the best buses for DGs installation in RDS. The presented approach is applied to IEEE 33-bus RDS to increase voltage profile and minimize the power losses. The results obtained prove that the developed approach can be highly effective in integrating DG into RDS compared to many other methods in the literature.

A metaphor-less based AI technique for optimal deployment of DG and DSTATCOM considering reconfiguration in the RDS for Techno-Economic benefits

The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial Intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy.

Optimal Operation of Smart Distribution Networks using Gravitational Search Algorithm

This paper proposes a methodology for an optimal operation of smart distribution network considering the network reconfiguration, distributed generation (DG) units allocation and optimally placing the shunt capacitors for reactive power compensation. In this work, the total power losses minimization objective is considered. By optimizing this objective, it can also results in the reduction of voltage deviation. The proposed problem is solved using evolutionary-based gravitational search algorithm (GSA). Simulation studies are performed on 33 bus radial distribution system (RDS). Simulation results reveal that there is a drastic reduction in the power losses by utilizing the network reconfiguration, DG allocation, and reactive power compensation.

User-Defined Dual Setting Directional Overcurrent Relays with Hybrid Time Current-Voltage Characteristics-Based Protection Coordination for Active Distribution Network

With the penetration of renewable energy sources based distributed generation (RES-DG) in the distribution network (DN), the operating time of protection relays becomes a major concern to avoid the mis-coordination and nuisance tripping of RES-DG. It is due to the bidirectional flow of current, variance in the fault current level, i.e. high fault current in case of Squirrel cage induction generator (SCIG) based RES-DG, and low fault current for an inverter-based RES-DGs. This paper proposes a novel User-defined dual setting direction overcurrent relay with hybrid time current-voltage (UDDOR-TCV) characteristics to intensify the proposed scheme’s flexibility without any communication assistance for radial DN. The proposed model is formulated in a constrained non-linear optimization fashion and solved with the MINLP solver of general algebraic modelling system(GAMS) software to determine the optimal relay settings. The propounded protection scheme is simulated on IEEE-33 bus radial distribution system and a local 40-bus system hosting the multiple SCIG and PV based RES-DGs at optimal locations. Detailed numerical studies are carried out to show the performance of the proposed scheme. 66.848% and 68.26% reduction in relay operation time with zero mis-coordination is achieved with SCIG-WTG case and PV-units case for the IEEE-33 bus system and a similar reduction in operation time is 68.937% and 66.563% for Local-40 bus system. Moreover, the relays numbers are reduced by 32.692% and 39.7% for IEEE-33 and Local-40 bus systems, respectively, due to the forward/reverse characteristics capability of the proposed UDDOR-TCV. The performance of the proposed scheme is also evaluated on IEEE-8 bus meshed DN. The results are compared with the existing protection schemes in recent literature.

A comparative analysis for optimal placement and sizing of distributed generator in grid connected and islanded mode of microgrid

Distributed generation offers a promising solution to the ever-increasing power demand. Other than supplying power it also reduces the power losses and improves the voltage regulation of the line. The performance of the distribution network with DG gets affected by its location, number of generators, and size. This paper describes separate techniques for estimation of optimal location and size of the DG's using power loss index (PLI) method and radial correction factor (RCF) in IEEE-15 bus radial distribution system. The performance of each method is tested for both the operating modes, i.e., grid connected and islanded mode of microgrid. Further, the optimal placement of capacitors is also done using voltage stability index (VSI) method for both the configurations. The performance of the proposed techniques is analysed by comparing the voltage profile and real power loss before and after the DG placement in the network. A comparative study of optimal location and size of DG is also presented considering the features; minimum bus voltage, real and reactive power losses using genetic algorithm (GA) and artificial bee colony (ABC) algorithms.

Load Flow Analysis With Newton–Raphson and Gauss–Seidel Methods in a Hybrid AC/DC System

In this study, a dc system was added to the IEEE 33-bus radial distribution system (RDS) test system using voltage-source converters (VSCs), and a hybrid ac/dc system was designed. For this designed system, a load flow analysis was made under the MATLAB platform. The Gauss-Seidel and Newton-Raphson methods, which are widely used in load flow analysis, are used. Comparisons of these methods were made according to the number of iterations, total line losses, and active and reactive powers generated and consumed in terms of different tolerance values. In the results obtained from the load flow analysis studies, the powers produced by the generator are calculated as close to each other according to the load demands for both methods. However, it was seen that the least iteration number and the least power loss were obtained by the Newton-Raphson method. According to the results obtained as a result of applying the power flow algorithm used in the hybrid ac/dc system, it has been proven that this algorithm is successfully applied to the solution of a power system problem. In addition, the results show that the designed system is accurate and reliable.

