DG Integration Research Articles

Premium Power Supply Scheme for Data Center With SMES and DG Integration

Premium Power Supply Scheme for Data Center With SMES and DG Integration

Performance evaluation of solar-PV integrated hybrid fuzzy-logic controlled multi-functional UPQC for enhancing PQ features

To improve distribution system voltage and current quality, a newly built solar-PV system connected multi-functional universal power quality compensator (MFUPQC) has been extensively used. The proposed MFUPQC mitigates both load and source-side concerns in a three-phase distribution system. Furthermore, as part of the distributed generation scheme, active power from solar PV is injected into the grid or source when solar PV is available. In this context, the proposed MFUPQC was tested in both PQ enhancement and DG integration modes using a feasible control scheme. The proportional-integral controller is used for shunt- voltage-source inverter (VSI) DC-link control, which is not suitable for regulating DC-link voltage at the desired level due to incorrect gain value selection. In this work, an intelligent hybrid-fuzzy-logic DC-link control of MFUPQC evidences the intelligent knowledge base for better regulation of power-quality issues. The suggested hybrid fuzzy-logic controlled MFUPQC device's performance for both power quality (PQ) improvement and DG integration is validated using the MATLAB/Simulink software tool, and simulation results are provided with an appealing comparison analysis.

A New Framework for Active Loss Reduction and Voltage Profile Enhancement in a Distributed Generation-Dominated Radial Distribution Network

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.

A Hybrid Adaptively Modified Firefly and Differential Evolution in DG Integration Optimization for Improving the Radial Power Distribution Networks

A Hybrid Adaptively Modified Firefly and Differential Evolution in DG Integration Optimization for Improving the Radial Power Distribution Networks

A comprehensive review of recent advances in optimal allocation methods for distributed renewable generation

AbstractDistributed generation (DG) has a key role in enlarging the implementation of renewable energy resources (RES). However, the intermittent and uncontrollable nature of RES can lead to several severe power quality‐related issues. Therefore, many efforts have been made to overcome these issues by optimizing DG sizes and locations. Hence, optimal DG allocation (ODGA) is significant for DG performance and provides advantages to the power system, such as improved power quality, voltage stability, reliability, and profitability. This study reviews recent ODGA studies eliminating the main DG integration problems. Often used ODGA methods have been categorized, and the main differences have been discussed, giving the details of features of optimization methods such as convergence performance and computational burden. A deep analysis for categorizing the objectives of ODGA has been done. In addition, optimization methods applied in ODGA studies have been presented by comparing the superiorities of algorithms and validated test network models. The objectives and significant findings of the ODGA applications are summarized with the advantages and disadvantages. It can be concluded that ODGA has a critical role in RES integration on the DG side and in reducing carbon emissions. This paper leads and provides a perspective for researchers working on recent ODGA methods.

A novel AI approach for optimal deployment of EV fast charging station and reliability analysis with solar based DGs in distribution network

The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network’s infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6.

The Optimal Integration of Multiple DGs under Different Load Models using Artificial Bee Colony-Hill Climbing Algorithm

With the continual advances in technologies, there is a significantly increased demand for electricity leads to a drastic increase in usage and penetration of DGs into the distribution network. The incorrect addition of DGs into the distribution system resulting in declining stability, power losses, voltage profile, reliability, and power quality of the system. Therefore, the optimal planning of DGs (OPDG) has always been under constant investigation and development for the maximum beneficiary. The proposed framework presents a novel meta-heuristic hybrid optimization technique called Artificial Bee Colony-Hill Climbing (ABC-HC) to find the best siting and sizing of multiple DGs using a weighted multi-objective function which includes real and reactive power loss index and voltage deviation index. The results clearly indicate the significant reduction in losses and voltage deviation achieved by integration of single and multiple DGs. The research work is also extended to assess the voltage harmonics of the system before and after the placement of DGs. The effectiveness of the proposed ABC-HC algorithm has been demonstrated on the standard IEEE-33 bus RDS under different voltage dependent load models using MATLAB 2017a.
 HIGHLIGHTS
 
