A Long-Term Network Expansion Planning Method for Practical Distribution Systems

Asakura, T.; Yura, T.; Hayashi, Naoki; Fukuyama, Yoshikazu

doi:10.1541/ieejpes1990.120.12_1574

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Residential PV sources are mainly connected to low‐voltage networks in a single phase, and the problems related to the massive introduction of those power sources were identified from early on. A considerable number of methods to maintain power quality or reduce power distribution losses have been developed and reported [1–11]. For example to counter voltage increase and fluctuation, voltage control methods using load tap change transformer, static reactive power compensator, and storage battery have been already developed [1–4]; to reduce power losses of distribution networks, high‐voltage distribution network configuration planning methods [5–7] and reconfiguration methods by switch operation have been so far reported [8–10].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Configuration of Low‐Voltage Distribution Networks for High Penetration of Photovoltaic Power Sources

Noriyuki

Zoka

Yorino

et al. 2021

IEEJ Transactions Elec Engng

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In recent years, massive introduction of photovoltaic power sources (PV) has compounded the problem of voltage fluctuation in distribution networks. The problem has been recognized from early on, and many research studies have been already reported. However, majority of the previous studies focused on high‐voltage distribution networks. Hence, in this study, the authors investigated appropriate configurations for low‐voltage distribution networks in Japan to address the problem as a planning problem. The on‐site practical measures were examined, and by means of the obtained knowledge, a network configuration problem was formulated for low‐voltage networks. In addition, an effective solution method is proposed, based on the priorities of measures applied to the network equipment. Numerical simulations were conducted for verifying the performance of the proposed method, wherein corroborative results were obtained. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Introductionmentioning

confidence: 99%

A Study on the Configuration of Low‐Voltage Distribution Networks for High Penetration of Photovoltaic Power Sources

Noriyuki

Zoka

Yorino

et al. 2021

IEEJ Transactions Elec Engng

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“…The equipment planning methods proposed so far for distribution facilities include selection of the equipment configuration so as to minimize power loss in distribution networks [1], to allow for the introduction of dispersed generators in the future [2], or to augment the equipment of existing distribution networks [3]. However, we do not know of any research dealing with the effects of fluctuation of the yearly load growth rate and other uncertain future factors on the operation of already configured distribution facilities.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment for optimum low‐voltage distribution facilities constructed by using genetic algorithms

Iioka

Narita

Yokomizu

et al. 2010

Electrical Engineering Japan

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SUMMARYThe influences of uncertainty of the load growth rate and interest rate on the total cost of low-voltage distribution facilities was investigated. We assumed that the total cost was composed of the initial construction cost, the improvement cost, and the cost of power loss. We also assumed that the distribution facilities were operated for 30 years. Genetic algorithms were used to determine how to construct cost-effective distribution facilities that did not compromise power quality. Two investment methods were compared: one in which the utility cut down the initial construction cost and improves the initial facilities along with the load growth, and one in which the utility bears the large initial cost without the improvement cost. It was found that the lower cost method depends on the load growth rate and the interest rate. It was also found that the influence of uncertainty in the load growth rate increases with an increase in the differential between the actual load growth rate and the expected one.

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“…Refs. [17][18][19][20] have employed a TS-based algorithm to obtain better results in the optimization problem. The power industry is interested in the use of TS in the practical applications due to the better performance than SA and GA. Ref.…”

Section: The Institute Of Electrical Engineers Of Japan (Ieej) Organimentioning

confidence: 99%

“…The power industry is interested in the use of TS in the practical applications due to the better performance than SA and GA. Ref. [17] evaluates long-term network expansion planning with Reactive Tabu Search(RTS) that considers parameter tuning of the tabu list. Ref.…”

Section: The Institute Of Electrical Engineers Of Japan (Ieej) Organimentioning

confidence: 99%

Applications of meta-heuristics to power systems in Japan

Mori

IEEE/PES Transmission and Distribution Conference and Exhibition

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This paper presents the applications of meta-heuristics to power systems in Japan. In recent years, metaheuristics is noteworthy for a new optimization methodology. Meta-heuristics is defined as an optimization technique that makes use of heuristics or rules to obtain a better solution [l] . Meta-heuristics takes advantage over the conventional mathematical programming in terms of the flexibility of the cost function and constraints, the efficient algorithm capable of handling nonlinear mixed integer programming problems, and the evaluation of globally optimal solutions or approximate ones, As the typical algorithm, SA[2,3], GA[4] and TS[5,6] are well-known in the engineering fields. SA is based on analogy of heat bath for annealing of metal or crystal. It introduces a parameter called temperature into the algorithm so that the search area is controlled by the cooling schedule at each iteration. The state freely moves to other states at high temperature while it has limitations to move to others at low temperature. In other words, the solution converges to a value as temperature cools down. It makes use of the stochastic optimization to escape from local minima. Next, GA makes use of the idea of natural selection in the evolution process of biology. The variable is regarded as genes that consist of many attributes. Creating new genes gives better solutions in optimization problems. In practice, genetic operations such as crossover and mutation, etc. are introduced to improve the quality of the solutions. GA is also one of the stochastic optimization methods in a sense that the random number is used in the genetic operations. SA and GA are effective for solving small-size problems, but they do not work so well for large-scale problems due to the stochastic algorithm. On the other hand, TS is one of deterministic optimization methods. TS is an extension of the hill-climbing method that evaluates solutions by repeating local search around a solution. The difference between the hill-climbing method and TS is that TS has adaptive memory called the tabu list that fixes some attributes not to get stuck in local minima. It aims at evaluating better solutions near a global minimum with the iterative process of simple rules or heuristics. The recent simulation results have shown that TS is better than SA and GA in terms of computational effort and solution accuracy[7-141. 0-7803-7525-4/02/$17.00 0 2002 IEEE.

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A Long-Term Network Expansion Planning Method for Practical Distribution Systems

Cited by 5 publications

References 4 publications

A Study on the Configuration of Low‐Voltage Distribution Networks for High Penetration of Photovoltaic Power Sources

A Study on the Configuration of Low‐Voltage Distribution Networks for High Penetration of Photovoltaic Power Sources

Risk assessment for optimum low‐voltage distribution facilities constructed by using genetic algorithms

Applications of meta-heuristics to power systems in Japan

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