Energy-aware scheduling has become a central challenge in modern manufacturing environments. As industries increasingly aim to reduce operational costs and carbon emissions, aligning production activities with electricity tariffs is increasingly important. The integration of battery energy storage system (BESS) further enhances the potential to reduce energy purchase costs; however, incorporating such systems introduces complex interdependencies between job scheduling and BESS management decisions, significantly complicating the problem structure.\\In this work, we study the problem of minimizing the energy cost of executing a set of jobs on a single machine within a fixed time horizon, where electricity prices follow a Time-of-Use (TOU) tariff. In addition, we consider the presence of a BESS that can be charged from the grid and discharged during high-price periods to reduce overall energy costs. To efficiently address this problem, we propose and analyze two matheuristic algorithm variants designed to effectively coordinate production scheduling and BESS usage decisions.
Escalating energy costs and peak power demand charges pose significant challenges to the manufacturing sector. In response, industries are increasingly adopting on-site renewable energy sources and Battery Energy Storage Systems (BESSs). However, maximizing their economic benefit requires sophisticated control strategi...
The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stag...
Edwin M. Garcia, C. Cuji, A. Aguila Téllez et al.· Sustainability· 0 citations
To address the challenges posed by the increasing penetration of renewable energy and electric vehicles (EVs)—such as output fluctuations, time-varying electricity prices, and battery degradation—this study proposes a multi-timescale optimal scheduling method for microgrids that incorporates vehicle–grid interaction-ba...
Shang-Da Xie, Shao-Yuan Li, Gen-Ke Yang· Journal of Renewable and Sus...· 0 citations
RAPID is proposed, a region-aware and power-informed scheduling framework that integrates static and online heuristic schedulers for large-scale AI request scheduling that significantly reduces carbon emissions, electricity costs, and total energy consumption.
B. Ding, Cai-Ning Wang, Ka-Fei Tang et al.· Sustainability· 0 citations
Data centers already draw a fast-rising slice of global electricity, and regulators and export markets are watching their carbon footprint more closely every year. This paper presents a genetic-algorithm (GA) framework for green task scheduling that jointly minimizes energy consumption, carbon emissions, and service-le...
Y.-F. Lu, Y.-D. Bao, C. Liu et al.· Advanced Electromagnetics· 0 citations
Simulation results indicate that the proposed coordinated optimization strategy for data center microgrids can effectively shift peak loads, significantly reduce the curtailment rate of photovoltaic power, and demonstrate good economic performance.
Yucheng Zheng, G. Rashed, Xin-Fa Jiang et al.· International Conference on...· 0 citations
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