 As demand grows, technologies advance, and power system infrastructure changes, there are more opportunities to integrate distributed generators into the distribution system
 The maximum benefit from the DG integration is achieved by selecting the optimal size and optimal site in the distribution system
 A new hybrid optimization technique has been developed for optimal planning of DGs under different voltage dependent loads. The work was also extended to carry out harmonic analysis
 
 GRAPHICAL ABSTRACT

An intelligent water drop approach for simultaneous reconfiguration and DG integration in distribution system

An intelligent water drop approach for simultaneous reconfiguration and DG integration in distribution system

Impact of Battery Storage with DG Integration in Distribution Network Using Combined Dispatch Strategy for Loss Minimisation

Impact of Battery Storage with DG Integration in Distribution Network Using Combined Dispatch Strategy for Loss Minimisation

An Analytical Approach to Find DG Integration Capacity by Load Analysis

An Analytical Approach to Find DG Integration Capacity by Load Analysis

Multi-objective Planning for Distributed Generation with Consideration of the Randomness of DG output

DG as an alternative clean energy has a rapid development with the advantages of economy, environment protection and so on. But the randomness of power output has an influence on the security and stability of power system. At the same time, the trade-off among multiple optimization goals is often involved in the actual planning process. Therefore, using multi-objective optimization method to plan DG has wider application prospects. In this paper, a multi-objective optimization model of DG planning is established from three aspects of economy, safety and environmental protection. The flexible parameters of power system are introduced to represent the multi-objective optimization sub-functions and constraint conditions. Considering the influence of the stochastic feature of DG output, the multi-objective programming model is transformed into a flexible integrated optimization problem. One of the goals is lower cost, the others are safety and environmental protection. At the same time the objectives of economy, security and environmental protection are optimized comprehensively after DG integration, which are greatly adapted to the actual needs of the power grid planning department. In addition, the introduction of the static voltage stability margin optimization sub objective is of great significance for solving the problems caused by the uncertainty of load forecasting during the planning process.

Implementation of an effective hybrid model for islanded microgrid energy management

Objective: To propose an effective hybrid model for predictive control (EHMPC) to efficiently manage demand and supply of energy for a microgrid operating in islanded mode operation. Due to the intermittent nature of renewable energy sources and variation in load in the microgrid, maintaining the system stability and reliability along with the economy is a critical issue to be addressed. In the islanded mode of operation, the voltage and frequency have to be monitored in addition to managing energy storage units. The different uncertainties occurring at various stages of microgrid should be taken into account to operate the microgrid with reliability under critical condition. Methods: This paper proposes an effective algorithm to efficiently control the operation of microgrid and to operate it with optimal efficiency and reliability. In this work, we have proposed a three-stage control of the microgrid where the first stage consists of the arbitrarily distributed generation (ADG) stage, the second stage has energy storage unit (ESU), and the final stage has the energy management scheme (EMS). Finding: A case study has been carried out and the proposed method is found to be better in performance, economical and robust in comparison with the conventional two-stage model predictive control (MPC) optimization approach. Further important parameters have been analyzed. Novelty: To overcome the limitations of conventional MPC algorithm, we propose a three-stage EHMPC algorithm it consists of three stages, ADG had the first stage of the algorithm the main features of this stage. To optimize the placement, sizing, power factor, minimize network losses and maximize DG integration. The second stage has ESU, the main features of this stage to improve control strategy for optimal power management of microgrid and the final stage has the EMS, to improve tested analysis for a real-time islanded microgrid under various load conditions. Keywords: Energy management; microgrid; renewable Energy; islanded mode; predictive control

DG integration to distribution system with active power injection control

Conventional methodology for electrical power generation is vulnerable due to environmental limitations and the availability of fuel. Distributed generation, offering virtuous benefits to the market partakers, is trending in electrical power system in modern era. This paper presents the distributed generation integration to grid with active power injection control. Distributed generation source delivers DC power which is processed through square wave inverter. Inverter circuit is controlled using a simple control technique to match grid code. Fixing the current reference and varying the same, analysis is carried out for grid integration scheme of distributed generation injecting active power to grid. Simulation work is carried out and results are shown using MATLAB/SIMULINK software.

