Improve System Voltage Research Articles

Simulation of Voltage Stability Enhancement at Primary Substation Using Static VAR Compensator

For the voltage stability enhancement of power system, the reactive power compensation is vital important. With the advancement in technology, the Flexible AC Transmission System Devices (FACTS) are widely used for stability improvement. In the primary substation, the stability is poor due to load conditions and system faults. In this research, the voltage stability enhancement for a Primary Substation using Static VAR (volt-amp reactive) compensator (SVC) is presented. SVC can be used to improve system voltage stability and addition of damping control. With the application of SVC, the voltage stability can be improved under normal condition as well as under contingency conditions. Shunt reactive power sources, including capacitive and inductive, are controlled by high power electronic switching devices in an SVC to enhance voltage stability. The goal of this attempt is to use SVC to improve the voltage stability of the 230/33 kV system. To evaluate the voltage stability improvement by SVC, a simulation model is implemented in Matlab/Simulink and performance analysis are carried out based on the simulation results.

Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer

Typically, the main control on alternating current (AC) power systems is performed by the scheduling of rotary machines of synchronous generators and static machines of on-load tap changer (OLTC) transformers and volt-ampere reactive (VAR) sources. Large machines of synchronous generators can be managed by utilizing terminal voltage control when synchronized in parallel to the power system. These machines are typically terminal voltage regulated. In addition, substation on-load tap changer (OLTC) transformers improve system voltage management by controlling variable turn ratios that are adjusted in different levels known as taps along either the primary or secondary winding. Moreover, volt-ampere reactive (VAR) sources of static VAR compensators (SVCs), which are automated impedance devices connected to the AC power network, are designed for voltage regulation and system stabilization. In this paper, scheduling of these machines is coordinated for optimal power system operation (OPSO) using a recent algorithm of social network search optimizer (SNSO). The OPSO is performed by achieving many optimization targets of cost of fuel, power losses, and polluting emissions. The SNS is a recent optimizer that is inspired from users in social networks throughout the different moods of users such as imitation, conversation, disputation, and innovation mood. The SNSO is developed for handling the OPSO problem and applied on an IEEE standardized 57-bus power system and real Egyptian power system of the West Delta area. The developed SNSO is used in various assessments and quantitative analyses with various contemporary techniques. The simulated findings prove the developed SNSO’s solution accuracy and resilience when compared to other relevant techniques in the literature.

Dynamic under-voltage load shedding scheme considering composite load modeling

This manuscript recommends an under-voltage load shedding scheme based on the sensitivity of dynamic voltage curves to mitigate voltage collapse in highly stressed power systems. The developed method can be used to determine the minimum amount and proper locations of load curtailment by considering power system dynamics including dynamic load modeling. The selected Indonesian regional system has a large number of dynamic loads in form of air conditioning and water pump loads whose dynamics contribute considerably to the complexity of the power system stability. Loss of the transmission line in the system caused by the impact of the composite load after major disturbance is studied. The proposed scheme involves an iterative procedure based on the sensitivity analysis of the dynamic voltage curve to solve the problem of load shedding. Furthermore, this work developed a new index named dynamic voltage-active power sensitivity (DVPS). The DVPS is calculated in all load buses to give information about the bus that has a predominant influence on improving system voltage stability through load shedding. Dynamic simulations are carried out with an Indonesian power system, the South Sulawesi interconnection power system with the proposed load shedding scheme. The results of this work show a noteworthy improvement in the magnitude of the voltage level as well as the voltage stability margin.

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

Considering the strong influence of distributed generation (DG) in electric distribution systems and its impact on network voltage losses and stability, a new challenge has appeared for such systems. In this study, a novel analytical algorithm is proposed to distinguish the optimal location and size of DGs in radial distribution networks based on a new combined index (CI) to reduce active power losses and improve system voltage profiles. To obtain the CI, active power losses and voltage stability indexes were used in the proposed approach. The CI index with sensitivity analysis was effective in decreasing power losses and improving voltage stability. Optimal DG size was determined based on a search algorithm to reduce active power losses. The considered scheme was examined through IEEE 12-bus and 33-bus radial distribution test systems (RDTS), and the obtained results were compared and validated in comparison with other available methods. The results and analysis verified the effectiveness of the proposed algorithm in reducing power losses and improving the distribution system voltage profiles by determining the appropriate location and optimal DG size. In IEEE 12 and 33 bus networks, the minimum voltage increased from 0.9434 p.u and 0.9039 p.u to 0.9907 p.u and 0.9402 p.u, respectively. Additionally, the annual cost of energy losses decreased by 78.23% and 64.37%, respectively.

