Distributed Generation Allocation Research Articles

Study of reducing losses, short-circuit currents and harmonics by allocation of distributed generation, capacitor banks and fault current limiters in distribution grids

The growing interest for researches that focus on renewable energies, as well as the increase in funding for this sector, is thanks to the advantages of renewable energy namely the reduction of environmental impacts and their great reliability. However, to maintain the correct operation of the electrical grid and its quality, it is essential to study the impacts that these generating sources have on the electrical system. The present research paper presents a novel methodology that considers the optimal allocation of photovoltaic distributed generation, capacitor bank, and fault current limiters reactors in distribution feeders, whose purpose is minimizing simultaneous the short-circuit current, active losses and harmonic distortion rate of distribution feeders. For this, a Multi-objective Gray Wolf Optimize has been applied and tested in three different distribution feeders. The result demonstrated that during the entire optimization process, the greatest reduction of the analyzed parameters, which are losses, harmonic distortion rate and short circuit current are associated with the location and number of devices that are allocated in the system.

Enhancing Voltage, Reliability, and Demand Balance via Combined Model of Distributed Generation Allocation and Distribution System Reconfiguration

The present study attempts to deal with two problems: optimal allocation of distribution generations (DGs) and reconfiguration of a distribution system to satisfy several multiobjectives including smoothing the voltage profile, minimizing the cost of reliability, and balancing the amount of load on the end-user side. These problems are merged into one single optimization problem with the aforementioned objective functions. The problem type is mixed-integer nonlinear programming (MINLP) and is subject to various limitations imposed by, for example, the operating status of DGs, demand equilibrium, bus voltages, and power passing on the distribution lines. Moreover, the paper adopts particle swarm optimization to find a solution to the MINLP problem under study. The test system was implemented in the MATLAB environment to validate the capability of the suggested approach.

Optimized allocation of distributed generation considering battery energy storage batteries and photovoltaic

The access of distributed generator (DG) to the distribution network becomes a voltage support point, which is conducive to optimizing the power flow and reducing the losses in the distribution network. The volatility of DG increases the uncertainty of grid operation and increases the difficulty of distribution grid operation control. To address the above contradictions, this paper researches the optimal allocation of distributed power sources. A bi-level optimal allocation model that integrates planning and operation is established. The upper takes the economic optimization of the distribution company as the target, while the lower layer optimizes the DG allocation scheme with minimum network loss and passes the target value to the upper layer to make the distribution network economically optimal. The DG output scenario generation method based on kernel density estimation and k-means clustering addresses the uncertainty of DG active power output. A computational analysis is carried out based on a typical 10kV distribution network with PV as an example to verify the validity of the model.

Fostering restoration and power delivery efficiency by distributed generators and line switches in electricity distribution network

ABSTRACT The distribution of power is being faced with challenges during and after extreme weather events. This contingent might cause a partial or full power blackout in the distribution territory. Hence, the restoration of the service is one of the key concerns of the utilities. A strategy is adopted to felicitate the power restoration process by the deployment of distributed generators (DGs) and line switches. A fuzzy-based system model is proposed to allocate line switches considering the impact of wind-driven extreme weather and the fault frequency character of the lines. The study further recommends an arrangement of dispatchable DGs in the sections of the distribution grid. A particle swarm optimization-based model is proposed for suitable capacity and location assessment of the DGs to foster the restoration of supply avoiding voltage and frequency disturbances in the developed sections of the grid. The results are further verified with the application of the grasshopper optimization algorithm on the multipurpose composite objective function. The proposed DG allocation approach escalates the network power loss reduction by 95.10%, improvement in average voltage stability level by 6.46%, and loadability margin enhancement by 37.37% when the DGs are operated in grid-connected mode. Moreover, power supply reliability is restored with successive employment of DG units. The proposed approach has been tested on an Indian 28-bus power distribution network. The outcome of the proposed approach is compared with existing DG allocation methods. The experiment confirms the credential of the proposed approach with better value addition in multiple objectives.

Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.

