Sizing Of DGs Research Articles

Simultaneous optimal location and sizing of DGs in distribution system considering different types of MGs in an electricity market

Simultaneous optimal location and sizing of DGs in distribution system considering different types of MGs in an electricity market

Sickle-shaped high gain and low profile based four port MIMO antenna for 5G and aeronautical mobile communication

The construction of the four-port MIMO antenna in the form of a sickle is provided in the article. Initially, the single port element is designed and optimized. Next, a structure with two ports is created, and lastly, a design with four ports is completed. This process is repeated until the design is optimized. Three types of parametric analysis are considered, including variations in length, widths of sickle-shaped patches, and varying sizes of DGS. The frequency range of 2–8 GHz is used for structural investigation. The − 18.77 dB of return loss was observed at 3.825 GHz for a single-element structure. The optimized one-port structure provides a return loss of − 19.79 dB at 3.825 GHz. The port design offers a bandwidth of 0.71 GHz (3.515–4.225). The four-port design represents two bands that are observed at 3 GHz and 5.43 GHz. Both bands provide the return loss at respectively − 19.79 dB and − 20.53 dB with bandwidths of 1.375 GHz (2.14–3.515) and 0.25 GHz (5.335–5.585). The healthy isolation among both transmittance and reflectance response is achieved. The low-profile material was used to create the design that was presented. The article includes a comparison of the findings that were measured and those that were simulated. The four-port design that has been shown offers a total gain of 15.93 dB, a peak co-polar value of 5.46 dB, a minimum return loss of − 20.53 dB, a peak field distribution of 46.43 A/m and a maximum bandwidth of 1.375 GHz. The values for all diversity parameters like ECC are near zero, the Negative value of TARC, Near to zero MEG, DG is almost 10 dB, and a zero value of CCL is achieved. All diversity parameter performance is within the allowable range. The design is well suited for 5G and aeronautical mobile communication applications.

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology.

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

An Optimal Multi-Objective Approach for Distributed Generation and AVR Placement in Distribution Networks Using IPMBSA

Inclusion of automatic voltage regulators (AVRs) and distributed generation units (DGs) in the distribution system (DS) will enhance the system performance if allocated optimally. Optimal allocation of AVRs and DGs is a complex combinatorial problem requiring a powerful optimization algorithm for solving the optimization problem. This paper proposes an improved pareto multi-objective backtracking search algorithm (IPMBSA) based Pareto front approach for optimal siting and sizing of DGs and AVRs. The proposed approach considers DS technical (such as minimizing of voltage deviation, power losses and voltage stability index), cost and emission indices as objective functions while solving the allocation problem. To evaluate the performance of the proposed approach, five different operational scenarios are considered for analysis on IEEE 33-bus, IEEE 69-bus and practical 43-bus Indian distribution test systems. The effectiveness of the proposed approach is highlighted by comparison of results obtained using a multi-objective approach based on NSGA-II and weighted sum method (WSM). Results propounded that allocation of DG along with AVR yielded better DS performance compared to the scenarios where only DG or AVRs are placed in DS.

Optimal Inverter-Based DG Placement considering Energy Loss, Reliability, Voltage Sag, DG, and Substation Power Cost Issues

This article presents a comprehensive multi-objective-based optimization problem formulation for finding the optimum size, site, and number of inverter-based DGs (IBDGs) for the operation of the distribution system so as to minimize annual energy cost comprising of costs for the energy loss during the normal operating state, running the IBDGs, substation power including penalty for reverse power flow (if any) and the average energy not supplied as a measure of reliability. Besides, the cost of average apparent load disturbed as a result of voltage sags during faults is also considered. The method is tested on the 33-bus distribution test system using particle swarm optimization. The results so obtained, demonstrate the effectiveness and applicability of the proposed formulation to find the optimum location(s), size(s), and number of IBDG(s) in the radial distribution system with appropriate constraints consideration.

Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

Optimal Placement and Sizing of DGs in Electrical Distribution System using Optimization Techniques

Optimal Placement and Sizing of DGs in Electrical Distribution System using Optimization Techniques

The Optimal Integration of Multiple DGs under Different Load Models using Artificial Bee Colony-Hill Climbing Algorithm

With the continual advances in technologies, there is a significantly increased demand for electricity leads to a drastic increase in usage and penetration of DGs into the distribution network. The incorrect addition of DGs into the distribution system resulting in declining stability, power losses, voltage profile, reliability, and power quality of the system. Therefore, the optimal planning of DGs (OPDG) has always been under constant investigation and development for the maximum beneficiary. The proposed framework presents a novel meta-heuristic hybrid optimization technique called Artificial Bee Colony-Hill Climbing (ABC-HC) to find the best siting and sizing of multiple DGs using a weighted multi-objective function which includes real and reactive power loss index and voltage deviation index. The results clearly indicate the significant reduction in losses and voltage deviation achieved by integration of single and multiple DGs. The research work is also extended to assess the voltage harmonics of the system before and after the placement of DGs. The effectiveness of the proposed ABC-HC algorithm has been demonstrated on the standard IEEE-33 bus RDS under different voltage dependent load models using MATLAB 2017a.
 HIGHLIGHTS
 
 As demand grows, technologies advance, and power system infrastructure changes, there are more opportunities to integrate distributed generators into the distribution system
 The maximum benefit from the DG integration is achieved by selecting the optimal size and optimal site in the distribution system
 A new hybrid optimization technique has been developed for optimal planning of DGs under different voltage dependent loads. The work was also extended to carry out harmonic analysis
 
 GRAPHICAL ABSTRACT

Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations

In existing power system networks, the positioning and sizing of multi-DG is critical at the optimum locations for effective energy management. Initially optimal power flow is assessed using the NR method (without DG) in which performance parameters such as real power loss, accuracy, selectivity and MSE are obtained, but in an undesirable manner. To meet load demand; multi-DGs are placed and their optimal locations are assessed by the proposed heuristic probability distribution methodology and an ANN because existing techniques provides poor performance parameters for selecting the location and sizing of DGs. The optimal positions of multi-DGs are estimated in terms of performance parameters including real power loss of transmission network, accuracy, selectivity and MSE, while the performance parameters obtained with the ANN are better than the heuristic pdf. Then, the sizing of multi-DGs is evaluated in relation to active and reactive power. It is found that that sizes of multi-DGs are smaller with ANN than with heuristic pdf. It is preferable to connect the buses having lowest real power losses with the smallest multi-DGs. The performance analysis is tested in the standard IEEE 9- bus and IEEE 57- bus systems on Simulink. To improve the distortion level in real and reactive power, multi-FACTS namely TCSC, TSC and STATCOM are used. The switching of TCSC and TSC is done by SPWM while STATCOM switching is controlled with ANFIS. The locations of multi-FACTS devices are chosen for buses having larger distortion and the sizing of multi-FACTS devices is also optimally decided. The application of multi-FACTS devices helps to improve power quality and fulfill load demand with minimal size in order to make the system economical.

Optimal Planning of Remote Microgrids with Multi-Size Split-Diesel Generators

This paper proposes a multi-size Split-diesel generator (Split-DG) model with three different sizes of DGs and more switching configurations compared to the existing split-DG models. The proposed multi-size Split-DG system is examined for optimal sizing of remote microgrids with and without renewable-battery system. As a novel concept, multi-size Split-DG is used to reduce contamination, cost, and dumped power by using multiple small DGs to replace the single-size large DG. As another contribution of this study, a practical model is developed by considering the capacity degradation of components, spinning reserve, as well as DG’s and fuel tank’s constraints. The optimization problem is solved using a variable weighting particle swarm optimization (VW-PSO) algorithm. The effectiveness of the proposed Split-DG systems, optimized by the developed VW-PSO, is verified by comparing the results with conventional single-size DG system and the system optimized by conventional PSO. While the formulated optimization problem is general and can be used for any remote microgrids, an aboriginal community in South Australia is examined in this study. For this purpose, realistic data of load and weather, as well as technical and economic data of components, are used. It is found that the Split-DG-PV-WT-BES system has the lowest electricity cost compared to the systems without BES, or without PV and WT.

