IEEE 33-bus Radial Distribution System Research Articles

Power system restoration in distribution network using minimum spanning tree - Kruskal’s algorithm

<span>Events such as natural and manmade interference, line, transformer and feeder outages that occur in electric power distribution system negatively impact the continuity of power supply, thus affecting the power demand supply as well as customer’s satisfaction. In that cases, the restoration of power needs to be carried out immediately in order to guarantee the system’s reliability. The power flow path identification is considered as a difficult task especially in a huge system due to large number of switches. Kruskal’s algorithm is presented in this paper to find the minimum power flow path in a power distribution network. The comparison of performance between presented Kruskal’s algorithm and Binary Particle Swarm Optimization (BPSO) was made in solving a problem regarding network reconfiguration. The proposed load restoration approach is tested on IEEE 33-bus single feeder radial distribution system using MATLAB software. From the results, it is found that the presented Kruskal’s algorithm was able to search for the minimal power flow path that contribute to loss reduction for power restoration after the occurrence of fault.</span>

Optimal Whole-Life-Cycle Planning of Battery Energy Storage for Multi-Functional Services in Power Systems

One battery energy storage system (BESS) can provide multiple services to support electrical grid. However, the investment return, technical performance and lifetime degradation differ widely among different services. This paper proposes a novel method for the whole-life-cycle planning of BESS for providing multiple functional services in power systems. The proposed model aims to balance between extending BESS life duration and maximizing its overall revenue by strategically allocates battery capacity for each application over its whole life cycle. Specifically, BESS are initially used in ancillary service market for frequency regulation service; after certain lifetime degradation, the used batteries are transferred in energy arbitrage market for load shifting service. BESS depreciation along with the services is considered to accurately model the battery degradation effect under different marketing rules. The proposed method is tested on a modified IEEE 33-bus radial distribution system. Numerical results demonstrate that the proposed method can achieve higher economic benefits and longer life span than a single application service.

Hybrid Salp Swarm Algorithm for integrating renewable distributed energy resources in distribution systems considering annual load growth

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority.

Symbiotic Organism Search for Sizing and Optimal Location of Distributed Generation Using Novel Sensitivity Factor

Electric power systems consist of many interconnected buses. The most widely used network topology is Radial Distribution Network (RDN), the bus located furthest from generation has a significant voltage drop and the number of buses without any support from Distributed Generation (DG) inflicting in increased power losses and increased generation cost. DG is advantageous to reduce power losses and increasing voltage on the power system, but the location and sizing of DG in the distribution network requires great consideration. In this paper the method used to determine the location is Loss Reduction Sensitivity Factor (LRSF) and Voltage Improvement Performance Index (VIPI) and for sizing DG uses artificial intelligence Symbiotic Organism Search (SOS). This DG locating and sizing problem is associated with real power loss minimization and voltage profile improvement objectives. The proposed method has been demonstrated on IEEE 33-bus radial distribution system. Based on this research the identification of using LRSF location is better than VIPI.

Optimal DG allocation for enhancing voltage stability and minimizing power loss using hybrid gray wolf optimizer

High penetration of photovoltaic and wind turbine-based distributed generators (DGs) can help reduce carbon emissions which is an important goal for the whole world. DG can be used to improve the voltage stability, present generation reserve/emergency, and consequently, the system power quality can be improved. However, it is very important to select the right size and location of a DG so that the power system can increase the gained benefits of such an installation to the maximum. In this paper, a hybrid optimization technique is proposed to determine the optimal allocation of DG in the standard IEEE 33-bus radial distribution system in order to improve the voltage stability and minimize the total power loss. The proposed hyprid technique is based on the gray wolf optimizer algorithm with loss sensitivity factor. The performance of the system is analyzed without DG installation, then it is compared with the performance of the system when DGs are installed with the predefined optimal sizes and locations. The study is performed by MATLAB M-Files and NEPLAN software.

