Sizing Of Distributed Generation Research Articles

Placement and Sizing of Inverter-Based Renewable Systems in Multi-Phase Distribution Networks

This paper develops a tractable formulation for optimal placement and sizing of inverter-based renewable systems in multi-phase distribution networks. The goal of the formulation is to minimize the cost of inverter installation, average power import, and average distributed generation curtailment. Three-phase and single-phase inverter models are presented that preserve the underlying mappings between renewable uncertainty to power injection. The uncertainty of distributed generators (DGs) and loads are characterized by a finite set of scenarios. Linear multi-phase power flow approximations are used in conjunction with scenario reduction techniques to arrive at a tractable two-stage stochastic formulation for optimal DG placement and sizing. First-stage decisions are locations for DG deployment and capacity sizes, and second-stage decisions include DG real power curtailment, reactive power support, as well as feeder voltage profile. The resulting formulation is a mixed-integer second-order cone program and can be solved efficiently either by existing optimization solvers or by relaxing the binary variables to the [0,1] interval. Simulation studies on standard multi-phase IEEE test feeders promise that optimal stochastic planning of DGs reduces costs during validation, compared to a scheme where uncertainty is only represented by its average value.

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

In this paper, heuristic optimization techniques, such as integrated particle swarm optimization (IPSO), teaching--learning-based optimization (TLBO), and Jaya optimization, were applied effectively for the first time to optimize the radial distribution network (RDN) by simultaneously considering reconfiguration of the network and allocation and sizing of the distributed generations (DG). The objectives were to maximize the voltage stability and to minimize the power loss of the network without violating the system constraints. In standard PSO technique, the movement of current particle depends upon global best position and its own best position up to current step. However, if the particle lies too close to any of these positions, the guiding role highly decreases and even vanishes. To resolve this problem and to find the global best solution, IPSO was utilized to optimize the network reconfiguration and DG allocation and sizing problem in the RDN. Also, the optimization techniques, such as TLBO and Jaya optimization, which do not require any tuning of parameters, unlike other heuristic optimization techniques, were implemented successfully in this paper. Seven test cases were generated from different combinations of network reconfiguration and DG allocation and sizing. Moreover, for comparison, the optimization techniques, such as particle swarm optimization (PSO), adaptive cuckoo search algorithm (ACSA), harmony search algorithm (HSA), and fireworks algorithm (FWA), were also applied to IEEE 33- and 69-bus distribution test networks. The comparison results prove overall superiority of Jaya optimization when applied on the two IEEE bus systems with seven test cases undertaken.

A Multi-Objective Framework to Improve Voltage Stability in A Distribution Network

Abstract This paper investigates the performance of a radial system while installing a Distributed Generator (DG) in existing Distribution Network (DN). The investigation has been performed with various types of DG units for different topologies of the DN. The present work uses a Voltage Stability Index (VSI) for identifying the location for installing a DG in the DN. A multi-objective framework is proposed to evaluate the size of the DG to be installed by reducing power loss and deviation in bus voltage. Genetic Algorithm (GA) is used to optimize the size of DG. The proposed method has been tested with different types of DG units on standard systems (IEEE-33 bus and IEEE-69) with different radial topologies of DN.

Optimal Distributed Generator Sizing and Placement by Analytical Method and PSO Algorithm Considering Optimal Reactive Power Dispatch

Optimal Distributed Generator Sizing and Placement by Analytical Method and PSO Algorithm Considering Optimal Reactive Power Dispatch

Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems

This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

Protection Coordination Toward Optimal Network Reconfiguration and DG Sizing

Research on network reconfiguration (NR) considering distributed generations (DG) is typically concerns on the issues of power loss, voltage deviation, DG sizing as well as its placement, which are important and required in the planning stage. On the other hand, another important aspect which often neglected in this stage is coordination of protection devices which is essential to prevent the network from damages following system breakdown. Without sufficient attention given to the protection coordination during NR, there is a possibility for the protective devices to miscoordinate and consequently lead to system blackout, due to changes in current flow and fault level. Therefore, this paper proposed an NR method for distribution networks with DG, incorporating protection devices. The proposed method aims to find the optimal configuration and DG size with minimum power loss, and at the same time ensuring protective devices operate correctly during normal and fault condition. Constraints on protection coordination and DG size are explicitly formulated in the proposed method. The validity of the proposed method is analyzed on three commonly used IEEE 33-bus, 69-bus and 118-bus distribution systems, employing the firefly algorithm (FA) and evolutionary programming (EP) algorithm. Comparative studies are done to prove the validity and robustness of the proposed method.

