Radial distribution network planning under uncertainty by applying different reliability cost models

Nahman, J.; Peric, D.

doi:10.1016/j.ijepes.2019.105655

Cited by 28 publications

(9 citation statements)

References 19 publications

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“…In this paper, we use the routing scheme based on the steepest gradient descent proposed in [51,52] because of the faster convergence and increased tractability compared to the above-listed methods. The method accounts for the grid operational constraints on voltage magnitudes and lines ampacities.…”

Section: Routing Of Medium Voltage Network 241 Routing Algorithmmentioning

confidence: 99%

Countrywide PV hosting capacity and energy storage requirements for distribution networks: The case of Switzerland

2021

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Distributed photovoltaic (PV) generation is typically connected to power distribution grids, which are not designed to host a large amount of production if it is significantly larger than their nominal electricity demand. Given the prominent role of PV in energy transition pathways, modeling the existing power distribution infrastructure's constraints and limitations is key for its reliable techno-economical analysis and expansion. As countrywide models of the distribution grids are, in general, not available, this paper first tackles the problem of estimating medium voltage (MV) distribution grids starting from publicly available datasets. It then proposes a method to estimate the PV generation hosting capacity of such grids and extend it through energy storage systems. As a final contribution and ultimate objective, this paper proposes a method to derive cost-optimal plans for countrywide deployment of PV generation and energy storage systems considering the MV power distribution infrastructure's technical limitations. The distributed PV generation potential is modeled with high-spatially resolved capacity factors. Results are discussed using Switzerland as a case study.

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Section: Routing Of Medium Voltage Network 241 Routing Algorithmmentioning

confidence: 99%

Countrywide PV hosting capacity and energy storage requirements for distribution networks: The case of Switzerland

2021

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“…In [8], a metaheuristic approach is proposed based on grey wolf optimizer (GWO) and particle swarm optimization (PSO) for the optimal DNP in the MV side, considering DGs and a battery storage system (BSS). In [9], a new approach to the DNEP problem in the MV network under uncertainty in the presence of wind energy is proposed to improve the reliability index. In [10], an MILP model is proposed for the DNEP problem, which chooses a set of candidate feeders with a minimum cost with specified reliability.…”

Section: Motivation and Aimmentioning

confidence: 99%

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Rastgou

Hosseini-Hemati

2022

International Transactions on Electrical Energy Systems

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Distribution network expansion planning (DNEP) is one of the important matters in the field of planning and operation of electrical power systems. Since many costs and losses have occurred in the distribution networks, it has increased attention towards this network. The electrical energy distribution network is divided into two parts: the medium voltage (MV) network and the low voltage (LV) network. The main problem in this field is that planning is done either only on the MV network or only on the LV network. While planning in each of these networks has a significant effect on the other networks, this important case has not been considered in most research studies. Therefore, this study has tried to do integrated planning in the form of a bi-level model in the presence of different types of distributed generations (DGs) and consider the uncertainties of renewable sources and load demand in both MV and LV networks so that the planning and operation costs are minimized. In the proposed bi-level model, the upper-level section aims to minimize the investment and the operation cost of the MV network, and the lower-level problem minimizes the investment and the operation cost of the LV network considering the DGs and pollution emission. The obtained results show the effectiveness of the proposed model.

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“…In the current subject, the goal is to minimize the cost function defined in Eq. (1). The problem constraints are the load capacity constraints and the bus voltage constraints defined in relations 6 and 7, respectively.…”

Section: Fitness Function and Generation Of Possible Solutionmentioning

confidence: 99%

“…Generally, the problem of optimal routing of distribution feeders, independently or in combination with other issues of the distribution network planning problem, such as determining the location and optimal size of substations, has been considered in a variety of studies, some of which are referred to hereafter. The combination of the steepest descent approach and the simulated annealing technique is used for optimal planning of radial distribution networks, taking the uncertainties of the inputs and the various models of determining the interruption costs into account [1]. In this paper, only the connectivity of network configuration is checked using a network connectivity matrix.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feeder Routing and DG Placement Using Kruskal’s Algorithm

Kamyab¹

2020

EJEE

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In this paper, the optimal feeder routing along with optimal distributed generator placement is formulated as an optimization problem. In this problem, the total cost of capital recovery, supply interruption and energy losses are minimized. Also, line loading capacity and bus voltage constraints are applied. By proposing a novel method to code the solutions of the optimization problem with the facilitation obtained from the utilization of the Kruskal's algorithm, it is guaranteed that graphs of all solutions would always be spanning trees. The main result of the implementation of this method within meta-heuristic algorithms is to limit the search space to radial networks leading to snap quicker answers with higher degree of optimality. A distribution network with a 24 load points and 42 candidate branches is used as a baseline to indicate the effectiveness of the proposed method which was tested using three meta-heuristic algorithms including genetic algorithm, particle swarm optimization and simulated annealing.

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Radial distribution network planning under uncertainty by applying different reliability cost models

Cited by 28 publications

References 19 publications

Countrywide PV hosting capacity and energy storage requirements for distribution networks: The case of Switzerland

Countrywide PV hosting capacity and energy storage requirements for distribution networks: The case of Switzerland

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Optimal Feeder Routing and DG Placement Using Kruskal’s Algorithm

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