A new distribution network reconfiguration and restoration path selection algorithm

Abstract

The distribution network restoration is one of the most important parts in the total power system restoration process. The distribution network restoration decomposes into the identification of a suitable network configuration, which is defined by the status of switches between the radially arranged power lines and the optimization of the restoration paths, which are schedules for toggling switches and booting network nodes. This paper presents a two-stage approach for the restoration process of radial high voltage distribution network (e.g. HOkV). A Pareto-based multi-objective genetic algorithm (NSGA-II) is used to optimize the network configuration regarding the load that can be picked up, load priorities, and switching activity. Then, a multi-objective fuzzy decision method (FDM) selects the restoration paths. FDMs choices rely on performance indexes defined by human experts and harmonized as well as linearized by the analytic hierarchy process (AHP). In this work, the node importance degree, the load priority, the influence on already restored network, and the length of distribution lines are considered by FDM. The feasibility and efficiency of the proposed method are validated on the IEEE 30 network

Generator Start-up Sequences Optimization for Network Restoration Using Genetic Algorithm and Simulated Annealing

“…This, and other electrical properties such as over-voltage problems caused by self-excitation and energizing unload transmission lines, frequency control during the load restoration, and cold load pick up inrush have to be either validated before starting or checked during the optimization procedure. In our previous work we have already presented handling these requirements in an optimization algorithm [10,9]. Therefore, we will skip this, as the focus of the current work is to find good performing encodings, operators and optimization algorithms for generator start-up sequence optimization.…”

“…This is the subsequent step after restoring power plants and has been investigated in [2] by reformalizing the process as an combinatorial problem and making it input to a quantum-inspired evolutionary algorithm. In [9] the restoration path selection has been solved by using a multi-objective evolutionary algorithm giving the system operator different solutions that are maximizing the load shedding and minimizing the switching operations. The similarity of restoration path and generator start-up sequence optimization comes not only from the fact that the two tasks are closely interleaved during the restoration process.…”