Optimal Sizing Of Distributed Generation Research Articles

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.

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 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.

Optimum placement and sizing of DG units based on improving voltage stability using multi-objective evolutionary algorithm

This paper presents a method for locating and sizing Distributed Generation (DG) units based on the enhancement of voltage stability and network total loss reduction in radial distribution systems. In this method, a voltage stability index is used to determine the weakest buses from the voltage stability point of view as the best candidates for installing DGs. Also, an evolutionary algorithm based on Pareto Strength is used for solving the DG sizing problem. This problem is configured as a multi-objective optimization problem. The objective functions are defined as minimizing total power loss, enhancement of voltage stability and voltage profile improvement. Also, the effects of DG operating power factor and DG penetration level on total network loss and voltage stability are investigated and the DGs with the ability of generating reactive power are used. The selected DG penetration level is considered as a constraint in the evolutionary algorithm for determining the optimal size of DGs. It is shown that the selection of optimum DG penetration level can optimize the objective functions simultaneously. The proposed method is tested on a standard IEEE 69-bus radial distribution network. The obtained results are compared with similar previous methods and the appropriate performance of the proposed method is verified.

An Improved Method for Reconfiguring and Optimizing Electrical Active Distribution Network Using Evolutionary Particle Swarm Optimization

The presence of optimized distributed generation (DG) with suitable distribution network reconfiguration (DNR) in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO) is proposed in this research. The objective function is formulated to minimize the total power losses (TPL) and to improve the voltage stability index (VSI). The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO) and iteration particle swarm optimization (IPSO). Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.

A Comparative Investigation on Optimization Techniques for Optimal Penetration of Distribution Generators in Power System Application

In deregulation of power system, different regulatory units, environmental concerns and economic concerns have lead to the penetration of Distributed Generation (DG) in power system. When DGs are penetrated, various parameters like DG technology, optimal size, optimal location, and its impact on various operating characteristics such electrical losses, voltage profile, stability and reliability of power system are to be monitored. The performance of DG penetration depends largely upon the selection of the optimal parameter values. Special optimizing techniques used to evaluate optimal value of parameter in context of heuristic nature of variable. A literature review has been carried out on different optimal technique used to optimize parameter values. The authors have reviewed various definitions related to DG and conventional and non conventional technologies on which DG are based. Various issues regarding DG integration have been discussed. Exhaustive reviews of many optimization techniques have been done for optimal location and size of DG.

Optimal Distributed Generator Sizing and Placement by Analytical Method and Fuzzy Expert System: a Case Study in Tezpur University, India

Loss minimization in a distribution network is a vital component for enhancing the efficiency and reliability of a power system. Integration of Distributed Generators (DGs) in the existing power system is comparatively an efficient method for loss reduction than other known solutions. Challenges lie in the optimal sizing and placement of distributed generator. Installation of DGs of higher size at non optimal buses leads to higher system losses. In this paper a two stage methodology is proposed for optimal sizing and placement of DGs. At first, the sizing of DGs is done by analytical approach based on exact loss formula for minimum loss at the allied buses. After that Fuzzy expert system (FES) is used for optimal Placement using Distribution Loss Reduction Index (DLRI) and Voltage Deviation Reduction Index (VDRI) as its input parameters. The proposed methodology is tested for a practical distribution system of Tezpur University, India. The results show the effectiveness of the algorithm in terms of the loss reduction and voltage profile improvement of the distribution system.

A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems

Distributed Generation (DG) offers the reliable and economic source of electricity to consumers. These are connected directly to the distribution system at consumer load points. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. However, Optimal DG (ODG) allocation and sizing is always a challenging task for utilities as well as consumers. The major objective of ODG allocation and sizing is to improve system overall efficiency with minimum power loss, maximum system security, voltage stability, and reliability. Analytical techniques are performing well for small and simple systems, not suitable for a system with large and complex networks. However, various meta-heuristic techniques are performing better in terms of accuracy and convergence for extensively large and complex networks. A hybrid optimization is a combination of two or more optimization techniques. This technique offers efficient and reliable global optimum solutions for complex multi-objective problems. In this context, a comprehensive literature review of DG fundamentals and the different technical approaches for DG integration into the distribution system are analyzed here. Furthermore, an attempt has been made for comparison of analytical, classical (non-heuristic), meta-heuristic and hybrid optimization techniques with respect to objective function, test system, advantages, and disadvantages. This present study will give in-depth knowledge and acts as a forthright reference for imminent investigators and investors for ODG allocation and sizing in a distribution system.Â

