• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1689-1696
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• 2017

In this paper, the voltage measurement-based coordinated Voltage/VAR control (VMCVVC) algorithm for conservation voltage reduction(CVR) is proposed. The proposed algorithm has the purpose of enhancing the CVR effect through coordinated control of the voltage control devices such as the distributed energy resources and the load tap changer(LTC) transformers. It calculates the references of the voltage control devices such that the bus voltages are maintained at as close to the lower operation limit as possible. For this purpose, firstly, the distribution system is divided into LTC transformer control zones through topological search. Secondly, the reactive power references of the reactive power control devices are determined such that the voltage profile of the section is flattened. Finally, the tap references of the LTC transformers are calculated to lower the voltage profile. The effectiveness of the proposed algorithm is demonstrated through case studies using IEEE test network.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.346-354
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• 1992

In the power-system, the active and reactive power levels of load bus randomly vary over days, months, and years which are stochastic in nature. This paper presents an algorithm for optimal Var planning considering the uncertainties of loads. The optimization problem is solved by a stochastic linear programming technique which can handle stochastic constraints to evaluate optimal Var requirement at load bus to maintain the voltage profile which results in probabilistic density function by stochastic Load Flow analysis within admissible range. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.984-993
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• 1992

Fuzzy L.P. with Multi-criteria function is adopted in this VAR planning algorithm to accomplish the optimization of comflicting objectives, such as the amount of the VAR installed and power system loss, while keeping the bus voltage profile within an admissible range. Fuzzy L.P. with Multi-criteria function, a powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective functions, enables us to search for the solutions which may contribute in VAR planning. This advantage is not provided by traditional standardized L.P. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.211-213
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• 1992

Fuzzy L. P. with Multi-criteria function is adopted in this VAR allocation algorithm to accomplish the optimization of co-conflicting objectives, such as the amount of the VAR Installed and power system loss, while keeping the bus voltage profile within an admissible range. fuzzy L. P., a powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective functions, enables us to search for the solutions which may contribute in VAR planning. This advantage Is not provided by traditional standardized L. P. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.786-792
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• 2011

The present paper presents an optimal capacitor placement (OCP) algorithm for voltagestability enhancement. The OCP issue is represented using a mixed-integer problem and a highly nonlinear problem. The hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the OCP problem. The HPSO algorithm combines the optimal power flow (OPF) with the primal-dual interior-point method (PDIPM) and ordinary PSO. It takes advantage of the global search ability of PSO and the very fast simulation running time of the OPF algorithm with PDIPM. In addition, OPF gives intelligence to PSO through the information provided by the dual variable of the OPF. Numerical results illustrate that the HPSO algorithm can improve the accuracy and reduce the simulation running time. Test results evaluated with the three-bus, New England 39-bus, and Korea Electric Power Corporation systems show the applicability of the proposed algorithm.

• Journal of IKEEE
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• v.27 no.4
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• pp.657-667
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• 2023

This paper proposes two-stage optimization framework to re-estimate the photovoltaic (PV) hosting capacity (HC) by improving parameters for voltage controls of the smart inverter. In the first stage, PV HC is estimated considering Volt-Var (VV) and Volt-Watt (VW) controls, aligning with IEEE Std 1547-2018 guidelines. In the second stage, adjust parameters of VV and VW to improve HC. To investigate the performance of the proposed algorithm, simulations conducted using OpenDSS on an IEEE 37-bus system. The results demonstrate that effectively increases PV HC.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.467-475
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• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.233-235
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• 2000

SVC(Static Var Compensator) is commonly installed with conventional mechanically switched existing reactor or capacitor banks for wide range voltage control. The frequencies of switching of external banks have a great impact on the quality of voltage, but is limited since the life time of the external banks depends severely on the number of switching. So it is a complete multi-objective nonlinear optimization problem with conflicting objectives. This paper presents a method to determine the optimal coordination of SVC and external banks using genetic algorithm based on the multi-objective criteria. Optimal dead band and delay time of external banks is sought for reliable and efficient operation

• The Transactions of the Korean Institute of Electrical Engineers
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• v.44 no.1
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• pp.21-24
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• 1995

The larger and the more complicated the system size and configuration grow, the more serious the system loss problem becomes. Exessive system loss causes severs system voltage depression, which even may result in system voltage collapse. This paper proposes an effective tool for minimizing the system power loss by optimal re-location of the static condenser based on the system loss sensitivity index .lambda.$_{Q}$. It is possible to determine the optimal location and amount of VAR investment for minimizing the system loss by priority of .lambda.$_{Q}$ index given for each bus. Several computational techniques for avoiding divergency of the load flow solution are proposed. The loss sensitivity index .lambda.$_{Q}$ uses information of normal power flow equations and their Jacobians. Two case studies proved the effectiveness of the algorithm proposed.posed.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.