# Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping

arXiv: https://arxiv.org/abs/2311.04838 · HTML: https://mlsanity.com/paper/2311.04838/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods](https://mlsanity.com/paper/1909.10461/) (2019)
- [DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems](https://mlsanity.com/paper/2007.01002/) (2020)
- [End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch](https://mlsanity.com/paper/2304.11726/) (2023)
- [Learning to Solve Optimization Problems with Hard Linear Constraints](https://mlsanity.com/paper/2208.10611/) (2022)
- [Computationally Efficient Safe Reinforcement Learning for Power Systems](https://mlsanity.com/paper/2110.10333/) (2021)
- [DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility](https://mlsanity.com/paper/2009.03147/) (2020)
- [Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time](https://mlsanity.com/paper/2006.01336/) (2020)
- [Teaching Networks to Solve Optimization Problems](https://mlsanity.com/paper/2202.04104/) (2022)
- [Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization](https://mlsanity.com/paper/2310.15468/) (2023)

## Abstract (arXiv)

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.