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

Bi-level programming approach for coordinated configuration of distributed generations and automation devices in distribution networks

Improving system reliability and voltage profiles in distribution networks requires the simultaneous installation of distributed generations and automation devices. This paper builds a bi-level programming model for the coordinated configuration of distributed generations and automation devices. The upper-level problem is to minimize the total investment cost of distributed generations and automation devices and the penalty cost of power shortages, and the lower-level problem is to minimize the voltage deviation during typical operation mode. To explicitly represent the relationship between the penalty cost of power shortages and installation locations of distributed generations and automation devices, an analytical expression of the failure rates of load nodes with respect to installation locations of distributed generations and automation devices is derived. The bi-level model can be transformed to a mixed-integer nonlinear programming problem by applying the Karush–Kuhn–Tucker theorem because the lower-level problem is a quadratically constrained linear programming problem. Case studies on an IEEE 33-bus distribution system and a practical 110-bus distribution system in China are used to verify the effectiveness of the proposed method.

Index-based optimal DG allocation for voltage quality improvement in an unbalanced network

Voltage quality is a one of the major concerns in distribution systems. Distributed Generations (DGs) have the potential to improve voltage quality, if optimally planned and operated. Considering the problem of DG siting and sizing, this paper aims at integrating technical factors, particularly voltage quality, in planning of DGs. Hence, a methodology is proposed which optimizes voltage quality in the presence of DGs and can be used as one of the objectives in a multi-objective problem or as an intermediate stage in usual DG planning. Modified voltage quality indices are proposed which consider factors including voltage profile, voltage variation due to DG disconnection, voltage regulation and voltage unbalance. The indices are defined in such a manner suitable for three-phase unbalanced networks. The system voltage quality is assessed by a new comprehensive voltage quality index. By applying this index, the DGs locating and penetration problem is formulated to improve system voltage quality. The method is tested on IEEE 13-bus feeder which is an inherently unbalanced network.

Optimal sizing and placement of multiple renewable distribution generation and DSTATCOM in radial distribution systems using hybrid lightning search algorithm‐simplex method optimization algorithm

AbstractIn this literature, the simultaneous placement of both the Renewable Distribution Generation (DG) and Distribution STATCOM (DSTATCOM) in the Radial Distribution System (RDS) for Power Loss Minimization (PLM) is discussed. Loss Sensitivity Factor (LSF) is initially applied to discover the candidate location of DG and DSTATCOM in RDS. The effective Hybrid Lightning Search Algorithm‐Simplex Method (LSA‐SM) is used to compute the optimal sizing of DG and D‐STATCOM. The performance of the proposed method is tested separately on both IEEE33 and IEEE69 RDS test systems. The improvement in system Voltage Profile and operational stability before and after the allocation of DG and DSTATCOM is discussed. The improved system Voltage Stability Index values for different cases are plotted. The performance of the proposed method is compared with similar existing methods.

Planning for Distribution System with Grey Wolf Optimization Method

Nowadays Distribution generation (DG) has achieved to further precious awareness, especially inside the power system fields, so the strength and dependability specifically in the distribution system. Optimum scheduling of DG not only focuses on the size of DG only too puts a load on the optimal location of generators. Install for DG at the optimum location along with optimal size into the distribution system would improve the system performance and also give price effectual solved to the planning of the distribution network. The positive impact of optimum DG position into the distribution system would improve system voltage profile, reduction in line losses, improved power standard, make better reliability and strength of the distribution network. GWO is modeled based on the unique hunt, searching for a target, encircling target, and attacking prey, are executing to perform the optimization. The GWO is determined to the IEEE-16, 30, 57 and 118-bus test systems radial distribution network as well as considering multiplier DG units in the system. The better study outcome of the attained to without DG, with DG, type 1 DG, type 2 DG, with type 3 DG at 0.9 pf and with type DG at unity pf. Moreover, the obtained is compared as well as the net outcome of the proposed procedure for the sequence to see the efficiency and effectual and the distribution systems.

Impact of DG Integration in Distribution System on Voltage Stability and Loss Reduction for Different Loading Conditions

One of the modern and important techniques in the electrical distribution systems is to solve the networks problems service availability, high loss, improve system voltage and reliability, these can be resolved by accommodating small scaled de-centralized generating stations in networks, which is known as Distributed Generation (DG).Distributed generation (DG) units placed at the consumer end will minimize electric power losses, improvexreliabilityyand voltage profile in the system. This paper, presents a method to determine the proper size and location of DG in a distribution system to reduce the losses and improve the voltage stability for different loading conditions. The IEEE 33-Bus system is used to simulate in Power WorldsSimulator (PWS) software and theevoltage profile and losses in the system are analyzed. The Simulated result shows that proper placement and sizing of DG will reduce losses in the system and improve voltage profile within the allowable limit and there by improve the voltage stability.

