Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method

This paper proposes an approach based on the Symbiotic Organism Search (SOS) for optimal determining sizing, siting, and number of Distributed Generations (DG) in distribution systems. The objective of the problem is to minimize the power loss of the system subject to the equality and inequality constraints such as power balance, bus voltage limits, DG capacity limits, and DG penetration limit. The SOS approach is defined as the symbiotic relationship observed between two organisms in an ecosystem, which does not need the control parameters like other meta-heuristic algorithms in the literature. For the implementation of the proposed method to the problem, an integrated approach of Loss Sensitivity Factor (LSF) is used to determine the optimal location for installation of DG units, and SOS is used to find the optimal size of DG units. The proposed method has been tested on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the SOS algorithm have been compared to those of other methods in the literature. The simulated results have demonstrated that the proposed SOS method has a very good performance and effectiveness for the problem of optimal placement of DG units in distribution systems

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Analysis of the Effect of Distributed Generation on Loss Reduction in Electrical Distribution Network

Distribution network is said to be the most visual part of the electric production and the most observed by the utilities for investment, maintenance and operation. The system have been operated under stressed conditions due to limited structure and increasing day to day requirement of power consumption, which have a significant economic and social impact on the system. Due to the system high resistance to impendence ratio, large amount of power loss occur in the network. This loss is the most severity factors affecting the power quality delivered to the end users and depend on power network expansion and load complexity. Among the support methods available for power loss minimization in distribution network, strategic allocation of Distributed Generation (DG) in distribution system is widely considered a viable option. DGs are electrical sources connected to the power network located to consumer’s side but very small when compared with the centralized power plant. They can be in form of wind, mini-hydro, photovoltaic and fuel-based system such as fuel cells and micro-turbines. Therefore, in this study, different approaches for power loss minimization in electrical distribution system with the incorporation of DG by various researchers were reviewed. These approaches have become powerful tools to overcome the problem of power loss minimization in distribution system. Keywords: Distribution System, Power Loss. Distributed Generation, Power Consumption, Photovoltaic System, Centralized Power Plant. DOI: 10.7176/JETP/11-6-02 Publication date: November 30th 202

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system

Headroom-based optimization for placement of distributed generation in a distribution substation

This paper presents a headroom-based optimization for the placement of distributed generation (DG) in a distribution substation. The penetration limits of DGs into the existing distribution substations are often expressed as a function of the feeder’s hosting capacity (headroom). Therefore, it is important to estimate the reliability of the network\u27s operation as well as that of the limits imposed by the power quality standards by evaluating of the hosting capacity (headroom) of the existing distribution feeder substation. This study aims at developing a novel algorithm for positioning a bus with permissible headroom capacity for DG positioning without causing voltage violations but maximizing the active power supply. Since DG increases short-circuit faults, the algorithm is useful for utility companies to select feeder substations that have permissible headroom capacity for DG installation and thus, contributing to reducing high DG penetration in the network. The modeling and optimization were carried out the Power System Software for Engineers (PSS/E) environment using the IEEE 14-bus test system. The results obtained from the case study show that only two (2) feeder substations out of fourteen (14) have the permissible headroom capacity for DG connections

The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems

The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques

Optimal integration and management of solar generation and battery storage system in distribution systems under uncertain environment

The simultaneous placement of solar photovoltaics (SPVs) and battery energy storage systems (BESSs) in distribution systems is a highly complex combinatorial optimization problem. It not only involves siting and sizing but is also embedded with charging and discharging dispatches of BESSs under dynamically varying system states with intermittency of SPVs and operational constraints. This makes the simultaneous allocation a nested problem, where the operational part acts as a constraint for the planning part and adds complexity to the problem. This paper presents a bi-layer optimization strategy to optimally place SPVs and BESSs in the distribution system. A simple and effective operating BESS strategy model is developed to mitigate reverse power flow, enhance load deviation index and absorb variability of load and power generation which are essential features for the faithful exploitation of available renewable energy sources (RESs). In the proposed optimization strategy, the inner layer optimizes the energy management of BESSs for the sizing and siting as suggested by the outer layer. Since the inner layer optimizes each system state separately, the problem search space of GA is significantly reduced. The application results on a benchmark 33-bus test distribution system highlight the importance of the proposed method

Using HBMO Algorithm to Optimal Sizing & Sitting of Distributed Generation in Power System

This paper analyzes of HBMO placement method efficiency in comparison with PSO and GA in order to sizing and sitting of distributed generation in distribution power system. These algorithms for optimization in this paper is tested on IEEE 33 bus reconfigured test system. The proposed objective function considers active power losses and the voltage profile in nominal load of system. In order to use of optimization algorithms, at first, placement problem is written as an optimization problem which includes the objective function and constraints, and then to achieve the most desirable results, Optimization methods is applied to solve the problem. High performance of the proposed algorithm in mention system is verified by simulations in MATLAB software and in order to illustrate of feasibility of proposed method will accomplish