Probabilistic assessment of static voltage stability in distribution systems considering wind generation using catastrophe theory

With increasing penetration of distributed generations, the importance of the voltage stability assessment of distribution networks has been increased. Considering this situation, a probabilistic model is proposed to evaluate the voltage stability of distribution network integrating wind turbine (WT) units. With this intention, a probabilistic voltage stability index (PVSI) of a radial distribution system with wind power generation is presented. This index investigates the probabilistic risk of voltage collapse for all the buses of system considering uncertainty of WTs. Therefore, the most sensitive bus to the voltage collapse can be identified. The problem model is based on the catastrophe theory that can find the bifurcation point of system. Three-point estimation method is employed to calculate the statistical moments of voltage and PVSI of nodes. Moreover, to estimate the cumulative distribution function of output random variables, the Cornish−Fisher series are used. The performance of the probabilistic index is tested on the IEEE 69-bus radial distribution system where different load models are considered. The results demonstrate that the PVSI can accurately predict the voltage instability condition of the system.

Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood

Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

Optimal Number of Electric Vehicles for Existing Networks Considering Economic and Emission Dispatch

The future transportation network is expected to be dominated by electric vehicles (EVs). With an increase in grid-connected vehicles, the power grid needs to increase its generation capacity to meet their charging demand. Thus, from a grid perspective, it is essential to find the maximum number of EVs that the existing infrastructure can handle. This research proposes a new framework to identify the fleet size capability of a power network in both uncontrolled and vehicle-to-grid (V2G) modes of operation. The proposed model is modeled as an optimization problem and tested on a system that is formed by combining IEEE 33-bus radial distribution system and a ten-unit generation system. Simulation results show that the current system can handle a fixed number of EVs when they operate independently. Participation of EVs in a V2G market significantly enhances the fleet size handling capability of the existing infrastructure, which is approximately four times the present capacity. Furthermore, a relation between the fleet size of EVs and operating points of plants is extracted, which is very beneficial for system planners. The result reveals that the time span requirements for power infrastructure upgradation can be increased by twofold if the V2G market is applied in the network.

A novel opposition-based tuned-chaotic differential evolution technique for techno-economic analysis by optimal placement of distributed generation

ABSTRACTOptimal placement of distributed generation (DG) units in a radial distribution network can reduce the system losses, improve the voltage profile of the network and thereby maximize the techno-economic benefits. The present problem considers various indices, namely the cost index, voltage deviation index and line flow capacity index, with several equality and inequality constraints, to analyse the techno-economic benefits. An opposition-based tuned-chaotic differential evolution (OTCDE) technique is proposed to avoid premature convergence. This technique uses an opposition-based scheme for initialization and logistic mapping to generate chaotic sequences within the specified range for the mutation factor, because wrong parameter selection may lead to premature convergence and even stagnation. The proposed algorithm has been applied on IEEE 33-bus, 69-bus and 118-bus radial distribution systems to verify its effectiveness. By several statistical analyses, it is observed that the proposed technique outperforms other newly proposed metaheuristic techniques.

Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis

Placement of optimally sized distributed generator (DG) units at optimal locations in the radial distribution networks can play a major role in improving the system performance. The maximum economic and technical benefits can be extracted by minimizing various objectives including yearly economic loss which includes installation, operation and maintenance cost, power loss as well as voltage deviation throughout the buses. The present problem is analysed considering these multi-objective frameworks and presents the best compromise solution or Pareto-optimal solution. Several equality and inequality constraints are also considered for the multi-objective optimization problem. In this paper, a novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi-objective problem in order to avoid premature convergence. Performance of population based meta-heuristic techniques largely depends on the proper selections of control parameters. It is reported that wrong parameters selection may lead to premature convergence and even stagnation. The proposed technique uses logistic mapping to generate chaotic sequence for control parameters. The proposed algorithm is implemented on IEEE-33 and IEEE-69 bus radial distribution systems for verifying its effectiveness. A comparative analysis with other modern multi-objective algorithms like NSGA-II, SPEA2 and MOPSO is also presented in this work. It is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile.

