The interconnection of distributed generators (DGs) in a power system increases the difficulty of managing the system. The minimization of the voltage deviation by network reconfiguration is an important requirement for dealing with the issue. We had previously developed a reconfiguration technique, named the intelligent flow algorithm (IFA), for determining the optimum or suboptimum network configuration within a short computation time. In the present paper, we propose an extension of IFA, named the extended flow algorithm (EFA), for more effective determination of the optimal network configuration of a distribution system containing massive installations of DGs. EFA is a two‐stage method in which the configuration that produces uniform power supply, referred to as the balanced configuration, is first generated, and then used to seek the optimal configuration using an improved branch exchange approach. Accordingly, EFA is more simplified to improve its computation speed on large‐scale systems. The algorithm was tested by case studies of different test distribution systems in the MATLAB environment, and was confirmed to have high performance to cope with DG installations and large‐scale systems. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

In a distribution line, power system control and power equipment investment are planned based on a measured power system current. However, recently the mass introduction of photovoltaic (PV) make it difficult for us to precisely measure the demand curve that is a current consumed by electrical equipment because the reversal power flow from PV systems is superposed. Therefore, the prediction of demand curves of distribution line is indispensable for power system management. In addition, it is also necessary to estimate the reliability of the predicted values as well as predicted current itself. In this paper, we propose the estimation method of the prediction interval that is the index of reliability based on the past demand curve database. The feature of the proposed method based on Just-In-Time (JIT) modeling make it possible for us to accurately estimate the prediction interval by the normalized database of demand curve. In this paper, some numerical examples are presented, which demonstrate the effectiveness of the proposed method. C⃝ 2017 Wiley Periodicals, Inc. Electr Eng Jpn, 202(2): 12-23, 2018; Published online in Wiley Online Library (wileyonlinelibrary.com).

No abstract

SUMMARY In a distribution line, power system control and power equipment investment are planned based on a measured power system current. However, recently the mass introduction of photovoltaic (PV) make it difficult for us to precisely measure the demand curve that is a current consumed by electrical equipment because the reversal power flow from PV systems is superposed. Therefore, the prediction of demand curves of distribution line is indispensable for power system management. In addition, it is also necessary to estimate the reliability of the predicted values as well as predicted current itself. In this paper, we propose the estimation method of the prediction interval that is the index of reliability based on the past demand curve database. The feature of the proposed method based on Just‐In‐Time (JIT) modeling make it possible for us to accurately estimate the prediction interval by the normalized database of demand curve. In this paper, some numerical examples are presented, which demonstrate the effectiveness of the proposed method.

No abstract