Types Of Distributed Generation Research Articles

Effects of Integration of Multi Type DGs on Voltage stability and Loss Minimization in the Distribution System

In recent days in distribution system the more importance is giving to Loss minimization techniques since electricity demand is continuously increasing and utility companies are struggling to meet this rapid increase in load due constraints on energy resource to increase the generation. Furthermore, voltage instability phenomena is also playing important role in distribution systems and caused a major blackout in the network. To solve the above problem new technology that is drawing more attention is Distributed Generation (DG). Placing DG in the distribution system inherently reduces the power loss and improves the system voltage. The selection of type of DG, proper placement and sizing of DG plays an important role. This paper presents an effective methodology to identify the optimum location of multi type DG in the distribution system in order to reduce the losses and improve the voltage. In this work the IEEE 33-Bus system is simulated in Power World Simulator (PWS) and the voltage stability and system losses are analyzed with different types of DGs and combination of different types of DGs are studied. Simulation result shows that optimal placement and sizing of DG will reduce the system losses and improve the voltage profile within the acceptable limits and the loss reduction and voltage profile improvement are depending on the types of DGs and combination of DGs placed in the distribution system. Index Terms: Distributed generation, Optimum size, Optimum location, Power loss, Power world simulator, Sensitivity analysis, Types of DG, Voltage stability.

Determination of location and capacity of distributed generations with reconfiguration in distribution systems for power quality improvement

The use of non-linear loads and the integration of renewable energy in electricity network can cause power quality problems, especially harmonic distortion. It is a challenge in the operation and design of the radial distribution system. This can happen because harmonics that exceed the limit can cause interference to equipment and systems. This study will discuss the determination of the optimal location and capacity of distributed generation (DG) and network reconfiguration in the radial distribution system to improve the quality of electric power, especially the suppression of harmonic distribution. This study combines the optimal location and capacity of DG and network reconfiguration using the particle swarm optimization method. In addition, this research method is implemented in the distribution system of Bandar Lampung City by considering the effect of using nonlinear loads to improve power quality, especially harmonic distortion. The inverter-based DG type used considers the value of harmonic source when placed. The combination of the proposed methods provides an optimal solution. Increased efficiency in reducing power losses up to 81.17% and %total harmonic distortion voltage (THDv) is below the allowable limit.

Comparative assessment of techno-economic and environmental benefits in optimal allocation of distributed generators in distribution networks

Integration of Distributed Generation (DG) into power systems, especially at the distribution end, is one of the verified approaches that has been utilized to reduce power loss, enhanced reliable electricity supply, and promote environmental sustainability by reducing Greenhouse Gas (GHG) emissions. In this study, an approach for solving an optimal DG allocation problem in distribution network with the objective function of maximizing the financial Techno-Economic and Environmental Benefits (TEEBs) of the grid is discussed. The TEEBs analysis of the DG planning problem is uniquely modeled as financial cost-benefit due to reduced power purchased and reduced Penalty Emission Cost (PEC) arising from the reduction of GHG emission in the network. A comprehensive and comparative analysis was carried out for the four classes of DG technology types to identify the environmental impact of integrating renewable and non-renewable DGs into the distribution system. Furthermore, The DG planning problem is solved using the Black Widow Optimizer (BWO) technique. The study implemented the proposed methodology on the standard IEEE 69-bus and Nigerian Shasa 59-bus distribution systems. The results show that renewable DGs’ optimal integration yielded higher TEEBs than non-renewable DGs despite the high capital cost of renewable DGs. Furthermore, the study affirmed the viability and efficiency of the proposed method by comparing the results of power loss obtained for the various DG types with that of techniques in open works of literature.

Optimal distributed generation allocation in distribution system for power loss minimization and voltage stability improvement

Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.

Optimal Locating and Sizing of DG in Radial Distribution System Using Modified Shuffled Frog Leaping Algorithm

