Optimal Sizing Of Distributed Generation Research Articles

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of 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..

Optimal sizing and siting of distributed generation systems incorporating reactive power tariffs via water flow optimization

Distributed generation (DG) systems are gaining popularity because they generate electricity closer to the point of consumption. This reduces transmission and distribution losses and enhances system reliability. For the efficient and cost-effective use of these systems, optimal sizing and siting of DGs is crucial. This paper introduces a Water Flow Optimization (WFO)-based method for the optimal placement and sizing of DG systems capable of injecting both active and reactive power simultaneously. This study assesses both the technical and economic facets of the system, taking into account power losses and the comprehensive annual economic costs, including capital investment, deployment, operation, and maintenance. A significant aspect of this research is the DG units' capability to enhance voltage and minimize losses, factoring in the reactive power tariff. The findings show that the WFO method surpasses other optimization techniques, such as particle swarm optimization, whale optimization algorithm, genetic algorithm and gray wolf optimization. Furthermore, the study emphasizes the cost advantages of injecting reactive power. The minimum annual economic losses (AEL) recorded in this study are $19,207 and $13,852 for IEEE-33 and IEEE-69 bus systems, respectively. In comparison, for DGs only injecting active power, the AEL increases to $24,749 and $41,381 for the corresponding systems.

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.

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

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.

A new optimal allocation of DGs in distribution networks by using coot bird optimization method

Energy is one of the most important topics in the world today and is considered as one of the most effective factors for the development of countries. Due to the limitation of non-renewable energy sources and undesirable effects of consuming these resources on the environment, the strategy of countries has changed towards the use of renewable energy. Renewable energy sources do not decrease over time and operate independently of price fluctuations and are more available, thus being able to play a greater role in modern power systems. Therefore, the optimal location and use of these resources will have an impact on modifying the parameters of the power grid. In this paper an analytical approach for optimal placement and sizing of distributed generation (DG) in power distribution networks to minimize the power loss, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. In the first step, determines the DG capacity causing maximum benefit at different buses, and then selects the best location for DG placement which corresponds to highest benefit in the buses. This method is applicable for sizing and siting of single as well as multiple DG units. The coot bird optimization method (CBOM) is proposed for solving optimal placement, size, and power factor (PF) of DG in distribution network. The suggested method is tested on the IEEE 33-bus, 69-bus, Distribution Networks. The proposed CBOM method has good performance to find optimal placement, size, and PF of DG and it can be applied for various distribution system.

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.

A Convex Micro-Grid-Based Optimization Model for Planning of Resilient and Sustainable Distribution Systems Considering Feeders Routing and Siting/Sizing of Substations and DG Units

Integration of distributed generation (DG) and renewable energy resources (DERs) has affected planning and operation of recent distribution networks (DNs). On the other side, resiliency and sustainability of DNs is the other concern of distribution network operators. In this paper, a new formulation is developed for optimal planning of resilient DNs based on optimal formation of resilient micro-grids (MGs). The formulation aims to implement optimal siting and sizing of conventional and renewable-based DGs, optimal routing and type of feeders, as well as optimal sizing and placement of HV/MV substations to configure adequate and resilient MGs against extreme events. The investment, operational, emissions, and resiliency costs have been included in the objective function, and all the problem constraints as well as uncertainty of renewable DGs have been taken into account. The new formulation uses line-flow-based (LFB) model of AC power flow equations, and reserve feeders (tie-lines) are employed to enhance resiliency after severe outages via reconfiguration concept. All the relations have been convexfied in order to compose a mixed-integer quadratically-constrained (MIQCP) model to be solved with global optimum solvers in GAMS. Efficiency of the conducted methodology has been evaluated by different experiments on the 24-node system, and the results are investigated.