Efficient sizing and placement of distributed generators in cyber-physical power systems

Abstract Now days, distribution system has experienced numerous significant changes due to the implementation of smart grid technology and integration of distributed and renewable energy resources. Optimal integration of distributed generators and reconfiguration of the radial network have overall positive impacts on the power system. In this paper our aim is to minimize line losses and total harmonic distortion (THD), and to improve the voltage profile of the system by optimal placement and sizing of distributed generators and optimal reconfiguration of the network simultaneously. The impact of total harmonic distortion on power factor is also evaluated in this paper. Genetic algorithm is used as a solving tool to find optimal size of distributed generators and optimal reconfiguration of the network in a varying load environment. Placement buses for distributed generators are found by sensitivity analysis of the network. Fast harmonic load flow analysis and forward/backward sweep methods based on bus current injection to branch current (BIBC) matrix and branch current to bus voltage (BCBV) matrix are developed according to the network topology. The methodology is executed on IEEE-33 bus radial distribution system and the results shows its effectiveness.

Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems

This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

Optimal Distribution Feeder Reconfiguration with Optimal Planning of Distributed Generation for Loss Reduction and Voltage Improvement using Deferential Evolution Algorithm

In the late years, there has been a noticeable influx of DGs onto the grid. This shift alters the manner in which electricity is being generated, transmitted, and managed. DGs conflicts have already risen between distribution systems designed for one-way power flow and DGs that want to force power flow in the opposite direction. Screening methods exist to avoid adverse impacts due to DGs, but this addressing the abundance of DGs interconnection requests and can result in higher overall costs if the resource is not fully integrated and located appropriately. A method to determine the optimal feeder reconfiguration and optimal allocation and size of DGs is proposed. The DG location is determined using the sensitivity of power losses. Then, the proposed differential evolution algorithm (DEA) is used to obtain the optimal size of the DG unit. The proposed algorithm is validated using the revised version of IEEE 33-bus radial distribution system.

Techno-Economic Evaluation of Mixed AC and DC Power Distribution Network for Integrating Large-Scale Photovoltaic Power Generation

This paper presents a methodology for evaluating the techo-economic feasibility of mixed AC and DC distribution network to replace the existing AC distribution network with large-scale integration of photovoltaic energy sources. Economic dispatch of mixed AC/DC system is formulated in the presence of power electronic transformer (PET) for the flexible control of AC and DC power flows while maintaining the reliability constraints in terms of bus voltage, branch current carrying capacity, and the efficient operation of power electronic converters. Furthermore, the configuration of the PET and power losses of converters with a changing operation efficiency is incorporated into the optimal dispatch problem to assess the energy savings provided by the AC/DC power network. Two objectives for minimizing the operating cost and total power losses are also considered in the formulated economic dispatch. To obtain the Pareto-optimal solutions of this reliability constrained multi-objective optimization problem, the non-dominated sorting genetic algorithm-II (NSGA-II) associated with the interior-point method are applied in the proposed two-stage optimal algorithm to solve the optimization for PET configuration and operation. The method has been tested on the IEEE 33-bus radial distribution system with excessive PV installation and updated PETs. In comparison with the optimal dispatch of existing AC power distribution system, the joint optimization of PET AC/DC ports and PV inverters improves the flexibility of power flow control for both AC and DC networks and thus minimizing the PV curtailment and total operating cost.

Optimal placement and sizing of DSTATCOM using firefly algorithm in the distribution system

The electric power distribution system must be intended to operate and deliver acceptable level of electrical energy to consumers. Distribution static compensator (DSTATCOM) is a custom power device implemented in the distribution system to improve the power quality. This paper presents the optimal size and placement of DSTATCOM in the distribution system by using firefly algorithm (FA). Minimisation of power loss and minimisation of installation cost of DSTATCOM are considered as the objective function (OF), where the voltage and current limits of the buses are chosen as the optimisation constraints. The performance of the proposed methodology using FA is demonstrated on IEEE 33-bus and 69-bus radial distribution systems using MATLAB software. Backward/forward sweep algorithm is used for load flow analysis of radial distribution system. The efficacy of the proposed methodology using FA is compared with the results obtained by using immune algorithm (IA).

