Location Of Distributed Generation Research Articles

A REVIEW ON OPTIMAL PLACEMENT AND SIZING METHODS OF DISTRIBUTION GENERATION SOURCES

This manuscript outlines various work carried out in the field of Distributed Generation (DG). Increase in power consumption and shortage in transmission capabilities are addressed by DGs. In order to maximize the potential benefits, it is imperative to place the DGs at optimal locations and the DGs should have optimal size pertaining to that location. There are several research works that are carried out on the placement and sizing of DGs. Nonetheless, the methodical principle for this issue is still unsettled. Various optimization strategies can be used to obtain the appropriate placement and sizing of distributed generation (DG) in grids. This study provides a comprehensive overview of several DG placement approaches, including stochastic fractal search algorithms, particle swarm optimization, symbiotic search algorithms, opposition-based tuneable chaotic differential evaluation, and more. The benefits and potential uses of each method are briefly covered in this study. The study sheds light on the efforts made to determine the best location and size of DGs.

Optimization of sustainable distributed power generation in distribution networks: a comprehensive analysis of location, capacity, and environmental impact

With the increasing integration of distributed generation (DG) into distribution networks, the impact of its output and configuration on the environment and overall benefits has become more pronounced. To enhance environmental benefits while minimizing costs, an optimization model for DG location and capacity considering environmental benefits (LCEB) is proposed. Initially, a mathematical model for DG location and capacity is established, with optimization objectives including voltage deviation, network loss, and environmental benefit. Subsequently, modified particle swarm optimization (MPSO) with a single-dimension search probability mutation is employed to solve this model, obtaining an optimized DG output and layout scheme. Finally, the feasibility and effectiveness of the proposed model are demonstrated through simulation and analysis by using the IEEE33 node system as an example. The results highlight the model’s capability to improve environmental and economic benefits, enhance voltage levels, and reduce network losses within the distribution network.

OPTIMAL LOCATION AND SIZING OF DG IN DISTRIBUTION NETWORK: IMPACT ON OVERCURRENT DISTRIBUTION PROTECTION SCHEME

The integration of distributed generation (DG) into the existing distribution networks minimizes active power losses and improves the voltage profile. The recent improvement in the development of DG technology has caused increase in the penetration of DG from different sources. The optimal location and sizing of DG is very challenging and introduces different protection issues. This is caused by penetration of current flow from different source which is contrary to the principles of operation of the conventional overcurrent scheme. This paper presents optimal method of DG placement and sizing using firefly algorithm and its impact of DG penetration on overcurrent protection scheme. Low voltage distribution network is modelled and simulated in PSCAD/EMTDC and DigSilent power factory software using IEEE 33 and Ayepe 34 standard bus. False tripping of feeders, Nuisance tripping, blinding of the protection scheme and unintentional islanding caused as result of Photovoltaic penetration into the distribution network were simulated. Coordination of overcurrent relays with and without DG penetration were analyzed and possible mitigation algorithm were proposed. The simulation result obtained showed that the proposed methods significantly improve the protection scheme coordination..

Effects of integration of distributed generation on reliability in distribution system

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Hybrid Siting and Sizing of Distributed Generators and Shunt Capacitors with System Reconfiguration using Wild Horse Optimizer

Injection of Distributed generators (DGS) and Shunt capacitors (SCS) simultaneously with system reconfiguration significantly promotes smart grid performance. In addition, system reconfiguration increases the injected distributed generation capacity in the system. This work proposes a wild horse optimizer (WHO) for the optimal siting and sizing of DGS and SCS in parallel with network reconfiguration. The proposed method aims to attain single and multi-objectives: minimizing active power loss, maximizing voltage stability index (VSI), and minimizing voltage deviation index (VDI). Five operational cases are introduced to elucidate the superior performance of the proposed method. The five cases are executed on IEEE 33-bus standard radial distribution test system. The single-objective function results are compared with other optimization algorithms. The simulation results belay that the proposed WHO optimizer has the best results for unfixed DGS and SCS locations.

Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.

Optimal size and location of dispatchable distributed generators in an autonomous microgrid using Honey Badger algorithm

Optimal size and location of dispatchable distributed generators in an autonomous microgrid using Honey Badger algorithm

PSO‐based optimal placement of electric vehicle charging stations in a distribution network in smart grid environment incorporating backward forward sweep method

AbstractThe transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Achieving energy independence in urban microgrids: Strategies for domestic resource utilization and environmental sustainability

The quest for energy independence within urban microgrids (MGs) has become increasingly crucial for ensuring domestic resource utilization and environmental sustainability. One of the pivotal challenges lies in the clustering of MGs, a complex task aimed at enhancing their robustness and economic performance during events. However, limited research has been conducted on this intricate clustering problem. This study addresses this gap by proposing an artificial intelligence-based method tailored for MG clustering. In this paper, we introduce a novel multi-objective optimization approach to determine the optimal clustering of standalone MGs. Our methodology considers a range of critical parameters, including distributed generation (DG) droop settings, DG locations and capacities, renewable energy resources from solar units and wind turbines, capacitors, and power transmission lines. By minimizing power losses and enhancing voltage stability, we identify a virtual cut-set line, often referred to as the "virtual line," characterized by minimal power exchange between clustered MGs. To achieve optimal and cost-effective performance, we develop an enhanced differential genetic evolution algorithm designed to address multi-objective problems within stringent constraints. Additionally, we employ Bayesian theory within artificial intelligence to estimate the optimal output power of renewable energy sources, specifically wind turbines and solar panels. To validate the effectiveness of our approach, we apply it to a 69-bus MG across various scenarios. Our findings demonstrate the proficiency of our method in achieving energy independence, domestic resource utilization, and environmental sustainability within urban microgrids.

Active and Reactive Power loss Minimization Along with Voltage profile Improvement for Distribution Reconfiguration

Optimal distribution network reconfiguration (DNR), distributed generations location and sizing (DGs-LS), tap changer adjustment (TCA), and capacitors bank location and sizing (CAs-SL) are different methodologies used to reduce loss and enhance the voltage profile of distribution systems. DNR is the process of changing the network topography by changing both sectionalized and tie switch states. The optimal location looks to find the optimal setting of the DG and CA within the distribution network. Optimal size seeks to find the optimal output generation of both DG and CA. The TCA looks to find the optimal position for TC. These methods are challenging optimization problems and resort to meta-heuristic techniques to find a globally optimal solution. This paper presents a new methodology with which to simultaneously solve the problem of DNR, DGs-LS, TCA, and CAs-SL in distribution networks. This work aims to minimize active and reactive power losses, including voltage profile improvement using a multi-objective decision approach. The firefly algorithm (FA) and analytic hierarchy process (AHP) are used to optimize the fitness function and determine the function weight factors through the use of MATLAB software. Several scenarios were considered on the IEEE 69-bus network. In terms of active power and reactive losses, reductions in the test system of 96.16% and 92.7%, respectively, were achieved, evidencing the positive impact of the proposed methodology on distribution networks.

A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.

OPTIMIZATION OF DISTRIBUTED GENERATION INSTALLATION IN CILEDUG TASIKMALAYA FEEDER TO REDUCE POWER LOSS WITH GENETIC ALGORITHM (GA) METHOD

