• Title, Summary, Keyword: Chance constrained programming

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Reliability Evaluation of a Microgrid Considering Its Operating Condition

  • Xu, Xufeng;Mitra, Joydeep;Wang, Tingting;Mu, Longhua
    • Journal of Electrical Engineering and Technology
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    • v.11 no.1
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    • pp.47-54
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    • 2016
  • Microgrids offer several reliability benefits, such as the improvement of load-point reliability and the opportunity for reliability-differentiated services. The primary goal of this work is to investigate the impacts of operating condition on the reliability index for microgrid system. It relies on a component failure rate model which quantifies the relationship between component failure rate and state variables. Some parameters involved are characterized by subjective uncertainty. Thus, fuzzy numbers are introduced to represent such parameters, and an optimization model based on Fuzzy Chance Constrained Programming (FCCP) is established for reliability index calculation. In addition, we present a hybrid algorithm which combines scenario enumeration and fuzzy simulation as a solution tool. The simulations in a microgrid test system show that reliability indices without considering operating condition can often prove to be optimistic. We also investigate two groups of situations, which include the different penetration levels of microsource and different confidence levels. The results support the necessity of considering operating condition for achieving accurate reliability evaluation.

Chance-constrained Scheduling of Variable Generation and Energy Storage in a Multi-Timescale Framework

  • Tan, Wen-Shan;Abdullah, Md Pauzi;Shaaban, Mohamed
    • Journal of Electrical Engineering and Technology
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    • v.12 no.5
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    • pp.1709-1718
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    • 2017
  • This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

Stochastic Programming Model for River Water Quality Management (추계학적 계획모형을 이용한 하천수질관리)

  • Cho, Jae Heon
    • Journal of The Korean Society of Civil Engineers
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    • v.14 no.1
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    • pp.231-243
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    • 1994
  • A stochastic programming model for river water quality management was developed. River water quality, river flow, quality and flowrate of the wastewater treatment plant inflow were treated as random variables in the model. Withdrawal for water supply and submerged weir reaeration were included in the model itself. A probabilistic model was formulated to compute the expectation and variance of water quality using Streeter-Phelps equation. Chance constraints of the optimization problem were converted to deterministic equivalents by chance constrained method. Objective function was total annual treatment cost of all wastewater treatment plants in the region. Construction cost function and O & M cost function were derived in the form of nonlinear equations that are functions of treatment efficiency and capacity of treatment plant. The optimization problem was solved by nonlinear programming. This model was applied to the lower Han River. The results show that the reliability to meet the DO standards of the year 1996 is about 50% when the treatment level of four wastewater treatment plants in Seoul is secondary treatment, and BOD load from the tributary inflows is the same as present time. And when BOD load from Tanchon, Jungrangchon, and Anyangchon is decreased to 50%, the reliability to meet the DO standards of the year 1996 is above 60%. This results indicated that for the sake of the water quality conservation of the lower Han River, water quality of the tributaries must be improved, and at least secondary level of treatment is required in the wastewater treatment plants.

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AN FORMULATION OF THE ENERGY MODEL FOR THE KOREAN ENERGY INDUSTRY

  • Kim, Jong Duck
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.12 no.20
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    • pp.55-61
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    • 1989
  • The main contribution this research is the development of methodology which is capable of solving problems associated with the capacity expansion and operating schedule of energy industries. The principal concern of such industries is the proper allocation of primary energy which are required for the production of sufficient supply of electricity and petroleum products for the Korea`s energy needs. Nonlinear programming models are developed for power generation expansion planning and for the oil refinery industry. In order to deal with uncertainties about future demands for final energy, chance-constrained programming is used to formulate appropriate constraints. The methodology of the model can be used to evaluate Korean energy and expansion planning in the energy industry, especially the electric power generation industry and the refinery industry.

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A Study on an Efficient Double-fleet Operation of the Korean High Speed Rail (한국 고속철도의 효율적 중련편성 운영방법에 대한 연구)

  • Oh, Seog-Moon;Sohn, Moo-Sung;Choi, In-Chan
    • Journal of the Korean Society for Railway
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    • v.10 no.6
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    • pp.742-750
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    • 2007
  • This paper presents a mathematical model for a double-fleet operation in Korean high speed rail (HSR). KORAIL has a plan to launch new HSR units in 2010, which are composed of 10 railcars. The double-fleet operation assigns a single-unit or two-unit fleet to a segment, accommodating demand fluctuation. The proposed model assumes stochastic demand and uses chance-constrained constraints to assure a preset service level. It can be used in the tactical planning stage of the rail management as it includes several real-world conditions, such as the capacities of the infra-structures and operational procedures. In the solution approach, the expected revenue in the objective function is linearized by using expected marginal revenue, and the chance-constrained constraints are linearized by assuming that demands are normally distributed. Subsequently, the model can be solved by a mixed-integer linear programming solver fur small size problems. The test results of the model applied to Friday morning train schedules for one month sample data from KTX operation in 2004 shows that the proposed model could be utilized to determine the effectiveness of double-fleet operation, which could significantly increase the expected profit and seat utilization rates when properly maneuvered.