Optimal Distributed Generation Research Articles

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.

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

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.

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.

Optimal DG allocation by Garra Rufa optimization for power loss reduction

<p>The rapid growth of distributed generation (DG) units has necessitated their optimization to address the increasing complexity of power grids and reduce power losses. The need for optimization of distributed generation (DG) units has been growing rapidly over the past few years. To minimize such losses, the optimal allocation of DG units needs to be correctly identified and applied. On the other hand, Garra Rufa optimization (GRO) is a mathematical optimization technique that is used to determine the high effective and efficient way to solve very complex problems to achieve optimal results. In this work, Garra Rufa optimization is used to identify the optimal placement and size of DG units in order to meet specific power loss requirements. A comparison between genetic algorithm (GA), particle swarm optimization (PSO), and GRO is done using MATLAB to validate the proposed method. The comparison shows that GRO is better than the other methods in DG allocation, especially in more than two DGs. The optimization techniques are evaluated using the IEEE standard power system case, specifically the 30-bus configuration.</p>

Optimizing Distributed Generation Placement and Sizing in Distribution Systems: A Multi-Objective Analysis of Power Losses, Reliability, and Operational Constraints

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.

Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods

The need for electrical energy is increasing in line with the increase in population and increasing progress in welfare. On the other hand, the availability of fossil fuels as the main fuel in generating electricity is dwindling; so, there is a need for policies that require the use of environmentally friendly renewable energy. The utilization of renewable energy does not necessarily apply freely due to several constraints. One effort is a generator or distributed generation (DG) which is placed in the distribution line close to the load. The utilization of DG must go through planning, especially the large capacity and position on the bus and on the feeder, which will result in small network losses and a voltage profile that meets tolerance limits. Thus, the purpose of this study is to optimize to obtain the capacity and location of the DG calculated by considering the variation in the load through the genetic algorithm method. As a result, the optimal DG position for normal load is obtained on bus 18, bus 20, and bus 32 with capacities of 190 kW, 463 kW, and 370 kW, respectively. The losses obtained decreased from 54.6733 kW to 9.9447 kW, and the voltage profile was maintained within the specified limits. Optimization was carried out for decreasing and increasing loads in percent. The result is that losses can be minimized, and the voltage profile remains within the required limits. The lower the load, the more stable the voltage and the smaller the losses; meanwhile, the larger the load, the more fluctuating the voltage is, but still within the limits specified in the optimization.

Optimal location and sizing of distributed generation for distribution systems: An improved analytical technique

ABSTRACT Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network.

Probabilistic Planning of Distribution Networks With Optimal DG Placement Under Uncertainties

This research paper presents an efficient methodology for distribution network planning under an uncertain environment. As an extension of our previous work presented at the ECCE Asia 2021 conference, here optimal placement and sizing of Renewable Energy Sources (RES)-based Distributed Generations (DGs) are determined considering the generation and load uncertainties. In addition, the optimal tap settings of off-load tap changing transformers present in a network are also determined. Probabilistic non-linear optimization is solved with a sensitivity-based technique to minimize the distribution network losses and improve its voltage stability. The proposed methodology is implemented on standard test systems like the IEEE 69 bus and the Indian 85 bus networks. Further, to determine its real-world functionality, the methodology is tested on a practical radial distribution network of 88 buses present in a remote Froan island of Norway. When compared with existing techniques, the proposed methodology provides much more efficient network planning solutions with lesser power losses. Developed on free and open-source software platforms, it also provides a reliable and cost-effective alternative to network operators to determine their network robustness.

Optimal Placement of Distributed Generation Units for Microgrid Planning in Distribution Networks

Due to increasing penetration of renewable distributed generation (DG), conventional distribution networks have been gradually transforming into their active form, where microgrids may serve as fundamental building blocks. As the primary step towards microgrid planning, optimal DGs placement and sizing can reduce the total energy losses by localizing power supply to loads. In this paper, a DG optimal placement method by minimizing the total energy losses is proposed, where the planning is formulated as a non-linear programming (NLP) problem. AC optimal power flow (OPF) is used to solve this planning problem by considering operational constraints and uncertainties in load and renewable power generation of the network. IEEE 33-bus test system and a real 404-bus distribution system operated by Saskatoon Light and Power in Saskatoon, Canada are used to validate the proposed method. The proposed method also shows superior performance compared to existing methods.

Performance enhancement of radial distribution system with optimal DG allocation

ABSTRACT During the modernization era, the power generation has been shifted to decentralized manner with more renewable distributed generation (DG) units integration at the distribution level in disperse manner. These DG units have to be properly placed for performance enhancement of distribution system. In this paper, the performance of radial distribution system has been enhanced by optimally allocating biomass type DG in the network. Here, a combination of sensitivity index method and a recently developed nature inspired algorithm known as Artificial Gorilla Troops Optimizer (AGTO) algorithm has been used for optimal siting and sizing of DG units in the 33- and 69-bus radial distribution network by taking real and reactive power loss minimization, voltage profile improvement and voltage stability enhancement as the primary objectives along with the system operational constraints. The composite type of load model has been considered in this paper. Out of all the load buses, some severe buses are selected by index vector sensitivity method. AGTO has been applied on these selected buses for appropriate allocation of DG. The effectiveness of this algorithm has been compared with Grey Wolf Optimization (GWO) algorithm and other reported results. The concept of load growth has also been studied in this work.

