Enhanced OPF for DG Penetrated Power System Network under Variable Load Conditions

2014 ◽  
Vol 984-985 ◽  
pp. 1301-1305
Author(s):  
P. Sivakumar ◽  
Arumugam Rajapandiyan
Keyword(s):  
Power Systems ◽  
New England ◽  
Distributed Generation ◽  
Renewable Energy Sources ◽  
Pso Algorithm ◽  
Test System ◽  
Variable Load ◽  
Swarm Optimization ◽  
Distributed Generators

In modern power systems, distributed generation turns out to be progressively significant. Conversely, the growing utilize of distributed generators origins the concerns on the growing system hazard owing to their probable breakdown or unruly power productivity based on such renewable energy sources as wind and the sun. Power contribution at the required proportion by the grids is the chief performance consideration which depends upon the penetration of distributed generation and the accessibility of conventional sources during the load transform. In this paper, the projected approach is that the essential load power is divided evenly between the grids composed of Distributed Generation (DG) units and the utility based on the PSO algorithm during the load transform. A case study is carried out based on the New England test system (10-Generator-39-Bus) as a standard by using Particle swarm optimization (PSO) algorithm.

Optimal Allocation Research of Distributed Generation Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 1070-1072 ◽  
pp. 657-665
Author(s):  
Peng Cheng Li ◽  
Zhong Xiao Cong ◽  
Jia Xiang Ou ◽  
Zhi Wei Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Total System ◽  
Swarm Optimization ◽  
Node Voltage ◽  
Searching Ability

Multi-objective optimization model on sitting and sizing of Distributed Generation (DG) was proposed in this paper, and it was based on the comprehensive consideration of total system network loss and total deviation of node voltage, aiming at the optimization of DG’s access, the simulation tests were carried out on the 13 bus test system using Particle Swarm Optimization (PSO) algorithm that belonged to swarm intelligence algorithm, receiving the improved network loss and node voltage as the evaluation index, the mutation operator was introduced into the basic PSO algorithm, which improved the possibility to find a more optimal value ,the results showed that IPSO algorithm had strong global searching ability and rapid convergence speed for optimal allocation of Distributed Generation in the distribution network, and it created a new idea for further Distributed Generation allocation.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots with Uncertainties

2021 ◽  
pp. 1-32
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Pso Algorithm ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design ◽  
Serial Robots ◽  
Simulation Based ◽  
Serial Robot ◽  
Simulation Results ◽  
Uncertainty Level

Abstract This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. The reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. The gravity balancing begins with a simulation-based analysis of the gravitational torques of a typical serial robot. Based on the simulation results, a gravity balancing design for the robot using mechanical springs is realized. A reliability-based design optimization (RBDO) method is also developed to seek a reliable and robust design for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

scholarly journals A New Indicator of Transient Stability for Controlled Islanding of Power Systems: Critical Islanding Time

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2975 ◽  
Author(s):  
Zhenzhi Lin ◽  
Yuxuan Zhao ◽  
Shengyuan Liu ◽  
Fushuan Wen ◽  
Yi Ding ◽  
...  
Keyword(s):  
New York ◽  
Power Systems ◽  
New England ◽  
Transient Stability ◽  
Time Frame ◽  
Test System ◽  
Crucial Importance ◽  
Interconnected System ◽  
Controlled Islanding ◽  
Simulation Results

Transient stability after islanding is of crucial importance because a controlled islanding strategy is not feasible if transient stability cannot be maintained in the islands created. A new indicator of transient stability for controlled islanding strategies, defined as the critical islanding time (CIT), is presented for slow coherency-based controlled islanding strategies to determine whether all the islands created are transiently stable. Then, the stable islanding interval (SII) is also defined to determine the appropriate time frame for stable islanding. Simulations were conducted on the New England test system–New York interconnected system to demonstrate the characteristics of the critical islanding time and stable islanding interval. Simulation results showed that the answer for when to island could be easily reflected by the proposed CIT and SII indicators. These two indicators are beneficial to power dispatchers to keep the power systems transiently stable and prevent widespread blackouts.

