Distributed Generation Allocation Research Articles

Optimal dispatchable DG allocation in a distribution network considering load growth with a mixed-PSO algorithm

In recent years, one of the major issues faced by distribution utilities is integrating distributed generation (DG) units in distribution networks. This paper proposes a population-based method called particle swarm optimization (PSO) for optimal planning of the location and sizing of different types of DG units in the distribution network, considering different loading conditions. The objective of this application is power loss minimization. In order tond the optimal location and size of DG units, continuous and discrete forms of PSO are deployed, respectively (mixed PSO). In addition, the optimal locations and sizes of the DG units are determined in the areas of signicant feeder load growth. This meta- heuristic approach makes little or no assumption about the issue being optimized and can search large spaces of possible solutions. The presented method is tested on the standard IEEE 33-bus and IEEE 69-bus test systems. In order to show the effectiveness of the proposed methodology, the results are compared with another method of DG allocation and another loss minimization technique.

Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods

Many methods have been proposed to determine the optimal location and capacities of distributed generation (DG) units to reach the lowest value for system losses. In this study, the combination of analytical and genetic algorithm methods is used for optimal allocation of multiple DGs in a distribution network to minimise the system losses. This combination guarantees the convergence accuracy and speed in multiple DG units allocation. In this study, the DGs active power, power factor, and location are simultaneously considered during distribution network losses minimisation. The utility will dictate only the maximum DG power generation if the DG is installed by DG owner. However, both of the size and the location of DG will be determined by the utility if the DG is installed by it. The proposed method is applied to 33-bus and 69-bus test distribution systems. Simulation results show that the proposed method results in lower losses compared with the other methods.

Multi-objective Allocation of Multi-type Distributed Generators along Distribution Networks Using Backtracking Search Algorithm and Fuzzy Expert Rules

This article presents a methodology for a multi-objective optimal allocation of multi-type distributed generators in radial distribution networks based on a backtracking search algorithm. The study aims to prove the validity and the strength of the backtracking search algorithm multi-objective method on distributed generator allocation. The multi-objective function is expressed to minimize the network power losses, to consolidate the static voltage stability indices, and to ameliorate the bus's voltage profile. The indicators of loss sensitivity factors and bus voltage magnitudes are incorporated to establish set of fuzzy expert rules to assort the preliminary buses for distributed generator placement. The proposed methodology allows the fuzzy decision maker to decide the best compromise solution among the offered Pareto-optimal solutions. The salient features of the backtracking search algorithm are demonstrated and marked on 33- and 94-node radial distribution networks with various scenarios. The cropped results are compared with those reported by others in the literature, validating and signifying the proposed approach. The study finds that the type-3 distributed generator unit (delivers P and injects Q) is most preferred to reduce power losses along network lines and to boost both the bus voltage profile and voltage stability indices.

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Distribution system planners usually provide dedicated feeders to its different class of customers, each of whom has its own characteristic load pattern which varies hourly and seasonally. A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers. This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers. The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile. Bat algorithm (BA) is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region, but its exploration is inadequate. The problem is solved by proposing the improved BA (IBA). The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.

A Stochastic Investment Model for Renewable Generation in Distribution Systems

A model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion planning. The problem is formulated using a stochastic two-stage multiperiod mixed-integer linear programming (MILP) model, where investment decisions are done in the first stage and scenario-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation (photovoltaic and wind) investment costs, substation expansion investment cost, operation and maintenance costs, energy losses cost, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints include voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a 34-bus system and conclusions are duly drawn.

