Bus Radial Distribution System Research Articles

Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach

The usage of Electric Vehicles (EVs) for transportation is expected to continue growing, which opens up new possibilities for creating new smart grids. It offers a large-scale penetration of Fast Charging Stations (FCE) in a local utility network. A severe voltage fluctuation and increased active power loss might result from the inappropriate placement of the FCE as it penetrates the Distribution System (DST). This paper proposes a multi-objective optimisation for the simultaneous optimal allocation of FCEs, Distributed Generators (DGs), and Shunted Capacitors (SCs). The proposed Pareto dominance-based hybrid methodology incorporates the advantages of the Grey Wolf Optimiser and Particle Swarm Optimisation algorithm to minimise the objectives on 118 bus radial distribution systems. The proposed method outperforms some other existing algorithms in terms of minimising (a) active power loss costs of the distribution system, (b) voltage deviations, (c) FCE development costs, (d) EV energy consumption costs, and (e) DG costs, as well as satisfying the number of FCEs and EVs in all zones based on transportation and the electrical network. The simulation results demonstrate that the simultaneous deployment technique yields better outcomes, such as the active power loss costs of the distribution system being reduced to 53.21%, voltage deviations being reduced to 68.99%, FCE development costs being reduced to 22.56%, EV energy consumption costs being reduced to 19.8%, and DG costs being reduced to 5.1%.

Distribution Networks Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Hybrid TLBO-BH Algorithm

In this paper, a new method based on the combination of the Teaching-learning-based-optimization (TLBO) and Black-hole (BH) algorithm has been proposed for the reconfiguration of distribution networks in order to reduce active power losses and improve voltage profile in the presence of distributed generation sources. The proposed method is applied to the IEEE 33-bus radial distribution system. The results show that the proposed method can be a very promising potential method for solving the reconfiguration problem in distribution systems and has a significant effect on loss reduction and voltage profile improvement.

A Comprehensive Modeling for Wind Turbine Generator and DSTATCOM into Backward/Forward Load Flow Algorithm

Backward/forward load flow algorithm is commonly used in the power system, particularly in radial distribution networks. However, the rapid development in the power electronics technologies makes power systems more flexible with the integration of Renewable Energy Resources (RESs) and compensation devices. Consequently, this paper presents simple models for wind turbine generators based on Squirrel Cage Induction Generator (SCIG) and Distribution Static Synchronous Compensator (DSTATCOM) into a backward/forward load flow algorithm. In the wind generator model, the mechanical powers are collected from the manufactures data where the electrical power is calculated through the SCIG equivalent circuit and injected as negative loads at the connected buses. The second model in this paper is the DSTATCOM devices which are utilized to recover the SCIG reactive power. In the proposed model, the connected DSTATCOM bus is converted to a voltage-controlled bus (PV-bus type) with zero active power. Moreover, the reactive power of the DSTATCOM is updated during the iterative process until the load flow convergence. The simplified models for both wind generators and DSTATCOM are validated based on standard IEEE 33-bus and 85-bus radial distribution systems. The obtained results show the feasibility and effectiveness of the proposed models.

GAMS applications to capacitors location and its sizing in a RDS

This paper presents a new approach for finding capacitor's location and it's sizing in a radial distribution system (RDS) optimally with an aim of reducing the active power loss. In this paper, the problem of minimizing power loss is converted into a Mixed Integer Non-Linear Program (MINLP) problem, and it will be solved by using Generalized Algebraic Modeling Systems (GAMS) software with MINLP-SBB Solver. The proposing GAMS approach is tested on IEEE 10 bus RDS. By using GAMS, the programming will be simple and more accurate results can be achieved with less execution time. The MATLAB R2020b is used to run the load flows program and analyze results. The results are compared with the other optimization techniques results.

Effect of change in number and power factor of DG on optimal allocation for minimal actual power loss in RDS

Due to its remarkable techno-economic advantages, Distributed Generator (DG) penetration is growing drastically. To minimize the power losses and enhance the voltage profile, determining the precise size & position of DG is critical. The proposed paper compares the effect of variations in the number & power factor of DG on its optimal allocation in the Radial Distribution System (RDS) for minimizing the active power loss & enhancing the voltage profile using Grey Wolf Optimization (GWO) algorithm. The Direct Load Flow (DLF) approach is applied to address the system's power flow. Under altering DG parameters, the proposed method computes and compares appropriate DG allocation in standard IEEE 33 & 69 bus RDS.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

<span>The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.</span>

Power loss mitigation and voltage profile improvement by optimizing distributed generation

