Types Of DG Research Articles

Performance Evaluation of Distance Relay Operation in Distribution Systems with Integrated Distributed Energy Resources

This article presents the evaluation of the performance of the distance relay (ANSI function 21) when integrating Distributed Energy Resources (DERs) in a Local Distribution System (LDS). The aim is to understand the impacts of and the necessary modifications required in the operation of distance relays, considering different levels of DER aggregation, and identifying any threshold levels before issues arise. To achieve this, first, a comprehensive review was carried out to analyze the impacts generated in the protection systems. Second, by using the DigSilent Power Factory software, the implementation of the distance relay using a IEEE 13 Node Test Feeder was validated. The aggregation of the three fundamental types of DG, synchronous machines, solar panels, and wind turbines, was evaluated. The threshold at which distributed generation power injection begins to compromise distance protection performance was identified. This study compares the outcomes of using mho and quadrilateral protection schemes.

An AQ-SCS Optimization Assisted Optimal Expansion Planning with Fixed and Switchable Capacitors for Distribution System

Distribution System has given renewable-based DGs like Solar PV and Wind turbines a lot of attention because of growing worries about global warming and the depletion of fossil fuels. This work proposes a unique hybridized optimization technique for a distribution system expansion plan utilizing an innovative theoretical framework. Two stages are used to tackle the issue with the distribution system expansion plan: master optimization and sub-optimization for every state of the system. The developed Aquila based Sand Cat Swarm (Aq-SCS) optimization method considers the distribution system factors, like DG type, size/capacity, location, PQ power, fixed capacitor, switchable capacitor, and the SPV/wind capacity with load demand uncertainties. The sub-algorithm is employed in conjunction with the DG allocation strategy produced by the master algorithm to deduce the state-dependent operating tactics for each individual DG unit with respect to active and reactive power. The suggested Aq-SCS optimization approach is created by combining the properties of the SCSO with the AOA models. The main objective of achieving the minimal possible cost for the DSEP is verified, and the cycle continues unless the best outcome (least cost) is reached. The analysis of the suggested approach is carried out in 4 cases, such as (i) Without DG and capacitor (ii) With capacitor and no DG (iii) With DG and no capacitor (iv) With both capacitor and DG. The analysis is made in IEEE 33 bus system. The Aq-SCS approach outperforms the conservative approaches SSI-CS, WHO, AQO, and SCSO, according to the analysis conducted in MATLAB/Simulink utilizing an IEEE-33 bus system with five system states.

Kajian Teknis Dan Finansial Interkoneksi Pembangkit Listrik Tenaga Biogas Dari Palm Oil Mill Effluent (POME) Pada Sistem 20 KV Di Kabupaten Musi Rawas

Using renewable energy is one way to reduce global warming. Minister of Energy and Mineral Resources Regulation No. 21 of 2016 concerning Purchase of Electricity and Biogas Power. The government has supported efforts to develop electrical energy from biogas. PT Evans Lestari is an oil palm plantation company operating in Mura with a plantation area of 9,220 ha and a mill of 60 tons/hour. In order to fulfill PLN's requirements to offer electricity supply, it is necessary to carry out a technical assessment of the connection and an economic analysis. The connection of the EBT PLT to PLN's 20 kV distribution system has been regulated in Directors' Decree No. 0357.K/DIR/2014. The benefits obtained from using DG include reducing power losses and improving the voltage profile of the distribution system. Among the types of DG that are commonly used, one of them is the Bio Gas Power Plant (PTBG) fueled by liquid palm oil waste.

ПОБУДОВА МОДЕЛЕЙ ОПТИМАЛЬНОЇ ВЗАЄМОДІЇ ДЖЕРЕЛ РОЗОСЕРЕДЖЕНОЇ ГЕНЕРАЦІЇ ТА СИСТЕМ АКУМУЛЮВАННЯ ЕНЕРГІЇ У МЕЖАХ MICROGRID СИСТЕМ

The Problems of integration of DG and RES, as well as energy storage systems (ESS) in the Microgrid systems are investigated in this paper. It is proposed to consider Microgrid models of systems with different types of DG and RES (DER) within the framework of the SGAM architecture, and to distinguish three types of DG and ESS: " Non controllable DER (T1)", "Controllable DER (T2)" and "DER with storage systems (T3)" with further division into subtypes. This method makes it possible to display the multifaceted interaction of systems with DER and ESS within the framework of Microgrid systems. On the basis of the proposed mechanism, the formulation of the problem of optimal interaction of DG sources and ESS within the boundaries of Microgrid systems within the framework of game theory is formalized. An optimization procedure is proposed within the framework of a formalized game, which is based on mechanisms of dynamic pricing and allows to find the most optimal solutions for the game task. References 13, figures 8, table 1.