Switching strategy for DG optimal allocation during repairing fault periods on loop distribution networks

Summary It is expected that the use of distributed generation (DG) will increase sharply in distribution systems, especially with the realization of smart grids. In spite of the benefits of DG in distribution systems, high penetration level of DGs could cause some challenges for the network. Therefore, the optimal sizing and siting of DGs should be identified carefully. In addition, different operating conditions have to be taken into account in this process. The DGs are usually installed depending on optimal siting and sizing techniques considering normal operating periods. During repairing fault periods, different locations and capacities of DGs may be preferable. In this paper, 2 scenarios of switching strategies are presented for attaining the optimal sizing and siting of DGs during repairing fault period starting from the optimal allocation obtained for normal operating period. Genetic algorithm is used for solving the optimization problem that is implemented for an actual loop distribution system in Egypt.

Multi-Objective Approach for Reconfiguration of Distribution Systems with Distributed Generations

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems.

Optimal DG Location and Sizing for Minimum Active Power Loss in Radial Distribution System using Firefly Algorithm

In this paper, a novel optimization algorithm is presented for optimal location and sizing of Distributed Generation (DG) units on distribution systems. For this purpose, a recently based meta-heuristic called Firefly Algorithm (FA) has been employed to minimize the total active power losses. The results show a considerable improved in voltage profiles of all the buses and enhance the voltage stability index. The investigations were tested on IEEE 33 bus radial distribution system. Simulation results demonstrate the effectiveness of firefly algorithm. Comparison with another method is also given.

Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems

This paper mainly focused on the impact of distributed generation (DG) placement on distribution system. The integration of DG is transforming the traditional radial distribution system into a multi-source system. Distributed generation is a term that refers to the production of electricity near the consumption place. The effects of distributed generation are short circuit levels are increased, load losses change, reliability change and voltage profiles change along the network. The above advantages can be accomplished by ideal position and sizing of DG units. The ideal positions are obtained from index vector method. Ant Lion Optimization (ALO), a novel meta heuristic algorithm is used to determine the optimal DG size. ALO is modeled based on the unique hunting behavior of ant lions. The ALO algorithm is evaluated on IEEE 15, 33, 69 and 85-bus test systems. ALO algorithm was compared with different types of DG units and other evolutionary algorithms. When compared with other algorithms the ALO algorithm gives better results. From the analysis best results have been achieved from type III DG operating at 0.9pf.

Optimal multiple microgrids based forming of greenfield distribution network under uncertainty

Multiple microgrid (MMG) operation concept of community microgrid (MG) provides enhanced resiliency and reliability through self-healing, enabling a high penetration of distributed generations (DGs), power interchange between MGs and real-time communication. In this study, the methodology of optimal design of low-voltage greenfield distribution grids based on multiple MGs in the presence of uncertainties is presented. The electrical and geographical borders of MGs community are determined using some important criteria such as power balance and spatial distribution of load. Within MGs, both of dispatchable and non-dispatchable DGs are considered. The external grid is assumed as a backup for each MG in abnormal conditions. The proposed method determines the optimal borders of MGs, optimal size and site of DGs for each autonomous MG simultaneously. The imperialist competitive algorithm is used to optimise the total cost of optimal MG clustering problem. The method is implemented to a vacant area with residential and commercial customers. The MGs optimal service area, DGs location, size and type within each MG and LV feeder's route are indicated and compared both for deterministic and probabilistic planning cases.

Optimal distributed generation placement in distribution system to improve reliability and critical loads pick up after natural disasters

The increase in frequency of natural disasters has necessitated the need of resilient distribution systems. Natural disasters lead to severe damage of power system infrastructure and the main grid may not be available to serve the loads. The integration of distributed generation (DG) into distribution system partially restores the loads after natural disasters and improves the reliability during normal operating conditions. After a natural disaster, objective of the system operators is to restore the critical loads as a priority. This enables the need of considering critical load pick up as an objective function while placing the DGs. A location based constraint is, thus, required to make sure the DGs are available to pick up the loads after natural disasters. Fuzzy multi criteria decision making (FMCDM) approach is used in this work to rank the load points and locations/feeder sections. This paper uses particle swarm optimization (PSO) to evaluate the optimal size and location of DGs using the proposed objective function. The obtained results are compared with the results of reliability as an objective function.

Linear Model for Optimal Distributed Generation Size Predication

This article presents a linear model predicting optimal size of Distributed Generation (DG) that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.

