Optimal Allocation Of Distributed Generation Research Articles

Optimal Allocation of Combined Renewable Distributed Generation and Capacitor Units for Interconnection Cost Reduction

In this paper, a new methodology for the optimal investment in distributed generation is presented, based on an optimal allocation of combined DG and capacitor units to alleviate network voltage constraints and reduce the interconnection cost of renewable generation integration in public medium voltage distribution networks. An analytical optimization method is developed, with the inclusion of practical considerations that are typically neglected in developed works: network topology reconfiguration and the geographical data of the generation land-use and network infrastructure. Powerful results concluded from a sensitivity analysis study of the most impacted parts of the network by the variation of active and reactive power injection under network topology reconfiguration are used as a basis for capacitor units placement. A case study, with two meshed IEEE 15-bus feeders and a new DG to connect, geographical dispersed, are used to simulate the performance of the proposed approach. A cost evaluation of the obtained results proves the effectiveness of the proposed approach to reduce the required charges for connecting new renewable generation units in medium voltage distribution system.

A novel multi-objective modified symbiotic organisms search algorithm for optimal allocation of distributed generation in radial distribution system

This article presents a novel optimization technique to allocate distributed generation (DG) units optimally in radial distribution system (RDS). Three renewable type DG units (such as wind turbine, solar photovoltaic and biomass system) have been integrated in the RDS. In this regard, an optimization problem is formulated considering multiple technical and economic objectives of the DG planning. A new metaheuristic, namely multi-objective modified symbiotic organisms search (MOMSOS), is proposed to solve this optimal DG allocation problem. A chaos-based cross-over operator is introduced in the parasitism phase of the proposed MOMSOS to enhance diversity in the population. The proposed MOMSOS is equipped with hierarchical non-dominated sorting technique which is superior to the existing fast non-dominated sorting strategy in terms of computational complexity. An adaptive penalty function is utilized for constraint handling. The proposed algorithm is tested on some CEC 2009 benchmark test problems to ensure its global optimization capability. Furthermore, performance of the proposed MOMSOS is validated on 69-node RDS and the obtained results are compared with other well-established multi-objective optimization algorithms.

Upgrading Conventional Distribution Networks by Actively Planning Distributed Generation Based on Virtual Microgrids

In addition to active energy management, this article proposes active planning as another critical feature of active distribution networks (ADNs). To develop this set of tasks, this article introduces a three-layer active planning framework consisting of a physical layer, cyber layer, and socioeconomic layer. Furthermore, a three-step developing strategy for ADNs based on a virtual microgrid (VM) is put forward. Then, according to this framework, this article focuses on a specific and fundamental issue that often arises: the optimal allocation of distributed generation (DG). A two-stage scheme based on VMs is a proposed solution. In the first stage, VM boundaries are determined based on the characteristics of a network structure. Using the identified VM boundaries as constraints, a bilevel hierarchical optimization method is applied to determine the optimal DG allocation in the second stage. The proposed method is verified in the popular PG&E 69-bus distribution network.

Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models

Renewable energy-based Distributed Generation (DG) is the most imperative part of modern power system and offers many potential benefits. To attain maximum benefits offered by DG integration, it is important to model the time varying characteristics of both load and generation. Therefore, this paper presents a new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads. At first, Probabilistic model is proposed for wind speed uncertainty modeling to calculate the hourly output power from wind DG. Afterwards, the values of output power are considered for determining optimal allocation and penetration of wind DG in distribution network to minimize the Average Multi-Objective Index (AIMO) using Particle Swarm Optimization (PSO). The strength of the proposed methodology is validated on IEEE 33 and 69-bus systems. Results depict that, proposed methodology is appropriate for wind speed modeling and is suitable for implementing in power system planning. • Time-varying characteristics of load and generation are assumed for DG allocation. • Weibull distribution-based PG model is proposed to determine hourly wind DG output. • Hourly output power values are considered for the optimal allocation of DG. • The penetration level of wind DG is calculated for different TVVD load models.

Optimal Allocation of DGs with network reconfiguration using Improved Spotted Hyena Algorithm

Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DGs) or system reconfiguration (SRC). However, very few researches have been exerted for incorporating both where this merge represents further complexities in searching for maximizing their benefits and minimizing the investments. This paper sheds light on the simultaneous allocation of DGs with SRC in distribution systems. It suggests a multi-objective formulation considering multiple objectives: active power loss minimization, total cost reduction, and voltage profile improvement. For solving this problem, an improved spotted hyena optimizer (ISHO) is developed. The SHO demonstrates a good feature in finding the optimal or nearly optimal subset with simple structure to minimize the given fitness function. To support the exploration characteristics of the proposed ISHO, it augments a switch strategy in the updating mechanism of the spotted hyena’s positions and random positions are generated instead of the violated spotted hyenas. The simulations are investigated for the integration of a single and multiple DGs with SRC for two standard systems, which are IEEE 33 and 69-node test systems. The obtained results prove the efficiency of the suggested method for the network manager to find the optimal DGs allocation with SRC considering multiple criteria. These results are compared with previous literatures to demonstrate the effectiveness of the proposed algorithm.