A Novel Constraints Reduction-Based Optimal Relay Coordination Method Considering Variable Operational Status of Distribution System With DGs

One of the factors that affect the level of fault currents is the number of short-circuit MVA sources present during the fault. A distribution system with distributed generators (DGs) is a multi-source system, in which DGs can be connected or disconnected to meet the high or low load requirements respectively. Also, during fault conditions, DGs can get disconnected due to their low fault ride-through capabilities. These changes in connections change the number of DGs and the operational status of the system, which in turn change the fault currents seen by relays and the ranges of maximum and minimum fault currents for which the relays settings work coordinately. This paper realizes that an increase in the proliferation of DGs makes the relay coordination problem a highly constrained optimization problem, and changes in the number of DGs make the problem more complex, which can decelerate the overall performance of relays. The aim of this paper is to reduce the complexity and design a fast, robust, and adaptive protection scheme which can efficiently work in the variable environment of the DG-distribution system. This paper proposes a novel constraints reduction-based relay coordination method which selects a small set of constraints out of large set of constraints while determining the optimal relays settings. The relay coordination problem is formulated as a linear programming problem. The performance of the proposed method is tested on the both configurations, radial and meshed, of the IEEE 38-bus distribution system. Comparative studies have been conducted to highlight the advantages of the proposed approach under changing system’s configuration with variable locations and sizes of DGs.

Distributed generation integration optimization using fuzzy logic controller

Distributed generators (DGs) are connected near load centers and integrated to grid for optimum power system operations. The challenging task for integration of DGs is the proper size and location, due to random character of generation and energy consumption. The objective of the research is to provide the proper size and location of DGs to optimize the voltage profile and reduction in active and reactive power losses. Optimization of distributed generators is performed through fuzzy logic controllers at Ramchandrapura 33/11 kV, 69 bus radial distribution substation, Kota, India. Fuzzy logic controller provides possible solution on selection of distributed generators on the basis of voltage profile of the bus and the impedance of the branch connected DGs at consumer’s end. Forward or backward sweep methodology is used to optimize the load flow analysis for calculation of real and reactive power losses and voltage profile with and without implementation of distributed generators. Proposed approach provides a possible solution for efficient integration of DGs.

Multi-Objective Optimal Location and Sizing of Hybrid Photovoltaic System in Distribution Systems Using Crow Search Algorithm

The optimal size and location of Distributed generation (DG) in a distribution system can minimize the power loss and improve the voltage profile of the distribution system. But the random placement of DG will not solve the problem of power loss and voltage profile. Therefore optimization algorithms are used to find the optimal size and location of DG to minimize the power loss and to improve the voltage profile. This research paper introduces a new metaheuristic algorithm which is the Crow search algorithm (CSA) to calculate the optimal size and location of multiple solar photovoltaic (PV) units for reducing the power loss and improving the voltage profile. This paper also discusses the complete mathematical modeling of probabilistic PV generation, time-varying load modeling, loss calculation, voltage stability, and optimal PV size calculation. The method proposed in this paper is tested on IEEE 30 and 57 bus test systems and compared with other existing methods like Genetic algorithm (GA), Particle swarm optimization (PSO) and Hybrid GA-PSO method. CSA has only 2 settings parameters while GA and PSO have 6 and 3 settings parameters. Because of this, computation time and complexity of the algorithm are less in comparison to other methods and it gives a better result. Therefore by using the CSA method, the optimal size and location of the DG can be found which gives less power loss and improved voltage profile, less computational time and easy to implement compared to other existing methods proposed here.