scholarly journals An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 573
Author(s):  
Mohamed Mokhtar ◽  
Mostafa I. Marei ◽  
Mariam A. Sameh ◽  
Mahmoud A. Attia
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
System Performance ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Adaptive Controller ◽  
Load Frequency Control ◽  
Energy Sources ◽  
Load Variations

The frequency of power systems is very sensitive to load variations. Additionally, with the increased penetration of renewable energy sources in electrical grids, stabilizing the system frequency becomes more challenging. Therefore, Load Frequency Control (LFC) is used to keep the frequency within its acceptable limits. In this paper, an adaptive controller is proposed to enhance the system performance under load variations. Moreover, the proposed controller overcomes the disturbances resulting from the natural operation of the renewable energy sources such as Wave Energy Conversion System (WECS) and Photovoltaic (PV) system. The superiority of the proposed controller compared to the classical LFC schemes is that it has auto tuned parameters. The validation of the proposed controller is carried out through four case studies. The first case study is dedicated to a two-area LFC system under load variations. The WECS is considered as a disturbance for the second case study. Moreover, to demonstrate the superiority of the proposed controller, the dynamic performance is compared with previous work based on an optimized controller in the third case study. Finally in the fourth case study, a sensitivity analysis is carried out through parameters variations in the nonlinear PV-thermal hybrid system. The novel application of the adaptive controller into the LFC leads to enhance the system performance under disturbance of different sources of renewable energy. Moreover, a robustness test is presented to validate the reliability of the proposed controller.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

scholarly journals A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania

2020 ◽  
Vol 12 (15) ◽  
pp. 6084
Author(s):  
Simona-Vasilica Oprea ◽  
Adela Bâra ◽  
Ștefan Preda ◽  
Osman Bulent Tor
Keyword(s):  
Fuzzy Logic ◽  
Renewable Energy ◽  
Power Systems ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Sources ◽  
Fuzzy Logic Systems

Electricity generation from renewable energy sources (RES) has a common feature, that is, it is fluctuating, available in certain amounts and only for some periods of time. Consuming this electricity when it is available should be a primary goal to enhance operation of the RES-powered generating units which are particularly operating in microgrids. Heavily influenced by weather parameters, RES-powered systems can benefit from implementation of sensors and fuzzy logic systems to dynamically adapt electric loads to the volatility of RES. This study attempts to answer the following question: How to efficiently integrate RES to power systems by means of sustainable energy solutions that involve sensors, fuzzy logic, and categorization of loads? A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. It is demonstrated that, without SASM, the RES generation is supposed to be curtailed in some cases, e.g., when batteries are fully charged, even though the weather conditions are favourable. In such cases, fuzzy rules of SASM securely mitigate curtailment of RES generation by supplying high power non-traditional storage appliances. A numerical case study is performed to demonstrate effectiveness of the proposed SASM architecture for a RES system located in Hulubești (Dâmbovița), Romania.

Impacts of Distributed Generators on Voltage Sag Analysis

2013 ◽  
Vol 732-733 ◽  
pp. 877-881
Author(s):  
Jenjira Boonnamol ◽  
Thavatchai Tayjasanant
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Test System ◽  
Voltage Sag ◽  
Swarm Optimization ◽  
Distributed Generators ◽  
Three Phase

This paper presents impacts of distributed generators (DGs) such as synchronous-based DG and inverter-based DG on voltage sag analysis in distribution systems. Voltage sag analysis is assessed through area of vulnerability (AOV), number of sags frequency (NSF) and voltage sag index (SARFI). Single line-to-ground and three-phase faults are investigated. Size and location of DG are carried out by using Particle Swarm Optimization algorithm (PSO) in order to minimize losses and number of sag frequency. Roy Billinton Test System (RBTS) Bus 2 is used for simulation cases. Results show that the distribution system with DG installed improves voltage sag performance compared with the system without DG installed.

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