In energy-oriented lot-sizing and scheduling research, it is often assumed that minimizing energy costs automatically leads to an improvement of the ecological footprint of a company, i.e., lower carbon dioxide emissions. More precisely, a close to one (positive) correlation between energy costs and carbon dioxide emissions is often supposed. In this contribution, we show that this conjecture does not always hold true due to fluctuating carbon dioxide emissions over the whole day. Therefore, we present a real-world business case study, combining lot-sizing and machine scheduling under time-varying electric energy costs and carbon dioxide emissions in a mixed integer optimization model; in this context, we also consider on-site power generation. The interplay between all these aspects is demonstrated via a numerical analysis.
Due to climate change and the increasing scarcity of resources, the sustainability performance of companies is increasingly becoming the focus of science and practice. Consequently, bicriteria energy-efficient production planning under price-dynamic electricity tariffs—e.g., real-time-pricing (RTP) or time-of-use (TOU)—is meanwhile well established, often fathoming the tradeoffs between electricity costs of production and another criterion such as makespan. However, tradeoffs between electricity costs and electricity consumption in general are rarely the focus of such analyses. So-called green power purchase agreements (PPAs), which are becoming increasingly popular in the European business community as a means of improving corporate sustainability performance, are also largely ignored. Thus, for the first time in the scientific literature, we put this type of electricity tariff to the test by analyzing the tradeoffs between electricity costs and electricity consumption in a lot-sizing and scheduling context. Here, we additionally consider a real-world redox flow battery storage system that may be the system of the future, which is also new to the literature on lot-sizing and scheduling. Even more: due to the complex nature of our bicriteria mixed-integer problem, we develop and present suitable heuristics. These include an energy-efficient allocation heuristic in the case of PPA and, among others, a fix-relax-and-optimize heuristic combined with a decomposition approach in the case of RTP and TOU. Ultimately, a scenario analysis demonstrates the performance of these heuristics.
Due to the war in Ukraine, the European Commission has released its “Save Gas for a Safe Winter” plan, communicating the goal of reducing gas consumption in the electricity sector, among others. In this paper, the gas consumption in the electricity sector is picked up and the well-established concept of demand response is brought into alignment with the consumption of gas in the electricity sector, leading to the concept of gas-to-power demand response. Two proposed programs based on this concept are then applied in a production planning approach that shows how companies could proactively contribute to easing the tense situation in Europe, particularly in Germany, especially using methods such as scheduling and/or lot-sizing. This article is intended to serve as a basis for further discussions in the political and economic sectors.
A correction to this paper has been published: https://doi.org/10.1007/s11573-021-01051-y
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