Performance enhancement of radial distribution system with optimal DG allocation

ABSTRACT During the modernization era, the power generation has been shifted to decentralized manner with more renewable distributed generation (DG) units integration at the distribution level in disperse manner. These DG units have to be properly placed for performance enhancement of distribution system. In this paper, the performance of radial distribution system has been enhanced by optimally allocating biomass type DG in the network. Here, a combination of sensitivity index method and a recently developed nature inspired algorithm known as Artificial Gorilla Troops Optimizer (AGTO) algorithm has been used for optimal siting and sizing of DG units in the 33- and 69-bus radial distribution network by taking real and reactive power loss minimization, voltage profile improvement and voltage stability enhancement as the primary objectives along with the system operational constraints. The composite type of load model has been considered in this paper. Out of all the load buses, some severe buses are selected by index vector sensitivity method. AGTO has been applied on these selected buses for appropriate allocation of DG. The effectiveness of this algorithm has been compared with Grey Wolf Optimization (GWO) algorithm and other reported results. The concept of load growth has also been studied in this work.

Network reconfiguration in the presence of optimally integrated multiple distributed generation units in a radial distribution network

This article presents a hybrid of two metaheuristic techniques, namely the plant growth simulation (PGS) algorithm and particle swarm optimization (PSO), for power loss minimization in a radial distribution network (RDN). The PSO algorithm determines the optimal allocation of multiple distributed generators (DGs), followed by the PGS algorithm for optimal network reconfiguration (NR). Type I and IV DGs were considered. The load flow solutions were obtained using a branch matrix formulation. IEEE 33 and 69 buses were used for performance analysis and the results were compared with previously published results to validate the method in providing solutions even in complex system configurations. The superior results demonstrate that the penetration of multiple DGs along with the NR permits the network to operate successfully under different loading conditions, without violating the constraints.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device’s impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method’s efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network.

Extended mixed integer quadratic programming for simultaneous distributed generation location and network reconfiguration

Introduction. To minimise power loss, maintain the voltage within the acceptable range, and improve power quality in power distribution networks, reconfiguration and optimal distributed generation placement are presented. Power flow analysis and advanced optimization techniques that can handle significant combinatorial problems must be used in distribution network reconfiguration investigations. The optimization approach to be used depends on the size of the distribution network. Our methodology simultaneously addresses two nonlinear discrete optimization problems to construct an intelligent algorithm to identify the best solution. The proposed work is novel in that it the Extended Mixed-Integer Quadratic Programming (EMIQP) technique, a deterministic approach for determining the topology that will effectively minimize power losses in the distribution system by strategically sizing and positioning Distributed Generation (DG) while taking network reconfiguration into account. Using an efficient Quadratic Mixed Integer Programming (QMIP) solver (IBM ®), the resulting optimization problem has a quadratic form. To ascertain the range and impact of various variables, our methodology outperforms cutting-edge algorithms described in the literature in terms of the obtained power loss reduction, according to extensive numerical validation carried out on typical IEEE 33- and 69-bus systems at three different load factors. Practical value. Examining the effectiveness of concurrent reconfiguration and DG allocation versus sole reconfiguration is done using test cases. According to the findings, network reconfiguration along with the installation of a distributed generator in the proper location, at the proper size, with the proper loss level, and with a higher profile, is effective.

Optimal Distributed Generation Allocation in Practical Distribution System in the Presence of Plug-in Electric Vehicles

Optimal Distributed Generation Allocation in Practical Distribution System in the Presence of Plug-in Electric Vehicles

Comparative assessment of techno-economic and environmental benefits in optimal allocation of distributed generators in distribution networks

Integration of Distributed Generation (DG) into power systems, especially at the distribution end, is one of the verified approaches that has been utilized to reduce power loss, enhanced reliable electricity supply, and promote environmental sustainability by reducing Greenhouse Gas (GHG) emissions. In this study, an approach for solving an optimal DG allocation problem in distribution network with the objective function of maximizing the financial Techno-Economic and Environmental Benefits (TEEBs) of the grid is discussed. The TEEBs analysis of the DG planning problem is uniquely modeled as financial cost-benefit due to reduced power purchased and reduced Penalty Emission Cost (PEC) arising from the reduction of GHG emission in the network. A comprehensive and comparative analysis was carried out for the four classes of DG technology types to identify the environmental impact of integrating renewable and non-renewable DGs into the distribution system. Furthermore, The DG planning problem is solved using the Black Widow Optimizer (BWO) technique. The study implemented the proposed methodology on the standard IEEE 69-bus and Nigerian Shasa 59-bus distribution systems. The results show that renewable DGs’ optimal integration yielded higher TEEBs than non-renewable DGs despite the high capital cost of renewable DGs. Furthermore, the study affirmed the viability and efficiency of the proposed method by comparing the results of power loss obtained for the various DG types with that of techniques in open works of literature.

Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%.

Optimal allocation of electric vehicle charging stations and renewable distributed generation with battery energy storage in radial distribution system considering time sequence characteristics of generation and load demand

The addition of electric vehicle (EV) charging station (EVCS)/EV battery swapping stations (EVBSSs) in radial distribution system (RDS) draws extra real power from the distribution substation. This paper proffers a novel strategy for obtaining the best location of EVCS/EVBSSs in the RDS. Further, the EV charger has been modeled as constant current load and the influence of EVCS/EVBSSs demand on the voltage profile, real power loss, total voltage deviation, energy loss cost and overall operating cost of the RDS have been investigated considering constant power (CP), industrial (IL), residential (RES) and commercial (COM) load models. In order to make the network more self-sustainable and reliable, it is obligatory to assimilate the distributed generations (DGs) of optimal size and at proper location in the RDS to diminish the impact of EVCS/EVBSS(s) load. In addition, non-dispatchable solar photovoltaic (SPV) and wind turbine (WT) units are converted into a dispactable SPV and WT units with a combination of battery energy storage (BES) (i.e., SPV-BES and WT-BES). This research work suggests a novel chaotic student psychology based optimization (SPBO) (CSPBO) algorithm to acquire the optimal size and site of SPV-BES, WT-BES and biomass in IEEE 33-bus and practical Brazil 136-bus RDS for CP, IL, RES and COM load models considering the average hourly load demand profile and the average hourly variation in generation profile of SPV and WT. The obtained results based on benchmark test problems reveals that chaotic maps are proficient to enhance the performance of the SPBO algorithm significantly in terms of local optima circumvention and faster convergence mobility. The obtained outcomes show that the attained size and site of renewable DGs in the RDS may be feasible ones. The attained outcomes of the proffered CSPBO algorithm are contrasted to SPBO and Harris hawk's optimization algorithm. The yielded results will definitely help the EV and distribution industry in improving the reliability and the efficiency of the system.

Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

Single objective constant PQ load models were extensively considered for site and size of distributed generation (DG) and shunt capacitor (SC) allocation. Which may lead to single non-dominated solution of unpredictable and misleading results about their site and size, loss reduction and payback period. Therefore, primary objective of this study is to investigate the effects of seven nonlinear voltage-dependent load models for the siting and sizing of DG and SC considering various conflicting Multiobjective functions. These objective functions are minimization of active power loss, voltage deviation, cost of energy loss per year, the total cost of installed DG and SC, and emission. Three study cases of simultaneous optimization of two and three objective functions are intended to find optimal integration of DG and SC in the standard 33 and 118-bus radial distribution network considering seven nonlinear voltage-dependent load models. A new Multiobjective evolutionary algorithm (MOEA) called Bidirectional Coevolutionary (BiCo) is applied to the proposed study cases to demonstrate the impact of load model on DG and SC allocation. Further, to show the superiority and performance of proposed algorithm, six state-of-the-art MOEAs are implemented and statistically compared with proposed algorithm using a representative hypervolume indicator (HVI). The maximum savings in annual cost of annual energy loss reaches 58.99% in case1 of PQ load model, 59.4% in case 2 and 64.96% in case 3 of industrial load model considering only DG allocation, whereas, 93.673% in constant PQ load model of case1, 78.908% in constant current load model of case2 and 93.403% of case3 of PQ load model considering simultaneous DG and SC. Simulation results show that the proposed algorithm is adept and suitable to find a better trade-off between various conflicting objective functions compared to other recently designed MOEAs.

Distribution network planning with DG units considering the network reconfiguration and reliability

The failure statistics of most utilities show that distribution networks (DN) make the most considerable individual contribution to the lack of supply to consumers. There are several advantages to optimizing distributed generation (DG) allocation in DNs, including loss reduction, voltage profile improvements, and reliability. A traditional objective function of optimal DG allocation is to minimize the total power loss and improve the voltage profile. Due to their ability to supply loads locally, DG units enhance the load-carrying capability of lines and, therefore, serve the same purpose as redundant lines. A DN’s reliability indices can also be improved by optimal DG allocation. As such, it is interesting to investigate how network reconfiguration (NR) may be further enhanced to improve the achievement of these critical objectives and ensure the system’s efficient operation in this context. The purpose of this paper is to present an intensive investigation of the reliability and performance improvement that NR can achieve when using DG units in modern DNs. An efficient metaheuristic rider optimizer algorithm (ROA) is applied to the complex combinatorial NR and DG allocation problem with stringent constraints. Analyses are conducted on benchmark IEEE 33-bus test DNs with explicit presentation of results.

