Distribution networks have undergone a series of changes, with the insertion of distributed energy resources, such as distributed generation, energy storage systems, and demand response, allowing the consumers to produce energy and have an active role in distribution systems. Thus, it is possible to form microgrids. From the active grid’s point of view, it is necessary to plan the operation considering the distributed resources and the microgrids connected to it, aiming to ensure the maintenance of grid economy and operational safety. So, this paper presents the proposition of a hierarchical model for planning the daily operation of active distribution grids with microgrids. In this case, the entire grid operation is optimized considering the results from the microgrid optimization itself. If none of the technical constraints, for example voltage levels, are reached, the grid is optimized, however, if there are some violations in the constraints feedback is sent to the internal microgrid optimization to be run again. Several scenarios are evaluated to verify the iteration among the controls in a coordinated way allowing the optimization of the operation of microgrids, as well as of the distribution network. A coordinated and hierarchical operation of active distribution networks with microgrids, specifically when they have distributed energy resources allocated and operated in an optimized way, results in a reduction in operating costs, losses, and greater flexibility and security of the whole system.

The electrical system is becoming more robust with the insertion of distributed energy resources (DERs) and the need for energy autonomy by consumers, given that the current scenario is a growth in demand for electric energy. This paper aims to apply a computational model capable of determining the optimal hourly allocation of controllable loads in residence, as well as studying the optimal dispatch of residential microgrids considering management on the demand side. In addition, this paper presents an economic feasibility analysis of residential microgrids considering distributed generation from wind and solar sources, distributed storage, electric vehicles, and residential controllable loads. Thus, it was possible to conclude that in residence, the insertion of distributed energy generation and storage elements can present a significant reduction in electric energy costs, which can be even greater if these elements are associated with optimized controllable load management. Keyword: distributed generation; residential microgrids; management on the demand side; optimal dispatch; electric vehicles. HIGHLIGHTS• Smart allocation of loads in residence generates savings on the energy bill.• The optimized management of distributed energy resources can reduce the energy bill.• Wind generation can be inviable in some specific areas.• Optimal management of loads impacts economic viability by reducing a residential microgrid payback time.

The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power HIGHLIGHTS• Novel data-driven approach to uncertainties in microgrid resources• Faster convergence that stochastic optimization• Reduced conservativeness compared to robust optimization• Microgrid system with comprehensive selection of distributed energy resources

No abstract

No abstract