Placement Of Distributed Generation Research Articles

Efficient multi-objective optimization approach for solving optimal DG placement and sizing problem in distribution systems

Incorporating distributed generations (DGs) like wind turbines (WTs) and solar photovoltaic (SPV) arrays into radial distribution systems (RDSs) has become increasingly important due to their benefits in enhancing system performance. This requires determining appropriate DG locations and their optimal power output when injected into the RDS. To this end, this paper introduces a novel application of a recent multi-objective optimization technique called multi-objective grey wolf optimization (MOGWO) to allocate multiple DG units into the RDS optimally. The primary objectives are to minimize total real power losses (RPL), reduce voltage deviation (VD), and enhance the voltage stability index (VSI) across the entire system while adhering to operational constraints. Various DG operational power factor (PF) scenarios are considered, including unity-PF, fixed-PF, and optimal-PF. The proposed approach generates a Pareto-optimal set of solutions to address the multi-objective problem, after which a fuzzy decision-making method is adopted to select the best trade-off solution from the set. The technique has been implemented on 69-bus and the actual Portuguese 94-bus RDS. Numerical results are compared with other recent well-known algorithms from the literature, highlighting the efficacy of the suggested MOGWO method in handling multi-objective problems. Comparatively speaking, it provides superior solutions than other methods in selecting appropriate control parameters.

Stability analysis and voltage improvement in DG-integrated distribution networks using VCPI-based critical buses and lines detection considering uncertain power factor

Voltage stability ensures reliable and efficient power transmission to end-users, making it essential for power system efficiency. Dynamic load changes and rising power requirements might cause voltage instability and strain performance. This paper investigates the voltage stability Indicators of distribution systems to address the voltage stability issue in power systems. The uncertain power factor and other numerous technical parameters that affect the voltage drop or voltage collapse are involved in predicting the system’s voltage stability. First, the analysis uses the voltage collapse proximity index (VCPI) technique, which considers bus voltage, impedance, uncertain power factor of load buses, and transmission line impedance at both ends. The applied technique correctly predicts and detects weak buses and transmission lines using different voltage stability indices since technical parameters are correlated with the VCP index. The index and line impedance correspond; consequently, the recommended indication compensates for voltage loss owing to impedance. Therefore, the VCPI is a better index for anticipating voltage collapse and finding network weak busses and lines. The implemented approach is simple, precise, and economical; the index technique detects weak buses and forecasts system collapse. Second, this work introduces EMFO-FPSO, a hybrid metaheuristic that improves voltage stability by combining Fractional-order particle swarm optimization (PSO) and Entropy design mouth flame optimization (MFO). The proposed approach is validated on the IEEE 33 bus system by identifying weak buses and optimizing distributed generation placement in the distribution system. The implemented solution approach based on VCPI and EMFO-FPSO optimization achieves a significant power loss reduction of 71.5% compared to the base case, whereas the reductions for ABC, ACO-ABC, and PSO are 57.3%, 60.7%, and 64.4%, respectively. Moreover, the proposed method attains a stable VCPI Index of 0.90, indicating a 10% enhancement in voltage stability relative to the base case. The study’s results, key findings, and comparisons show the relevance of the proposed method for voltage stability enhancement.

Implementasi Metode Artificial Bee Colony Untuk Penempatan Distributed Generator Pada Penyulang Rayon Tanjung

Distributed Generation (DG) is a power plant that is spread over the power distribution network. In principle, electrical power output is generated by power plants located far from the load center and electrical power is transmitted to the load center through transmission and distribution networks. The longer the cable from the power plant or substation, the lower the voltage at the end load. Sub-optimal DG placement and size can result in increased power losses and affect the system voltage profile. In addition, suboptimal generation capacity can lead to voltage instability, reduced system security, and potential impacts on system frequency and emissions. Therefore, this study will use the Artificial Bee Colony (ABC) method to determine the optimal DG capacity and location. In the base case condition, the power losses in the system are 118 KW and 99 Kvar. Power losses after the installation of 1 DG active power loss becomes 37.4 KW and reactive power loss becomes 31.65 Kvar. Placement of 2 DGs reduces active power loss to 17 KW and reactive power loss to 15 Kvar. While the placement of 3 DG reduces active power loss to 33 KW and reactive power loss to 28 KVar.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Analysis of Relay Coordination in Electrical Distribution Networks with Optimally Positioned Distributed Generation Units

As power demand increases and transmission and distribution capacity is limited, Distributed Generation (DG) units are gradually being incorporated into electrical power distribution networks. However, this integration may result in a significant impact on the coordination of protective relays, potentially generating issues such as blindness of protection and false tripping of overcurrent relays. To overcome these issues, an adaptive protection method is presented that adjusts relay sensitivity in response to changes in network design. The suggested technique dynamically modifies relay settings based on the line current, which fluctuates depending on the system design. Digital relays are needed for this method in order to save operational data in real time. Based on periodic observations, the Plug Multiplier Setting (PMS) and Time Multiplier Setting (TMS) parameters are optimized using a Fuzzy Inference System (FIS). Fuzzy rules are used by the adaptive algorithm for every relay. These rules are produced using the fuzzy editor using the inputs provided. An initial investigation was carried out to evaluate the effect of DG on the coordination of relay protection., as well as the optimal placement of DG to reduce losses and improve voltage profiles, using two base distribution networks. The algorithm was further tested on various configurations of IEEE distribution networks, and its effectiveness in achieving optimal relay coordination with adaptive settings was validated through the relay coordination module in ETAP.

