Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures Based on Active Constraints Identification and Integer Variables Prediction

TL;DR

This paper introduces a real-time optimization approach for wind-to-hydrogen infrastructure, utilizing active constraints identification and integer prediction to efficiently manage large-scale mixed-integer convex problems under wind power uncertainty.

Contribution

It presents a novel fast solution method for large-scale mixed-integer convex optimization problems in wind-to-hydrogen systems, leveraging historical data for real-time decision making.

Findings

The proposed method significantly reduces computation time.

It effectively handles wind power uncertainty in system operation.

Validation on case studies demonstrates practical applicability.

Abstract

This paper proposes a concept of wind-to-hydrogen-driven critical infrastructure (W2H-CI) as an engineering solution for decarbonizing the power generation sector where it utilizes wind power to produce hydrogen through electrolysis and combines it with the carbon captured from fossil fuel power plants. First, a convex mathematical model of W2H-CI is developed. Then, an optimization model for optimal operation of W2H-CI, which is a large-scale mixed-integer convex program (MICP), is proposed. Moreover, we propose to solve this problem in real-time in order to hedge against the uncertainty of wind power. For this purpose, a novel solution method based on active constraints identification and integer variable prediction is introduced. This method can solve MICP problems very fast since it uses historical optimization data to predict the values of binary variables and a limited number of constraints which most likely contain all active constraints. We validate the effectiveness of the proposed fast solution method using two W2H-CI case studies.