Rekonfiguracija i otočna kompenzacija u prisustvu distribuiranih izvora u razgranatoj distributivnoj mreži

Distribution utilities witness many changes nowadays. If the network voltages must be in 0.9-1.1 p.u. limits and grid input power factor greater than 0.85 all three modern strategies of smart distribution network as reconfiguration, capacitors switching and presence of distribution generation (DG) ought to be applied. Even in this case On Load Tap Changer (OLTC) in the supplying substation has to be used. In this paper, detailed technical analysis of 21 realistic operation cases is presented by hybrid algorithm of Minimum spanning tree (MST) plus Simulated Annealing (SA). MST is used for tackling reconfiguration first and SA for capacitor switching afterwards. Implemented transparent grafical Monte Carlo method for locating of distributed generators and capacitor banks is unique and simple. Assumption is that DG is already present. Gauss and Weibull changing nodal loads and wind generators output, respectively as well as daily load curves for working and weekend day with insolation of solar panels are included in analysis. The objective function comprising of the cost of power losses, price of capacitors and undelivered energy is minimized. IEEE 118 bus network is analyzed which has 118 nodes and 132 branches (15 ties), all of which can commutate, that is considered to be large scale. The basic switching logic of uniform distribution of wind generators to the nodes and capacitors in accordance with objective function, that changes every hour is unrealistic. More realistic one was issued with fixed nodes for allocation of DG and capacitors (the most frequently visited nodes). The programme final objective function indicates the price of peak power losses, losses of delivered electrical energy, of the banks, of undelivered electrical energy and commutations for the period of month and a half (1008 network operating hours).

Photovoltaic-based Distributed Generation Allocation in Distribution Network for Energy Loss Minimization

Nowadays, there is a global consensus that integrating renewable energy sources (RESs) is highly needed to meet the increasing electricity demand and reduce the overall carbon footprint of energy production. However, large-scale integration of RES-based distributed generation (DG) units often poses several technical challenges in the system from stability, reliability, and power quality perspectives. However, these problems are usually mitigated by the optimal integration of DG units in the distribution networks (DNs). In this regard, the optimal sizing and placement of the DGs are crucial. Otherwise, network performance will deteriorate. This paper proposes to apply a novel population-based technique called the dung beetle optimization (DBO) algorithm for the optimal allocation of Photovoltaic (PV) based DG units to minimize total active energy loss subject to equality and inequality constraints in the DN. A DBO is inspired by the behaviours of dung beetles, including ball-rolling, dancing, foraging, stealing, and reproducing. A standard 33-bus system has been used to demonstrate the proposed method's effectiveness. The simulation results and comparison with other techniques demonstrate our proposed approach's significant energy loss reduction and a suitable voltage profile.

Optimal distributed generation allocation in distribution system for power loss minimization and voltage stability improvement

Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.

Simultaneously Distributed Generation Allocation and Network Reconfiguration in Distribution Network Considering Different Loading Levels

Simultaneously Distributed Generation Allocation and Network Reconfiguration in Distribution Network Considering Different Loading Levels

Optimal Placement of Multiple DG Units With Energy Storage in Radial Distribution System by Hybrid Techniques

In recent years, distributed generations (DGs) are extremely fast in detecting their location, which helps to satisfy the ever-increasing power demands. The placement of energy storage systems (ESSs) could be a substantial opportunity to enhance the presentation of radial distribution system (RDS). The major part of DG units in RDS deals with the detection of ideal placement and size of the DGs, which efficiently balance the power loss and voltage stability. The ideal location and size of ESSs are examined in standard IEEE-33 and 69 bus systems, which is important to reduce power losses. Nowadays, several algorithms or techniques are modified for the development of hybrid algorithms to improve the quality of DG allocation. In this research, a hybrid shuffled frog leap algorithm (SFLA) with ant lion optimizer (SFLA-ALO) is proposed for the optimal placement and size of the DG and ESS in the RDS to reduce power losses and maintain the stability of voltage. The performance of the proposed SFLA-ALO technique is compared with the implemented BPSO-SFLA technique.