Techno-economic and environmental approach for optimal placement and sizing of renewable DGs in distribution system

This paper presents an approach for optimal placement and sizing of dispatchable and non-dispatchable renewable distributed generators (DG) units in a radial distribution network. In this work, wind and solar energy based DG units are considered as non-dispatchable DG, while biomass energy based DG units are considered as dispatchable. The formulated multi-objective problem comprises of technical performance indices for real power loss, maximum branch current capacity, voltage deviation, environment impact reduction index; and economic index. The appropriate weighting factors for different indices has been decided by Analytic Hierarchy Process (AHP). To solve the developed formulation, Particle Swarm Optimization (PSO) based approach has been used and applied on a 51-bus distribution networks. Also, in order to validate the use of AHP, the prioritization to different indices leads to formation of four scenarios and results are obtained in terms of the technical, economic and environmental parameters. The results obtained by the proposed method have been compared with other multi-objective problems considering different weighting factors to the indices and emerged as a simple and efficient approach.

Hybrid approach for congestion management using optimal placement of distributed generator

ABSTRACTCongestion management (CM) in a large power system network is a difficult task which can be solved by placing one or more distributed generators (DGs) on congested lines. The first concern is to determine the exact location of congested line for the placement of optimal size of DG so that cost can be minimised. In this work, hybridisation of firefly technique and differential evolution optimisation search has been proposed, which manages congestion effectively by rescheduling of generators satisfying the system constraints both technically and economically in the deregulated market scenario. To validate the proposed hybrid approach, results have been compared with firefly optimisation technique results. It is observed that the hybrid approach is an efficient tool in handling CM resulting in a secure operation to reduce flows in the heavily loaded lines with low system loss and increasing power capability with improved stability of network by controlling power flows in the network.

Optimal Allocation of DG and DSTATCOM in Radial Distribution System Using Cuckoo Search Optimization Algorithm

This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) simultaneously in the distribution network. The objective function is formulated to minimize the total power losses of the system subjected to equality and inequality constraints. Loss sensitivity factor (LSF) and Voltage Stability Index (VSI) are used to predetermine the optimal location of DG and DSTATCOM, respectively. Recently developed nature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM. In the present work, five different cases have been considered during DG and DSTATCOM placement to access the performance of the proposed technique. To check the feasibility, the proposed method is tested on IEEE 12-bus, 34-bus, and 69-bus radial distribution system and the results were compared with other existing techniques.

Analyzing the Diverse Impacts of Conventional Distributed Energy Resources on Distribution System

In recent years, the rapid boost in energy demand around the globe has put power system in stress. To fulfill the energy demands and confine technical losses, researchers are eager to investigate the diverse impacts of Distributed Generation (DG) on the parameters of distribution network. DG is becoming even more attractive to power producing companies, utilities and consumers due to production of energy near to load centers. Reduction in power losses, better voltage profile and less environmental impact are the benefits of DG. Besides renewable energy resources, conventional energy resources are also a viable option for DG. This research aims to analyze the impact of localized synchronous and induction generators on distributions network. The main objectives are to find optimal type, size and location of DG in distribution network to have better impact on voltage profile and reduction in power losses. Using worldwide recognized software tool ETAP and Kohat road electricity distribution network as a test case. Results depicted that at certain buses, positive impacts on voltage profile were recorded while almost 20% of power losses were decreased when synchronous generator as DG unit was injected in distribution network. Injecting induction generator as DG unit, the results showed increase in power losses due to absorption of reactive power, while improving voltage profile by injecting active power.

The Proper Location and Sizing of Multiple Distributed Generators for Maximizing Voltage Stability Using PSO

Energy technologies and their efficient use plays a vital role in socio-economic development of any country. In the recent years, the restructuring of electricity market evolves some major improvements in the technologies of energy production and thus, it has paved the way for increasing the applications of Distributed Generation (DG) with renewable energy sources. In this research, the optimal placement and sizing of multiple DGs are achieved by a novel indicator United Bus and Line Voltage Firmness Factor (UBL_VFF). The objectives of this work are the minimization of system losses and maximization of voltage stability and they are achieved by identifying the weakest voltage bus due to the weakest link in the system. Particle Swarm Optimization (PSO) is used for solving this optimization problem. The effectiveness of this proposed approach is tested in 33 and 69 bus radial distribution test systems. The results of this proposed method is compared with the results reported in the contemporary literature. The results have proved to be robust in terms of reduction in system losses and maximization of bus and line voltage stability.