In a developing country, electricity has become the necessity of the growth industries; thus, the distribution system power quality and reliability are crucial. With low carbon initiatives, renewable energy or distributed generation (DG) is a promising source of electricity and leads the complex distribution system. Vital rises in DGs in power grids will significantly impact the system reliability and security, especially in power losses and voltage profiles parameters. This research focuses on an optimization placement and size of DGs in distribution systems to minimize power loss and improve voltage profile using the Modified Lightning Search Algorithm (MLSA). This research has modelled the practical 69-bus radial distribution system. Then MLSA with a weight summation approach is used to identify the suitable location and size for the DGs in the design proposal stage. The optimization objectives are to reduce power losses and improve the voltage profile, especially at the connection point of DGs. Besides that, load profile, DGs constant load and the solar load in distribution system modelled using MATLAB software. The results of the simulation using MLSA indicated that the optimization allocation and sizes of solar DGs applied with current load and load changes can minimize the power losses and improve voltage profile. These results verify the proposed approach’s effectiveness and success in determining the optimal location and sizing of solar DGs to reduce power losses as well as improve voltage profiles.

A Novel Technique for Optimal siting and Rating of Shunt Capacitors Placed to the Radial Distribution Systems

The overall efficacy of power distribution is reduced through power loss in distribution systems owing to branch resistance. For providing quality power to customers, power losses must be diminished, and voltages must be enhanced. This paper introduces a novel improvement for the traditional Salps Swarm Algorithm (SSA) called Enhance SSA (ESSA) for selecting Optimal Shunt Capacitor Ratings and their Locations (OSCRL). ESSA is viewed as an enhanced version of SSA with the aim of improving the SSA's poor performance. A multi-objective function was designed in this work for reducing real power loss and Total System Cost (TSC), as well as, improving feeder voltage profile and maximizing Net Savings (NSs) per year under security restrictions. For validating and evaluating the proposed ESSA technique, a practical 33-bus local Iraqi and standard IEEE 69-bus Radial Distribution Systems (RDSs) were used as test systems through utilizing MATLAB. The experimental results attained by the proposed ESSA are compared with other approaches currently available in the literature for addressing OSCRL problem. The experimental results prove the efficacy and supremacy of the ESSA to other literature algorithms with respect to diminishing power loss as well as TSC, reinforce bus voltage profile and increase NSs per year for multiple RDSs.

A Mayfly Algorithm-Based Optimal Placement of Capacitor for the Minimization of Losses and Improvement of Voltage Profile in a Microgrid

In this work, an algorithm based on mayfly is presented towards the optimal capacitor placement for the minimization of losses and improvement of the voltage profile. It employs the concept of fixed and switched capacitor with the former serves as an alternative to later. A factor based on loss sensitivity was employed in locating a candidate bus, this helps in search space reduction and optimal sizing and placement of capacitor were realized using the mayfly algorithm. The effectiveness of this technique was tested on the standard 10 bus and 15 bus radial distribution system which upon comparison the result surpassed that of the previous method .

Behavior of the Social Spider Technique on Network Reconfiguration

The goal of this paper is to offer a new strategy for solving the network reconfiguration problem with the aim of decreasing real power loss and enhancing the voltage profile in the distribution system. Social spider optimization (SSO), a new swarm algorithm, is employed to concurrently reconfigure and find the best network. The proposed method was tested on 30-bus mesh and 33-bus radial distribution systems at fixed load levels. To show the performance and efficacy of the suggested method, it was compared to optimization methodology, such as the genetic algorithm, harmony search algorithm, Kruskal's maximal spanning tree, discrete evolutionary programming, and cuckoo search algorithm. The findings reveal that SSO is a strategy worth investigating for tackling the network reconfiguration problem.

Particle Swarm Optimization Technique Used for Optimal Network Reconfiguration with Dispersed Generation

Dispersed Generation and Network Reconfiguration have been generally engaged to maintain voltage level with permissible limit and decrease the energy losses in the Distribution Systems. In this article, a Particle Swarm Optimization (PSO) technique is developed and it is used to reduce the energy losses in Network Reconfiguration and Dispersed Generation placement. Different scenarios are used to analyze the performance of developed method. The proposed and designed technique is efficiently tested on IEEE 33-bus RDS (Radial Distribution System) with three various load models. The voltage summary in IEEE 33- buses is better when compared to other basic voltage profile.

