• 대한산업공학회지
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• 제4권2호
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• pp.97-105
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• 1978

A mathematical model is developed in order to describe the network configuration and heating distribution to a Central District Heating System for an Urban Residential Community. The purpose of using this model is to optimize operating costs and to distribute heat to the Residential Community efficiently. In particular, because of the inherent nonlinearity and dual optimization of the problem a dyamic programming approach is taken. It is turned out that the optimal cost of the system is a strong non-linear function of the network. In particular, it is found that increasing N, the number of houses, may not necessarily imply increased costs. It is felt that past failure of producing economical systems may be due to the improper attention given to the network.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• 한국건설관리학회논문집
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• 제16권6호
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• pp.53-62
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• 2015

공기비용 트레이드오프 모델은 건설프로젝트의 계획 및 관리에 있어 매우 중요하다. TCTO 모델은 연속모델과 분절모델 두 가지 모델이 개발되어왔다. 그러나 한 종류의 모델만을 사용하여 현실적인 공기단축 시나리오를 적용하기에는 한계가 있다. 이에 TCTO 의 연속적인 모델과, 분절모델을 결합하여 진보된 모델을 제시하였으며 또한, 비선형 관계, 인센티브 및 지체보상금 고려가 가능하도록 TCTO모델에 포함되어 있다. 이런 특성들은 건설프로젝트에 적용가능하다. 6개의 activities로 구성된 CPM 네트워크는 연구에서 제안된 모델을 설명하기 위해 사용되었다. 제시한 모델은 모든 제약 조건을 만족시키는 최적 스케쥴 계산이 가능하다. 결과적으로 본 연구에서 제시한 진보된 TCTO모델은 기존의 모델보다 최적화된 공기단축이 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5701-5722
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• 2018

The continuous increase in the cost of energy production and concerns for environmental sustainability are leading research communities, governments and industries to amass efforts to reduce energy consumption and global $CO_2$ footprint. Players in the information and communication industry are keen on reducing the operational expenditures (OpEx) and maintaining the profitability of cellular networks. Meanwhile, network virtualization has been proposed in this regard as the main enabler for 5G mobile cellular networks. In this paper, we propose a generic framework of slice resource provisioning and customized physical resource allocation for energy-efficiency and quality of service optimization. In resource slicing, we consider user demand and population resources provisioning scheme aiming to satisfy quality of service (QoS). In customized physical resource allocation, we formulate this problem with an integer non-linear programming model, which is solved by a heuristic algorithm based on minimum vertex coverage. The proposed algorithm is compared with the existing approaches, without consideration of slice resource constraints via system-level simulations. From the perspective of infrastructure providers, traffic is scheduled over a limited number of active small-cell base stations (sc-BSs) that significantly reduce the system energy consumption and improve the system's spectral efficiency. From the perspective of virtual network operators and mobile users, the proposed approach can guarantee QoS for mobile users and improve user satisfaction.