• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.303-308
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• 2005

The restructure of the electrical power industry is accompanied by the extension of the electrical power exchange. One of the key pieces of information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). The traditional ATC deterministic approach is based on the severest case and it involves a complex procedure. Therefore, a novel approach for A TC calculation is proposed using cost optimization in this paper. The Jeju Island interconnected HVDC system has inland KEPCO (Korean Electric Power Corporation) systems, and its demand is increasing at the rate of about $\10[%]$ annually. To supply this increasing demand, the capability of the HVDC system must be enlarged. This paper proposes the optimal transfer capability of the HVDC system between Haenam in the inland and Jeju in Cheju Island through cost optimization. The cost optimization is based on generating cost in Jeju Island, transfer cost through Jeju-Haenam HVDC system and outage cost with one depth (N-1 contingency).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.81-86
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• 2005

This paper deals with power system stabilization problem using optimal foraging theory, which formulates foraging as an optimization problem and via computational or analytical methods can provide an optimal foraging policy that specifies how foraging decisions are made. It is possible that the local environment where a population of bacteria live changes either gradually (e.g., via consumption of nutrients) or suddenly due to some other influence. This objective scrutinizes to possibilities for power system stabilization by utilizing how mobile behaviors in both individual and groups of bacteria implement foraging and optimization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권3호
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• pp.171-177
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• 2013

This paper presents a fuzzy set method to enforce complementarity constraints (CCs) in a nonlinear interior point method (NIPM)-based optimization. NIPM is a Newton-type approach to nonlinear programming problems, but it adopts log-barrier functions to deal with the obstacle of managing inequality constraints. The fuzzy-enforcement method has been implemented for CCs, which can be incorporated in optimization problems for real-world applications. In this paper, numerical simulations that apply this method to power system optimal power flow problems are included.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.377-382
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• 2004

This paper presents a novel optimization method for the air- and gas-supplying network comprised of several air compression systems and air and gas streams in an industrial chemical plant. The optimization is based on the hybrid model developed by Han and $Han^1$ for predicting the power consumption of a compression system. A constrained optimization problem was formulated to minimize the total electric power consumption of all the compression systems in the air- and gas-supplying network under various operating constraints and was solved using a successive quadratic optimization algorithm. The optimization approach was applied to an industrial terephthalic acid manufacturing plant to achieve about 10% reduction in the total electric power consumption under varying ambient conditions.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2840-2851
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• 2022

Electric boilers (EBs) are the backup steam source for the auxiliary steam system of high-temperature gas-cooled reactor nuclear power plants. When the plant is in normal operations, the EB is always in hot standby status. However, the current hot standby operation strategy has problems of slow response, high power consumption, and long operation time. To solve these problems, this study focuses on the optimization of hot standby operations for the EB system. First, mathematical models of an electrode immersion EB and its accompanying deaerator were established. Then, a control simulation platform of the EB system was developed in MATLAB/Simulink implementing the established mathematical models and corresponding control systems. Finally, two optimization strategies for the EB hot standby operation were proposed, followed by dynamic simulations of the EB system transient from hot standby to normal operations. The results indicate that the proposed optimization strategies can significantly speed up the transient response of the EB system from hot standby to normal operations and reduce the power consumption in hot standby operations, improving the dynamic performance and economy of the system.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.