An enhanced fuzzy logic based strategy for non-disruptive load shedding and the predictive energy management system

Electrical energy capacity mismatch and immense power shortages due to energy deficit in the power system keep on increasing because of inadequate power capacity, insufficient investment, demand growth, and the rise in living standards. To maintain a balanced relationship between production and consumption, several load shedding schemes have been implemented for load management in the last few years, but their inconsistency poses a challenge. This research work will apply the fuzzy logic algorithm (FLA) and optimise the existing conventional power systems with and without dispersed generators (DGs). In critical circumstances, the algorithm will find node sets or desired locations where limits are violated and system operators may request the utility or industrial customer to shed a required amount of load by operating distributed generators to maintain its system integrity. The performance and suitability of the proposed scheme are demonstrated and proved by testing on a standard 33-bus radial distribution system in MATLAB Simulink having an objective function of minimising its total load curtailed and reducing power system losses by improving its system stability.

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system loss is taken as single-objective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm.

Optimal Allocation/Sizing of DGs/Capacitors in Reconfigured Radial Distribution System Using Quasi-Reflected Slime Mould Algorithm

Increased load demands worsen distribution system problems such as greater line losses, voltage deviation, and a plethora of other concerns. This current work presents an approach stressing simultaneous optimal allocation and sizing of capacitor banks and distributed generations, as well as optimal radial distribution system (RDS) reconfiguration, to address these difficulties. The above objectives are accomplished through the maiden application of the proposed quasi-reflection-based slime mould algorithm (QRSMA). The efficacy of QRSMA is established by testing it on different benchmark functions. A new modified backward forward load flow approach is also proposed and validated by comparing its results to those obtained using MATPOWER software for IEEE 69, 85, and 118 bus RDSs. The proposed load flow technique is independent of the sequential bus numbering scheme and may be applied to any RDS network topology. The proposed QRSMA is tested on 118 bus RDS and to prove its effectiveness; its results are compared to those of other studied algorithms. The study takes into account both fixed and variable loading scenarios. A cost-benefit analysis of the strategy is also performed in order to make the methodology more realistic.

DG Allocation in Distribution Networks with Considering of Voltage Stability Improvement and Loss Reduction

The improvement of the line Load-ability (LL) for voltage stability establishes the main criterion for distribution networks. The Distributed Generation (DG) resources play an important role in the supply of active and reactive power loads, bus voltage profiles, and voltage stability in distribution systems. In this paper, a new technique has been proposed for optimization of the location placement and sizing of DGs, considering the Generalized voltage Stability Index (GSI) to determine the maximum load-ability. Also, an analytical method has been applied to infer the effects of DGs on the distribution systems’ characteristics. The study also considers three types of DG modes, which are voltage control mode (PV mode), constant power mode (PQ mode), and also voltage control mode with reactive power constraint (PV mode with VAR constraint). The proposed method is applied to 12-bus, modified 12-bus, 69-bus and 94-bus radial distribution systems.

Analysis of Photovoltaic & Battery Energy Storage System Impacts on Electric Distribution System Efficacy

Uncertain nature of renewable energy sources like solar irradiation poses a serious concern of loss of power supply reliability. Battery energy storage (BES) system helps in improving system reliability by storing surplus energy generated and supplying the load in case of energy deficit. Thus BES allows improvement of microgrid performance and reduces operational cost by increasing the utilization of renewable energy sources. This paper presents an energy management strategy (EMS) to dictate the power flow among photovoltaic (PV) panels, BES and the load considering a proposed time-of-use (TOU) pricing as the control factor. Its efficacy in improving power supply reliability as well as power quality issues of a 69-bus radial distribution system (RDS) is evaluated from technical performance indices like power loss, voltage deviation index and security margin and economic performance considering costs of power import from the grid and active power loss and financial benefit from battery discharge. Grasshopper Optimization Algorithm (GOA), is used to optimize the sizes and placements of three PV-BES units to minimize an objective function aptly formulated combining the technical performance indices using weighted sum method. The results are contrasted against another two cases of with only PV and without PV and BES integration. Finally, the proposed system is analysed from economic perspective and the benefits obtained are compared. The results are evident of both technical and economic advantages of integrating both PV and BES units at optimal locations (load bus). The optimization results obtained from GOA have been compared with that from Genetic Algorithm (GA). GOA proves to be fast, effective and reliable in resolving power flow optimization problem.

