Abstract
In the past decade, substantial investments have been made in researching and developing concepts and technologies to support the smart grid, renewable integration, and grid-interactive buildings. Adaptation of integrated solar photovoltaics with energy storage is increasing in residential buildings as consumers and utilities are becoming aware of their economic benefits and resilience benefits. Effective integration and control of these systems with other building loads is critical for providing load flexibility to improve building energy efficiency, reduce carbon footprint, and support grid resiliency. In recent years vendors are shifting towards device-level optimization and defining more sophisticated operational modes for controlling energy storage systems rather than charge and discharge power. As a result, optimization techniques must encompass the characteristics of these modes and their interactions with other system disruptions and attributes. This complexity gives rise to a nonlinear optimization problem that cannot be effectively addressed by an open-source solver and is impractical to implement in real-world scenarios. In this paper, we designed and evaluated a linear multi-objective model-predictive control optimization strategy for integrated photovoltaic and energy storage systems in residential buildings by using manufacturer-defined operational modes. The optimization goal is to minimize the power-purchasing cost from the grid and maximize the power selling cost to the grid. We developed a generalized method to keep the optimization linearized, even with operational modes consideration while coupling the modes with the overall system charging and discharging power. Our simulation results were aligned with real-world measurements and validated the linearized optimization formulation for each operational mode and for the economic use-case. The optimization results for the economic use-case demonstrated that the power associated to grid charge is mostly larger than the grid discharge power which means the optimization tried to maximize the power selling to the grid when the price is high and avoid power purchasing from the grid during high price.