Superimposed current based differential protection scheme for AC microgrid feeders

Legacy protection schemes face numerous challenges with the emergence of microgrids. Due to the complex controls, the fault responses in microgrids differ from conventional behaviour and vary with the operation mode. In this paper, a differential protection scheme based on fundamental frequency superimposed current phasors is proposed for microgrid feeders. The phasors are extracted using dq components instead of conventional Fourier algorithms. The performance of proposed method is tested for different fault scenarios by carrying out simulations in MATLAB/SIMULINK software. The results confirm that internal faults are detected in a few milliseconds. At the same time, the scheme remains insensitive to external faults with CT saturation and other system disturbances. The proposed scheme is unaffected by the microgrid mode of operation, direction of power flow and DG type.

Impact of Distributed Generators Penetration Level on the Power Loss and Voltage Profile of Radial Distribution Networks

The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.

Density-based clustering and probabilistic classification for integrated transmission-distribution network security state prediction

The proliferation of renewable energy sources (RESs) into the distribution network necessitates the need for the capability to predict the security state of the grid. This paper proposes a density-based clustering and probabilistic classification approach to predict the security state of the modern grid. Firstly, an approach to predict the critical clearing time using the changes in inertia constant and system load is proposed. Secondly, an algorithm for training dataset development from the transient stability responses of the grid considering different operation scenarios is proposed. An expectation-maximization (EM) algorithm using the density-based clustering technique was applied to the dataset to obtain clusters representing the network's security states. Finally, a predictive model was obtained from the labeled dataset using a Naïve-Bayes (NB) probabilistic classifier. The feasibility of this approach is demonstrated using the IEEE 14 bus system. Respectively, an APA and RMSE of 98% and 3.6% were obtained. Also, an MAE of less than 1% was obtained when the proposed model was tested with seven different datasets having a different number of instances. The results show that the proposed technique can predict the security of the integrated transmission-distribution networks considering different DG types, penetration levels, and network disturbances with a high degree of accuracy.

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DNs.

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

PurposeThe purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).Design/methodology/approachThe identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.FindingsThe proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.Originality/valueA comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

Single and Multiple Placements of Different DG Types On the Power Distribution System

Integration of distributed generation on power distribution system impacts the network for improved voltage stability and power quality. However, inaccurate sizing and placement of the energy sources can worsen the network performance. This paper proposes a hybrid particle swarm optimization/whale optimization algorithm for the optimal placement of different distribution generation types on a power network. Standalone metaheuristics are efficient and robust optimization tools but are mostly challenged with convergence and sub-optimal solutions. The exploration potential of particle swarm optimization with the selection of higher inertial weight is annexed with the exploitation phase of the whale optimization algorithm. The proposed technique is verified on IEEE 33 – bus distribution system. Results show 86.12% and 89.84% improvement in voltage deviation for Type I and Type III DG injection respectively. Besides, the convergence is achieved in less than 50 iterations compared to standalone methods.

A novel [formula omitted]PMUs assisted loss-of-mains detection technique for active distribution systems

In this paper, a novel Loss-of-Mains (LoM) detection scheme is proposed using the Adjoint Network principle with limited number of installed micro Phasor Measurement Units (μPMU)s. The proposed scheme is comprised of two main stages viz., Off-line and On-line. In first stage, the network graph is divided into separate zones with two different Adjoints for each zonal area. The phasor information of the Adjoints are stored in matrices, as needed in the real-time process. The next step begins with Zonal Event Detection (ZED), which shrinks the LoM search area and ends up identifying the exact Point of Disconnection (PoD) using the Tellegen’s Theorem in a ZED defined area. The major advantage of the suggested approach is that its reliability is independent of both the circumstances of the power mismatch and DG type. The efficacy of the proposed method is demonstrated through various simulated test cases and also, validated through Hardware-in-the-Loop (HIL) testing using Real-Time Digital Simulator (RTDS) and dSPACE1104 micro-controller on an unbalanced IEEE 13-bus distribution system integrated with PhotoVoltaic (PV) DGs.