Optimal planning of RDS considering PV uncertainty with different load models using artificial intelligence techniques

This article presents the optimised planning of RDS and proposes the artificial intelligence technique using hybrid optimisation combined with phasor particle swarm optimisation and a gravitational algorithm, called PPSO/GSA for optimal planning of RDS considering photovoltaic distributed generators in RDSs. The main objective is to maximise the RDS performance by optimally allocating the PV generators. The proposed PPSO/GSA is implemented and validated on 94-bus practical RDS located in Portuguese considering single and multiple scenarios of PV generators installation along with various loading conditions. The results reveal that the optimised planning of RDS enhance the system reliability in term of a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile. Moreover, the convergence characteristics, computational efficiency, and applicability of the proposed artificial intelligence technique are evaluated by comparative analysis and comparison with other optimisation techniques.

Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

In recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

WIPSO, PSO and GA Techniques to Locate UPFC Effectively in Power System to Improve Voltage Stability and Reduce Losses

The domestic and industrial demand for electricity has been increasing extensively making the power system more expensive. With this increases in demand for electricity, the losses also increase in demand for electricity the losses also increase from power generation to distribution Flexible alternating currents transmission system (FACTS) is used to maintain flexible operation of the power system from power generation level to the distribution level. The reliability of the network system can be enhanced by using FACTs devices in the power system more reliably, the inventions in the advanced power electronics devices can be implemented in the design of FACTs, serices, shunt, serices-shunt and shunt-shunt are some of the FACTs devices. One way to operate the power system with less power losses and improved system voltage profile is to use FACTs. Unified power flow controller (UPFC) is one of the serices-shunt FACTs systems. This paper throws light on how UPFC can be used to improve the voltage profile and reduce the installation cost of UPFC, the system loss, in the electrical power system. Analyst and soft computing methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Weight Enhanced Particle Swarm Optimization (WIPSO) are used to determine ideal FACTs device settings and FACTs device place.

Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems

The increased penetration of renewable energy sources has prompted the integration of battery energy storage systems in active distribution networks. The energy storage systems not only participate in the backup power supply but also have the potential to provide various distributed ancillary services. In this paper, a new bi-level optimization framework is developed to optimally allocate the intense wind power generation units and battery energy storage systems with the provision of central and distributed ancillary services in distribution systems. Two battery energy storage systems and one shunt capacitor are strategically allocated for coordination of wind power generation. One of the battery is deployed at grid substation to participate in central ancillary services whereas second is participating in distributed ancillary services. At level-1, all the distributed energy resources are optimally allocated while minimizing the annual energy loss of distribution systems. Whereas, level-2 performs hourly optimal energy and ancillary services management of distributed resources deployed at level-1. The objectives considered at level-2 are the minimization of hourly load deviation, reverse power flow towards the grid, power loss, and node voltage deviation. The proposed framework is implemented on a real-life Indian 108-bus distribution system for different cases and solved by using a genetic algorithm. The comparison of simulation results reveal the promising advantages of the proposed optimization framework. It provides more energy loss and demands deviation reduction, improved system voltage and power factor at higher wind penetration as compared to the cases in which distributed ancillary services are ignored in the planning stage.

Optimally selecting the location of a multiple of D‐statcoms for the improvement of SARFIX due to faults in the IEEE 33‐bus distribution system

This article introduces a new method for optimizing the placement of a multiple of D‐Statcoms for voltage sag mitigation in distribution systems. The D‐Statcom's placement is optimally selected not only for improving system voltage sag caused by a single fault event but also for all possible fault events in the system of interest. Therefore, D‐Statcom's placement is optimized in a problem of optimization where the objective function is to minimize the system voltage sag index—SARFIx. D‐Statcom's effectiveness for voltage sag mitigation is modeled based on the method of Thevenin's superimposition for the problem of short‐circuit calculation in distribution systems. This article considers the case of using a multiple of D‐Statcoms with a proposed voltage compensating principle that can be practical for large‐size distribution systems. This article uses the IEEE 33‐buses distribution feeder as the test system for voltage sag simulation and influential parameters to the outcomes of the problem of optimization are considered and discussed. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Strategically incorporation of RES and DSTATCOM for techno‐economic‐environmental benefits using search space reduction‐based ICSA