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Integrated planning of active distribution network and DG integration in clusters considering a novel formulation for reliability assessment

Integrated planning of active distribution network and DG integration in clusters considering a novel formulation for reliability assessment

Optimal placement and sizing of DG in Radial Distribution System using PSO Technique

Power distribution system having various buses, branches and loads that ultimately rises the power losses and from few decades, integration of solar and wind distributed generations has ever increased attention in radial distribution network (RDN). The main reason for DG integration in radial distribution system is increased load growth, transmission line loading, deregulated power system, power losses, increased greenhouse gas emissions, voltage drop and increased reliability issues in RDN. The main focus of this paper is to find optimal size and site of solar PV to improve voltage at all the nodes with in permissible limits and to reduce power losses in RDN using particle swarm optimization (PSO) algorithm. Here two scenarios were considered. Scenario one was the integration of 1DG and second scenario was the integration of 2DGs. Furthermore, Results obtained shows that greater number of DG integration increases the system reliability, reduces losses and improves voltage at all the nodes of the RDN.

Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO 2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi-objective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Selection of Sensitive Buses using the Firefly Algorithm for Optimal Multiple Types of Distributed Generations Allocation

Power loss is one aspect of an electric power system performance indicator. Loss of power can have an impact on poor voltage performance at the receiving end. DG integration in the network has become one of the more powerful methods. To get the maximum benefit from synchronizing the system with DG, it is necessary to ascertain the size, location, and type of DG. This study aims to determine the capacity and location of DG connections for DG type I and type II. To address the aim of this paper, a metaheuristic solution based on a firefly algorithm is used. FA can cover up the lack of metaheuristic algorithms that require a long computational time. To ensure that the load bus location solution is selected as the best DG connection location, the input of the load bus candidate has been filtered based on stability sensitivity. The proposed method is tested on IEEE 30 buses. The optimization results show a decrease in power loss and an increase in bus voltage, which affects an increase in system stability by integrating three DG units. FA validation of the evolution-based algorithm shows a significant reduction in computational time.

Faults Signature Extraction in Wind Farm Integrated Transmission Line Topology

<p>The integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photo Voltaic (PV) systems into convention power system as a future solution to the increase in global energy demands, generation cost reduction, and limited climate impact. The innovation introduced protection compromise challenges in power system due to in-feeds fault current penetration from RGES on existing system, leading to an undesired trip of the healthy section of TL, equipment damages, and safety failure. A comparison study of extracted faults signature from two proposed Transmission Line (TL) network topologies with and without WFG integration, for onward fault identification, and classification model design. Descrete wavelet multiresolution Analysis (DWMRA) of extracted one-cycle fault signal signatures from 11 faults type’s scenarios in Matlab. Result demonstrated a unique fault signatures across all simulated faults scenarios harness for future work of an adaptive unit protection model for this new area of DG integration.</p>

An adaptability evaluation for DG Integration based on hierarchical and regional frame

In order to effectively evaluate the adaptability of distribution network to the DG planning scheme, based on hierarchical and regional frame, this article proposes an evaluation method for DG. On the basis of determining the extent of the distribution network, the hierarchical and regional analysis method is put forward. Then, the adaptability evaluation index system is built from four aspects of reliability, load rate, short circuit current, power quality, to get the grid adaptability to planning DG. The analysis results show that the proposed method can effectively evaluate the acceptance capacity of the regional grid for the DG.