Optimization Approach for Planning Soft Open Points in a MV-Distribution System to Maximize the Hosting Capacity

Abstract

Distributed energy resources (DERs) based on renewable power, such as photovoltaic (PV), have been increasing worldwide. To support this growth, some technologies have been developed to increase the hosting capacity (HC) of distribution networks (DNs), such as the Soft Open Point (SOP), which can replace normally open switches in DNs with the advantage of allowing power and voltage control. The benefits of SOPs in terms of increasing distributed generation (DG) hosting capacity can be enhanced by network reconfiguration (NR). In this work, an optimization-based approach is proposed for placing SOP in DN with simultaneous NR; that is, the proposed algorithm consists of a promising alternative to previous works in the literature that deal with SOP placement and NR in an iteratively way or in a two-step procedure, considering that better results can be obtained by simultaneously handling both options, as shown in the introduced case studies. The optimization problem is modeled as nonlinear mixed-integer programming, and solved by a Multi-objective Artificial Immune System (MOAIS). The proposed algorithm is applied to a well-known medium-voltage (MV) test system that is widely used for the problem at hand, and the results show the effectiveness of the proposed approach to maximize the HC by optimizing the SOP installation site in the tested system. An important outcome is that the association of SOP planning and NR in a simultaneous manner tends to provide better quality solutions, where HC can overcome 400% for multiple SOPs. Another outcome is that the proposed MOAIS is able to provide good concurrent solutions to support the decision-making of the DN planner.

Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective

“…Multi-objective optimisation problems have been implemented in distribution networks for planning purposes, for example, to increase the line's capacity [26], for solving facility location problems [27] and also for redispatch strategies of renewable energy under extreme conditions [28]. Different types of multi-objective approaches have also been applied for solving the demand response strategy [29] and for integrating grid-connected networks [30], in which they apply master-slave methodologies.…”