This chapter addresses the power quality of grid-connected microgrids in steady state. Three different power quality issues are evaluated: the voltage drop, the harmonic distortion, and the phase unbalance. A formulation for an energy management algorithm for microgrids is proposed under the form of a mixed-integer linear optimization including harmonic load flows. It handles both the optimization of the scheduling of all the generation, storage, and load assets, and the resolution of power quality issues at the tertiary level of control by adjusting the levels of certain types of loads within the system. This algorithm is simulated for different scenarios on a conceptual test-case microgrid with residential, industrial, and commercial loads. The results show that the demand-side management mechanism inside the algorithm can adapt efficiently the consumption behavior of certain loads, so that the voltage drop, the voltage total harmonic distortion, and the voltage unbalance factor meet the required standards at every node of the microgrid during the day. It is also highlighted that the microgrid can gradually reduce the purchase of power from the utility grid to which it is connected if the electricity price on the spot market increases.
Part of the book: Micro-grids
In this Chapter, we consider a microgrid with a certain number of distributed energy resources (DER) components connected to an office building (in a university campus) provided with electricity by a utility company. We develop the initial version of the energy management system which is responsible for the optimal energy scheduling of the microgrid’s distributed energy resources. These resources include a photovoltaic (PV) installation, a Storage Energy System (ESS), a small Combined Heat and Power (CHP) unit, and a fleet of electric vehicles (EVs) used for work-related trips. The mobility behavior of the EVs fleet is modeled considering deterministic realizations of the probabilistic distributions used for the arrival/departure, and the time EVs remain parked. To investigate the impact of renewable generation and load unpredictability on the energy management system (EMS) operation, PV production and electric load are modeled under uncertainty using actual smart meters data for the scenarios formulation. We also assume that each DER component, through an EMS, can communicate and control the power exchange from and towards this component and that, two way communication with the utility company can be reached through aggregators using advanced metering equipment. We also consider a simplified thermal model that provides a specific level of thermal comfort to the building’s occupants, by meeting the predicted heating load. The energy produced by the DERs can be sold back to the grid by the microgrid manager and/or it can be stored for future utilization.
Part of the book: Smart Cities