arXiv subjects

Zhenyu Pu

Publications and source records attributed to Zhenyu Pu.

3 recordsLinked to original sources

arXiv2026

End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers

Buildings and data centers (DCs) are energy-intensive sectors, playing a critical role to achieve the low-carbon and sustainable energy transition targets. To this end, integrated energy system (IES) that incorporates diverse renewables, energy generation, conversion, and storage technologies to enable coordinated multi-energy supply have been widely investigated for both buildings and DCs. However, few works consider the two sectors jointly within IES to exploit their substantial synergistic benefits. Meanwhile, the operational optimization of IES remains challenging due to the difficulty to predict the multi-energy demand and supply accurately. To address these gaps, this paper investigates IES for coordinated multi-energy supply of buildings and DC, where the waste heat from DCs is recovered and reused to enhance energy efficiency. Moreover, an end-to-end learning-based method is proposed for the operational optimization of IES under uncertainty. Unlike conventional predict-then-optimize approaches, the proposed method integrates the training of prediction models for uncertain variables with the constrained optimization of IES into a unified learning framework, guiding the training of prediction models to improve operational performance, rather than prediction accuracy, thereby mitigating the impacts of predictions errors. Case studies based on real-world datasets show that the proposed methods improves the operational performance of IES by about 7-9% compared to existing predict-then-optimize methods. In addition, coordinating buildings and DCs within IES shows substantial economic benefits. In particular, the waste heat recovery from DCs leads to approximately 10% of total energy cost reduction of the IES.

eess.SY↗

arXiv2026

Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP

Mixed-integer programming (MIP) extends linear programming by incorporating both continuous and integer decision variables, making it widely used in production planning, logistics scheduling, and resource allocation. However, MIP remains NP-hard and cannot generally be solved to optimality in polynomial time. Branch-and-bound, a fundamental exact method, faces significant parallelization challenges due to computational heterogeneity and strict determinism requirements in commercial applications. This paper presents the first fully open-source implementation of deterministic parallel branch-and-bound for HiGHS, a high-performance MIP solver. Our approach introduces a novel data-parallel architecture ensuring strict determinism by replicating complete solver state across worker threads and eliminating non-deterministic synchronization primitives. A key innovation is our AI-driven load balancing mechanism employing multi-stage workload prediction models that estimate node computational complexity based on structural characteristics and historical performance data, coupled with dynamic parameter adjustment strategies. The framework executes orchestrated parallel phases including concurrent dive operations, systematic data consolidation, and intelligent node selection. Comprehensive experimental evaluation on 80 MIPLIB 2017 benchmark instances demonstrates effectiveness, achieving a geometric mean speedup of 2.17 using eight threads while maintaining complete deterministic guarantees. Performance gains become increasingly pronounced for higher node counts, with speedup factors reaching 5.12 for computationally intensive instances and thread idle rates averaging 34.7%.

cs.DC↗

arXiv2026

Representation Learning Enhanced Deep Reinforcement Learning for Optimal Operation of Hydrogen-based Multi-Energy Systems

Hydrogen-based multi-energy systems (HMES) have emerged as a promising low-carbon and energy-efficient solution, as it can enable the coordinated operation of electricity, heating and cooling supply and demand to enhance operational flexibility, improve overall energy efficiency, and increase the share of renewable integration. However, the optimal operation of HMES remains challenging due to the nonlinear and multi-physics coupled dynamics of hydrogen energy storage systems (HESS) (consisting of electrolyters, fuel cells and hydrogen tanks) as well as the presence of multiple uncertainties from supply and demand. To address these challenges, this paper develops a comprehensive operational model for HMES that fully captures the nonlinear dynamics and multi-physics process of HESS. Moreover, we propose an enhanced deep reinforcement learning (DRL) framework by integrating the emerging representation learning techniques, enabling substantially accelerated and improved policy optimization for spatially and temporally coupled complex networked systems, which is not provided by conventional DRL. Experimental studies based on real-world datasets show that the comprehensive model is crucial to ensure the safe and reliable of HESS. In addition, the proposed SR-DRL approaches demonstrate superior convergence rate and performance over conventional DRL counterparts in terms of reducing the operation cost of HMES and handling the system operating constraints. Finally, we provide some insights into the role of representation learning in DRL, speculating that it can reorganize the original state space into a well-structured and cluster-aware geometric representation, thereby smoothing and facilitating the learning process of DRL.

cs.LG↗