Optimal Energy Management of Microgrids using Sampling-Based Model Predictive Control Considering PV Generation Forecast and Real-time Pricing

This paper presents a novel control solution capable of handling the intermittent nature of solar power generation by optimally managing an energy storage system and an HVAC responsive load under a real-time price scheme. The proposed solution controls the charging and discharging operations of an energy storage system (ESS) and the duty cycle of an HVAC system with the objective of minimizing the estimated incurred energy costs. The model makes use of forecasted load and PV generation, along with real-time price information, and generates a graph of control actions that can be traversed to find the optimal path that the system needs to take in order to minimize incurred costs. Simulations are presented to compare the proposed method with two baseline power control schemes based on predefined charge and discharge operations and a hysteresis control of the HVAC. Simulations show that the proposed control solution achieves substantial cost savings when compared with the baselines.