Several advantages have been achieved in distribution systems by adding Distributed Generation (DG). These benefits comprise minimizing power losses and improving the voltage profiles. Such benefits can be enhanced if DGs are optimally sized. This paper presents a DG in the distribution scheme to increase node voltages and reduce the power loss of radial distribution systems by using the Modified Shuffled Frog Leaping Algorithm (MSFLA) technique. The core objective is to decrease active-power losses of the system while keeping the voltage profiles within the defined limits of the system. This paper optimally allocates and sizes three types of DGs in various standard radial test systems and a practical radial feeder. Single and multiple DGs are considered for the proposed work by including some special cases. The standard test systems on which proposed work is performed are 33-bus and 69-bus radial feeders. A practical 43-bus radial feeder of factory area Faisalabad has also been taken under consideration for analysis. The Forward–Backward Sweep (FBS) scheme is selected for the load flow analysis, whereas MSFLA is chosen for finding of optimum size(s) and site(s) of DG(s). Various types of DGs results are retained separately and collectively. We have reduced the losses up to 85.14% and 96.1% for the 33-bus system and 69-bus respectively. The results will depict a significant improvement, both in the minimization of losses and also in improving the voltage profile. The results of multi DGs placement will be much more promising than those achieved after placing them independently.

Optimal DG Placement in Power Systems Using a Modified Flower Pollination Algorithm

There is a huge requirement for power systems to reduce power losses. Adding distributed generators (DGs) is the most common approach to achieving lower power losses. However, several challenges arise, such as determining the ideal size as well as location of the utilized distributed generators. Most of the existing methods do not consider the variety of load types, the variety and size of the utilized DGs besides reducing the convergence time and enhancing the optimization results. The paper performed an optimization algorithm that integrated a golden search-based flower pollination algorithm and fitness-distance balance (FDB) to find out the optimal size as well as the location of the distributed generators. It was then compared with different optimization methods to determine the best optimization technique, and it was determined to be the best technique. In addition, different types of DGs are considered, including solar energy, wind energy, and biogas, along with optimizing the size of the utilized DGs to reduce the system cost. Testing with different types of bus systems, and different types of DGs in a radial distribution system was done to reveal that the modified flower pollination with golden section search was superior in comparison to others with regards to convergence and power loss reduction.

Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review

For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks.

A Novel Approach for Secure Hybrid Islanding Detection Considering the Dynamic Behavior of Power and Load in Electrical Distribution Networks

In the arena of modern electrical power distribution systems, distributed generators (DGs) are emerging as a manifestation of electric power personalization. Even though DGs have various advantages, unintentional islanding phenomena caused by DGs during abnormal grid operations can damage equipment connected to the grid. Therefore, islanding detection mechanisms are essential for DGs in grid-connected mode to disconnect the DG from the grid in case of grid abnormalities by obeying to specific grid codes. In this regard, a novel approach to develop a secure hybrid islanding detection method (IDM) is presented in this paper. The proposed hybrid IDM is developed by combining two passive IDMs known as rate of change of active power and rate of change of reactive power with an active IDM called load connecting strategy. An 11 kV Malaysian distribution system integrated with three types of DGs, namely synchronous generator, photovoltaic, and biomass, has been chosen as a testbed for the verification of the proposed hybrid IDM. Seven different case studies have been conducted in the PSCAD/EMTDC platform to validate the performance of the proposed IDM for islanding and non-islanding events. The simulation results confirm that the proposed IDM can detect islanding within 0.09 s, which is within 2 s complying with IEEE and IEC standards. Further, a comparative study based on the detection time and non-detection zone has been carried out, which has confirmed that the proposed IDM demonstrates better performance compared to the previously developed hybrid IDMs.

A Novel Hybrid Fuzzy-Metaheuristic Strategy for Estimation of Optimal Size and Location of the Distributed Generators

Optimal size and location of the distributed generators (DGs) has become a new avenue applied to achieve a proper design and to provide a better performance for the distribution system. A hybrid Fuzzy-Metaheuristic strategy has been proposed in this work to provide an optimal design for sizing and placement of different types of DGs. In the introduced strategy, the fuzzy logic based adaptive weights subroutine has been combined with a metaheuristic optimizer in conjunction with a power system model, load flow software, input/output software modules and three proposed approaches software modules suitable for various types of DGs. Minimizing active and reactive power losses as well as enhancing the voltage characteristics over the whole system using a multi objective function with dynamic adjustment of weights has been introduced in this work. Furthermore, a novel approach of variable power factor has been established and investigated as well as the previously published constant power factor and unity power factor approaches. To prove the validity of the new strategy and the novel approach, IEEE distribution systems of 33 and 66 buses have been used to test the efficiency and accuracy of the proposed strategy. A novel hybrid optimization strategy named Hybrid Fuzzy Equilibrium Optimizer (HFEO) is established to estimate the optimal size and location of three DGs inserted in the selected distribution systems. The hybrid technique is based on merging fuzzy logic to adapt the weights of the objective function dynamically with a newly developed metaheuristic Algorithm named equilibrium optimizer to achieve a better performance in the optimization process. For fair verification of the proposed technique, its results have been compared with that of five algorithms presented in literature to prove its superiority and reliability over the other state of art. For intensive comparisons, some statistical analysis has been established to show the minimum objective function, the highest speed of convergence, the least execution time and the most consistent results.

Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system

The increasing demand for clean energy has resulted in the growth of renewable energy-based distributed generation (DG) penetration in the power distribution networks (PDN). The crucial point here is to find the optimum allocation of DGs to minimize the power loss and enhance the system performance overall. In this study, the Equilibrium Optimizer (EO) algorithm-based method is proposed to determine the location, size, connection type, and power factor of three different types of multiple DGs based on a three-phase 123-bus unbalanced PDN. The complex 123-bus UPDN has been very little studied to date, and most existing research works on the DG allocation problem have been conducted using a co-simulation platform. In this work, an unbalanced three-phase backward forward load flow (UBFLF) algorithm is written and executed under the MATLAB environment. This approach has decreased the complexity of co-simulation and provided advantages like optimizing the power factor value and the connection type (Delta/Wye) of DGs easily. Six more recent and well-established optimization algorithms such as PSO, JFO, BO, SMA, FDA, and GBO are also applied under the same test conditions to solve the Type-III DGs allocation problem. The results obtained are compared with the proposed method to show the effectiveness of the method in terms of elapsed time, statistical variables (such as standard deviation, median, variance and so on), converge speed, and best/worst case active power loss values. The efficacy of the presented method is also validated by comparing the results obtained using three distinct platforms as MATLAB/Simulink, IEEE-PES data, and OpenDSS. The detailed and comprehensive analyses show that the proposed approach demonstrates the lowest power loss, more enhancement in voltage profile, and the superiority of the EO algorithm in the application of a 123-bus unbalanced power distribution network.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Reliability Analysis of MV Electric Distribution Networks Including Distributed Generation and ICT Infrastructure

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures.

A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms.

Optimal distributed generation penetration considering relay coordination and power quality requirements

Although the electric energy supply from distributed generation (DG) resources is increasing, these sources also face limitations. Protection and power quality constraints are the most important issues that limit the maximum DG penetration in power systems. Changing the short circuit level due to DGs' presence—especially synchronous-based (SDG) type—may lead to miscoordination of the protective relays. Also, standard harmonic limits may be violated due to harmonic injection of inverter-based DGs and non-linear loads. A protection coordination problem is solved for the basic configuration, and the optimal settings are applied. Then, using a proposed approach and a new objective function, the location, size, and type of DGs are optimized to maximize the DG penetration and the speed of protection system, while reducing power losses. Finally, the protection setting of SDGs is optimized and applied. In these optimizations, the protection coordination constraints and the power quality requirements are considered and met. IEEE 14-bus system and a real sub-transmission system are simulated to evaluate the proposed approach and the new objective function. The obtained results show significant power loss reduction and protection speed improvement for different scenarios and case studies.

A new passive islanding detection technique for a microgrid based on transient energy

A new passive islanding detection technique based on transient energy that is measured and analysed at targeted distributed generation (DG) or the point of common coupling (PCC) is presented. The proposed technique measures the superimposed components and then calculates the transient active energy (TAE) and the transient reactive energy (TRE). Using the relation between TAE and the TRE, the relay can determine the islanding situation even in a worst-case operational scenario, and this is a notably shorter response time according to the current standards. The performance of the proposed technique is simulated in the MATLAB environment. Islanding and non-islanding scenarios for different power match conditions and types of DGs are simulated. The evaluation of the simulation results shows that the proposed technique identifies the islanding with a less than zero power mismatch, operates accurately under the condition of load variation, and does not conflict with the power system operation during normal conditions. It removes the passive technique drawbacks by having a tiny non-detection zone (NDZ) and a faster response.

A multi-objective framework for distributed energy resources planning and storage management

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD.

Resilience-Oriented Framework for Microgrid Planning in Distribution Systems

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost–benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG planning scenario by evaluating the possibility of future operations based on the segmentation of both the system configuration and type of DG to improve the resilience of distribution systems.

