Enhancement Of Power System Performance Research Articles

Promoted Osprey Optimizer: a solution for ORPD problem with electric vehicle penetration

This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35 MW to 1.93 MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21 MW to 3.53 MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration.

A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

A Review on Optimization Objectives for Power System Operation Improvement Using FACTS Devices

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented.

Power System Performance Enhancement with FACTS Devices

Power System Performance Enhancement with FACTS Devices

Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

Optimal integration of multi-type DG in RDS based on novel voltage stability index with future load growth

In this article, a novel voltage stability index is proposed for the optimal placement and an analytical as well as particle swarm optimization method is implemented for optimal sizing of different types of distributed generation (DG) in a radial distribution system. The three types of DG considered are Type I, Type II and Type IV model. The methods account for changes in long-term system load profile in the planning phase to enhance power system performance. The load model chosen is composite. The optimal power factor and the cost of the DG are also considered. The efficiency of the proposed technique is tested and validated on IEEE 33-bus and 69-bus systems. The significant savings in annual energy loss, reduction in system power losses with upgraded voltage profile is observed for Type IV DG at optimal PF over other types. The results attained are further compared to other analytical and nature-inspired methods to exemplify the dominance of the proposed methodology. The statistical analysis has also been carried out for both bus systems without and with considering all types of DG.

Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and Statcom Device

The complexity of a power system operating with transient stability/security constraints increases with increased interconnection of power transmission networks. Many of the power system’s secure operations are affected with the voltage/transient instability problems. Thereby, the modern power systems have considered solving optimal power flow (OPF) problems using voltage/transient stability constraints as a tedious and challenging task. Algebraic and differential equations of the voltage/stability constraints are included in non-linear optimal power flow optimization problems. In this work, the OPF problems with voltage/stability constraints are solved using a newly developed reliable and robust technique. Moreover, the impact of a FACTS device such as STATCOM device was investigated to test its impact in the enhancement of power system performance. An adaptive unified differential evolution (AuDE) technique is proposed to search in the non-convex and nonlinear problems to obtain the global optimal solutions. Compared to other existing methods and basic DE, the proposed AuDE algorithm has achieved better results under simulation conditions. The power system’s performance is considerably enhanced with STATCOM device. Efficiency of the proposed method in solving the transient and security constrained power systems for optimal operations were demonstrated using the numerical results obtained from IEEE 39-bus, 10-generator system and IEEE 30-bus, 6-generator system. Due to page limitation only 30-bus systems results are presented.

Optimal Placement and Control of UPFC for Enhancement of Power System Performance Using Multi-objective θ-CPCE Algorithm

This paper addresses an optimal design for simultaneously locating unified power flow controller and tuning the parameters of its controllers to enhance the performance of the power system. A mixed integer nonlinear problem is acquired for the design procedure due to the characteristics of the considered objective functions. A new θ-hybrid particle swarm optimization-based co-evolutionary cultural algorithm called θ-culture-PSO-co-evolutionary (θ-CPCE) is used to solve this problem. The best Pareto-optimal solutions are also obtained by defining this problem as a multi-objective function. The simulations results on IEEE 39-bus power system show the effective performance of the proposed method as compared with other algorithms.

Enhancement of power system performance with wind farm disturbances

Enhancement of power system performance with wind farm disturbances

Enhancement of power system performance with wind farm disturbances

This paper discussed how wind farm disturbance, especially wind speed variability, can affect the performance of the power system. Also, it discussed how blades angels of wind turbine can be controlled to increase the energy efficacy of the power system. It showed that the optimized pitch angel controller using harmony search algorithm could enhance blades angels’ adjustments performance. First part, the paper explained the advantages of doubly fed induction generator in wind turbine system. Paper also enumerated the most probable ways to tame the wind speed variability challenge focusing on the pitch angle controller technique. After that, paper compared the system parameter`s result before optimization with values after optimized pitch angle controller gain (Kp). This comparison would be held in three cases, case of variable wind speed with normal operation condition, constant wind speed with line to ground fault condition and variable wind speed with line to ground fault condition. Finally, It demonstrated the MATLAB/SIMULINK model used illustrating results appeared and conclusion.

Enhancement of power system performance with wind farm disturbances

This paper discussed how wind farm disturbance, especially wind speed variability, can affect the performance of the power system. Also, it discussed how blades angels of wind turbine can be controlled to increase the energy efficacy of the power system. It showed that the optimized pitch angel controller using harmony search algorithm could enhance blades angels’ adjustments performance. First part, the paper explained the advantages of doubly fed induction generator in wind turbine system. Paper also enumerated the most probable ways to tame the wind speed variability challenge focusing on the pitch angle controller technique. After that, paper compared the system parameter`s result before optimization with values after optimized pitch angle controller gain (Kp). This comparison would be held in three cases, case of variable wind speed with normal operation condition, constant wind speed with line to ground fault condition and variable wind speed with line to ground fault condition. Finally, It demonstrated the MATLAB/SIMULINK model used illustrating results appeared and conclusion.

