Placement Of Distributed Generation Research Articles

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.

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.

Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization Algorithm (GOA) and Mountain Gazelle Optimization Algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltage-dependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

The growing demand for electricity has raised concerns about power dissipation in distribution systems. To mitigate these losses, capacitors and distributed generator (DGs), particularly solar PV are strategically placed within the system. This project is committed to reducing power losses and improving the voltage profile through an in-depth analysis, optimizing the placement of capacitor and DG along the distribution feeder. The application of forward and backward sweep (FBS) algorithms assists load flow analysis in distribution networks with high R/X ratios, while the incorporation of the Genetic Algorithm (GA) within MATLAB identifies optimal locations & size for capacitors and DGs inside the large solution space of this complex, nonlinear optimization problem. Test outcomes, conducted on an IEEE 33-bus test system as its convincing representation of medium sized distribution network providing a versatile platform for evaluating proposed methodologies with practical implementation, showcase load flow examination, improvements in voltage profiles and minimized energy dissipation. The methodology is further applied to the real distribution network of the Sallaghari-Thimi 11 kV feeder in Bhaktapur, Nepal, sustaining the approach's effectiveness in mitigating power losses and increasing voltage profiles. Distributed generation with capacitor outperforms capacitors, and DG integration in the power system results in significant reductions of 72.91% in real power loss and 63.45% in reactive power loss, with a notable 6.542% increase in voltage magnitude. Application of these strategies in the Thimi Sallaghari 11 kV feeder demonstrates significant power loss saving (up to 82.72%) and worthy improvements in voltage profiles (up to 5.32%), focusing on their effectiveness in enhancing operational efficiency. This approach provides a practical solution for optimizing capacitor and solar PV distributed generator placement in distribution networks considering various case scenarios.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Optimization of Distributed Generation in Radial Distribution Network for Active Power Loss Minimization using Jellyfish Search Optimizer Algorithm

The inclusion of distributed generation (DG) units in the distribution network (DN) effectively cuts down the power losses (PL) and strengthens the voltage profile (VP). This paper examines the effect of allocating different distributed generation (DG) in radial distribution networks (RDN) through an implementation of an optimization technique using a recently introduced bio- inspired algorithm known as a jellyfish search optimizer (JSO). Unlike the other optimization algorithms, the JSO algorithm evades the local optimal trap and reaches the optimal solution in less time. The DG position(s) and size(s) are optimized for active power loss (APL) minimization with respect to several constraints. The effectiveness and robustness of the proposed optimization technique using JSO algorithm is investigated on a balanced IEEE RDNs with 33, 69 and 118-buses. The simulation outcomes are obtained for different types (type I, II and III) of DG placement. Additionally, a comprehensive comparative study has been performed for the JSO and other algorithms. The comparison exemplifies that the proposed JSO optimization approach produces a better optimal solution with steady convergence than other techniques reported in the literature. Also, the simulation findings show the potentiality of JSO optimization method for solving complex optimization problems.

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.

Optimal inverter-based distributed generation in ULP Way Halim considering harmonic distortion

Integration of distributed generation (DG) based on the use of new renewable energy is considered to be able to increase the capability of the electric power distribution system. However, the use of inverter-based DG is not optimal, it can worsen the condition of the system, especially in terms of the spread of harmonic distortion which can damage the equipment. This is due to the inverter-based DG technology, apart from supplying electrical energy, DG also injects harmonic currents from existing semiconductor components. This research discusses optimization placement of inverter-based DG using the multi objective particle swarm optimization (MOPSO) method which was tested on the Unit Layanan Pelaksana (ULP) Way Halim 88-bus radial distribution system based on MALTAB 2020b to increase the efficiency of the electrical system by reducing losses and %THDv. The inverter-based DG placed on 24 bus points with a capacity of 690 kW can reduce losses by up to 12.74 kW or 14.96% and all %THDv values for each bus are below 5%.

Optimal placement and sizing of multiple distributed generators in power distribution networks

The primary objective of power systems is to meet the total power requirement, as well as to retain system reliability and security with the economic operation of the system. In recent years, exponential growth in power demand, lack of investments in up-gradation of electrical infrastructure, shortage in power generation and environmental concerns have been the key factors for unreliable and poor operation of the power systems. To tackle these problems, there is a need to rely on supplementary sources of generation and fortification of transmission capacity. Whereas, installation of large scale new generating units is not economical. An adequate number of small generating units incorporated is a more effective solution to meet the growing power demand. This research work focuses on the enhancement of voltage profiles while curtailing power loss by the optimal sizing and optimal placement of multiple distributed generators (DGs) at different buses. Different types of DG sizing and placement are carried out at 100%, 120% and 140% loading conditions.

Solar and Wind-based Renewable DGs and DSTATCOM Allotment in Distribution System with Consideration of Various Load Models Using Spotted Hyena Optimizer Algorithm

Background: This article presents a novel strategy that utilizes the nature-inspired Spotted Hyena Optimizer Algorithm (SHOA) to optimize the placement of solar and wind-based renewable distributed generation (RDG) and distribution static compensators (DSTATCOMs) in radial distribution systems (RDS). Methods: The proposed technique aims to determine the optimal locations of DSTATCOM and RDGs based on the loss sensitivity factor (LSF), while the appropriate sizes are determined using the newly developed SHOA. To facilitate efficient load flow calculations, a fast and effective backward/forward sweep algorithm (BFSA) is employed. Results: The primary objective of this method is to minimize overall power losses within the system. The effectiveness of the optimization approach based on SHOA is demonstrated through extensive simulations conducted on a standard IEEE 33-bus test system with diverse load models. Conclusion: The results of the simulations and comparisons of multiple case studies clearly indicate that the allocation of DSTATCOMs leads to significant reductions in power losses and improvements in voltage profiles.

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>

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.

Robust Distribution Network Reconfiguration in the Presence of Distributed Generation Under Uncertainty in Demand and Load Variations

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.