• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2236-2244
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• 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)
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• 제14권4호
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• pp.1579-1602
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• 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.

• 한국방재학회 논문집
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• 제10권1호
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• pp.89-102
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• 2010

미래의 기후조건과 생활패턴의 불확실성으로 인해 미래용수수요 또한 불확실성을 가지며, 이는 충분한 용수공급을 목적으로 하는 댐 운영에 어려움을 초래한다. 따라서 가용 수자원을 최대한 활용하여 충분한 용수분배를 하는 동시에, 홍수와 가뭄에 대한 대비까지 가능한 댐의 운영은 매우 중요하다. 본 연구에서는 미래의 불확실한 용수수요량을 정확히 알지 못하는 상태에서 저수지의 운영을 통한 저류량을 1단계에서 결정하고, 2단계에서 용수수요에 따른 용수공급량과 하천유지유량을 결정하기 위한 최적화 모형을 2단계 추계학적 선형계획법을 이용하여 구축하고, 목표저류량과 실제 저류량의 차이, 용수공급과 하천유지유량의 부족량을 최소화하기 위한 저수지 운영 규칙을 최적화하였다. 또한 가뭄시 보다 현실적이고 효율적인 저수지 운영을 위해 댐저류량에 따라 댐 계획방류량을 일정비율 줄여주는 Hedging Rule을 사용하여 모형의 적절성과 적용성을 향상시켰다. 제안된 모형은 한강수계의 댐들 중 다목적댐인 충주, 횡성, 소양강 댐과 용수전용댐인 광동 댐, 그리고 발전용 댐이지만 비교적 큰 저류용량을 가진 화천 댐을 연계 운영 대상으로 하여, 미래 용수수요량 시나리오를 고려한 최적화를 실시하였다. 그 결과 모든 시나리오에서 생공용수, 농업용수, 하천유지용수 공급량을 대부분 만족시킬 수 있었고, 댐의 저류량 역시 갈수기 용수공급에 대비하여 홍수기인 6월 말에서 9월 중순에 저류량을 확보하면서도 홍수피해저감까지 고려하는 운영이 가능하였다. 이는 다목적 댐들의 연계운영을 위한 저수지 운영규칙결정에 매우 중요한 지표가 될 수 있을 것으로 판단된다.

• 산업경영시스템학회지
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• 제39권4호
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• pp.137-146
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• 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
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• 제13권6호
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• pp.2227-2235
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• 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.