Quantitative proteomics revealed the molecular characteristics of distinct types of granulated somatotroph adenomas.

Somatotroph adenomas are obviously heterogeneous in clinical characteristics, imaging performance, pathological diagnosis and therapeutic effect. The heterogeneity of the tumors, especially for SG and DG type adenomas, have attracted great interest in identifying the specific pathological markers and therapeutic targets of them. However, previous analyses of the molecular characteristics of the subtypes of somatotroph adenomas were performed at genomic and transcriptome level. The proteomic differences between the two subtypes of somatotroph adenomas are still unknown. Tumor samples were surgically removed from 10 sporadic pituitary somatotroph adenoma patients and grouped according to the pathological type. Tandem mass tag (TMT)-based quantitative proteomic analysis was employed to analyze the proteomic differences between SG and DG tumors. In total, 228 differentially expressed proteins were identified between SG adenomas and DG adenomas. They were enriched mainly in extracellular matrix (ECM)-receptor interaction, leukocyte transendothelial migration, arrhythmogenic right ventricular cardiomyopathy and DNA replication pathways. Protein-protein interaction (PPI) network analysis indicated that Cadherin-1 and Catenin beta-1 were the most important key proteins in the differences between SG and DG adenomas. Immunohistochemistry (IHC) confirmed the expression levels of the key proteins. This study provides large-scale proteome molecular characteristics of distinct granulation subtypes of somatotroph adenomas. Compared with DG adenomas, The differential protein of SG adenomas mostly enrich in invasive and proliferative functions and pathways at the proteomic level. Cadherin-1 and Catenin beta-1 play key roles in the different biological characteristics of the two tumor subtypes.

Reducing LMP and resolving the congestion of the lines based on placement and optimal size of DG in the power network using the GA-GSF algorithm

One of the important factors in the discussion of power transmission is filling the capacity of network lines, which limits the transmission of power and increases the locational marginal price (LMP) in the network. Today, one of the techniques for resolving congestion and reducing LMP is to install DG units in the network, taking into account two factors: place and optimal size of DG based on operating costs and network constraints, all of which depend on the awareness of the capacity of network lines. In this article, in the first step, using the GA-GSF algorithm, identifying the network's weaknesses such as the lines that have reached their maximum capacity, and then by considering three terms and two scenarios condition of the buses connected to the filled lines will be discussed and we review and extract the best places for installing DG on the network. Then, considering the cost of operating DG and applying it to the objective function of the problem, by positively denoting DG profit and reducing line congestion, the optimization problem is solved by GA-GSF algorithm, and finally, the algorithm output including place and optimal size of DG unit in the network is minimized by line congestion and LMP, and DG installation is economically justified. Finally, the proposed algorithm was tested on the IEEE 14-BUS network for seven different DG types, and the results were such that out of these seven DGs, three DGs were able to reduce the total capacity of the network lines to less than the full capacity of the lines and the price of LMP has been raised to the price of UMP, and the other four DGs have managed to lower the price of LMP compared to their cost function.

Effect of DG Types and Penetration Level in Transmission System using Hybrid Optimization Technique for Loss Control

Effect of DG Types and Penetration Level in Transmission System using Hybrid Optimization Technique for Loss Control

Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO)

The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

Multi-Objective Tabu Search for the Location and Sizing of Multiple Types of FACTS and DG in Electrical Networks

Flexible AC transmission systems and distributed generation units in power systems provide several benefits such as voltage stability, power loss minimization, thermal limits enhancement, or enables power system management close to the limit operation points; and by extension, economic benefits such as power fuel cost and power loss cost minimization. This work presents a multi-objective optimization algorithm to determine the location and size of hybrid solutions based on a combination of Flexible AC transmission systems devices and distributed generation. Further, the work expands the types of FACTS usually considered. The problem is solved by means of a Tabu search algorithm with good results when tested in a network of 300 nodes.