Exploration of DG Placement Strategy of Microgrids via FMFO Algorithm: Considering Increasing Power Demand and Diverse DG Combinations

Distributed energy resource (DER) has been widely deployed, and distributed generation (DG) can complement the distribution system. Favorable DG deployment provides the grid-connected microgrid (MG) with stable voltage and reduces emission and power generation costs. DGs are considered distributed feeders, and MG is required to be operated under the optimal state. Reconfiguration is a practical approach to optimizing resource allocation. The optimal global solution is obtained via optimization algorithms. In this paper, three objectives are defined, namely, minimization of economic cost (ECC), emission cost (EMC), and voltage deviation (VD). Consequently, a fuzzy moth-flame optimization (FMFO) algorithm is proposed to coordinate the interests of multiple objectives. Moreover, the simulation is conducted based on the standard IEEE 33-bus radial distribution system (RDS), under which the impact of deployment of various DG type and quantity on the MG is explored. In particular, diverse DG combinations are tried under the increasing power demand, and a high-stable voltage strategy is proposed to meet the specific demands. The simulation results reveal that: (1) the DG type has a significant impact on ECC and EMC; (2) penetration level of DG shows a positive-like relationship with the MG stability; and (3) the proposed FMFO algorithm exhibits an efficient performance in convergence.

Performance Enhancement for Microgrids Under the Demand Uncertainties with the Presence of Multiple DGs Through Stochastic Ranking Algorithm

The integration of Distributed Generators (DGs) in the reconfigurable microgrid is evolved to enhance the power delivery performance. The DGs are categorized as controllable and uncontrollable based on their power generation control behaviors. In addition to the control behavior, under the uncertain conditions of the microgrid, the performance of DGs is limited due to their respective drawbacks. Therefore, the identification of the most suitable DG under uncertain situation becomes more critical. This paper investigates the performance behavior of the hybrid DG, which uses the benefits of two kinds of DGs and overcomes their limitations. This work addresses five objective functions namely minimization of power loss, total generation cost, total emission cost and voltage deviation, and the maximization of the percentage DG penetration. The performance investigation of DGs is carried out for the system under five different test cases, including the uncertainty in power supply and demand. The modeling of the demand variation is done with the support of Probability Density Function (PDF). The many objective multi-indicator Stochastic Ranking Algorithm (SRA) is used for the optimization purposes. The simulations are performed on IEEE 33-bus Radial Distribution System (RDS) to assess the capability of the proposed investigation. Furthermore, the proposed strategy is applied under 24-h timely varying load pattern of IEEE 33-bus RDS to verify its applicability in uncertain load conditions. Moreover, the performance enhancement realization with the presence of hybrid DG is observed from the obtained simulation results.

Electric vehicles reactive power management and reconfiguration of distribution system to minimise losses

This study investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in smart distribution system integrated with electric vehicles (EVs) and wind power generation. A two-stage framework is proposed for real and reactive power management and DFR. In the first stage, demand dispatch of EVs is coordinated to utilise available wind power and minimise the total load variations. In the second stage, a comprehensive problem formulation is developed to minimise the losses in the system with simultaneous EVs reactive power management and DFR. A genetic algorithm is used to solve this two-stage optimisation problem. A case study of 33-bus radial distribution system is presented to highlight the efficacy of proposed approach. The obtained results are quantitatively assessed in terms of losses and node voltages. A comparative benchmarking analysis is presented to substantiate the role of DFR and reactive power capability of EVs in improving the system performance.

전력손실 최소화를 위한 심층 강화학습 기반 배전계통 재구성

Distribution network reconfiguration (DNR) is a technique that changes the status of sectionalizing and tie switches for various purposes such as loss minimization, voltage profile improvement, load leveling, and hosting capacity increase. Although previous algorithms for DNR show good performance, they still have practical limitations. Most of the algorithms assumed that a central coordinator knows all parameters and/or perfect states in a distribution network. Reinforcement learning which is a model-free optimization technique can be a key way to overcome these limitations. This work proposes a DNR scheme using deep reinforcement learning to minimize power loss defined by the amount of line loss and renewable energy curtailment. We model the DNR problem as a Markov decision process (MDP) problem and apply the reinforcement learning algorithm to solve this problem in real-time. Simulation result using 33-bus radial distribution system shows that the proposed scheme shows similar performance compared to an existing method which uses all information on the distribution network.

Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

Simultaneous Reconfiguration and Optimal Capacitor Placement Using Adaptive Whale Optimization Algorithm for Radial Distribution System

The losses in the distribution networks due to the line resistance decrease the overall efficiency of the power distribution. Reducing the power losses and regulating the voltages within the limits are necessary to provide quality power to the consumers. The power loss can be minimized by optimum network reconfiguration and the placement of the capacitors. Considering independent network reconfiguration and placement of the capacitor is not effective during heavy loading conditions. This paper proposes a simultaneous network reconfiguration and capacitor placement in radial distribution network to minimize the real power losses, operating cost and to improve the bus voltages. The Johnson’s algorithm is used to find the minimal spanning tree during the network reconfiguration and an adaptive whale optimization algorithm is used as an optimization method. The proposed methodology is tested on standard IEEE 33-bus and 69-bus radial distribution systems. The effectiveness of the proposed method is validated by comparing the results with previous result reported in the literature in terms of cost saving and loss reduction.

A Mathematical Model for the Scheduling of Virtual Microgrids Topology into an Active Distribution Network

This article presents a method based on a mathematical optimization model for the scheduling operation of a distribution network (DN). The contribution of the proposed method is that it permits the configuration and operation of a DN as a set of virtual microgrids with a high penetration level of distributed generation (DG) and battery energy storage systems (BESS). The topology of such virtual microgrids are modulated in time in response to grid failures, thus minimizing load curtailment, and maximizing local renewable resource and storage utilization as well. The formulation provides the load reduced by bus to balance the system at every hour and the global probability to present energy not supplied (ENS). Furthermore, for every bus, a flexibility load response range is considered to avoid its total load curtailment for small load reductions. The model has been constructed considering a linear version of the AC optimal power flow (OPF) constraints extended for multiple periods, and it has been tested in a modified version of the IEEE 33-bus radial distribution system considering four different scenarios of 72 h, where the global energy curtailment has been 27.9% without demand-side response (DSR) and 10.4% considering a 30% of flexibility load response. Every scenario execution takes less than a minute, making it appropriate for distribution system operational planning.

A strategy for PV and BESS allocation considering uncertainty based on a modified Henry gas solubility optimizer

Recently, solar photovoltaic (PV) is becoming widespread overall the world as it is a renewable free source of energy. PV alone is considered as a non-dispatchable source as it relies on variable source during the day. Therefore, the primary goal of this paper is to integrate the battery energy storage (BES) with PV as a dispatchable source in radial distribution system (RDS). In addition, this paper proposes a modified version of Henry gas solubility optimization algorithm (modified HGSO) to improve the performance of the conventional HGSO algorithm. This modified version is created by inserting the simulated annealing (SA) algorithm into the conventional HGSO algorithm. The proposed algorithm is used to determine the best size of PV and BES, considering the probabilistic of PV generation and time-varying load, in order to minimize the total system power loss. IEEE 69-bus RDS is used to demonstrate the effectiveness of proposed algorithm. The results show that integration of PV and BES in RDS reduces the system power loss, enhances the system voltage and increases the system capacity. The results also prove that the proposed algorithm is highly effective in integrating multiple PV and BES units in distribution system compared with the conventional algorithms.

Optimal placement of protective devices and switches in a radial distribution system with distributed generation

Optimal placement of protective devices and switches in the distribution system improves the system reliability at the cost of increased investment. In this study, a new bi-directional analytical model has been developed to solve the problem of optimal placement of protective devices and switches in various zones of a distribution system with distributed generation (DG). The model determines the optimal number and locations of reclosers, switches, ‘fuse-save fuses’ and ‘fuse-blow fuses’ for increasing the system reliability while reducing the investment and outage costs, considering system data uncertainties. For considering uncertainties in load data, failure rates (temporary and permanent) and repair rates, the model utilises three-point estimate method. The formulated problem has been solved for 69-bus distribution system and radial distribution system connected to bus 4 of the reliability busbar test system, using genetic algorithm optimisation technique. After analysing the test results, it is concluded that considerable profit to the utility is achieved by optimal placement of protective devices and switches in various zones of the distribution system with DG units.