The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constrained methods are equipped to model these complexities and uncertainties

solving these problems using traditional iterative solvers is often time-consuming

limiting their suitability for real-time applications. To overcome the shortcomings of today's solvers

we propose a fast

scalable

and explainable machine learning-based optimization proxy. Our solution

called Learning to Optimize the Optimization of Joint Chance-Constrained Problems <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathcal{LOOP}-\mathcal{JCCP})$

is iteration-free and solves the underlying problem in a single-shot. Our model uses a polyhedral reformulation of the original problem to manage constraint violations and ensure solution feasibility across various scenarios through customizable probability settings. To this end

we build on our recent deterministic solution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathcal{LOOP}-\mathcal{LC}\ 2.0)$ by incorporating a set aggregator module to handle uncertain sample sets of varying sizes and complexities. Our results verify the feasibility of our near-optimal solutions for joint chance-constrained power dispatch scenarios. Additionally

our feasibility guarantees increase the transparency and interpretability of our method

which is essential for operators to trust the outcomes. We showcase the effectiveness of our model in solving the stochastic energy management problem of Virtual Power Plants (VPPs). Our theoretical analysis

supported by empirical evidence

reveals strong flexibility in parameter tuning

adaptability to diverse datasets

and significantly improved computational speed.