In this study, an improved crow search algorithm (ICSA) is proposed for the optimal allocation of renewable energy sources (RESs) and distributed static compensator in a practical distribution network (PDN) to improve system voltage, reduce line losses, maximise economic benefit, and decrease pollutants’ emission. To improve the performances, the control parameters of ICSA are logically tuned to make them adaptive with the iteration. Voltage stability index is used to identify healthier and weaker buses (HBs and WBs) of the system and comparative performance analysis is conducted after placing the devices at the identified HBs and WBs. The proposed technique is applied for both the cases of reduced search space (only WBs) and full search space (all the load buses) to establish that the optimal placement is always obtained at the WBs of the network. The optimal allocation for each type of device is conducted considering the time-varying characteristics of load demand and RES's power output, and their optimal performances are analysed in details, which will be helpful for the distribution companies to identify the most suitable device for incorporating in PDN. Furthermore, to show the effectiveness, the proposed algorithm is also compared with other algorithms.

Strategic Allocation and Energy Management of BESS for the Provision of Ancillary Services in Active Distribution Networks

The increased penetration of renewables has prompted the integration of battery energy storage systems (BESSs) in active distribution networks. The BESS not only act as backup power supply but also provides various ancillary services with extra degree of flexibility to distribution networks by changing its’ role as generation and load. In this study, a new optimization framework is developed to optimally allocate the intense wind power generation units and BESSs considering its ancillary services in distribution systems. The BESSs are strategically allocated at two sites in coordination with the renewable generating source. One is installed at grid substation to participate in central ancillary services whereas; second will be used to participate in distributed ancillary services. The objective is to minimize the cost of annual energy loss while performing various ancillary services in distribution network. The proposed optimization framework is implemented on a real-life 108-bus Indian distribution system for different cases and solved using genetic algorithm. The comparison of simulation results show that the proposed optimization framework provides more energy loss reduction, improved system voltage and power factor at higher penetration of wind power generation units as compared to the cases in which ancillary services are not considered in planning.

Adequate Topology for Efficient Energy Resources Utilization of Active Distribution Networks Equipped With Soft Open Points

Active distribution networks concerned with providing efficient control technologies for large-scale integration of distributed generation (DG) units into the distribution systems. This research proposes a methodology for distribution networks reconfiguration by controlling number, sharing, size, and location of DG units. Also, the soft-open points (SOPs) are added instead of the tie line switches. The SOPs are benefited with its high capability in controlling active/reactive power flow to enhance transmission system performance. The main target of this work is to increase the efficiency of energy utilization through minimizing the power system losses and improving system voltage profile while preserving all system constraints within permissible limits with reliable and flexible networks in normal and abnormal conditions with a suitable penetration level of DG. A modified particle swarm optimizer is developed to find the best system configuration, size, and placement of DG units as well as the size and allocation of SOPs. Research methodology is tested on two standards: the IEEE 33-node and 69-node distribution networks under different operating cases. This paper compares the obtained results with those in the literature to prove the capabilities of the proposed work. Finally, the suggested work pursues the optimal number of DG units with their appropriate penetration levels and selects the most convenient location of SOPs for adequate network reconfiguration.

A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model

In this paper, a bio-geography-based optimization techniqueis proposed for optimal sizing and placement of multiple distributed generators to reduce system loss and to improve system voltage profiles in electric distribution systems. Distributed generation paves the way for installing generating sources in the vicinity of the loads, for improving the power factor of the system, and thereby significantly reducing total system losses. An effective power factor model is proposed to pre-set the power factor of each distributed generator placed at various locations in a distribution system. The bio-geography optimization algorithm is enhanced with a differential learning scheme to deal with the problems of high dimensionality and complex constraints. A sensitivity factor approach is presented to reduce the search space for the placement of distributed generators in the system. Numerical simulations are performed on the IEEE 33-bus and IEEE 69-bus systems and the results of the proposed method are compared with other methods reported in the literature.

Cuckoo Search approach enhanced with genetic replacement of abandoned nests applied to optimal allocation of distributed generation units

Here, it is presented a novel Cuckoo Search (CS) algorithm called Cuckoo-GRN (Cuckoo Search with Genetically Replaced Nests), which combines the benefits of genetic algorithm (GA) into the CS algorithm. The proposed method handles the abandoned nests from CS more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33-bus and 119-bus distribution systems. Simulation results show that Cuckoo-GRN can lead to a substantial performance improvement over the original CS algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.