Multi-Objective Optimal DG Placement Approach Using Analytical Hierarchy Process

Distributed generation (DG) has been increasingly integrated into distribution systems in the last decade. The selection of the optimal location and capacity of these DGs is an important issue. This selection is based on minimizing losses, reducing emissions, reducing voltage deviation, and much more. This paper proposes a new optimization approach with a multi-objective function for maximizing DG capacities and minimizing both system losses and voltage deviation, which are subject to system constraints. The weight of each attribute of the multi-objective function is estimated using the analytical hierarchical process (AHP). The optimal DG size is calculated for all possible buses, and hence, the optimal bus can be located. Moreover, an algorithm is introduced to decrease the number of iterations when integrating more than one DG. The proposed method is an iterative linear programming method based on the familiar Newton-Raphson sensitivity matrix. The proposed approach has been tested on 69 and IEEE 33 bus systems. The simulation results are compared with previous literature results.

IEEE-69 Distribution Network Performance Improvement by Simultaneously Optimal Distributed Generation Sizing and Location Using PSO Algorithm

Integrated distributed generation (DG) to the network is the most popular technique to improve the network performance since DG sizing and location could reduce the power loss. However, using a non-optimal DG sizing and location could cause voltage fluctuation and power loss increase. Therefore, it is very important to develop a suitable optimization method that determines the optimal DG sizing and location. This research proposes a new strategy that leads to finding the simultaneous optimal solution involving both the optimal DG sizing and location. The proposed method aims to reduce the overall fitness function that taking into account both the voltage profile index and the power loss reduction. The particle swarm optimization (PSO) technique is the method that is used in this work. MATLAB simulations carried out on IEEE 69-bus radial distribution networks were used to prove the ability of the suggested method. The results obtained demonstrate the effectiveness of the presented method to determine the simultaneously optimal sizing and location of the DG units that minimize the overall power loss and improve the overall voltage profile.

Extended mixed integer quadratic programming for simultaneous distributed generation location and network reconfiguration

Introduction. To minimise power loss, maintain the voltage within the acceptable range, and improve power quality in power distribution networks, reconfiguration and optimal distributed generation placement are presented. Power flow analysis and advanced optimization techniques that can handle significant combinatorial problems must be used in distribution network reconfiguration investigations. The optimization approach to be used depends on the size of the distribution network. Our methodology simultaneously addresses two nonlinear discrete optimization problems to construct an intelligent algorithm to identify the best solution. The proposed work is novel in that it the Extended Mixed-Integer Quadratic Programming (EMIQP) technique, a deterministic approach for determining the topology that will effectively minimize power losses in the distribution system by strategically sizing and positioning Distributed Generation (DG) while taking network reconfiguration into account. Using an efficient Quadratic Mixed Integer Programming (QMIP) solver (IBM ®), the resulting optimization problem has a quadratic form. To ascertain the range and impact of various variables, our methodology outperforms cutting-edge algorithms described in the literature in terms of the obtained power loss reduction, according to extensive numerical validation carried out on typical IEEE 33- and 69-bus systems at three different load factors. Practical value. Examining the effectiveness of concurrent reconfiguration and DG allocation versus sole reconfiguration is done using test cases. According to the findings, network reconfiguration along with the installation of a distributed generator in the proper location, at the proper size, with the proper loss level, and with a higher profile, is effective.

Optimal Placement and Sizing of Renewable and Non-Renewable Resources in Smart Grid

Distributed Generators (DGs) play a key role in existing power distribution networks together with significant innovations in smart grid technology. It is now more essential to evaluate the various types of DGs within the system. Renewable energy types of DGs such as PV and wind promise low emissions and abundant availability. When it comes to the installation of DG, the size, location, and type of DG should be given high importance because improper placement of DG units leads to reducing the benefits of the distribution system and even endangers the entire system operation. If DG size exceeds a certain value limit, power loss at that bus becomes negative. This situation must be avoided. As a result, optimal DG placement aids in the reduction of losses, improvement of voltage profiles, reliability, and overall system efficiency. Considering this, in this paper, a Simultaneous Particle Swarm Optimization (PSO) algorithm is implemented for placement allocation and sizing of multiple types and multiple numbers of DGs in power distribution systems with the objectives of minimization of active and reactive power loss and enhanced voltage profile. Along with the meta-heuristic optimization algorithm, sensitivity techniques such as index vector, loss sensitivity factor, and voltage stability margin methods have been analysed. The outcomes are obtained using the aforementioned sensitivity-related methods on the IEEE 15, 33, 69, and 85-radial bus systems and compared with simultaneous PSO for efficacy.

Economic Analysis by Optimal Placing of DGs in Distribution Networks by Particle Swarm Optimisation and Gravitational Search Optimisation Algorithm

The particle swarm optimisation and gravitational search optimisation algorithm (PSOGSA) is a hybrid algorithm which is used to determine size of optimal Distributed Generation (DG) in this paper. The PSOGSA integrates the social thinking ability (gbest) in PSO to capability of local search in GSA. The algorithm combines the searching capability of PSO and with enhanced exploration ability of GSA. Distributed generations are connected in distribution systems to consumers to reduce losses, enhance the voltage profile, reliability and economic benefits. DG optimal positioning and loss minimisation have a significant role for economic operation and overall reduction of energy costs. For evaluation of proposed algorithm, the test bus sets IEEE15, 33 and 69 are chosen. For considered objectives i.e., optimal DG sizing and economic analysis, this PSOGSA algorithm gives better results as compared to other methods and better outcomes has been achieved when DG unit of type III operates at power factor of 0.9 lag

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 DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.

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.