Optimal distribution transformer sizing in a harmonic involved load environment via dynamic programming technique

Hajipour, Ehsan; Mohiti, Maryam; Farzin, Nima; Vakilian, Mehdi

doi:10.1016/j.energy.2016.12.113

Cited by 19 publications

(19 citation statements)

References 15 publications

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“…The features of the transformers are shown in Table Ⅴ [36]. ℎ , , and are calculated according to the value of based on a method which is introduced in [37]. The transformer purchase price is 166.1 $/kVA [17].…”

Section: Simulation Resultsmentioning

confidence: 99%

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Madahi

Nafisi

Abyaneh

et al. 2021

IEEE Trans. Transp. Electrific.

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The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers expected life. In this paper, a new day-ahead cooptimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is minimizing the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results. Index Terms-plug-in electric vehicle (PEV), transformer aging, energy losses, daily operating cost reduction. NOMENCLATURE A. Indices and Sets ℎ Harmonic order ℎ Maximum harmonic order Set of power system nodes Time slot B. Plug-in Electric Vehicle and Parking Lot Parameters PEV battery capacity [kWh] Capacity of the n-th parking lot

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Section: Simulation Resultsmentioning

confidence: 99%

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Madahi

Nafisi

Abyaneh

et al. 2021

IEEE Trans. Transp. Electrific.

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“…The distribution company (or planner in general) is always looking to reduce the costs of the project in a planning time horizon. To implement economic studies in the field of the OTS, the two leading solutions presented are the use of i) cash flow and ii) dynamic programming methods [10]. The former one is an older method that has been fully described in the IEEE C57.120:2017 standard [25]; the other one is a more up-to-date method used in this paper.…”

Section: 2mentioning

confidence: 99%

“…VoLL y i and P y i are the value of lost load in $/kWh and the contracted demand of i th customers connected to this DT (in kW) at year y, respectively. FIGURE 3 A typical output of the optimal transformer sizing problem [10] For more details about the OTS problem and its corresponding parameters, the interested readers are referred to [10].…”

Section: 2mentioning

confidence: 99%

Distribution transformer relocation problem: an integer programming solution

Hosseini

Hajiaghapour-Moghimi

Hajipour

et al. 2020

IET Generation Trans & Dist

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The short-term expansion planning of the private utilities, as well as the emerging technologies such as photovoltaic panels (PVs), plug-in hybrid electric vehicles (PHEVs), cryptocurrency mining, and storage elements spread, make the long-term load estimation of distribution transformers (DTs) noticeably imprecise. In response, the number of overload and underload transformers is growing in recent years. The utilities normally analyse the loading of their DTs annually to determine the DTs, which should be replaced. It is a common practice for utilities to relocate these DTs to reduce the investment needed to purchase new transformers. Therefore, the utility needs a systematic algorithm to determine the optimal schedule to relocate/replace the DTs with minimum required time and cost. This paper introduces a two-stage procedure to obtain the optimal schedule to replace DTs. In the proposed algorithm, the maximum permitted number of work-hours per day for field workers is considered, which converts the problem to a daily-task based optimisation problem. Also, an useful life investigation of the in-service DTs is considered in the relocation problem. A real case study in Qom province, Iran, has been studied to prove the effectiveness of the proposed algorithm.

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“…To solve the allocation problem, both classical methods and artificial intelligence techniques can be applied . Some classical methods applied to the transformer allocation problem are geographic information system (GIS)‐based method, Voronoi diagram, and dynamic programming (DP) . Most classical approaches are derivative‐based search methods that are sensitive to the initial guess and may be trapped in local optima.…”

Section: Introductionmentioning

confidence: 99%

“…For this goal, features related to the principles of transformer siting have been extracted and integrated into a neural network. In Hajipour et al, a very fast and easy to implement method has been introduced to solve transformer sizing problem based on DP. The proposed methodology considers transformer thermal equations and does not need any heuristic algorithm to solve problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal MV/LV transformer allocation in distribution network for power losses reduction and cost minimization: A new multi‐objective framework

Khandani

Askarzadeh

2020

Int Trans Electr Energ Syst

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Among distribution network planning (DNP) problems, medium voltage (MV) to low voltage (LV) transformer allocation is one of the most important and challenging ones. This article presents a multi‐objective optimization framework for solving MV/LV transformer allocation problem considering two goals: power losses and investment cost. Transformer allocation problem has been solved in three scenarios: (1) base case, (2) mid‐term planning, and (3) long‐term planning. In order to effectively and efficiently solve this nonlinear problem, multi‐objective crow search algorithm (MOCSA) and multi‐objective particle swarm optimization (MOPSO) have been applied and compared in terms of Pareto front. Simulation results show that solving transformer allocation problem in multi‐objective framework results in a set non‐dominated solutions which provide a trade‐off between losses and cost. Moreover, MOCSA produces more promising results than MOPSO.

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Optimal distribution transformer sizing in a harmonic involved load environment via dynamic programming technique

Cited by 19 publications

References 15 publications

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Distribution transformer relocation problem: an integer programming solution

Optimal MV/LV transformer allocation in distribution network for power losses reduction and cost minimization: A new multi‐objective framework

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