  The evolution towards a more distributed and interconnected grid necessitates
large-scale decision-making within strict temporal constraints. Machine
learning (ML) paradigms have demonstrated significant potential in improving
the efficacy of optimization processes. However, the feasibility of solutions
derived from ML models continues to pose challenges. It's imperative that ML
models produce solutions that are attainable and realistic within the given
system constraints of power systems. To address the feasibility issue and
expedite the solution search process, we proposed LOOP-LC 2.0(Learning to
Optimize the Optimization Process with Linear Constraints version 2.0) as a
learning-based approach for solving the power dispatch problem. A notable
advantage of the LOOP-LC 2.0 framework is its ability to ensure near-optimality
and strict feasibility of solutions without depending on computationally
intensive post-processing procedures, thus eliminating the need for iterative
processes. At the heart of the LOOP-LC 2.0 model lies the newly proposed
generalized gauge map method, capable of mapping any infeasible solution to a
feasible point within the linearly-constrained domain. The proposed generalized
gauge map method improves the traditional gauge map by exhibiting reduced
sensitivity to input variances while increasing search speeds significantly.
Utilizing the IEEE-200 test case as a benchmark, we demonstrate the
effectiveness of the LOOP-LC 2.0 methodology, confirming its superior
performance in terms of training speed, computational time, optimality, and
solution feasibility compared to existing methodologies.