Multi-Objective Optimal Placement and Sizing of DGs by Hybrid Fuzzy TOPSIS and Taguchi Desirability Function Analysis Approach

Multi-objective optimal placement and sizing of Distributed Generation in a distribution system is a challenging endeavor. It is seen that in most cases DG placement is required for multiple criteria, however, placement is done using a single criterion. It is imperative to take into account all criteria and their importance for DG placement. Optimal placement of DG is portrayed as a multi-criteria decision-making (MCDM) problem in this paper. An optimal place is found by applying the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Voltage stability has been studied in context with DG sizing, however, power stability has not been given due consideration. In this paper, maintaining power stability is one of the objectives for finding optimal DG size. It is observed that the addition of this objective restricts DG size. A robust Taguchi Desirability Function Analysis (TDFA) technique is applied for estimating the optimal size of DG. TDFA can optimize almost a thousand objectives simultaneously. Each objective can be added effortlessly. Moreover, the proposed approach offers a set of near-optimal solutions. IEEE 33-bus radial distribution system and restructured IEEE 118-bus radial distribution system are used for testing.

Butterfly optimizer-assisted optimal integration of REDG units in hybrid AC/DC distribution micro-grids based on minimum operational area

This paper presents the impact of optimal location and sizing of renewable and non-renewable-based distributed generators in the AC/DC micro-grid system using the latest optimizer called butterfly optimization algorithm with an aim to minimize power loss. Generally, hybrid AC/DC micro-grids systems are modeled by separating AC and DC feeders with the help of high-power converters (HPC).AC grids sustained by substation and DC grids are maintained by their individual DG units. While planning of DGs in the hybrid AC/DC systems, the power loss incurred by HPCs is not considered avoiding complexity by many authors. In this paper, the sizing of DGs is determined by the operational area required by the type of DG technology as one variable and all possible candidate buses in the respective zones of AC/DC micro-grid system are another variable with due consideration of HPC losses in AC/DC micro-grid system. A hybrid AC/DC MG system is developed by classifying the existing benchmark 33-bus and 69-bus radial distribution systems into various AC/DC zones. To evaluate the proposed approach, it is implemented on aforementioned micro-grid systems and the obtained results are verified with other existing approaches in the literature. The results proved that the proposed approach is better than the other approaches in technical aspects.

Optimal DG placement in power markets from DG Owners’ perspective considering the impact of transmission costs

• The DG placement problem has been solved from the DG owners' perspective based on locational marginal prices (LMPs). • The impact of transmission costs is considered in DG placement. • The contribution of DGs from the power flow is calculated using generalized generation/load distribution factors (GGDFs/GLDFs). • The transmission cost allocated to DGs is determined by the MW-Mile method. • The method provides DG owners with an effective tool to make optimal decisions. Among many studies proposed to solve the DG placement problem, only a few papers have addressed this issue from DG owners' perspective, none of which has taken the important role of transmission costs allocated to DGs into account. In this paper, optimal placement of DG units in an optimal power flow based wholesale electricity market with the aim of maximizing DG owners' economic benefits is carried out, with specific consideration given to transmission costs allocated to DGs. The proposed approach takes both production and transmission cost components into account in the DG placement process, so that in order to achieve the most optimal size and site of DGs from their owners' point of view, the problem of optimal placement is formulated for maximizing the DG owners' revenue while minimizing their allocated costs (production and transmission cost alike). Generalized load distribution factors (GLDFs) are used to determine DGs' share of the power flow, and transmission costs are allocated using the MW-Mile method. Finally, two criteria are proposed to identify suitable load buses for optimal DG placement. Two case studies based on IEEE 14-bus and IEEE 30-bus are used to demonstrate the applicability and validity of the proposed method.

Optimal Placement of DG with Battery Energy Storage in Distribution Network for Power loss Minimization using Combined Dispatch & Combined PLS Strategy

This paper presents a novel application of General Algebraic Modelling System (GAMS) and MATLAB interfacing for optimal location and placement of DG with battery. The main contribution of this paper is: (i) optimal placement of DG based on combined-Power Loss Sensitivity (PLS) method, (ii) optimal placement of battery energy storage using combined dispatch strategy, (iii) optimal size of DGs and Battery have been carried out in such a way to minimize the total power loss without violating the constraints using MINLP solver in GAMS . (iv) Two types of DGs have been considered for analysis and the impact of DG with and without battery placement on total power loss and voltage improvement. Voltage profile, real and reactive power flow pattern, total power loss, computational time, fuel cost, total installation cost, total cost, State of Charge (SOC) status of battery and power obtain by DG with the battery has been determined. The result shows the importance of DG with battery size and location. The results are obtained on IEEE-33bus radial distribution test system and compare with another existing method also.