Гибридное моделирование распределенной генерации в электроэнергетических системах

As many countries seek to use distributed energy more and move away from conventional centralized electricity delivery, the functioning of grids carrying distributed generation facilities becomes a relevant matter. Adoption of distributed generation fundamentally alters the properties of distribution grids, transforming them from passive transports and distributors into active distribution systems that have novel properties and dynamic characteristics, which inevitably causes the processes at major generation nodes and remote load nodes to affect each other in the context of reverse, rapidly changing power flows. Analysis presented herein shows that the mutual influence of distributed generation facilities and in-grid processes are ambiguous, and each case calls for analysis of its own, since, while being an advantage for the grid, distributed generation has negative impact, too, which pertains to the operating parameters of various DG types and units. How DG facilities and the grid affect each other can only be found by mathematical modeling; to maximize the accuracy, such modeling has to reproduce the most complete and detailed topology of grids carrying DG facilities; however, there are known limitations of software and hardware that prevent such modeling from returning accurate results. The authors hereof propose an alternative, hybrid approach to modeling electric systems (grid) that combines digital, analog, and physical-level modeling; the approach has been implemented as the All-Mode Real-Time Grid Simulation Complex. A specialized hybrid wind-farm processor has been designed and tested as a distributed generation unit of Tomsk Electric Grids.

Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

To ensure the safe operation of the active distribution network (ADN), accurate fault diagnosis and location are crucial to improve the reliability indices and reduce the outage time. This paper proposes a characteristic model-based method for the single phase to earth fault in the ADN system. Firstly, the characteristic model of the fault factor extracts the phasor distribution characteristics of the voltage and current along the distribution feeder line and estimates the current contribution of DG units to the fault point. Based on the minimum entropy theory, the solution of nonlinear characteristic model is transformed to a single-objective optimization of the characteristic entropy and the diagnosis criteria is formulated. Then, the two-stage fault location scheme for single phase fault is proposed. The fault diagnosis stage estimates the suspicious fault section to reduce the search area and the fault location stage locates the exact fault distance. The Fibonacci search algorithm is utilized for fault location to the optimize iterative and minimize the respond time. The proposed scheme is general for all DG types and overcomes the requirement of its individual model parameters. The proposed method is validated in the IEEE 34-bus distribution test system using the phasor measurement unit (PMU). Test results of the model-based diagnosis and location method can reveal accurate fault location and rapid response at different fault impedance and small time delay comparing with the radial basis function neural network (RBF), and wavelet neural network (WNN) method.

Sequential Power Flow Calculation of AC/DC Hybrid System Considering Converter Station Loss and Its Capacity Constraints

In the power flow calculation of AC/DC hybrid distribution network, the loss and capacity of the converter station have a great influence on the power flow calculation, and the AC/DC alternately solves the power flow, which has the disadvantage of poor convergence performance. In this paper, the power flow model of AC/DC hybrid distribution network with VSC is established. The loss mechanism of the converter station is analyzed and the capacity limit of the converter station is fully considered. The sequential power flow algorithm with VSC AC-DC hybrid distribution network is proposed. Its convergence performance is improved. In the improved 33-node AC/DC hybrid power distribution network with multiple types of DG, the correctness, effectiveness and algorithm fastness of the algorithm are verified.

OPTIMAL DISTRIBUTED GENERATION PLACEMENT AND SIZING TO REDUCE ACTIVE POWER LOSS USING GA AND ACO ALGORITHM

In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniquesare 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 powerlosses as a function of power generators. This strategy based on a strong coupling between activepower and power flow taking into consideration the voltage angles. With the end goal to exhibitthe adequacy of the proposed method, the proposed strategy is applied on IEEE 30-bus standardsystems. Different maximum penetration level capacity of DG units with three ranges such as,10%, 20% and 30% of maximum power load and various possible places of DG units amongseveral types of DG (active, reactive or active and reactive power) are considered. Results showthat the optimization tools employing GA and ACO are effective in reducing active power lossesand cost loss by finding the optimal placement and sizing of DG units.