scholarly journals Optimal TCSC placement for congestion management in deregulated power systems using antlion optimization algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 77
Author(s):  
Majid Moazzami ◽  
Hossein Shahinzadeh ◽  
Gevork B. Gharehpetian ◽  
Abolfazl Shafiei
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Congestion Management ◽  
Test System ◽  
Production Costs ◽  
High Price ◽  
Facts Devices ◽  
Antlion Optimization ◽  
Deregulated Power Systems

Congestion management is one of the important issues in the deregulated power systems. There are several methods to eliminate congestion. Utilizing FACTS devices is an appropriate option for large-scale and quick control of flows of transmission lines. FACTS devices such as Thyristor Controlled Series Capacitor (TCSC) can help to mitigate the transmitting flow of power in the congested lines, which leads to an increase in the network loading ability as well as reduction of both losses and production costs. Due to the considerably high price of FACTS devices, it is important to determine their optimum location on the network. Accordingly, in this paper, the Antlion optimization algorithm (ALO) has been employed to conduct a congestion management analysis to determine the optimal location for the installation of TCSC, which is simulated on an IEEE 14-bus test system subject to satisfy the constraints of the market environment.

scholarly journals An Effective Method for Minimizing Electric Generation Costs of Thermal Systems with Complex Constraints and Large Scale

2020 ◽  
Vol 10 (10) ◽  
pp. 3507
Author(s):  
Bach Hoang Dinh ◽  
Thanh Van Pham ◽  
Thang Trung Nguyen ◽  
Gabriela Nicoleta Sava ◽  
Minh Quan Duong
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
High Performance ◽  
Large Scale ◽  
Original Method ◽  
Thermal Systems ◽  
Generation Costs ◽  
Antlion Optimization ◽  
Electrical Generation ◽  
Almost All

In this paper, an improved antlion optimization algorithm (IALO) was proposed to search for promising solutions for optimal economic load dispatch (ELD) problems to minimize electrical generation fuel costs in power systems with thermal units and to ensure all constraints are within operating ranges. IALO can be more effective than the original method, called the antlion optimization algorithm (ALO), because of the high performance of the applied modifications on the new solutions searching process. In order to evaluate the abilities of the IALO method, we completed many tests on thermal generating systems including 10, 15, 20, 30, 60, 80, and 90 units with different constraints and fuel-consuming characteristics. The results suggest that the offered method is superior to the ALO method with more stable search ability, faster convergence velocity, and shorter calculation times. Furthermore, the obtained results of the IALO method are much better than those of almost all the other methods used to solve problems for the same systems. As a result, IALO is suggested to be a highly effective method, and it can be applied to other problems in power systems instead of ALO, which has a lower performance.

scholarly journals Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2301
Author(s):  
Yun-Sung Cho ◽  
Yun-Hyuk Choi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
State Estimation ◽  
Measurement Errors ◽  
Large Scale ◽  
Test System ◽  
The State ◽  
Inner Function ◽  
Outer Function ◽  
Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

scholarly journals An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1231-1237
Author(s):  
S T Suganthi ◽  
D Devaraj
Keyword(s):  
Power Systems ◽  
Congestion Management ◽  
Test System ◽  
Photovoltaic System ◽  
Management Technique ◽  
Optimization Approach ◽  
Solar Photovoltaic ◽  
Teaching Learning Based Optimization ◽  
Solar Photovoltaic System ◽  
Teaching Learning

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

RELIABILITY MANAGEMENT AND IMPROVEMENT FOR PLANNING AND OPERATIONAL PROCESS ENHANCEMENT MEASURES IN DEREGULATED POWER SYSTEMS USING ATC

2010 ◽  
Vol 17 (03) ◽  
pp. 275-289
Author(s):  
V. VIJAY VENU ◽  
A. K. VERMA
Keyword(s):  
Power Systems ◽  
Planning Horizon ◽  
Available Transfer Capability ◽  
Reliability Management ◽  
Flexible Ac Transmission ◽  
Flexible Ac Transmission System ◽  
Facts Devices ◽  
Transfer Capability ◽  
Ac Transmission ◽  
Deregulated Power Systems

In this paper, beginning with a concise overview of the Available Transfer Capability (ATC) evaluation methods, we make a proposition for reliability management in the planning horizon of deregulated power systems through the concept of Adequacy Resiliency. The derived indices are meant as indicators of adaptability of power systems to ensure the required reliability levels. Improvements to this conceptualization upon the deployment of Flexible AC Transmission System (FACTS) devices are then put forward. We also explore the option of employing the created indices to the operational horizon of power systems, explaining the means of market enhancement. Core reliability issues arising out of the usage of FACTS are then discussed.

scholarly journals Voltage Coordination via Communication in Large-Scale Multi-Area Power Systems. Part II: Simulation Results

2012 ◽  
Vol 433-440 ◽  
pp. 7183-7189
Author(s):  
Mohammad Moradzadeh ◽  
René Boel
Keyword(s):  
Power Systems ◽  
Power System ◽  
Electricity Market ◽  
Large Scale ◽  
Test System ◽  
Distributed Model ◽  
Future Evolution ◽  
Distributed Model Predictive Control ◽  
Control Actions ◽  
Simulation Results

This two-part paper deals with the coordination of the control actions in a network of many interacting components, where each component is controlled by independent control agents. As a case study we consider voltage control in large electric power systems where ever-increasing pressures from the liberalization and globalization of the electricity market has led to partitioning the power system into multiple areas each operated by an independent Transmission System Operator (TSO). Coordination of local control actions taken by those TSOs is a very challenging problem as poorly coordinated operation of TSOs may endanger the power system security by increasing the risk of blackouts. This second part of the paper presents simulation results on a 12-bus 3-area test system, using the distributed model predictive control paradigm in order to design a coordinating model-based feedback controller. Coordination requires that each agent has some information on what the future evolution of its power flows to and from its neighbors will be. It will be shown that how the communication between agents can avoid voltage collapse in circumstances where classical uncoordinated controllers fail.

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