• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.187-197
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• 2004

This paper presents a window-based load management system (LMS) developed as a decision-making tool in the competitive electricity market The developed LMS can help the users to monitor system load patterns, analyze their past energy consumption and schedule for the future energy consumption. The LMS can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. Therefore. this LMS can be used to plan the optimal demand schedule and consumption strategy.

• 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.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.441-443
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• 2002

This paper presents a window-based load management system (LMS) package developed as a decision-making tool in the competitive electricity market. The presented LMS package can help the users to monitor system load patterns, analyze their past energy consumption and schedule the future energy consumption. The LMS package can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. The developed LMS package can be used to establish the optimal demand schedule and consumption strategy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.21-28
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• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.904-913
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• 2005

This paper deals with an integrated problem of inventory control and dynamic pricing strategies for a commodity with price and sales-period dependent demand pattern, where a seller and customers have complete information of each other. The problem consists of two parts; one is each buyer's benefit problem which makes the best decision on price and time for buyer to purchase items, and the other one is a seller's profit problem which decides an optimal sales strategy concerned with inventory control and discount schedule. The seller's profit function consists of sales revenue and inventory holding cost functions. The two parts are closely related into each other with some related variables, so that any existing general solution methods can not be applied. Therefore, a simplified model with single seller and two customers in considered first, where demand for multiple units is allowed to each customer within a time limit. Therewith, the model is generalized for a n-customer-classes problem. To solve the proposed n-customer-set problem, a dynamic programming algorithm is derived. In the proposed dynamic programming algorithm, an intermediate profit function is used, which is computed in case of a fixed initial inventory level and then adjusted in searching for an optimal inventory level. This leads to an optimal sales strategy for a seller, which can derive an optimal decision on both an initial inventory level and a discount schedule, in $O(n^2)$ time. This result can be used for some extended problems with a small customer set and a short selling period, including sales strategy for department stores, Dutch auction for items with heavy holding cost, open tender of materials, quantity-limited sales, and cooperative buying in the on/off markets.

• Journal of KIBIM
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• v.9 no.2
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• pp.1-10
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• 2019

In this research, schedule optimization is defined as balancing the number of workers while keeping the demand and needs of the project resources, creating the perfect schedule for each activity. Therefore, when one optimizes a schedule, multiple potentials of schedule changes are assessed to get an instant view of changes that avoid any over and under staffing while maximizing productivity levels for the available labor cost. Optimizing the number of workers in the scheduling process is not a simple task since it usually involves many different factors to be considered such as the development of quantity take-offs, cost estimating, scheduling, direct/indirect costs, and borrowing costs in cash flow while each factor affecting the others simultaneously. That is why the optimization process usually requires complex computational simulations/modeling. This research attempts to find an optimal selection of daily maximum workers in a project while considering the impacts of other factors at the same time through OPEN BIM based multiple computer simulations in resource leveling. This paper integrates several different processes such as quantity take-offs, cost estimating, and scheduling processes through computer aided simulations and prediction in generating/comparing different outcomes of each process. To achieve interoperability among different simulation processes, this research utilized data exchanges supported by building SMART-IFC effort in automating the data extraction and retrieval. Numerous computer simulations were run, which included necessary aspects of construction scheduling, to produce sufficient alternatives for a given project.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.453-463
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• 2013

The probabilistic nature of renewable energy, especially wind energy, increases the needs for new forms of planning and operating with electrical power. This paper presents a novel approach for determining the short-term generation schedule for optimal operations of wind energy-integrated power systems. The proposed probabilistic security-constrained optimal power flow (P-SCOPF) considers dispatch, network, and security constraints in pre- and post-contingency states. The method considers two sources of uncertainty: power demand and wind speed. The power demand is assumed to follow a normal distribution, while the correlated wind speed is modeled by the Weibull distribution. A Monte Carlo simulation is used to choose input variables of power demand and wind speed from their probability distribution functions. Then, P-SCOPF can be applied to the input variables. This approach was tested on a modified IEEE 30-bus system with two wind farms. The results show that the proposed approach provides information on power system economics, security, and environmental parameters to enable better decision-making by system operators.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.266-271
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• 2000

The electric power industries are moving from the conventional monopolistic or vertically integrated environments to deregulated and competitive environments, where each participant is concerned with profit maximization rather than system-wide costs minimization. Consequently, the conventional least-cost approaches for the generation resource schedule can not exactly handle real-world situations. This paper presents a game theory application for analyzing power transactions and market design in a deregulated energy marketplace, where the market participants determine the net profits through the optimal bidding strategies. The demand elasticity of the energy price is considered for the realistic modeling of the deregulated marketplace.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.489-497
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• 2003

A water management system is developed to reduce the unit cost of production in wide-area waterworks. Improving productivity in waterworks is to save power rate. We suggest a method to schedule the supply of water according to the time-varying power rate and pump control scheme. Water pipeline analysis package (SynerGEE Water) is utilized to obtain optimal pump control solution adaptation to water demand. Our evaluation results show that developed scheme is more efficient than the conventional.

• IE interfaces
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• v.11 no.2
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• pp.13-24
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• 1998

A distribution system is composed of multiple levels from a producer to customers, and it's objective is to supply customers with goods timely with a prescribed level of quality at a minimum cost. For the installation and operation of multi-echelon distribution system, DRP(Distribution Resource Planning) is widely used. However, because of the characteristic difference of material flow dynamic of each distribution center, it is almost impossible to get the optimal distribution scheduling. In this paper, an improved DRP method to schedule multi-echelon distribution network is proposed so that the lot-size and order point is dynamically obtained to meet the change of demand rate and timing. The experiment is done with various demand pattern, forecast errors of demand and lead times of central distribution center. The proposed method is compared with traditional statistical approach.