• Journal of Electrical Engineering and Technology
• /
• v.13 no.6
• /
• pp.2236-2244
• /
• 2018

We make optimal consecutive offer curves for an energy storage system (ESS) integrated wind power producer (WPP) in the co-optimized day-ahead energy and regulation markets. We build the offer curves by solving multi-stage stochastic optimization (MSSO) problems based on the scenarios of pairs consisting of real-time price and wind power forecasts through the progressive hedging method (PHM). We also use the rolling horizon method (RHM) to build the consecutive offer curves for several hours in chronological order. We test the profitability of the offer curves by using the data sampled from the Iberian Peninsula. We show that the offer curves obtained by solving MSSO problems with the PHM and RHM have a higher profitability than offer curves obtained by solving deterministic problems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1579-1602
• /
• 2020

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system's multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.1
• /
• pp.89-102
• /
• 2010

Future uncertainty on water demand caused by future climate condition and water consumption leads a difficulty to determine the reservoir operation rule for supplying sufficient water to users. It is, thus, important to operate reservoirs not only for distributing enough water to users using the limited water resources but also for preventing floods and drought under the unknown future condition. In this study, the reservoir storage is determined in the first stage when future condition is unknown, and then, water distribution to users and river stream is optimized using the available water resources from the first stage decision using 2-stage stochastic linear programming (2-SLP). The objective function is to minimize the difference between target and actual water storage in reservoirs and the water shortage in users and river stream. Hedging rule defined by a precaution against severe drought by restricting outflow when reservoir storage decreases below a target, is also applied in the reservoir operation rule for improving the model applicability to the real system. The developed model is applied in a system with five reservoirs in the Han River basin, Korea to optimize the multi-reservoir system under various future water demand scenarios. Three multi-purposed dams - Chungju, Hoengseong, and Soyanggang - are considered in the model. Gwangdong and Hwacheon dams are also considered in the system due to the large capacity of the reservoirs, but they are primarily for water supply and power generation, respectively. As a result, the water demand of users and river stream are satisfied in most cases. The reservoirs are operated successfully to store enough water during the wet season for preparing the coming drought and also for reducing downstream flood risk. The developed model can provide an effective guideline of multi-reservoir operation rules in the basin.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.4
• /
• pp.137-146
• /
• 2016

In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer's capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer's skill-level. This study intends to social contribution through attempts to optimize enterprise's goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource's capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz's MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.6
• /
• pp.2227-2235
• /
• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.