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks

This paper aims to apply an improved meta-heuristic method to optimize the allocation of distributed generation (DG) units in radial distribution networks (RDNs). The proposed method, namely the Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS) algorithm, is the improved version of the original SOS algorithm. QOCSOS incorporates the Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies into SOS to improve the global search capacity. In this study, the objective of the optimal DG allocation (OGDA) problem is to optimally reduce the real power loss, improve the voltage profile, and increase the voltage stability in RDNs. The proposed QOCSOS algorithm was applied to find the optimal locations and sizes of DG units with different DG power factors (unity and non-unity) in the RDNs including 33, 69, and 118-bus. It was found that the operation of DG units with optimal power factor significantly improved the performance of RDNs in terms of voltage deviation minimization, and voltage stability maximization, especially for power loss reduction. After the DG integration, for the case of DG units operating with unity power factor, the power loss reduction was reduced by 65.50%, 69.14%, and 60.23% for the 33, 69, and 118-bus RDNs, respectively. In addition, it should be emphasized that for the cases of DG units operating with optimal power factor, the power loss reduction was reduced up to 94.44%, 98.10%, and 90.28% for these RDNs, respectively. The obtained results from QOCSOS were evaluated by comparing to those from SOS and other optimization methods in the literature. The results showed that the proposed QOCSOS method performed greater than SOS, and offered better quality solutions than many other compared methods, suggesting the feasibility of QOCSOS in solving the ODGA problem, especially for a complex and large-scale system.

Optimal Distributed Generation Allocation and Sizing Using Genetic and Ant Colony Algorithms

Distributed generators (DG) which installed into distribution network to face the increasing of load demand. DG can used to enhance power generation systems and improve distribution network efficiency. However, the distributed generators units’ implementation at not convenient position and sizing can lead to negative impacts such as a growing in power losses and invasion of system constraints. Because the rising of demand in energy. The appropriate placement and sizing of DG’s units is a credible solution to much problems in distribution system, for example power loss reduction and voltage regulation. The allocation of distributed generators into the distribution network can significantly impact in the transmission and distribution systems. Therefore, a method, which can identify an optimum DG location and size, is necessary. In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniques are proposed to find optimal sizing and location for distributed generation in electrical networks. The objective function of the work relies upon a linearized model to compute the active power losses as a function of power supplied from the generators. This strategy based on a strong coupling between active power and power flow taking into consideration the voltage angles. With the end goal to exhibit the adequacy of the proposed method, the proposed strategy is applied on IEEE 57-bus standard systems. Different maximum penetration level capacity of DG units and various possible places of DG units among several types of DG (active, reactive or active and reactive power) are considered. Results show that the optimization tools employing GA and ACO are effective in reducing active power losses by finding the optimal placement and sizing of DG units

Simultaneous Allocation of Multiple Distributed Generators and Shunt Capacitor Banks in Radial Distribution Systems using Grasshopper Optimization Algorithm

Electricity has become a part of day to day life of the people. With the increase in population, electricity demand is also raised. The development of distribution systems (DSs), the optimal utilisation of shunt capacitor banks (SCBs) and distributed generators (DGs) in the DSs are heightened. In this paper, a methodology is proposed to allocate both DGs and SCBs simultaneously on the DS. The objectives are to minimise power loss, improving the voltage profile and voltage stability index (VSI) of the DS. First locations of DGs and SCBs are found out by loss sensitivity factor (LSF) technique. Next grasshopper optimisation algorithm (GOA) is used to determine the rating of these sources. The methodology is implemented on IEEE 33 and 69 bus test systems considering optimal allocation of multiple DGs without and with placement of capacitors. Finally, the results obtained by the proposed method are compared with GA/PSO and PSO methods.

Simultaneous optimal allocation and sizing of DGs and capacitors in radial distribution systems using SPEA2 considering load uncertainty

The rapid growth in load demand results in higher line loses in distribution systems and also demands distribution system augmentation. Apart from this, due to fluctuating loads, it becomes a challenge for the utility sectors to maintain voltage stability of the system under healthy condition. For addressing such problems, simultaneous optimal placement of distributed generations (DGs) and capacitors in radial distribution systems employing multi-objective optimisation technique based approach is explored in this work. In line with this, the present work uses a simple and powerful multi-objective strength Pareto evolutionary algorithm 2 (SPEA2) for solving distributed generator and capacitor placement problem considering load uncertainty. The uncertainty characteristics of load are designed by probabilistic approach and the same is utilised during the optimisation process. Thereafter, a set of non-dominated Pareto optimal solution is obtained according to the objective function value. A compromised solution is selected from the set of Pareto optimal solutions by using fuzzy set theory. Along with the above, the impact of reverse power flow is studied by taking different test cases. The studied algorithm has been tested on different standard IEEE 33-bus and 69-bus radial distribution test systems.

