Method for an Energy-Cost-Oriented Manufacturing Control to Reduce Energy Costs: Energy Cost Reduction by Using a New Sequencing Method

Willeke, Stefan; Ullmann, Georg; Nyhuis, Peter

doi:10.1109/icimsa.2016.7504018

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Moreover, a predictive-reactive, short-term load-management for peripheral energy consumers like heating or ventilation is applied. Reference [36] presents another reactive approach to energy-oriented production control in order to exploit volatile prices. However, no market constraints, e.g., the required due time, are considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

et al. 2020

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The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

et al. 2020

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“…For companies to be competitive in the global era, one strategy that can be carried out is to reduce operating or production expenses. Production costs can be reduced by production improvement (Hoseinpour et al ., 2020), proper risk assessment (Taghipour et al ., 2015), improvement in material procurement (Arnold et al ., 2009) and reducing energy costs (Willeke et al ., 2016). The paper focuses on the strategy to reduce energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Maintenance strategies and energy efficiency: a review

Firdaus

Ab-Samat

Prasetyo³

2023

JQME

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PurposeThis paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.Design/methodology/approachA literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.FindingsEach maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.Practical implicationsThe literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.Originality/valueThe novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.

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“…In recent years, electricity prices have continuously increased for manufacturing companies in industrialized countries ( Willeke, Ullmann & Nyhuis, 2016 ). In Norway, the industrial electricity price, including taxes, increased by 47% between 2017 and 2018 ( BEIS, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel machine scheduling

Rego

Pinto

Cota

et al. 2022

PeerJ Computer Science

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In many countries, there is an energy pricing policy that varies according to the time-of-use. In this context, it is financially advantageous for the industries to plan their production considering this policy. This article introduces a new bi-objective unrelated parallel machine scheduling problem with sequence-dependent setup times, in which the objectives are to minimize the makespan and the total energy cost. We propose a mixed-integer linear programming formulation based on the weighted sum method to obtain the Pareto front. We also developed an NSGA-II method to address large instances of the problem since the formulation cannot solve it in an acceptable computational time for decision-making. The results showed that the proposed NSGA-II is able to find a good approximation for the Pareto front when compared with the weighted sum method in small instances. Besides, in large instances, NSGA-II outperforms, with 95% confidence level, the MOGA and NSGA-I multi-objective techniques concerning the hypervolume and hierarchical cluster counting metrics. Thus, the proposed algorithm finds non-dominated solutions with good convergence, diversity, uniformity, and amplitude.

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Method for an Energy-Cost-Oriented Manufacturing Control to Reduce Energy Costs: Energy Cost Reduction by Using a New Sequencing Method

Cited by 9 publications

References 12 publications

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

Maintenance strategies and energy efficiency: a review

A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel machine scheduling

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