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In this paper, based on all spanning trees of undirected graph, a novel genetic algorithm for electric distribution network reconfiguration is proposed.
Today, the stability of the power distribution system is influenced by the significant number of photovoltaic power plants connected to the electric distribution network.
In the smart grid (SG) scenario, where control and management activities of the electric distribution network are expected to play a relevant and increasing role, distribution system state estimation (DSSE) is conceived as a fundamental monitoring tool.
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Giorgi also contributed to the development of hydroelectric installations, electric distribution networks, and urban trolley systems.
A recent paper, [1], has proposed a methodology for taking into account uncertainties in the load evolution within the design of electric distribution networks.
At the same time, the price and technology of MW-class Battery Energy Storage Systems (BESSs) have considerably improved, which opens up the possibility to make electric distribution networks dispatchable.
In the recent years, the regulators employ various forms of reward/penalty schemes (RPS) in electric distribution networks to ensure the reliability of services.
In this paper, we investigate the impact on the bulk power system of dispatchable electric distribution networks that host a large share of stochastic resources.
The result is a software tool with high abstraction and easy customization, aimed at a self-healing system for a reconfiguration of an electric power distribution network.
The optimal expansion planning of electric power distribution network to meet system load growth and overcome to pseudo dynamic behavior of network parameters considering a large number of constraints is a hard satisfactory multiobjective optimization problem.
One of such problems is the understanding of the effects that occur on electric energy distribution networks with distributed generation (DG), mainly with photovoltaic (PV) cells.
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