Reducing greenhouse gas emissions from industrial operations is essential for achieving net-zero targets and advancing sustainable manufacturing. Peak electricity demand increases costs for both industrial sites and grid operators, and can trigger fossil-based generation to alleviate constraints in the grid. Managing peak loads, i.e., periods of maximum power demand, is therefore essential for both economic and environmental performance. This study presents a data-driven method to diagnose and mitigate peak loads in industrial manufacturing. Using one year of electricity consumption data from a heavy-duty vehicle production site in Sweden, we identify peak loads, analyze their temporal distribution, and quantify the contributions of individual consumers. The results show that peak demand is concentrated: the top 10% of consumers account for ~40% of demand during peak loads, with foundry ovens and compressor systems being the largest contributors. Consumers were classified using two indicators—mean power (indicating efficiency potential) and the ratio of peak to average load (indicating flexibility potential). This approach distinguishes consumers suited for efficiency measures (high mean power, low load spread) from those with greater flexibility potential (high load spread). The method provides a transparent screening tool for targeting reductions in peak load and is transferable to other industrial sites. These results support net-zero strategies by aligning operational measures with grid requirements and cost incentives.
Schmitt et al. (Thu,) studied this question.