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Foam-based internal electrical insulation of hollow-core composite insulators (HCIs) is regarded as a superior solution to gases for not requiring continuous monitoring, with polyurethanes (PUs) being the main solution to date. Potential restrictions facing PUs as well as the need for higher dielectric strengths have prompted research activities into alternative foams. This work follows an extensive experimental and simulative approach to investigate the dielectric strength of a novel polymeric foam, known as dry syntactic foam (DSF). Seven different densities in two thicknesses are manufactured and tested under ac, dc, and lightning impulse (LI) voltages. Two finite-element (FE) models are created based on microscope images to reproduce electrostatic- and conduction-dominated voltage distributions and complement the experiments. Under ac and LI, the dielectric strength is found to rise with foam density, whereas no distinctive effect is observed under dc stress. The simulations indicate that under ac and LI, the highest electrical fields are reached uniformly inside gas pores and, that under dc, certainly arranged pores observe significant higher stresses. The ac and LI results are discussed according to Townsend discharges, whereas a mechanism based on a transient process is suggested for dc. [ABSTRACT FROM AUTHOR]

This article presents models and algorithms to simultaneously solve both the long-term grid planning and the distributed energy resource scheduling optimization problem for medium voltage grids. An emphasis is on evaluation of electric vehicle scheduling and demand side management. The main benefit of this new simultaneous optimization approach is that it converges towards a global optimum by considering not only infrastructure investments but also energy cost changes due to scheduling measures as for example curtailment or demand side management. The article firstly analyzes degrees of freedom in grid planning and in scheduling to derive models. The sections thereafter present the algorithm for simultaneous optimization. It integrates a fast scheduling optimization into a meta-heuristic grid-planning algorithm. The grid-planning algorithm uses Delaunay triangulation and an ant colony systems approach. The scheduling optimization combines dynamic programming and a fast heuristic to consider grid constraints. The last section presents exemplary results and illustrates effects of different regulatory regimes on costs. Results suggest that market-based scheduling will prevail due to energy cost-savings. However, purely market-based scheduling is unattractive when grid costs are considered. Distribution system operators can reduce long-term grid costs significantly, if they may perform demand side management. [ABSTRACT FROM AUTHOR]

As part of transmission network expansion planning (TNEP), a technical and economical assessment of several planning alternatives must be performed in order to ensure fulfillment of the network security criteria and to estimate the alternatives’ expected operating costs. This task requires performing load flow calculations for different operating points (OPs) of the power system. Due to the high computational burden, considering all possible OPs is simply not possible. As a consequence, only a set of representative OPs is usually taken into account. Most works in the TNEP focus on issues related to optimization algorithms and modeling, neglecting the selection process of the representative OPs. Furthermore, most works only consider a few OPs, providing little or no insight about the criteria used in the selection process or about the error made when evaluating planning alternatives using a limited number of OPs. In this work, a novel methodology for selecting representative OPs to consider within the TNEP is presented. The proposal pays special attention to critical situations, where the network security may be endangered. Furthermore, the methodology allows quantifying the error made when evaluating network operation using a limited number of representative OPs. [ABSTRACT FROM AUTHOR]