Distributed generation (DG) is a small-scale power plant that is generally located close to the load. The installation of DG can be an alternative to reduce power losses in the distribution system. In this study, the placement and capacity optimization of DG to Ciledug feeder is carried out. Optimization is carried out using the genetic algorithm (GA) method which aims to reduce power losses in the system. The simulation is divided into 2 stages. In the first simulation before the installation of DG, the power loss obtained was 755,807 kW, and the voltage profile experienced under voltage from bus 31 to bus 104. The second simulation will be carried out by adding DG to the network, the DG used amounted to 2, 3, and 4 DG units. The location and capacity of DG are searched using the genetic algorithm optimization technique. The optimization results using 2 DGs, showed that the DGs were connected at bus 95 and bus 97 with a capacity of 0.969 MW and 0.480 MW, respectively. The addition of 2 DGs to the network reduces the power loss to 398,899 kW. Next, the optimization results using 3 DGs, showed that DGs were connected at bus 96, bus 78, and bus 67 with a capacity of 0.977 MW, 0.977 MW, and 0.013 MW, respectively. The addition of 3 DGs to the network reduces the power loss to 236,920 kW. Finally, the optimization results using 4 DGs show that DGs are connected at bus 67, bus 80, bus 46, and bus 26 with a capacity of 0.951 MW, 0.799 MW, 0.616 MW, and 0.257 MW, respectively. The addition of 4 DGs to the network reduces the power loss to 141,465 kW.

Weighted Minkowski distance-based differential protection for active distribution networks considering the uncertainty of frequency-domain characteristics

With the increasing use of DGs (distributed generators) in the power system, the distribution network is becoming more and more complicated. The rapid change of DG output power and the diversity of DG location make the computing calculation of relay protection difficult, bringing severe challenges to the current protection based on single-end electrical volume. Therefore, double-end-based protection is a good choice in view of the DG uncertainty and the network complexity. However, the power frequency offset after fault and the uncertainty of DG fault current result in uncertainty in the frequency-domain characteristics, which can also negatively affect the differential protection. In view of this, a new differential protection method based on the weighted Minkowski distance algorithm is proposed in this paper. First, the Minkowski distance algorithm is used to describe the similarity of both currents considering the uncertainty in the frequency-domain characteristics. Second, each current similarity including the phase current, the positive current, and the negative current is calculated separately to increase the protection sensitivity. Through the weighted algorithm distribution, the weight is assigned. Furthermore, a high-performance protection mechanism is designed to improve the protection sensitivity. This method is suitable for the active distribution network with different penetration rates of DGs and different types of DGs. The simulations show that the proposed method has good feasibility and high superiority.

Strategic Sizing and Placement of Distributed Generation in Radial Distributed Networks Using Multiobjective PSO

Distributed generators (DGs) offer significant advantages to electric power systems, including improved system losses, stability, and reduced losses. However, realizing these benefits necessitates optimal DG site selection and sizing. This study proposes a traditional multiobjective particle swarm optimization (PSO) approach to determine the optimal location and size of renewable energy-based DGs (wind and solar) on the Namibian distribution system. The aim is to enhance voltage profiles and minimize power losses and total DG cost. Probabilistic models are employed to account for the random nature of wind speeds and solar irradiances. This is used in an algorithm which eventually optimizes the siting and sizing of DGs using the nearest main substation as reference. The proposed method is tested on the Vhungu-Vhungu 11 kV distribution network in Namibia. Four cases were considered: base case with no DG, solar power, wind power, and a hybrid of both wind and solar. Optimal values for each case are determined and analyzed: 0.69.93 kW at 26 km for solar PV-based DG and 100 kW at 42 km for wind-based DG. These findings will serve as a valuable blueprint for future DG connections on the Namibian distribution network, providing guidance for optimizing system performance.

Optimal placement of time-varying distributed generators by using crow search and black widow - Hybrid optimization

This paper focuses on the optimal reorganization of radial distribution system (RDS) and the superlative location of time-varying power generation distributed generators, considering time-varying loads. The electric generators, utilizing non-conventional energy sources, are directly connected to the distribution grids with lower ratings compared to conventional source-driven generators. In this study, a combination of optimization techniques, such as the black widow and crow search techniques, is employed to enhance performance in terms of optimal reorganization and superlative location of distribution generation. The reorganization problem is examined and analyzed by considering various scenarios in a standard 69-bus grid, as well as in larger-scale 119-bus and 135-bus systems, with and without the presence of distributed generation resources. The analysis of the proposed optimization techniques with the obtained results is well discussed in this paper.

Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity

One of the most famous methods for minimizing power loss is distribution network reconfiguration (DNR). Accordingly, many researchers have focused their work on finding a network’s optimal configuration in planning mode. However, few address the switching sequence process during operation mode. This paper introduces an innovative approach to minimize power loss in distribution networks. It addresses the often-overlooked real-time switching sequence order (SSO) during network operation, ensuring a smooth transition to the optimal configuration within operational constraints. Simultaneously, it optimizes distribution network reconfiguration (DNR) and distributed generation location and sizing (DG-LS) to maximize load capacity. The primary goal is to reduce power losses, improve voltage profiles, and enhance network efficiency. Utilizing multi-objective decision methods based on AHP, particle swarm optimization (PSO), and firefly algorithm (FA), this study achieves efficient results for SSO, DNR, and DG-LS optimization.

Optimal allocation of distributed generation in active distribution power network considering HELM-based stability index

Traditionally, assessment of static voltage stability for distribution power networks with distributed generation (DG) utilizes the continuous power flow (CPF) method by calculating load margin index (LMI) at various load levels, which suffers from slow calculation speed and low accuracy when power flow calculation becomes ill-conditioned. To overcome this issue, this paper presents a novel holomorphic embedding load flow method (HELM)-based approach that calculates voltage stability indices for various types of nodes in a distribution network. Different orders of sensitivity information are calculated using the HELM method, and the criteria for assessing voltage stability are derived, which are used as constraints to optimize the location and capacity of DG. Such formulation of the optimization problem does not need to calculate the maximum load margin using CPF. Moreover, it can be effectively solved using the Genetic Algorithm and easily adaptable to various operating conditions of DG, which improve the calculation speed of the stability index. The effectiveness of the proposed approach is verified on IEEE 33-node and IEEE 69-node distribution power network models. Results show that considering the HELM VSI, the location, and capacity of DG in the distribution network can be better optimized. For the IEEE 33-node model, the total cost is reduced by approximately 2.33%, the network loss is reduced by 1.2%, and the HELM voltage stability criteria index is increased by about 4.23%; while for the IEEE 69-node system, the total cost is reduced by about 2.98%.

Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

Comparison of Optimal DG Placement Using GA, PSO and BA for Minimum Real Power Loss in Radial Distribution System

The below paper concentrates on the efficient integration of Renewable Energy Systems as Distributed Generators (DGs) within the distribution sector, aiming to minimize losses occurring from their presence in the system. To achieve this objective, the study proposes the strategic placing and sizing of DGs at optimal locations within the distribution network. To tackle this challenge, four heuristic search-based methods have been carefully selected and applied. The primary objectives of the paper are twofold: Firstly, it seeks to find the ideal location and sizing of DGs by loss minimization criteria. Secondly, the study aims to compare the performance of four chosen heuristic search-based methods TO achieve the loss reduction goals. By evaluating these methods' outcomes, researchers can identify the most effective method for DG placement and its sizing in the radial network. For the analysis, the IEEE 33 radial bus test system is utilized as a representative distribution system.

Distribution network forecasting and expansion planning with optimal location and sizing of solar photovoltaic-based distributed generation

Load forecasting is the prediction of demand for a future period, and it is a critical necessity for distribution system design and growth while also meeting all operational and technical constraints. In this paper, an analytical least squares extrapolation technique is applied to determine the optimal size and location of solar photovoltaic (SPV)-based distributed generation (DG) in the distribution system. The integration of SPV DG changes the operating characteristics of modern power systems, resulting in significant economic and technical outcomes such as minimizing losses and improving the voltage profile. The proposed work was performed on a practical Yirgalem-I distribution network in Ethiopia. The modeling and simulation of this feeder have been carried out using ETAP and MATLAB software. Simulation results show that the injection of active and reactive powers at the optimal buses reduces the active and reactive power losses by 94.03% and 96.07%, respectively, with an improved voltage profile.