Optimal placement and sizing of DSTATCOM using firefly algorithm in the distribution system

The electric power distribution system must be intended to operate and deliver acceptable level of electrical energy to consumers. Distribution static compensator (DSTATCOM) is a custom power device implemented in the distribution system to improve the power quality. This paper presents the optimal size and placement of DSTATCOM in the distribution system by using firefly algorithm (FA). Minimisation of power loss and minimisation of installation cost of DSTATCOM are considered as the objective function (OF), where the voltage and current limits of the buses are chosen as the optimisation constraints. The performance of the proposed methodology using FA is demonstrated on IEEE 33-bus and 69-bus radial distribution systems using MATLAB software. Backward/forward sweep algorithm is used for load flow analysis of radial distribution system. The efficacy of the proposed methodology using FA is compared with the results obtained by using immune algorithm (IA).

A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration

Transformations are taking place within the distribution systems to cope with the congestions and reliability concerns. This paper presents a new technique to efficiently minimize power losses within the distribution system by optimally sizing and placing distributed generators (DGs) while considering network reconfiguration. The proposed technique is a hybridization of two metaheuristic-based algorithms: Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO), which solve the network reconfiguration problem by optimally installing different DG types (conventional and renewable-based). Case studies carried out showed the proposed hybrid technique outperformed each algorithm operating individually regarding both voltage profile and reduction in system losses. Case studies are carried to measure and compare the performance of the proposed technique on three different works: IEEE 33-bus, IEEE 69-bus radial distribution system, and an actual 78-bus distribution system located at Cairo, Egypt. The integration of renewable energy with the distribution network, such as photovoltaic (PV) arrays, is recommended since Cairo enjoys an excellent actual record of irradiance according to the PV map of Egypt.

Multi‐objective approach to maximise loadability of distribution networks by simultaneous reconfiguration and allocation of distributed energy resources

In recent years, continuously increasing load demand and deficiency of capital resources vis-a-vis a competitive electricity market have forced transmission and distribution utilities worldwide to maximise the efficiency and utilisation of their existing infrastructure. This study presents a multi-objective approach to maximise the loadability of distribution networks by simultaneous reconfiguration and optimal allocation of distributed energy resources using a comprehensive teaching-learning-based optimisation algorithm. The proposed technique is based on the e-constraints method and uses a graphical approach for network reconfiguration. Loadability enhancement is validated on the IEEE 33-bus and 69-bus radial distribution systems. Several case studies are carried out to demonstrate the effectiveness of the proposed approach in reducing the network active power losses, improving the kVA loading margins and enhancing the voltage profiles while considering voltage and thermal limit constraints. Results show that maximum loadability is obtained with simultaneous network reconfiguration and multiple DER allocation with varying power factors.

Integrated Volt-VAr Optimization with Distributed Energy Sources to Minimize Substation Energy in Distribution System

Volt-VAr optimization (VVO) is the tool which improves the efficiency of the distribution system by coordinating the voltage and reactive power. Due to voltage-dependence of loads, both real and reactive power will be affected by variation in voltage. Consequently, rather than minimizing active energy of the system, the focus of this article to reduce the daily apparent energy of the substation using VVO. In this, on load tap changers (OLTC), and voltage regulators (VRs) are used for voltage optimization, whereas capacitor banks (CBs) inject reactive power (VAr) into the system provided operational and system constraints satisfied. This study focuses on minimization of substation apparent energy (kVAh) by the optimal scheduling of OLTC, VRs, and CBs. The photo-voltaic (PV) and wind turbine (WT) generators are also assumed to be connected in the distribution system. Moreover, this study is extended to consider the effect of harmonics in the load current for associated loads. All the simulations are performed on modified IEEE 33-bus and IEEE 123-bus radial distribution systems. More savings in terms of substation kVAh are accomplished when DGs are connected into the system. Reductions up to 20% in substation kVAh and 37% in energy losses have been achieved.