Optimal allocation of microgrid considering economic dispatch based on hybrid weighted bilevel planning method and algorithm improvement

Abstract During the microgrid search process, optimal allocation and economic dispatch are very important and are interrelated, having interaction and mutual checks. In this paper, we adopt a bilevel planning method to search for the multi-objective optimal allocation of a microgrid, considering economic dispatch. The upper level is the optimal allocation, which aims at minimizing the daily fixed cost of investment (DFCI), load loss probability (LLP), and excess energy rate (EER), with which it determines the capacity of each power supply. The lower level is the economic dispatch, which aims at minimizing the cost of operation and management (COM) and cost of pollutant disposal (CPD), with which it determines the output power of each distributed generator (DG). Considering the different dimensions of multi-objective functions of the upper level, a hybrid weighted method based on the judgment matrix method and variation coefficient method is proposed. For the objective function of the lower level, the penalty function method is adopted to handle the unbalanced active power. We propose an improved adaptive genetic algorithm (IAGA), which can avoid premature phenomena and local convergence and can find the global optimal solution more quickly and stably. We also discuss the two different scheduling strategies of the microgrid. With the example system, the correctness and effectiveness of the model and algorithm are verified. The simulation results show that the hybrid weighted bilevel planning model can efficiently realize the optimal allocation of DGs, reasonably schedule the output power of DGs, and eventually achieve optimal multiple objectives, considering optimal allocation and economic dispatch. The presented research can provide some reference information for microgrid applications.

Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

Abstract Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.

Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm

This paper presents a distribution generation (DG) allocation strategy for radial distribution networks under uncertainties of load and generation using adaptive genetic algorithm (GA). The uncertainties of load and generation are modeled using fuzzy-based approach. The optimal locations for DG integration and the optimal amount of generation for these locations are determined by minimizing the network power loss and maximum node voltage deviation. Since GA is a metaheuristic algorithm, the results of multiple runs are taken and the statistical variations for locations and generations for DG units are shown. The locations and sizes for DG units obtained with fuzzy-based approach are found to be different than those obtained with deterministic approach. The results obtained with fuzzy-based approach are found to be comparatively efficient in working with future load growth. The proposed approach is demonstrated on the IEEE 33-node test network and a 52-node Indian practical distribution network.

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

Abstract The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.

Power loss minimization in distribution systems using multiple distributed generations

In this work, an efficient analytical method is proposed for optimally allocating distributed generations (DGs) in electrical distribution systems to minimize power losses. The proposed analytical method can be employed for obtaining the optimal combination of different DG types in a distribution system for loss minimization. The validity of the proposed method is demonstrated using two test systems with different configurations by comparing with the exact optimal solution obtained from the exhaustive optimal power flow (OPF) algorithm. The calculated results and the comprehensive comparisons with existing methods prove the superiority of the proposed method in terms of accuracy and calculation speed. The proposed loss minimization method can be a useful tool for any general DG allocation problem since it provides effective and fast loss evaluation taking into account other benefits. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Study impact of various load models on DG placement and sizing using backtracking search algorithm

In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation.

The implementation of capital budgeting analysis for distributed generation allocation problems

For the last 7 years, the Republic of Croatia has been witnessing a sudden increase of distributed energy resources connecting to its power system. However, a significant problem is how to find the financial equilibrium which will satisfy the independent power producers on one side and the distribution system operator on the other. In this paper, the authors discuss the capital budgeting analysis of distributed generation projects with a goal of maximizing their net present values. The evaluation of net present values will be the central tool for finding the optimal position and size of distributed generation units in the network. By simultaneously minimizing active power losses the interests of distribution system operator will not be neglected in this study. The power production of distributed generation and power consumption of network loads will be modeled with characteristic average daily power curves with discrete hour intervals. The problem will be solved using genetic algorithm, and realized in Matlab programming environment.

Optimal allocation and adaptive VAR control of PV-DG in distribution networks

The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme.

