Visualization of distribution system planning for engineers and decision-makers

Abstract

Abstract:Planning of an electric distribution network is a complex task, requiring a comprehensive approach that includes collecting and analyzing large amounts of technical and nontechnical data. In addition, the outcome of the planning procedure needs to be supported by economic analysis that provides pathways for future investments. In order to present the planning results to various groups of people, such as technical staff, managers, and politicians, different visualization techniques are used. Data visualization, as a branch of descriptive statistics, is important to understand and present information in an effective manner. The basic idea of visualization is to present data in a visual form, allowing a person to get a quick insight into the data, draw conclusions, and directly interact with the data. Some traditional methods, such as graphs, histograms, 2D column and line charts, single line diagrams, and 3D surfaces with contours, are mandatory for visualizing power data. However, in the electric power system business there is a growing need for geographically referenced visualization. While there is a lot of research being done on visualizing data in transmission power systems, the number of studies on display methods in distribution power systems is limited. The focus of this paper is the visualization of data used during the distribution network planning process.

A new method for optimal expansion planning in electrical energy distribution networks with distributed generation resources considering uncertainties

“…Due to the network vastness, installation of the measuring equipment at all load points is impossible. Also, estimation methods are usually used to evaluate loads in the network, which depend on several unpredictable factors such as behavior of the customers [22]. Since load uncertainty can improve network model, the probability theory is a conventional method to estimate uncertainty.…”

“…In the fuzzy model, for set of ( f T min , f Emin ) the optimal solution is (1, 1) and the worst solution for set of (f T max , f Emax ) is (0, 0). µ k is the normalized value of nonlinear components of the objective function, shown in Equation (22).…”