A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids
For microgrid operators, this work provides a computationally efficient method for optimizing power exchanges and energy storage use, though it is an incremental improvement over existing MILP-based scheduling.
The paper proposes a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for microgrid power management, achieving a 97.6% reduction in computation time (from 38.27s to 0.92s) compared to a MINLP formulation, enabling real-time implementation.
This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusingon external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programmig (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILP-PS) optimization through McCormick's relaxation to reduce the computational complexity. A predictive window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrationg the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources whie shifting the eexternal power transfers to specified time durations.