Voltage Control Scheme with Distributed Generation and Grid Connected Converter in a DC Microgrid

Direct Current (DC) microgrids are expected to become larger due to the rapid growth of DC energy sources and power loads. As the scale of the system expends, the importance of voltage control will be increased to operate power systems stably. Many studies have been performed on voltage control methods in a DC microgrid, but most of them focused only on a small scale microgrid, such as a building microgrid. Therefore, a new control method is needed for a middle or large scale DC microgrid. This paper analyzes voltage drop problems in a large DC microgrid and proposes a cooperative voltage control scheme with a distributed generator (DG) and a grid connected converter (GCC). For the voltage control with DGs, their location and capacity should be considered for economic operation in the systems. Accordingly, an optimal DG allocation algorithm is proposed to minimize the capacity of a DG for voltage control in DC microgrids. The proposed methods are verified with typical load types by a simulation using MATLAB and PSCAD/EMTDC.

Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm

Abstract In this article, a very recently swarm optimization technique namely a backtracking search optimization algorithm (BSOA) is addressed to assign the distributed generators (DGs) along radial distribution networks. One of the main features of the BSOA is a single control parameter and not over sensitive to the initial value of this factor. The objective function is adapted with weighting factor to reduce the network real loss and enhance the voltage profile with the purpose of improving the operating performance. In addition, the combined power factor and reduction in network reactive power loss are spotted. Set of fuzzy expert rules using loss sensitivity factors and bus voltages are employed to identify the initial DG’s locations. The proposed approach is attuned to tackle the shortfall of loss sensitivity factors and to decide the final placement of the DGs. Two types of the DGs are studied and investigated. The proposed method is demonstrated and validated thru many radial distribution networks with different sizes and complexities. The BSOA-based methodology can efficiently generate high-quality solutions compared to other competitive techniques in the literature.

Optimal Distributed Generator Allocation in Active Distribution Network Considering Reconfiguration

For the structure of active distribution network can flexibly adjust, an optimal distributed generator allocation model considering reconfiguration is proposed. This model is implemented in the GAMS software and solved using SBB solver based on branch and bound method, due to it is a 0-1 nonlinear mixed integer programming. IEEE 33 standard example is used to simulate for this model. Results show that reconfiguration can increase the capacity of absorbing DG, providing thoughts and guidance for confirming the capacity of DG, and for planning and operation of active distribution network.

Mesh distribution system analysis in presence of distributed generation with time varying load model

With the implementation of competitive electricity supply system all over the world, there is the need of optimal utilization of the existing generation sources at the same time to curtail the generation from the conventional power plants and add the renewable sources generation looking into the environmental concern and limitation of the fossil fuels. This has lead to the motivation for the studies on the integration of distributed resources to the grid. In this paper optimal locations and sizes for DG is determined for weakly meshed distribution networks based on the sensitivity method. Novel method based on loss sensitivity is used in this paper to determine optimal size and location of DGs. The modified Novel method is proposed for DG allocation. The main contribution of the paper is: (i) distributed generation allocation for mesh network using sensitivity approach, (ii) modified Novel method for DG allocation and sizes calculation for meshed distribution system with load variations, (iii) comparison of the results obtained with single and two DG placement with load variations, (iv) the loss savings and overall cost savings per annum with single and two DGs placement with load variations. In this paper we considered the impact of time varying load flow with realistic load model. The realistic ZIP load model has been considered for study. The results have been obtained for a distribution network of UK Distribution Corporation consisting of 38 buses. The results have also been obtained for radial distribution system for comparison.

Optimal reconfiguration and DG allocation in balanced and unbalanced distribution systems

This paper investigates feeder reconfiguration in balanced and unbalanced networks and presents an efficient method to optimize practical distribution systems by means of simultaneous reconfiguration and distributed generation (DG) allocation. A precise and robust load flow algorithm is applied and a composite multi-objective function is formulated to solve the problem which includes: 1. power loss saving, 2. voltage profile, 3. voltage unbalance, and 4. current unbalance of the system. The genetic algorithm (GA) is utilized to search for optimal solution. Results show significant reduction in power loss and number of voltage violations. Moreover, in unbalanced cases, ability of proposed method in three-phase balancing is demonstrated.