Search arXivSearch

arXiv · 2607.17248

Feasibility-Aware Energy Management of a Hydrogen-Enabled Community Microgrid: A Proof-of-Concept Study

Abstract

Hydrogen-enabled community microgrids require coordinated control of intermittent renewable generation and coupled battery and hydrogen storage. This paper presents a feasibility-aware proximal policy optimization (PPO) energy management system for a grid-connected microgrid comprising photovoltaic and wind generation, battery storage, an electrolyzer, a hydrogen tank, a fuel cell, and diesel backup. Raw continuous actions are projected onto the feasible operating set before evaluating the hourly power balance, ensuring operationally valid dispatch. The proof-of-concept study uses 8,760 hourly observations for a 1,000-household community in Rockhampton, Australia. The same annual chronology and one random seed were used for training and evaluation. Under a 1 percent independent hourly grid outage probability, the system achieved an annual net operating cash balance of AUD 195,690.67, load satisfaction of 99.77 percent, and a gross renewable share of 91.2 percent. After removing duplicated hydrogen electricity emissions, annual emissions were 1.342 kt CO2, equivalent to 0.328 kg CO2 per kWh of served demand and 0.087 kg CO2 per kWh of export-inclusive delivered energy. At a 5 percent outage probability, the cash balance decreased to AUD 169,892.21 and load satisfaction fell to 98.79 percent. Battery discharge and diesel generation increased more than fuel cell output. The results demonstrate feasible dispatch for the studied chronology, but broader validation requires unseen testing, multiple random seeds, benchmark controllers, export limits, and sustained outage scenarios.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mohamed Atef, Sanath Alahakoon, Umme Mumtahina, Peter Wolfs, Tamer Khatib, Moslem Uddin. 2026-07-31. Feasibility-Aware Energy Management of a Hydrogen-Enabled Community Microgrid: A Proof-of-Concept Study. https://arxiv.org/abs/2607.17248

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Failure-Aware Iterative Learning of State-Control Invariant Sets

In this paper, we address the problem of computing maximal state-control invariant sets for deterministic linear systems using failing trajectories. We introduce the concept of state-control invariance, which extends control invariance from the state space to the joint state-control space. The maximal state-control invariant (MSCI) set simultaneously encodes the maximal control invariant set (MCI) and, for each state in the MCI, the set of control inputs that preserve invariance. We prove that the state projection of the MSCI is the MCI and the state-dependent sections of the MSCI are the admissible invariance-preserving inputs. Building on this framework, we develop a Failure-Aware Iterative Learning (FAIL) algorithm for deterministic linear time-invariant systems with polytopic constraints. The algorithm iteratively updates a constraint set in the state-control space by learning predecessor halfspaces from one-step failing state-input pairs, without knowing the dynamics. For each failure, FAIL learns the violated halfspaces of the predecessor of the constraint set by a regression on failing trajectories. We prove that the learned constraint set converges monotonically to the MSCI. Numerical experiments on a double integrator system validate the proposed approach.

eess.SY

Consensus and Synchronization of Multi-agent Systems over Finite Fields - Graph Topologies

This paper presents cooperative protocols for multi-agent systems with agents having a finite state-space. Both scalar single-integrator consensus and general LTI system synchronization are considered. Systems having a finite state-space describe agents with minimal memory capacity processing only a finite alphabet. Such systems are remarkably resilient to communication noise. The crucial problem, however, is to construct the admissible communication topology, which is NP-hard. We address this by efficiently exploring the subsets of admissible graph matrices and propose two new algorithms to generate them. Simulations validate the proposed approach.

eess.SY

Extracting Exact Lie Derivatives Without Backpropagation: A Dual Compiler for Neural Control Barrier Functions

A safety filter based on a neural control barrier function (CBF) deployed in an embedded control loop evaluates, at each control cycle, the trained network and its Lie derivatives along the system vector fields, under the memory and worst-case execution time (WCET) constraints that safety-oriented coding standards impose. Reverse-mode automatic differentiation, by which training frameworks obtain these derivatives, retains an activation cache whose size grows with the sum of the layer widths, and general-purpose differentiation runtimes allocate the computational graph from the heap at each call. This paper presents a compiler that evaluates a neural CBF and its exact Lie derivatives by forward-mode dual-number arithmetic. The compiler emits self-contained C++ code in which a single forward pass, without backpropagation, returns the barrier value and its exact Lie derivative along a given vector field; the drift and input Lie derivatives of the safety constraint are obtained from one such pass per vector field, and a second-order extension based on hyper-dual numbers returns the exact second-order Lie derivatives required by CBFs of relative degree two. The dual forward pass requires a workspace bounded by four times the widest layer, independent of network depth, and the emitted code contains no allocation call sites, so the absence of dynamic allocation is verifiable by inspection of the code. On an ESP32-S3 microcontroller, the compiled filter assembles the complete safety constraint in under one millisecond from statically allocated buffers of at most 768 bytes, and the maximum execution time over 1000 evaluations lies within 5% of the median in all three examples, whereas a heap-allocating reverse-mode baseline shows maxima 33% and 70% above its median in the two first-order examples. The compiler and the embedded experiments are released as open-source software.

eess.SY