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A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids

Neethu Maya, Bala Kameshwar Poolla, Seshadhri Srinivasan, Narasimman Sundararajan, Suresh Sundaram

TL;DR

This paper addresses real-time microgrid power management among heterogeneous prosumers by optimizing external exchanges. It proposes Dynamic Internal Predictive Power Scheduling (DIPPS), which formulates the internal scheduling as a MINLP-PS and then reduces it to a MILP-PS via McCormick relaxation, with a dynamic objective incorporating a time-varying binary parameter $b_S(t)$. A predictive horizon of length $N_p$ is used to anticipate load, PV, and tariffs, enabling decision-making under uncertainty. Empirical results using real-world load and PV data show a mean solution time per window of about $0.92$ s versus $38.27$ s for the MINLP-PS (a 97.6% improvement), and three case studies demonstrate the ability to shift external exchanges to predetermined periods while maintaining a fixed end-of-day SoC, underscoring DIPPS' viability for real-time, resource-aware microgrid coordination.

Abstract

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.

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids

TL;DR

This paper addresses real-time microgrid power management among heterogeneous prosumers by optimizing external exchanges. It proposes Dynamic Internal Predictive Power Scheduling (DIPPS), which formulates the internal scheduling as a MINLP-PS and then reduces it to a MILP-PS via McCormick relaxation, with a dynamic objective incorporating a time-varying binary parameter . A predictive horizon of length is used to anticipate load, PV, and tariffs, enabling decision-making under uncertainty. Empirical results using real-world load and PV data show a mean solution time per window of about s versus s for the MINLP-PS (a 97.6% improvement), and three case studies demonstrate the ability to shift external exchanges to predetermined periods while maintaining a fixed end-of-day SoC, underscoring DIPPS' viability for real-time, resource-aware microgrid coordination.

Abstract

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.
Paper Structure (6 sections, 16 equations, 4 figures, 1 table)

This paper contains 6 sections, 16 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Dynamic Internal Predictive Power Scheduling Problem Formulation
  3. Modeling preliminaries
  4. Problem Formulation
  5. Performance of Dynamic Internal Predictive Power Scheduling approach
  6. Conclusions

Figures (4)

  • Figure 1: Microgrid power flow schematic
  • Figure 2: The results for internal power scheduling across three cases.
  • Figure 3: Variation of the total cost of power exchange with the grid with an increase in predictive scheduling window.
  • Figure 4: Time taken to solve the problem in each window compared with and without using McCormick's relaxation