Economic analysis of HRES-based distributed generation for mitigation of power loss and voltage profile problem

Abstract Power loss, voltage deviation and reliability are the issues that are widely encountered in the power system operation of developing countries. The Eastern region of Ethiopian Electric Power (EEP) is characterized by long transmission lines with no power generation units and large loads. Power loss and voltage profile deviations are experienced repeatedly in this region. The aim of this study is to present an extensive analysis of the role of Hybrid Renewable Energy Sources (HRES) used as Distributed Generation (DG) for power loss minimization, voltage profile and reliability improvement in EEP. The research work initially proves the superior performance of Manta Ray Foraging Optimization (MRFO) for the sizing and placement of DG. Optimal sizing and location of DG are then performed for the case study system using MRFO. The results indicate considerable power loss reduction and improvement in the voltage profile of the network. The reliability of the system is also improved by having an alternative energy source from RES. Wind and solar are considered as part of DG, the combination of which is then optimized through MRFO in the next part of the research. An economic feasibility analysis of the proposed result is also presented for this real-world problem. MATPOWER is used for executing the load flow whereas MATLAB programming is used for implementing MRFO. The result shows that the utility got economic benefits due to the introduction of DG to the network.

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.

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.

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%.

Optimal Placement and Sizing of Distributed Generation for Power Loss Minimization in Distribution Network using Particle Swarm Optimization Technique

Electrical power distribution network is the final stage in the delivery of electric power to consumers. It suffers from high power losses along its radial lines, and there is a need to minimize these losses. One of the technologies used in reducing losses is the application of Distributed Generation (DG). However, inappropriate sizing or placement of DG could inadvertently increase losses in the network. Therefore, this study carries out Optimal Placement and Sizing of DG (OPSDG) in the distribution system using Particle Swarm Optimization (PSO) in reducing the total power loss of the distribution network. A mathematical model of distribution system without and with DG was developed from one voltage source representation to generate a set of equations using Bus Injection to Branch Current (BIBC) and Branch Current to Bus Voltage (BCBV) load flow technique. The model was optimized using PSO and implemented with MATLAB. In each case, the Loss Reduction Index (LRI) was computed. The approach was used on a Nigeria Distribution network 11 kV 34-bus Ayepe feeder of the Ibadan Electricity Distribution Company (IBEDC). The total LRI obtained using analytical technique for sizing and placement of DG is 0.1808 p.u. With the incorporation of DG using PSO, the total LRI is 0.2636 p.u. The best location and size of the DG unit in the network after optimization is at bus 14 with an active power of 5.00 MW. The results established that using PSO for DG placement and sizing significantly reduced the distribution network total power loss.

Optimal Placement and Sizing of DGs in Distribution Networks Using Dandelion Optimization Algorithm: Case Study of an Algerian Distribution Network

The optimal placement of distributed generation (DG) in power distribution systems involves identifying the best locations for the generators to be installed and sizing them appropriately, to optimize the performance of the system. In this paper, the recently proposed nature-inspired optimization algorithm namely: Dandelion Optimizer (DO) has been used for the optimal placement and sizing of DG in the radial distribution network. The objectives are to minimize active power loss and voltage deviation and to enhance the voltage stability of the distribution network. The efficiency of the proposed method has been verified over the IEEE 33-bus and Algerian 112-bus distribution systems. The result comparisons indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the DO algorithm can be a very effective method for solving the optimal allocation of the DG problem.

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.

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.

A Critical Review of Optimization Strategies for Simultaneous Integration of Distributed Generation and Capacitor Banks in Power Distribution Networks

This paper reviews the optimization strategies for the optimal simultaneous allocation of distributed generation (DG) and shunts capacitor banks (SCBs) in electrical distribution networks. These optimization strategies aim to determine the optimal size, location, and combination of DGs and SCBs to constitute a techno-economic system while satisfying the constraints and energy demand of the load. The optimization strategies explicitly reviewed include the problem formulations, optimization techniques, restrictions posed for optimization problems, decision variables, and network operating modes typically assumed while allocating the DGs and SCBs. In addition, there is an attempt to highlight the limitations of the existing literature and future research directions. This study undertakes a comprehensive review of the literature that systematically considers the simultaneous application of DGs and SCBs to advance the existing literature, which lacks such a review. Expectedly, this review will serve as a principle platform for researchers intending to explore the subject area for further improvement.