• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1064-1072
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• 2017

The power of controlled generators in microgrids randomly fluctuate because of the stochastic volatility of the outputs of photovoltaic systems and wind turbines as well as the load demands. To address and dispatch these stochastic factors for daily operations, a dynamic economic dispatch model with the goal of minimizing the generation cost is established via chance-constrained programming. A Monte Carlo simulation combined with particle swarm optimization algorithm is employed to optimize the model. The simulation results show that both the objective function and constraint condition have been tightened and that the operation costs have increased. A higher stability of the system corresponds to the higher operation costs of controlled generators. These operation costs also increase along with the confidence levels for the objective function and constraints.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.80-89
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• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.881-885
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• 2005

Scheduling repetitive projects under limitations on the amounts of available resources (labor and equipment) has been an active subject because of its practical relevance. Traditionally, the limitation is specified as a deterministic (fixed) number, such as 1000 labor-hours. The limitation, however, is often exposed to uncertainty and variability, especially when the project is lengthy. This paper presents a stochastic optimization model to treat the situations where the limitations of resources are expressed as probability functions in lieu of deterministic numbers. The proposed model transfers each deterministic resource constraint into a corresponding stochastic one and then solves the problem by the use of a chance-constrained programming technique. The solution is validated by comparison with simulation results to show that it can satisfy the resource constraints with a probability beyond the desired confidence level.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.663-665
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• 1998

This paper investigates a facility location problem where there are possible demand points with demand occuring probabilites and actual distances between these points and the facility site to be determined are ambiguous, Further we define the fuzzy goal with respect to the maximum value among the actual distances between demand points and the facility. We determine the site of facility maximizing the minimal satisficing degree under the chance constraint. We propose the geometric algorithm to find this optimal site.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.191-193
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• 1989

This paper presents a optimal Var allocation algorithm for minimizing power loss and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A stochastic Linear Programming technique based on chance constrained method is applied to solve the probabilistic constraint. The test result in IEEE-14 Bus model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before Var allocation.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.863-865
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• 1988

This paper presents a optimal Var allocation algorithm for minimizing transmission line losses and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A Stocastic Linear programming technique based on chance constrained method is applied, to solve the var allocation problem with probabilistic constraint. The test result in 6-Bus Model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before var allocation.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.83-93
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• 2020

Bivariate measure of risk and error(BMORE) plot is originally designed to depict bivariate output data and related statistics obtained from a stochastic simulation such as sample mean, median, outliers, and a boundary of a certain percentile of simulation data. When compared to the static numbers, the plot has a big advantage in visualization that enables scholars and practitioners to understand the potential variability and risk in the simulation data. In this study, beyond just the construction of the plot to depict the variability of a certain system, we add a chance constraint to the plot and apply it for decision making such as checking the feasibility of systems, comparing performances of the systems on statistical background, and also analyzing the sensitivity of the problem parameters. In order to demonstrate an application of the plot, we employ an inventory management problem as an example. However, the techniques and algorithms suggested in this paper can be applied to any other problems comparing systems on bivariate performance measures with simulation/experiment results.

• 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.