Optimizing routing and tower spotting of electricity transmission lines: An integration of geographical data and engineering aspects into decision-making

Santos, Afonso Henriques Moreira; Lima, Rodolfo Mendes de; Pereira, Camilo Raimundo Silva; Osis, Reinis; Medeiros, Giulia O. S.; Queiroz, Anderson Rodrigo de; Flauzino, Bárbara Karoline; Cardoso, Arthur Rohr Paschoal Corrêa; Czank, Luiz; Santos, Renato A. dos; Carvalho, Eden L.

doi:10.1016/j.epsr.2019.105953

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…Similar to [26,21], we model power infrastructure planning with a graph G = (V, E) with n := |V | vertices representing transmission towers, and m := |E| directed or undirected edges, corresponding to the cable between towers. Figure 2a visualizes the graph layout: Each vertex is one cell in the raster, i.e.…”

Section: Graph Modelmentioning

confidence: 99%

“…In AQSPP the objective is to minimize an interaction cost of adjacent edges along with the normal edge costs. Even though AQSPP was shown to be NP-hard when the graph is not acyclic [25,17], an auxiliary graph structure, namely the line graph or dual graph, has been exploited in applications to deal with di-edge costs, including power line [26,21] and other infrastructure planning [7,32].…”

Section: From Resistances To Edge Costsmentioning

confidence: 99%

“…An additional challenge in power infrastructure planning is the avoidance of angles, since angles increase construction costs and electrical resistance. Work on angle-constrained shortest path computation in a graph model similar to ours is given by Santos et al [26]. Building up on Piveteau et al [21], they construct a line graph in a pre-computed corridor to model angle resistance.…”

Section: Introductionmentioning

confidence: 99%

“…• Provably efficient algorithms to compute the minimal-angle shortest path. The baseline approach, a line graph [26,21], has size O( v∈V δ − v • δ + v ) for a graph with in-degree δ − v and out-degree δ + v at vertex v ∈ V . The Bellman Ford (BF) algorithm thus requires O(|V | • v∈V δ − v • δ + v ) operations.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Framework for Power Infrastructure Planning

Wiedemann¹,

Adjiashvili²

2021

Preprint

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The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In the view of investments of more than a million dollars per kilometre, planning authorities aim to minimise the resistances posed by multiple stakeholders. Mathematical optimisation research offers efficient algorithms to compute globally optimal routes based on geographic input data. We propose a framework that utilizes a graph model where vertices represent possible locations of transmission towers, and edges are placed according to the feasible distance between neighbouring towers. In order to cope with the specific challenges arising in linear infrastructure layout, we first introduce a variant of the Bellman-Ford algorithm that efficiently computes the minimal-angle shortest path. Secondly, an iterative procedure is proposed that yields a locally optimal path at considerably lower memory requirements and runtime. Third, we discuss and analyse methods to output k diverse path alternatives. Experiments on real data show that compared to previous work, our approach reduces the resistances by more than 10% in feasible time, while at the same time offering much more flexibility and functionality. Our methods are demonstrated in a simple and intuitive graphical user interface, and an open-source package (LION) is available at https://pypi.org/project/lion-sp.

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Section: Graph Modelmentioning

confidence: 99%

Section: From Resistances To Edge Costsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimization Framework for Power Infrastructure Planning

Wiedemann¹,

Adjiashvili²

2021

Preprint

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show abstract

“…Afonso Henriques Moreira Santos et al used graph theory and the Dijkstra's shortest path algorithm for vertex location, completed tower positioning based on dynamic programming to find the optimal vertex set along the route (2019) [8]. This solution was used to solve the expansion planning problem of the new Transmission Line (TL), and its goal was to find a design solution with minimum cost.…”

Section: Introductionmentioning

confidence: 99%

Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

2020

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Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.

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Distribution Route Planning of Fresh Food E-commerce Based on Ant Colony Algorithm

Wang¹,

Almajed²

2023

Lecture Notes on Data Engineering and Communications Technologies

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Optimizing routing and tower spotting of electricity transmission lines: An integration of geographical data and engineering aspects into decision-making

Cited by 19 publications

References 21 publications

An Optimization Framework for Power Infrastructure Planning

An Optimization Framework for Power Infrastructure Planning

Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

Distribution Route Planning of Fresh Food E-commerce Based on Ant Colony Algorithm

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