Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives

Zhang, Qi; Grossmann, Ignacio E.

doi:10.1016/j.cherd.2016.10.006

Cited by 124 publications

(72 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the analysis of the scientific literature, it is thus evident that no studies focus on the integration of electric MMHE activity scheduling and the optimal management of their charging stations. In this context, the application of intelligent DSM to industrial electric equipment is rapidly gaining momentum in the scientific research and industry [11]. Similarly to the residential DSM, where energy users schedule their domestic activities in accordance with demand response mechanisms [42], load scheduling has recently attracted significant attention in the industrial sector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the energy management of warehouses plays a fundamental role in identifying strategies for companies to pursue higher performance in terms of productivity, competitiveness, and sustainability at the same time. The active management of electricity demand, commonly known as demand-side management (DSM), has been recognized as an effective approach to provide significant environmental and economic benefits for large electricity consumers such as power-intensive industries and manufacturing companies [11]. Currently, renovation of the electricity market is leading these consumers to pay stratified electricity rates that depend on the time of the day (e.g., peak load, midload, and off-peak load).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model

et al. 2020

View full text Add to dashboard Cite

In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The newly emerging field of enterprise-wide optimization is a further step that expands the scope of PSE past supply chain management to aid in making decisions in firm R&D efforts, corporate finance and management, demand modeling, etc. [228,229].…”

Section: Modeling and Optimal Design Of Supply Chains For Distributedmentioning

confidence: 99%

Modeling and Simulation of Energy Systems: A Review

2018

View full text Add to dashboard Cite

Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

show abstract

“…Over the past decade, the economic benefits of DSM measures have been evaluated for a variety of processes using quasi‐stationary process models (cf. the recent review of Zhang and Grossmann). Although the assumption of quasi‐stationarity is valid for many processes, for example, desalination or electrolysis, many chemical processes are characterized by non‐negligible response times or time delays.…”

Section: Introductionmentioning

confidence: 99%

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

et al. 2019

View full text Add to dashboard Cite

The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

show abstract

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives

Cited by 124 publications

References 85 publications

Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model

Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model

Modeling and Simulation of Energy Systems: A Review

Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

Contact Info

Product

Resources

About