Two States for Optimal Position and Capacity of Distributed Generators Considering Network Reconfiguration for Power Loss Minimization Based on Runner Root Algorithm

Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.

Application of genetic algorithm for problem of optimizing location and capacity of distributed generation considering distributed network reconfiguration

This paper presents a method of determining the location and size of distributed generation (DG) considering to operate the configuration of distribution network to minimize the real power loss. The proposed method which is based on the genetic algorithm (GA) is divided into two stages. In the first stage, GA is used to optimize the location and size of DG in the mesh distribution network, while in the second stage, GA is used to determine the radial network configuration after installing DG. The simulation results on the 33-nodes and 69-nodes systems show that the proposed method can be an efficient method for the placing DG problem and that is considering to solve the problem of distribution network reconfiguration.

Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm

In this paper, authors proposed a new methodology based on bio-inspired Cuckoo Search Algorithm (CSA), which can significantly be envisaged with simultaneous allocation of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) in the radial distribution systems (RDS). In the proposed method, optimal locations of the DG and DSTATCOM are determined by using Voltage Stability Index (VSI) and Loss Sensitivity Factor (LSF) respectively. The optimal size of DG and DSTATCOM are found by using a newly developed nature-inspired CSA in the RDS. Further, in the proposed method load flow calculations are carried out by using a fast and efficient Backward/Forward Sweep (BFS) algorithm. The objective function of the proposed method is derived in such a way that to minimize the system total power losses and enhancing the bus voltages. The effectiveness of the proposed method is tested by considering a well-known IEEE 33-bus and large 136-bus RDS. The attained results of the proposed method and comparison of different cases for allocation of DG and DSTATCOM are presented in result section. From the presented results, it can be clearly understood that the proposed method is able to significantly reduce the power losses and enhance the voltage profile of the RDS.

A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model

In this paper, a bio-geography-based optimization techniqueis proposed for optimal sizing and placement of multiple distributed generators to reduce system loss and to improve system voltage profiles in electric distribution systems. Distributed generation paves the way for installing generating sources in the vicinity of the loads, for improving the power factor of the system, and thereby significantly reducing total system losses. An effective power factor model is proposed to pre-set the power factor of each distributed generator placed at various locations in a distribution system. The bio-geography optimization algorithm is enhanced with a differential learning scheme to deal with the problems of high dimensionality and complex constraints. A sensitivity factor approach is presented to reduce the search space for the placement of distributed generators in the system. Numerical simulations are performed on the IEEE 33-bus and IEEE 69-bus systems and the results of the proposed method are compared with other methods reported in the literature.

Protection Coordination of Properly Sized and Placed Distributed Generations–Methods, Applications and Future Scope

The radial distribution networks are designed for unidirectional power flows and are passive in nature. However, with the penetration of Distributed Generation (DG), the power flow becomes bidirectional and the network becomes active. The integration of DGs into distribution network creates many issues with: system stability, protection coordination, power quality, islanding, proper placement and sizing etc. Among these issues, the two most significant are optimal sizing and placement of DGs and their protection coordination in utility network. The proper coordination of relays with high penetration of DGs placed at optimal location increases the availability and reliability of the network during abnormal operating conditions. This research addresses most of the available methods for efficient sizing and placement of DGs in distribution system (numerical, analytical and heuristic) as well as the developed protection coordination techniques for utility networks in the presence of DGs (Artificial Intelligence (AI), adaptive and non-adaptive, multi-agent, hybrid). This paper indicates the possible research gaps and highlights the applications possibilities and methods’ limitations in the area of DGs.

Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

PurposeThis paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption.Design/methodology/approachGenerally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution.FindingsIn AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN).Practical implicationsThe AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources.Originality/valueIn this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

Probabilistic Assessment of Hosting Capacity in Radial Distribution Systems

High penetration of distributed generation (DG) is mainly constrained by voltage-related issues. Due to the uncertainties associated with type, size, and location of DGs, it is difficult to quantify their integration limits in distribution networks, i.e., hosting capacity (HC). To address this issue, this paper proposes a probabilistic-based framework to determine the maximum integration limits of DGs considering the voltage rise and voltage deviation constraints. Such framework requires the use of the HC model, which can be formulated as a nonlinear optimization problem. Adding the voltage deviation constraint in the HC problem makes the model unsolvable. We address this issue by proposing a two-step algorithm to linearize the HC model. Then, using the linearized model, a probabilistic framework is proposed for considering the load variability and DGs uncertainties. To validate the efficacy and accuracy of the proposed framework, we identify the HC of a balanced and an unbalanced distribution networks and compare our results with those obtained from comprehensive power flow method and the traditional conservative planning. Finally, using the proposed framework, the impact of voltage deviation constraint, load growth, DG type and network structure on the HC are comprehensively studied using different DG technologies (i.e., Photovoltaics and wind).

A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids

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.

Optimal siting and sizing of distributed renewable energy in an active distribution network

The distributed generation (DG) plays an important role in the context of the environmental problems and sustainable development throughout the world. This paper proposes a DG siting and sizing model in an active distribution network (ADN). The objective is to minimize the total cost, including investment, operation and maintenance costs. The proposed model is transferred to a Mixed Integer Second-Order Cone Programming (MISOCP) model based on a distribution network forward backward-sweep power flow and constraint relaxation. The CVX platform and GUROBI solver are used for the solution. The scenario analysis is used for the uncertainties of load and DG. Different numbers of operational scenarios are considered in order to analyze the effect of a non-network solution to the final planning result and total investment. The planning results with and without consideration of active managements, and the planning results with and without taking environmental profits into consideration, are compared and analyzed. The proposed methodology is verified with a modified IEEE 33 example.

Optimal Placement and Sizing of Photovoltaic Based Distributed Generation Considering Costs of Operation Planning of Monocrystalline and Thin-Film Technologies

Distributed generation (DG) technology has been growing rapidly in industries as this technology can increase the overall efficiency to the power systems. Improper placement and sizing can lead to power losses and interrupt the voltage profile of distribution systems. Studies have been done to solve the DG placement and sizing problem considering several factors, and one of the common factor is minimizing the power losses. However, it is not adequate by only considering the power losses, whereas, the costs of the generation, investment, maintenance, and losses of the distribution system must be taken in consideration. In this research, DG chosen to study is photovoltaic (PV) type which is monocrystalline and thin-film. Costs of operation planning with respect to the power losses is considered which include the costs of investment, maintenance, power loss, and generation that are determined for optimal placement and sizing of DG. The proposed method improved gravitational search algorithm (IGSA) is used in the matlab environment to find the optimal placement and sizing of DG and is tested with the IEEE 34-bus system. The performance of IGSA is then compared with gravitational search algorithm (GSA) and particle swarm optimization (PSO) to find out which algorithm gives the best fitness value and convergence rate. The purpose of this research is to identify the operation planning cost based on the optimization results and improves the optimal placement and sizing of DG in future, to provide maximum economical, technical, environmental benefits, and increase the overall efficiency to the power system.

Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm

High energy losses and voltage instability issues in distribution system are prevalent for energy planners and utilities due to high penetration of Electric Vehicles (EVs). Battery Swapping Stations (BSS) are important component for energizing EVs equipped with battery charging mode. In this paper, energy loss reduction and voltage stability factor are enhanced by optimal allocation of Distributed Generation (DG) and BSS, using new Grasshopper Optimizer Algorithm (GOA). The zone based strategy with dispersed DG ensures sufficient distance among BSS's to adequately serve the motorists and enhance system performance. The operational constraints of BSS, DG and loads in each zone for 33-and 69-bus networks are considered. GOA method is compared with established and well-known techniques, namely Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). Using GOA, energy losses are reduced to 82% and 92%, while loadability is increased to 13% and 19% in 33- and 69-bus systems, respectively. The results have shown superiority of GOA in terms of system performance and convergence characteristics. Optimal sites and sizes for DG-BSS placement in the system are beneficial to EV users, BSS developers, electricity grid and utility.