Voltage Stability Enhancement by Coordination of SVC and TCSC Using Particle Swarm Optimization

The Flexible AC Transmission System (FATCS) plays a vital role in power system performance enhancement. They attributes to accomplish faster and having answers for many issues in power system study. With regards to this, a particles warm optimization (PSO) algorithm is implemented so that to design the coordinated parameters of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). Transient stability of a power system is based on the generator relative rotor angles obtained from time domain simulations outputs. A self-sufficient model of IEEE fourteen bus systems has been given with full detail and voltage stability analysis is done by considering load changing at a bus. PSO applied here is built on searching the values of L and C of SVC, then one can achieve desired and fine coordination. PSO is available with few modification, as change in PSO range & number, difference in selection criteria technique, control technique etc, with due respect to normalize PSO. The results are compared and found that coordination of FACTS devices with each other promises the efficiency of the suggested method for revamping Power flows. When function of TCSC is impelled by some curb, adjustable SVC can supply additional support to upgrade the gross performance.

Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization

The Flexible AC Transmission System (FACTS) plays a vital role in power system performance enhancement. They attributes to accomplish faster and having an answer for many issues in power system study. With regards to this, a particle swarm optimization (PSO) algorithm is implemented so that to design the coordinated parameters of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). With behavioral nonlinearities of the power system, linear methods cannot be used to design coordinated parameters of controllers. Simulation is carried out on IEEE-14 bus system with help of MATLAB. PSO applied here is built on searching the values of L and C of SVC, then one can achieve desired and fine coordination. PSO is available with few modification, as change in PSO range & number, difference in selection criteria technique, control technique etc., with due respect to normalize PSO. The results are compared and found that coordination of FACTS devices with each other promises the efficiency of the suggested method for revamping Power flows. When function of TCSC is impelled by some curb, adjustable SVC can apply additional support to upgrade the gross performance.

A Novel Hybrid Fuzzy-JAYA Optimization Algorithm for Efficient ORPD Solution

This paper is concerned with the application of hybrid fuzzy-JAYA optimization algorithm to find the solution of non-linear optimal reactive power dispatch (ORPD) problem in power systems. The proposed hybrid optimization algorithm combines the merits of fuzzy principle and the Jaya optimizer. Fuzzification of the ORPD variables is employed by pseudo goal strategy. Two technical objectives are minimized individually and simultaneously to enhance the overall power systems performance. These objectives are transmission active power losses and voltage deviation at load buses. The ORPD objectives are optimized considering both inequality and equality constraints that reflect the operation needs. The hybrid fuzzy-JAYA is established as efficient optimization method that is achieving the global optimal solution. The effectiveness of the proposed hybrid algorithm for solving the ORPD problem is proven by using three standard IEEE test networks. An assessment of the proposed hybrid algorithm is carried out compared with other optimization algorithms those reported in the literature. The simulation results assure that the fuzzy Jaya hybrid algorithm leads to significant power system performance enhancement for different scale power systems.

Application of a hybrid evolutionary algorithm on reactive power compensation problem of distribution network

Development of evolutionary optimization algorithms has provided more capability for enhancement of power system performance. To improve the performance of these algorithms, the combination of them can be a suitable option. This paper has proposed a hybrid algorithm based on combination of particle swarm optimization (PSO) and electromagnetic-like (EM) algorithms. The proposed algorithm covers the problems of trapping in local optimum problem of PSO and low convergence rate of EM. The optimal capacitor siting and sizing in distribution networks as the one of important problems of power system operators have been studied for validation of proposed algorithm performance. A comprehensive comparison has been carried out between proposed and other popular evolutionary algorithms. The results show that proposed algorithm has better performance for increasing the net saving value as the optimization objective function. Two well-known test systems of IEEE 34-bus and IEEE 85-bus power distribution networks have selected for these investigations.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

Power System Performance Enhancement through Compensation Techniques

Demands for electricity had increased tremendously over the years and subsequently power systems need to be augmented in size and performance. One possible way of enhancing the performance of power systems is through transmission lines performance upgrading. Series compensation technique is found to be very effective in heightening the power transmission line’s loadability; hence it is practiced widely due to its cost effectiveness. In this research paper, a percentage compensation software package has been developed to illustrate the benefits of series compensation. This package consists of various compensation configurations such as: single capacitor configuration, twin capacitors-optimum configuration, twin capacitors-optional capacitors configuration and twin capacitors-optional placement configurations. The MATLAB GUI platform is used for the development of the software package.

The Opportunity of Power Electronics on Improving the Quality of Voltage and Power Flow in the West Algeria Network

The Flexible Alternate Current Transmission Systems (FACTS) controllers improve quality of the supply power, enhance power system performance and also provide an optimal utilization of the existing resources. Especially the Thyristor Controlled Series Compensator (TCSC) and the Static Var Compensator (SVC) has been proposed to enhance the power transfer capability and improve the quality of the voltage by adjusting the line reactance. This paper, present a study of the west Algerian 2012 network. Furthermore, we will try to ask some problems encountered in practice, to find solutions and moving towards the FACTS devices in particular the TCSC and SVC controller, its application and technical advantage. The software NEPLAN is used to analyse the behaviour of the West Algerian electrical network without and with FACTS devices.