Network reconfiguration and optimal allocation of distributed generations for Mayangone distribution system

This paper focuses on reconfiguration of radial distribution system incorporated with optimal allocation of Distributed Generation units (DGs). The network reconfiguration is to optimize the structure of radial feeders by changing the status of normally closed sectionalizing and normally open tie switches. The optimal allocation of DGs in distribution system has to determine the DGs location and their correspondent sizing for enhancing the system effectively. There are many investigations with both two researches either separately or incorporated simultaneously. But network reconfiguration by using Particle Swarm Optimization (PSO) incorporated with optimal allocation of DGs based on Voltage Stability Index (VSI) and analytical approach of Forward Backward Sweep (FBS) method in radial power flow is the novelty of this research. The main objective is to achieve the minimum losses and improvement of voltage profile while all distribution constraints are satisfied. In proposed algorithm, optimal allocation is determined by finding the best sizing and location of DGs based on VSI and FBS load flow, and then PSO reconfiguration finds out the optimal location of tie and sectionalizing switches simultaneously. The study of test system is carried out on Mayangone Distribution System (MDS) and the obtained results are inefficient for this research.

Optimal Sizing and Placement of Capacitor Banks and Distributed Generation in Distribution Systems Using Spring Search Algorithm

AbstractIntegration of capacitor banks (CBs) and distributed generation units (DGs) in distribution systems aims to elevate the system performance. Optimal sizing and placement of CBs and DGs using Spring Search Algorithm (SSA) is proposed. The SSA aims to attain economic, technical, and environmental advantages. Various objective functions: total emissions produced by generation sources, voltage deviation, total electrical energy cost, and power losses are evaluated. SSA simulates Hawk’s Law among weights and springs. In SSA, agents are weights connected to each other by springs. Performance of the proposed method is evaluated on the IEEE 33-bus system. The results indicate superiority of SSA compared with other optimization algorithms.

A novel [formula omitted] bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses

This paper proposes a new DG placement and sizing technique by introducing a novel Q−PQV bus pair in distribution networks (DNRs) with seasonal variation of load demand. The principle aim of this paper is to boost and maintain the voltage of remotely located buses to predefined optimal level for seasonal load demand by integrating biomass DGs in DNRs. The selection of locations and sizes of DGs is determined by novel bus pair, which is different from conventional buses present in power system studies. For PQV bus along with active and reactive power, the voltage magnitude also is known, whereas, for Q bus, only reactive power is known. The power injection of Q bus with the load connected determines DG sizes. The inclusion of this new bus pair modifies the load flow technique, which is also given in the paper. The proposed methodology is applied on 33 bus DNR, while active power loss is taken as principal objective function. Both upf and lpf DGs are considered in this paper, and the results are compared with existing methods available in literature. The cost-benefit analysis shows the profit gained from it.

Minimization of Power Losses with Different Types of DGs Using CSA

Proper allocation of DGs in distribution network is important from all the aspects like technical, economical and environmental. In this paper, Crow search algorithm (CSA) technique has been proposed to find the optimal size and location of multiple DGs of different types to reduce the active power loss of the distribution network (DN). The proposed method has only two parameters to be tuned. The proposed method is tested on 33 bus and 69 bus test system and the obtained result is compared with IA and PSO method. The proposed method gives better results compared to existing PSO and IA methods.

Simultaneous placement of tie-lines and distributed generations to optimize distribution system post-outage operation and minimize energy losses

This study proposes a new method which aims to optimally install tie-lines and distributed generations simultaneously. This is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution systems. The number and location of tie-lines, as well as the number, size, and location of DGs, are pinpointed through teaching the learning-based optimization (TLBO) method. The objective function in the current research is to minimize the costs pertaining to the investment, operation, energy losses, and energies not supplied. In addition to the normal operational condition, fault operational condition is also evaluated. Therefore, the optimal post-fault reconfigurations for fault occurrences in all lines are established. Moreover, the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational modes. Finally, the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.