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Commercial buildings consume a substantial amount of energy, underscoring the need for consumption optimization. Embedded microgrids combined with demand side management strategies have potential to help end-users and utilities to better manage both the supply and demand side of the grid. This paper presents an integrated optimal control strategy for a grid-tied solar PV-battery microgrid powering a public building under demand response program. Current literature predominantly focuses on residential buildings and, most importantly, only addresses a limited range of energy management objectives. In this study, a mixed-integer non-linear programming (MINLP) mathematical model that minimizes grid energy costs, grid peak demand, battery degradation cost, and end-user inconvenience associated with rescheduling of appliances out of their baseline schedules is presented. To test and validate the model, two case study scenarios are considered: Case I applies the proposed model to a grid-tied, solar-PV-battery powered public building without appliance scheduling, while Case II integrates appliance scheduling as a demand response program into the resulting hybrid energy system. Simulation results from Case I show the model’s potential to reduce the building’s energy costs by 49%, with an additional 4.4% reduction achieved in Case II. Compared to the baseline model, the Case II model achieves a 37.5% reduction in system peak demand (coincident peak) and a 21.8% decrease in the building’s non-coincident peak demand. The proposed microgrid retrofit model is economically viable, with a payback period of 9-10 years. The obtained results can be used as a decision-making reference for planning embedded microgrids in public buildings with demand response programs. • Formulated an optimal control model to maximize microgrid benefits in demand response. • Simulated the model using a real-world case study with actual load profiles and climate data. • Developed a techno-economic model to assess the feasibility of retrofitting the microgrid.
Wamalwa et al. (Sun,) studied this question.