• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.2
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• pp.97-105
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• 2008

This paper presents simulated and experimental test results of optimal control algorithm for an encapsulated ice thermal storage system with full capacity chiller operation. The algorithm finds an optimal combination of a chiller and/or a storage tank operation for the minimum total operation cost through a cycle of charging and discharging. Dynamic programming is used to find the optimal control schedule. The conventional control strategy of chiller-priority is the baseline case for comparing with the optimal control strategy through simulation and experimental test. Simulation shows that operating cost for the optimal control with chiller on-off operation is not so different from that with chiller part load capacity control. As a result from the experimental test, the optimal control operation according to the simulated operation schedule showed about 14 % of cost saving compared with the chiller-priority control.

• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.69-77
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• 2017

The amount of solar irradiation obtained by a photovoltaic (PV) solar panel is the major factor determining the power generated by a PV system, and the array tilt angle is critical for maximizing panel radiation acquisition. There are three types of PV systems based on the manner of setting the array tilt angle: fixed, semi-fixed, and tracking systems. A fixed system cannot respond to seasonal solar altitude angle changes, and therefore cannot absorb the maximum available solar radiation. The tracking system continually adjusts the tilt angle to absorb the maximum available radiation, but requires additional cost for equipment, installation, operation, and maintenance. The semi-fixed system is only adjusted periodically (usually seasonally) to obtain more energy than a fixed system at an overall cost that is less than a tracking system. To maximize semi-fixed system efficiency, determining the optimal tilt angle adjustment schedule are required. In this research, we conducted a simulation to derive an optimal operation schedule for a semi-fixed system in Seoul, Korea (latitude $37.5^{\circ}$). We implemented a solar radiation acquisition model and PV genereation model on MATLAB. The optimal operation schedule was derived by changing the number of tilt angle adjustments throughout a year using a Dynamic Algorithm. The results show that adjusting the tilt angle 4 times a year was the most appropriate. and then, generation amount of PV system increased 2.80% compared with the fixed system. This corresponds to 99% compared to daily adjustment model. This increase would be quite valid as the PV system installation area increased.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1347-1354
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• 2016

This paper presents optimal operational scheduling model and economic assessment of Li-ion battery energy storage systems installed in non-residential customers. The operation schedule of a BESS is determined to minimize electric bill, which is composed of demand and energy charges. Dynamic programming is introduced to solve the nonlinear optimization problem. Based on the optimal operation schedule result, the economics of a BESS are evaluated in the investor and the social perspective respectively. Calculated benefits in the investor or customer perspective are the savings of demand charge, energy charge, and related taxes. The social benefits include fuel cost savings of generating units, construction deferral effects of the generation capacity and T&D infra, and incremental CO2 emission cost impacts, etc. Case studies are applied to an large industrial customer that shows similarly repeated load patterns according to days of the week.

• Journal of IKEEE
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• v.23 no.3
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• pp.1027-1032
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• 2019

In this paper, we had developed an algorithm that can cope with all the economic variables that may occur in the operation of PV-ESS system by developing the algorithm for the operation method of PV-ESS connected system. Based on this, the optimal operation schedule of PV-ESS was decided to minimize the electric charges of customers. Through this, we developed a three-dimensional dynamic planning method based on case generation to determine the optimal PV-ESS operation schedule stably even when exogenous variables change.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1028-1031
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• 1996

Dynamic scheduling is very important in constructing CIM and improving productivity of chemical processing systems. Computation at the scheduling level requires mostly a long time to generate an optimal schedule, so it is difficult to immediately respond to actual process events in real-time. To solve these problems, we developed dynamic scheduling algorithms such as DSMM(Dynamic Shift Modification Method), PUOM(Parallel Unit Operation Method) and UVVM(Unit Validity Verification Method). Their main functions are to minimize the effects of unexpected disturbances such as process time variations and unit failure, to predict a makespan of the updated dynamic schedule and to modify schedule desirably in real-time responding to process time variations. As a result, the algorithms generate a new pertinent schedule in real-time which is close to the original schedule but provides an efficient way of responding to the variation of process environment. Examples in a shampoo production batch process illustrate the efficiency of the algorithms.

• ETRI Journal
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• v.37 no.5
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• pp.888-897
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• 2015

Group scheduling is an operation whereby control packets arriving in a time slot schedule their bursts simultaneously. Normally, those bursts that are of the same wavelength are scheduled on the same channel. In cases where the support of full wavelength converters is available, such scheduling can be performed on multiple channels for those bursts that are of an arbitrary wavelength. This paper presents a new algorithm for group scheduling on multiple channels. In our approach, to reach a near-optimal schedule, a maximum-weight clique needs to be determined; thus, we propose an additional algorithm for this purpose. Analysis and simulation results indicate that an optimal schedule is almost attainable, while the complexity of computation and that of implementation are reduced.

• Water for future
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• v.29 no.4
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• pp.131-138
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• 1996

The objective of this study is to develop an optimal operation model for the sewage treatment plants using nonlinear programming (NLP) technique and the QUAL2E model. The model finds the minimum-cost operation of sewage treatment plants while satisfying all design constraints and water quality (BOD) standard. The model is applied to four sewage treatment plants in Han River for the city of Seoul. It has been found that optimal operation schedule for the sewage treatment plants can be computed and it is more economic to operate the plants according to the schedule which satisfies the water quality constraints in the river. In addition, the water quality in the river can be predicted using the model under the treatment policy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.6
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• pp.242-248
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• 2006

This paper proposes a coordinated control method for under-load tap changers (ULTCs) with shunt capacitors to reduce the operation numbers of both devices. The proposed method consists of two stages. In the first stage, the dispatch schedule is determined using a genetic algorithm with forecasted loads to reduce the power loss and to improve the voltage profile during a day. In the second stage, each capacitor operates according to this dispatch schedule and the ULTCs are controlled in real time with the modified reference voltages considering the dispatch schedule of the capacitors. The performance of the method is evaluated for the modified IEEE 14-bus system. Simulation results show that the proposed method performs better than a conventional control method.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1369-1375
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• 2017

This paper presents an optimal operation scheduling algorithm for a smart home with energy storage system, electric vehicle and distributed generation. The proposed algorithm provides the optimal charge and discharge schedule of the EV and the ESS. In minimizing the electricity costs of the smart home, it considers not only the cost of energy purchase from the grid but also the life cost of batteries. The life costs of batteries are calculated based on the relation between the depth of discharge and life time of battery. As the life time of battery depends on the charge and discharge pattern, optimal charge and discharge schedule should consider the life cost of batteries especially when there is more than one battery with different technical characteristics. The proposed algorithm can also be used for optimal selection of size and type of battery for a smart home.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.597-602
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• 2005

The introduction of competition in electricity market emphasizes the importance of sufficient transmission capacities to guarantee various electricity transactions. Therefore, when dispatch scheduling, transmission security constraints should be considered for the economic and stable electric power system operation. In this paper, we propose an optimal dispatch scheduling algorithm incorporating transmission security constraints. For solving these constraints, the dispatch scheduling problem is decomposed into a master problem to calculate a general optimal power flow (OPF) without transmission security constraints and several subproblems to inspect the feasibility of OPF solution under various transmission line contingencies. If a dispatch schedule given by the master problem violates transmission security constraints, then an additional constraint is imposed to the master problem. Through these iteration processes between the master problem and subproblems, an optimal dispatch schedule reflecting the post-contingency rescheduling is derived. Moreover, since interruptible loads can positively participate as generators in the competitive electricity market, we consider these interruptible loads active control variables. Numerical example demonstrates efficiency of the proposed algorithm.