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Real-Time Operational Optimization of Microgrid via Piecewise Linear Basis Function-Based Galerkin Approximation
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DOI：10.1109/TSG.2026.3688063.png)
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Abstract
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This letter proposes a novel approximate dynamic programming method for real-time operational optimization of microgrids. The general idea is to directly approximate the value function via Galerkin theory through an analytical approach, thus eliminating the need for iterative training and enhancing the computation efficiency. More specifically, the piecewise linear basis function is designed for Galerkin approximation considering the influence of limit constraints. Therefore, it effectively fits the piecewise characteristic of actual value function for a high approximation accuracy, and thus the superior solution optimality. Simulation tests and comparisons with the state-of-the-art methods demonstrate the advantages of the proposed method.
Keywords:
Approximate dynamic programming
real-time operational optimization
Galerkin theory
piecewise linear basis function
limit constraints
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