Chaotic slime mould optimization algorithm for optimal load-shedding in distribution system

• Introduces Chaotic Slime Mould Algorithm (CSMA) as an optimal load shedding scheme of islanded DG integrated systems. • A fitness function comprising of maximum amount of remaining load and voltage stability margin (VSM) is adopted. • IEEE 33 bus and IEEE 69 bus distribution systems are used to assess the algorithm’s efficacy. • The proposed CSMA algorithm is compared with Slime mould algorithm (SMA) and Backtrack search algorithm (BSA). In addition, non-parametric statistical analysis have been conducted to adequately compare the algorithms. • In terms of fitness value, time consumption, and convergence characteristics, CSMA outperforms both BSA and SMA. In addition, CSMA ensures a better voltage profile and provides a solution with the least amount of load to be shed. The critical challenge for an efficient islanding operation of a distribution system having Distributed Generation (DG) is preserving the frequency and voltage stability. Contemporary load shedding schemes are inefficient and do not adequately assess the optimum amount of load to shed which results in either excessive or inadequate load shedding. Hence, this paper presents an optimal load shedding technique using Chaotic Slime Mould Algorithm (CSMA) with sinusoidal map in order to achieve greater efficiency. A constrained function with static voltage stability margin (VSM) index and total remaining load after load shedding was applied to accomplish the evaluation. A total of three islanding scenarios of IEEE 33 bus and IEEE 69 bus radial distribution systems were used as test systems to assess the efficacy of the proposed load shedding approach using MATLAB software. To identify performance enhancements, the developed method was compared to Backtrack Search Algorithm (BSA) and the original SMA. According to the results, CSMA outperforms both BSA and SMA in terms of remaining load and voltage stability margin index values in all the test systems.

Strategic integration of battery energy storage and photovoltaic at low voltage level considering multiobjective cost-benefit

Renewable energy sources, such as solar photovoltaic (PV) systems and battery energy storage systems (BESS), help reduce greenhouse gas emissions while fulfilling the world?s growing energy demand. The inclusion of BESS reduces the peak hour demand, and control of charging and discharging of BESS can be economical for distributors facing time-based energy pricing. This paper discusses a novel multiobjective Horse herd optimisation algorithm (MOHHOA) approach, which is inspired by the social behaviour of horses in herds for PV and BESS optimal allocation in the radial distribution system. The proposed algorithm combines multiple benefits like benefits from economic gain per day, reduction in CO2 emission per day, and reduction in energy loss per day. IEEE 69-bus radial distribution system (RDS) is used for testing the suggested approach. The case studies and simulation results show that the suggested model effectively accommodated PV power generation in IEEE 69-bus RDS without violating any system constraints. Results indicate improvement in node voltage profiles, security margins and energy losses, and peak energy savings, and system characteristics as a whole, and there were significantly improved techno-economic performance of the distribution system.

Techno-economic analysis of the DNO operated distribution system for active and reactive power support using modified particle swarm optimisation

The distribution network operator (DNO) needs to allocate DG and DSTATCOM capabilities efficiently to satisfy growing load demand. The optimal allocation of single and multiple DG and DSTATCOM units under various operational limitations is obtained by utilising a novel and modified PSO technique based on butterfly sensitivity and nectar probability parameters. The butterfly-based PSO (BFPSO) is implemented in balanced IEEE 33 and unbalanced 25 bus radial distribution systems using single- and multi-objective functions formulated for different cases. The simulation results demonstrated that the BFPSO algorithm is robust, rapidly converges, and produced superior results when compared to the original PSO technique. Constant power and battery charge load models are considered for the techno-economic analysis. Active and reactive power losses are hugely decreased with the voltage profile enhancement, advancement in the VSI graph, and raised a net profit of DNO, demonstrating the proposed approach’s effectiveness for DS’s excellent technical and economic achievement.

Power loss minimization with simultaneous location and sizing of distribution generation units using artificial algae algorithm

Power loss is one <span lang="EN-US">of the important pointers used to measure the performance of distributions networks. Many optimization algorithms have been proposed to solve various optimal power flow problems in Electrical Engineering. In this paper, a novel technique, artificial algae algorithm is developed to robustly detect the optimal location and size of distributed generation (DG) units for minimization of total power losses without violating the equality and inequality constraints. The main objective of optimal power flow (OPF) is to maximize or minimize the objective function using various constraint so that steady-state operation point is achieved. The concept of optimal power flow in power system helps to minimize real power loss. In the proposed approach, various control variables like generator bus, voltage magnitudes, and transformer tap settings are considered. The proposed algorithm is simulated in MATLAB and effectiveness is carried on IEEE 33 bus radial distribution system and satisfactory results are achieved when compared with other optimization techniques. A notable improvement in reduction of active power losses with 3 DG operating at different power factors are 65.5%, 42.4%, and 77.8% respectively, were achieved in comparison to the system without DGs and as compared with other research papers.</span>

Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

This work proposes a distribution locational marginal price (DLMP)-based transactive energy (TE) framework for distribution systems with enthusiastic or smart prosumers. The framework uses a multi-agent system (MAS) as the basis on which the proposed TE model, i.e., distribution locational marginal price (DLMP) based TE management system (DTEMS), is implemented. DTEMS uses a novel metric known as the nodal earning component, which is determined by the optimal power flow (OPF) based smart auction mechanism, to schedule the TE transactions optimally among the stakeholders by alleviating the congestion in the distribution system. Based on the individual contributions to the congestion relief, DTEMS ranks the prosumers and loads as most valuable players (MVP) and assigns the energy trading price according to the category of the player. The effectiveness of the proposed TE model is verified by simulating the proposed DTEMS for a modified 33 bus radial distribution system fed with various plug-able energy resources, prosumers, and microgrids.

Cost-based analysis and optimization of distributed generations and shunt capacitors incorporated into distribution systems with nonlinear demand modeling

This paper optimizes the allocations of Distributed Generators (DG) and Shunt Capacitor (SC) banks to minimize a multi-objective problem consisting of four different indices. These indices include active/reactive power loss reduction, voltage profile improvement, and system stability enhancement. Consequently, the costs of energy loss, allocated devices, and total operation are estimated per year for different installed DG/SC pairs for studied systems. Multi-DG and SC banks are optimally installed in the standard 69-bus and 136-bus radial distribution systems considering six nonlinear load models. The six models are generated from the nonlinear relationship between the system voltage and demand. Furthermore, the load type affects the DG/SC allocation optimization problem and other performance parameters such as the total voltage deviation (TVD), stability index (SI), and the power drawn from the utility. A new metaheuristic optimization called the Jellyfish Search Algorithm (JSA) is adopted and applied to allocate the DG units and the SC banks optimally into the distribution systems. The savings in the annual energy loss reach 65.88%, 95.99%, and 97.91% with industrial load demand for 1DG/1SC, 2DG/2SC, and 3DG/3SC allocations, respectively for the 69-bus RDS. While for the 136-bus RDS, the saving reaches 39.71% with constant-impedance demand, 58.84%, and 73.87% with commercial demand for 1DG/1SC, 3DG/3SC, and 6DG/6SC pairs, respectively. It is found that the JSA is practical and suitable for solving these nonlinear optimization problems, and it gives better results as compared to other algorithms in the literature.

A Comparison Study of Multi-Objective Bonobo Optimizers for Optimal Integration of Distributed Generation in Distribution Systems

In this paper, the three newly published Multi-Objective Bonobo Optimizer (MOBO) variants are assessed and evaluated using statistical analysis for solving the multi-objective optimization of Distributed Generation (DG) into distribution systems. The main objectives of the study are to minimize system loss and enhance voltage profile. While the first variant, MOBO1, depends on the sort and grid-index approach, the second variant, MOBO2, relies on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm technique. The last variant, MOBO3, is inspired by the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The three MOBO algorithms are compared to themselves and to other algorithms solving the same optimization problem. These algorithms include the MOJAYA, Multi-Objective Artificial Ecosystem-Based Algorithm (MOAEO), Multi-Objective Gravitational Search Algorithm (MOGSA), and Multi-Objective Particle Swarm Optimization (MOPSO). The 33-bus and 85-bus radial distribution systems are used test systems for solving the optimal allocation of single- and three-DG units operating at unity power factor. In order to find the best compromise solution, the Pareto Optimal front method is adopted with the help of a fuzzy-based function. The obtained results show the effectiveness of the MOBO variants compared with other algorithms in terms of different statistical parameters and multi-objective performance metrics such as diversity, hypervolume, spacing, and set coverage. While the MOBO algorithm reduces power loss and TVD by 39.59 and 68.31% for a single DG, they are reduced to 58.13 and 88.44% for three DG units allocated to the 33-bus distribution system, respectively. On the other hand, the MOBO algorithm reduces power loss and TVD by 37.28 and 66.84% for a single DG, respectively, they are decreased to 46.35 and 82.53% for three DG units assigned to the 85-bus distribution system. Among the three MOBO variants, it is found that the MOBO1 is superior for a single-DG allocation, while the MOBO3 is the best for the allocation of three-DG units.

A hybrid algorithm for voltage stability enhancement of distribution systems

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.