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

The massive increase in the utilization of Distributed Generation (DG) units in the traditional Electric Distribution Networks (EDNs) enforces the distribution companies’ operators to enhance the technical performance of EDNs while considering economic perspectives. This challenge paves the way for developing a multi-objective optimization platform to tackle the techno-economic problems while respecting system uncertainties as well as the operational policy of the distribution companies. As a motivating solution for this multi-objective problem, this paper introduces the application of three nature-inspired algorithms as multi-objective optimization techniques for enhancing the techno-economic performance of EDNs through the integration of multiple Renewable Energy Resources (RERs). Grasshopper Optimization Algorithm (GOA), Salp Swarm Algorithm (SSA) and Moth Flame Optimization Algorithm (MFO), have been employed in this comparative study to minimize the active power losses, enhance the Fast Voltage Stability Index (FVSI) and reduce the total costs, considering the penetration level specified margin as well as and the framework of the DG units’ operating power factor constraints. The proposed algorithms have been implemented in the MATLAB environment and applied on various benchmark IEEE test systems (33-bus, 57-bus and 300-bus) as a mimic, small and large EDNs. A realistic part of the Egyptian distribution network (171-bus) is also introduced as a practical, applicable case study. The attained results show that the suggested optimization platform especially using MFO, is more effective and successful in determining and finding the optimal locations and capacities of different DG types for getting the optimal value of the objective function in minimum time within a minimum number of iterations.

Increment of Fault Current due to integration of Distributed Generation and its impact on Distribution Network protection

One of the new technologies in generating power near the distribution system is called distributed generation which has supportive and destructive characteristics to the power system protection. One of the destructive characteristics of distributed generation is increasing the level of fault current to the protective equipment of the power system that affects the existing time intermission coordination of the protective overcurrent relay. In addition to increment of fault, it also alters the radial nature of the power distribution system and cause the power bidirectional rather than unidirectional. Integration of distributed generation to the distribution network causes increment of fault current effect, reliability drop, and affects security of protection system. The level of failure of protection be contingent on type, size, location and number of distributed generation. This fault current can cause a great damage to the electrical equipment with the miss operations of protective devices. The main aim of this paper is analysis of the fault current level to the protection of distribution network due to the integration of distributed generation which concerns on solar distributed generation, wind distributed generation and combination of solar and wind distributed generations at a time. This paper conducts the increment of fault current by the integration of distributed generation to the distribution system and its impact on distribution network protection. The analysis and the modeling are conducted on the 15KV distribution network of the radial feeder in Debre Markos town. This paper has covered the ling to ground, line to line and three phase fault analysis and their impact on the protection of distribution system for the wind and solar distributed generation types. After the integration of the distributed generation the fault current is increased by 0.529KA for three phase, 0.74KA for line to ground, 0.467KA for line to line and 0.523KA for line to line to ground. This paper confirms designing distribution network without forecasting the future demand of electric power users give the protection equipment additional requirement. As the result, the fault current after the integration of distributed generation to the distribution network have great value in terms of power system protection.

A comparative analysis for impact of distributed generations with electric vehicles planning

In the approaching years, the utilization of Distributed Generations (DGs) with Electric Vehicles (EVs) will accelerate. Future issues will be exacerbated by the relationship between the distribution system of DGs solely, DGs with Fuel Cell Electric Vehicle (FCEV), and DGs with Extended Plug-in Hybrid Electric Vehicles (Ex-PHEV). This paper discusses the use of a multi-tasking Genetic Algorithm to optimize DGs with EVs planning in distribution systems for load models for rating and placement determination. From a system perspective, the goal of this research is to reduce total real power loss. The various types of DGs with EVs are taken into account from the standpoint of the analysis. Photovoltaic, diesel engines, synchronous condensers, and doubly-fed induction generators are examples of various types of DGs. In this analysis, EV types such as FCEV and Ex-PHEV are considered. Different forms of constant impedance (Z), constant current (I), and constant power (P) load models are included in DGs with EVs planning for improving system performance indices, such as Light-emitting diode, Laptop charger, Personal computer, Tungsten light, and Vacuum tube. The real power loss index (%ILP), the reactive power loss index (%ILQ), the voltage deviation index (%IVD), the short circuit current reduction (%IC), the active power DGs with EVs penetration (% PPWDGs & EVs), and the reactive power DGs with EVs penetration (%PQWDGs & EVs) are all measured in this paper. On the 37-bus test distribution system, the effectiveness of the listed approach was verified.This article is very much useful for researchers, industrial, scientific persons, and those who are working in the smart grid with DGs and EVs.