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats’ self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

The continuous challenge of ensuring energy supply is a significant concern. Traditionally, utilities have addressed increasing load demands by building new central power plants or expanding existing ones. However, these methods are time-consuming and costly, providing only short-term solutions. Additionally, power plants are often far from load centers, resulting in higher power losses during long-distance transmission. The integration of distributed generation (DG), characterized by smaller scale and lower capital costs, with nearby consumers can effectively address these challenges. Due to the significant influence of DG penetration and their specific locations on the distribution system's performance, this study offers a novel Zebra Optimization Algorithm (ZOA). The suggested ZOA is used to optimize the capacity and position of various DG units connected to a radial distribution system to reduce power losses and study the stability of the system after DG allocation using the Voltage Stability Index (VSI). The evaluation is conducted using the IEEE 33-bus test system, considering various scenarios including single and multiple DGs of different types. The results demonstrate that in the best scenario, the ZOA can reduce losses by up to 94.25 % and improve the VSI from 0.69643 to 0.9605. These findings highlight the superior performance of the ZOA over other techniques in optimizing DG placement and sizing for power system performance.

Effect of distributed generation placement to load noise on the KH-01 feeder at PT. PLN (PERSERO) ULP Krueng Geukueh

Effect of distributed generation placement to load noise on the KH-01 feeder at PT. PLN (PERSERO) ULP Krueng Geukueh

A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

Novel JAYA-IWO Approach for Optimal Distributed Generation Placement to Minimize Power Losses

Novel JAYA-IWO Approach for Optimal Distributed Generation Placement to Minimize Power Losses

Characteristics of Various Single Wind-Power Distributed Generation Placements for Voltage Drop Improvement in a 22 kV Distribution System

A major challenge in distribution systems is the issue of voltage drop along the distribution line resulting from an increased load capacity connected to the utility. A significant voltage drop can affect the performance of a distribution system and cause quality issues for end users, impacting the system’s long-term sustainability and reliability. Therefore, regulations have been set stating that the voltage level should not be more that 5% higher or lower than the rated voltage. Thus, in this study, we aimed to evaluate the voltage level characteristics of a 22 kV distribution system that replicates the actual distribution system in the Provincial Electricity Authority. A voltage improvement technique based on distributed generation placement was proposed. In addition, the distribution system characteristics with and without distributed generation placement were evaluated under fault conditions. The results indicate that distributed generation placement in the distribution system can improve the voltage level along the distribution line. However, the level of increase in voltage depends on the size of the load, the capacity of the distributed generation, and the location of the distributed generation system on the distribution line. Furthermore, placing a distributed generation system with a minimum capacity at the proposed location can improve the voltage within the utility’s standard level. Thus, the installation of a distributed generation system in the distribution system is beneficial in terms of voltage improvement in the distribution system and provides the power system with a sustainable method to address the issue of voltage drop.

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.

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Performance improvement of radial distribution system with distributed generation and intermittent load

The distribution network's load situation is typically variable and non-uniform. Therefore, a modified Distributed Generation (DG) algorithm is proposed in this paper, which uses the analytical approach and applies to all kinds of radial distribution feeder networks. The specified parameter of the distribution network is given accurately to the programme manually or through the file uploading mode, and the power flow analysis is done by MATLAB software. The results are obtained, including the specific location of DG placement which is optimised analytically. Here, the voltage and power profiles are monitored seriously, and the minimisation of voltage drop, and power loss is obtained, which is precise of a high value. The system is implemented for the IEEE 33 bus system which is got from Panchasakha Feeder Network. In this proposed method, DGs are deployed to reduce the loss in the transmission line as well as to maintain the voltage stability in the system by an analytical algorithm approach.

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 allocation of controllable power factor distributed generation with network reconfiguration on electric vehicle charging station loaded distribution network

This paper presents a hybrid optimisation technique to solve the problem of optimal allocation of controllable power factor Distributed Generators (DGs) together with network reconfiguration on Electric Vehicle Charging Stations (EVCSs)-loaded distribution networks. The suggested hybrid optimisation strategy combines the Artificial Gorilla Troops Optimizer (GTO) algorithm with genetic operators to improve convergence characteristics and prevent premature convergence to local optima. The objective is to minimise power loss, bus voltage deviation and voltage stability index reduction, which are formulated as a multi-objective function. The effectiveness of the proposed approach is evaluated on 69 and 118-bus distribution networks. Additionally, a comparative analysis is performed to evaluate the efficacy of the suggested approach in comparison with standard GTO and other well-known optimisation algorithms. The results demonstrate that simultaneous optimal placement of controllable power factor DGs with network reconfiguration demonstrates superior effectiveness in mitigating the adverse impact of EVCSs on distribution networks compared to optimisation of fixed-power factor DG placement with network reconfiguration. Furthermore, the hybrid optimisation approach outperforms other applied algorithms in terms of solution quality.

Optimizing Distributed Generation Placement and Profit Maximization in Active Distribution Networks with Harmonics: A GEPSO Approach

Optimizing Distributed Generation Placement and Profit Maximization in Active Distribution Networks with Harmonics: A GEPSO Approach

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.