Optimal Distributed Generation Allocation in Distribution Systems with Non-Linear Loads Using a New Hybrid Meta-Heuristic Algorithm

Abstract This paper presents a new hybrid meta-heuristic algorithm based on the Phasor Particle Swarm Optimization (PPSO) and Gravitational Search Algorithm (GSA) for optimal allocation of distributed generation (DG) in distribution systems with non-linear loads. Performance of the algorithm is evaluated on the IEEE 69-bus system with the aim of reducing power losses, as well as improving voltage profile and power quality. Results, obtained using the proposed algorithm, are compared with those obtained using the original PSO, PPSO, GSA and PSOGSA algorithms. It is found that the proposed algorithm has better performance in terms of convergence speed and finding the best solutions.

Simultaneous Allocation of Distributed Generators and Shunt Capacitors in a Distribution System

Performance of a distribution system is negatively affected with the usage of non linear loads and rapid growth in electricity demand. It is possible to improve the voltage profile and reduce the power loss in a distribution system, by integrating distributed generators (DGs) and shunt capacitors (SCs). Identifying the optimal location and capacity of DGs and SCsare the crucial factors affecting the DS performance. This paper aims to reduce the power losses in the DS and facilitates an improvement in voltage profile with optimal allocation of DGs and SCs. First, the vulnerable nodes for placement of DGs and SCs are identified by loss sensitivity factor (LSF) technique. Next, the sizes of SCs and DGs at these corresponding locations are determined using a recently developed swarm intelligent technique dragonfly algorithm (DFA). Various constraints of the DS are included to estimate the objective function. To analyze the performance of the proposed method it is investigated on IEEE 69 bus radial distribution systems (RDS) considering constant power load at different load levels. Several case studies are conducted to analyze the performance of the DS. Three different load levels at different power factors are considered in the study. Initially few case studies are performed by considering single DG and single SC. Further the analyses are extended with multiple DGs and SCs. Finally, the proposed method is compared with other prominent methods accessible in the literature. It can be inferred from the analyses that simultaneous allocation of DGs and SCs in DS improves the overall performance of the system.

Effect of Inertia Weight of PSO on Optimal Placement of DG

Integrating Distributed Generation (DG) at appropriate location in distribution system can reduce its real power losses and can also increase the voltage regulation. Optimal allocation of DG has two parts, i) location identification of DG, ii) capacity determination of DG. This article uses loss sensitivity methods for fixing of optimal place of DG and then Particle Swarm Optimization (PSO) technique is implemented for determination of optimal size of DG at that location. During optimization, effect of inertia weight of PSO on the optimal placement of DG is demonstrated in this paper. For detailed study, IEEE 33-bus system is considered and impact of integration DG in the system is also shown.

Optimal DG allocation for enhancing voltage stability and minimizing power loss using hybrid gray wolf optimizer

High penetration of photovoltaic and wind turbine-based distributed generators (DGs) can help reduce carbon emissions which is an important goal for the whole world. DG can be used to improve the voltage stability, present generation reserve/emergency, and consequently, the system power quality can be improved. However, it is very important to select the right size and location of a DG so that the power system can increase the gained benefits of such an installation to the maximum. In this paper, a hybrid optimization technique is proposed to determine the optimal allocation of DG in the standard IEEE 33-bus radial distribution system in order to improve the voltage stability and minimize the total power loss. The proposed hyprid technique is based on the gray wolf optimizer algorithm with loss sensitivity factor. The performance of the system is analyzed without DG installation, then it is compared with the performance of the system when DGs are installed with the predefined optimal sizes and locations. The study is performed by MATLAB M-Files and NEPLAN software.

Optimal Single Point Injection of PV based DG Maximizing Technical, Social and Environmental Benefits in Radial Distribution Systems using JAYA Algorithm

Power crisis is becoming a major challenge in modern day world due to ever increasing load demand leading to fast depletion of energy resources. As the number of loads connected to the distribution network increase, maintaining the voltage profile of the distribution network is becoming increasingly difficult for the network operator. The pollutants from a fossil fuel based power plant have adverse effects on the environment. This paper presents a comprehensive study on the technical, economic and environmental impacts on single point optimal allocation of DG on a radial distribution network. The test systems considered are standard IEEE 33 bus, 69 bus and 85 bus test systems.

Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic

Many efforts on distributed generation (DG) allocation are based on deterministic methods in previous studies. However, the influence of stochastic fluctuation characteristics of DGs and loads is enormous. Therefore, affine arithmetic (AA) is used to represent the uncertainty, and a multi-objective uncertainty optimization model for DG allocation with minimum investment cost, highest income, lowest environmental cost, and minimal network loss is built in this paper. Then multi-objective interval decision-making and bi-layer optimization method based on D-S evidence theory (ET) and genetic algorithm (GA) is proposed to achieve the optimal allocation of DGs. In the proposed method, GA is used as an outer layer optimization for multi-point searches to generate different allocation schemes. Moreover, the ET method is employed as the inner layer to evaluate the candidate allocation schemes which from the outer layer optimization. Also, the ET method guides the evolution direction of GA in the outer layer. For validating the effectiveness and performance, the proposed method is applied to a typical 33-bus distribution system. The comparison with the existing TOPSIS method demonstrates the advantages when handling the uncertainty of the proposed method and achieves a more robust optimal solution.

Optimal Placement of Distributed Generation in Power System for Power System Loss Reduction Using ETAP

Because of increasing interest in renewable energy sources in recent times, the studies concerning integration of Distributed Generation (DG) to power grid have been increased rapidly. Apart from other benefits, loss reduction and voltage profile improvement are its salient features. Non-optimal locations of DG units may lead to increase power losses. Optimal location of DGs in power systems is vital to maximize overall system efficiency. In this approach, optimization techniques have been applied to determine the optimal allocation and impact of DG on electric power system in terms of power loss reduction are analyzed. The Newton Raphson load flow analysis has been carried out on 10 bus system using ETAP software which shows that active power losses were reduced from 3302.2 KW to 400.7 KW after the installation of 5MW.

Optimal Placement of Distributed Generation in Power System for Power System Loss Reduction Using ETAP

Because of increasing interest in renewable energy sources in recent times, the studies concerning integration of Distributed Generation (DG) to power grid have been increased rapidly. Apart from other benefits, loss reduction and voltage profile improvement are its salient features. Non-optimal locations of DG units may lead to increase power losses. Optimal location of DGs in power systems is vital to maximize overall system efficiency. In this approach, optimization techniques have been applied to determine the optimal allocation and impact of DG on electric power system in terms of power loss reduction are analyzed. The Newton Raphson load flow analysis has been carried out on 10 bus system using ETAP software which shows that active power losses were reduced from 3302.2 KW to 400.7 KW after the installation of 5MW.

Optimal Allocation and Sizing of Multiple DG in Radial Distribution System Using Binary Particle Swarm Optimization

Optimal Allocation and Sizing of Multiple DG in Radial Distribution System Using Binary Particle Swarm Optimization

An Efficient Probabilistic Approach Based on Area Grey Incidence Decision Making for Optimal Distributed Generation Planning

The increase in the scale of distribution networks significantly reduces the efficiency of intelligent planning for distributed generation (DG). To improve the efficiency of intelligent DG planning and avoid the impact of uncertainty concerning renewable energy on it, this paper proposes a sensitivity index for the bus-embedded multi-objective estimation of distribution algorithm (MEDA) based on the semi-invariant probabilistic power flow approach to achieve an optimal solution. The sensitivity indices of the buses are comprehensively enabled to obtain a new index and determine their sensitivity sequences based on the area grey incidence decision-making method. Subsequently, according to the uncertainty of wind turbine generators and photovoltaics, a probability model is established, and the semi-invariant method is used to solve for the probabilistic power flow according to a correlation model. Finally, the sensitivity of the proposed bus-embedded MEDA to enhancing the efficiency of the solution is examined. The optimal DG allocation scheme is obtained with the goal of achieving the lowest total cost in the planning year. Finally, the feasibility and effectiveness of the proposed model and method are verified using simulations of the IEEE 33-bus, IEEE 69-bus, and IEEE 118-bus test systems.

Optimal allocation of distributed generation units for converting conventional radial distribution system to loop using particle swarm optimization

Closed loop distribution system is one of the promising techniques to overcome the shortage of the radial distribution system. Converting the conventional radial distribution system to loop is an interesting research area. The voltage difference during connecting any two buses in radial distribution system, to convert a radial part to loop, is the main problem that should be solved. In this work, a new technique based on applying distributed generators (DGs) to overcome this problem is introduced. This paper proposes a particle swarm optimization (PSO) based algorithm for optimal allocation of DGs in a primary radial distribution system to minimize the difference in voltage between specified two buses so that they could be tied to loop a part of the system. The proposed algorithm makes use of a backward/ forward load flow method for the load-flow analysis of the radial distribution system. The proposed PSO-based algorithm is tested on the IEEE 33 –bus radial distribution system and results are obtained using MATLAB. Discussions are provided based on the obtained results.