A chance constraint microgrid energy management with phase balancing using electric vehicle demand aggregation

Microgrids are sustainable distribution systems which integrate plug-in electric vehicles (EVs) and renewable energy sources to minimize grid dependency. The uncertainty in the forecast data of renewables and erratic consumption of energy by consumers aggravates voltage stability in a three-phase microgrid. Voltage instability and ineluctable uncertainty in the system inevitably make energy management of resources infeasible. To address the challenges of voltage stability and uncertainty in microgrid energy management, a novel two-stage energy management framework using stochastic chance constraint model predictive control (MPC) is proposed to optimize operational costs with reduced reserve costs. In the primary stage, an optimal day-ahead resource dispatching strategy is modeled using robust chance-constrained to handle the uncertainty of the forecasted data. Wasserstein metric ambiguity set is developed against uncertainty risk operations for low conservativeness and tractable constraints. The secondary stage control, with a shorter timescale, regulates the deviations in the primary stage state parameters using the MPC technique. Also, a fuzzy rule-based unbalance voltage control for EV parking lots (EVPL) is proposed in the secondary stage by modifying each phase’s power using phase switches. The proposed energy management framework adopts a mixed-stage optimization structure, where the concerned problem is progressively optimized over diverse time scales. The results of the proposed strategy show that lowering the confidence interval and increasing sampled data reduces operating costs by 7.57% and 5.34%, while penalty costs are reduced by 51.9% and 74.9%, respectively. It is also observed that this strategy minimizes the voltage unbalance on a modified IEEE 123 bus system and validates with benchmark approaches.