• Journal of Electrical Engineering and Technology
• /
• v.5 no.4
• /
• pp.511-521
• /
• 2010

This paper presents a new algorithm to determine accurate bus-wise transmission loss allocation utilizing path-integrals dictated by the transaction strategy. For any transaction strategy, the total sum of the allocated transmission losses of all buses is equal to the actual loss given by the AC power-flow calculation considering the distributed slack. In this paper, the bus-wise allocation of the transmission loss is calculated by integrating the differential loss along a path determined by the transaction strategy. The proposed algorithm is also compared with Galiana's method, which is the well-known transmission loss allocation algorithm based on integration. The performance of the proposed algorithm is evaluated by case studies carried out on the WSCC 9-bus, IEEE 14-bus, New England 39-bus, and IEEE 118-bus systems. The simulation results show that the proposed algorithm is fast and accurate with a large step size.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.6
• /
• pp.924-931
• /
• 2008

This paper presents a buswise transmission loss allocation algorithm utilizing the transaction strategy. We prove that whatever calculated by any transaction strategy, the total of the allocated transmission losses of each bus, including no-load loss allocation, almost equals the total loss of AC power flow algorithm and the loss is perfectly slackbus-independent. In this paper, the allocated transmission losses of each bus is calculated by the method of integrating loss sensitivities using by the load level parameter ${\lambda}$. The performance of the proposed algorithm is evaluated by the case studies carried out on the WSCC 9-bus and IEEE 14-bus systems.

• Proceedings of the KIEE Conference
• /
• 2001.11b
• /
• pp.424-428
• /
• 2001

The methodologies of power transmission pricing are normally divided into two categories such as marginal cost method and embedded cost allocation method. This paper, first, discusses the possible problems that can occur when the marginal cost method is applied to pricing the transmission services. Next, the paper proposes a method to apply the power flow tracing to the transmission network charge. The result of the power flow tracing method is then used in MW-mile method to charge individual loads for the use of transmission network. Effectiveness of the algorithm is verified by computer simulations.

• Journal of Korean Society for Quality Management
• /
• v.15 no.2
• /
• pp.20-26
• /
• 1987

An algorithm for the minimum total process percent defective under marginal costs is presented. Initial process is single series system, which is constructed by k process units, and each process is considered priority. Minimum of total process percent defective for parallel-series system exists when additional process setup costs are larger than marginal costs.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.2
• /
• pp.53-58
• /
• 2002

This paper describes methodologies to charge for power transmission network use. These methodologies are normally divided into two categories such as marginal cost method and embedded cost allocation method. This paper, first, discusses the possible problems that can occur when the marginal cost method is applied to pricing the transmission services. Next, the paper proposes a method to apply the electricity tracing method to the transmission network charge. The result of the electricity tracing method is then used in MW-mile method to charge individual loads for the use of transmission network. Effectiveness of the algorithm is verified by computer simulations and it is estimated that the results can be used to compute the cost of electric power transmission under deregulated environment in electric power industries.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.35 no.1
• /
• pp.97-109
• /
• 2010

In this paper, we study a joint design problem of sampling inspections and service capacities for multi-products. Products are supplied in batches after rectifying inspections, that is, rejected lots are 100% inspected and defective products are reworked to good ones. When supplied, all defective products are uncovered through total inspection and returned to service. By exploiting the first and second order properties of the objective function, we identify the optimal inspection policies and service capacities for individual products, and develop allocation algorithms to obtain an optimal allocation of the limited total service capacity to products with the small number of computations.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.28 no.3
• /
• pp.49-60
• /
• 2003

In this paper, we study the joint design of sampling inspections and service capacities for multi-products. Products of different defect rates which are either deterministic or random variables are supplied in batches after sampling inspection and rework. When supplied, all defective products that have not been inspected in batches are uncovered through total inspection and returned to service. We identify the optimal inspection policies and service capacities for multi-products reflecting the relationships between inspection rework costs and service provision costs. We also develope a marginal allocation algorithm for the optimal allocation of the limited total service capacity to products as well as inspection quantities.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.6
• /
• pp.114-120
• /
• 2004

The paper proposes a method calculate the allocations of real power losses in transmission lines to individual loads based on loss distribution factors and compares them with those using marginal loss factors. The proposed method is implemented by defining loss distribution factors and analysing the individual loads' shares in the transmission line losses. Computer simulations on a 9-bus sample system verify effectiveness of the algorithm proposed and give an assertion that it is desirable to allocate power losses to loads using loss distribution factors rather than based on marginal loss factors.

• The Journal of Information Technology
• /
• v.3 no.2
• /
• pp.73-85
• /
• 2000

The efficient algorithms are suggested in this study for solving the multicommodity network flow problems applied to Communications Systems. These problems are typical NP-complete optimization problems that require integer solution and in which the computational complexity increases numerically in appropriate with the problem size. Although the suggested algorithms are not absolutely optimal, they are developed for computationally efficient and produce near-optimal and primal integral solutions. We supplement the traditional Lagrangian method with a price-directive decomposition. It proceeded as follows. First, A primal heuristic from which good initial feasible solutions can be obtained is developed. Second, the dual is initialized using marginal values from the primal heuristic. Generally, the Lagrangian optimization is conducted from a naive dual solution which is set as ${\lambda}=0$. The dual optimization converged very slowly because these values have sort of gaps from the optimum. Better dual solutions improve the primal solution, and better primal bounds improve the step size used by the dual optimization. Third, a limitation that the Lagrangian decomposition approach has Is dealt with. Because this method is dual based, the solution need not converge to the optimal solution in the multicommodity network problem. So as to adjust relaxed solution to a feasible one, we made efficient re-allocation heuristic. In addition, the computational performances of various versions of the developed algorithms are compared and evaluated. First, commercial LP software, LINGO 4.0 extended version for LINDO system is utilized for the purpose of implementation that is robust and efficient. Tested problem sets are generated randomly Numerical results on randomly generated examples demonstrate that our algorithm is near-optimal (< 2% from the optimum) and has a quite computational efficiency.