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Tag Archives: Operations Management
Journal Paper Accepted at Journal of Enterprise Transformation – Axiomatic Design Based Human Resources Management for the Enterprise Transformation of the Abu Dhabi Healthcare Labor Pool
- Sharing all input datasets used to conduct the research for which no prior proprietary or security commitments have been made.
- Producing scientific publications in such a way that scientific peers can accurately verify & validate the work.
- Making the content of all conference, journal and book-chapter publications freely available in author preprint form. (Note: Most publishers allow self-archiving and open-distribution of author preprints).
Journal Paper Accepted at Applied Energy Journal – Demand Side Management in a Day-Ahead Wholesale Market: A Comparison of Industrial & Social Welfare Approaches
The LIINES is pleased to announce the acceptance of the paper entitled: “Demand Side Management in a Day-Ahead Wholesale Market: A Comparison of Industrial & Social Welfare Approaches” to Applied Energy Journal for publication. The paper is authored by Bo Jiang, Prof. Amro M. Farid, and Prof. Kamal Youcef-Toumi.
The intermittent and unpredictable nature of renewable energy brings operational challenges to electrical grid reliability. The fast fluctuations in renewable energy generation require high ramping capability which must be met by dispatchable energy resources. In contrast, Demand Side management (DSM) with its ability to allow customers to adjust electricity consumption in response to market signals has been recognized as an efficient way to shape load profiles and mitigate the variable effects of renewable energy as well as to reduce system costs. However, the academic and industrial literature have taken divergent approaches to DSM implementation. While the popular approach among academia adopts a social welfare maximization formulation, defined as the net benefit from electricity consumption measured from zero, the industrial practice introduces an estimated baseline. This baseline represents the counterfactual electricity consumption that would have occurred without DSM, and customers are compensated according to their load reduction from this predefined electricity consumption baseline.
In response to the academic and industrial literature gap, our paper rigorously compares these two different approaches in a day-ahead wholesale market context. We developed models for the two methods using the same mathematical formalism and compared them analytically as well as in a test case using RTS-1996 reliability testing system. The comparison of the two models showed that a proper reconciliation of the two models might make them dispatch in fundamentally the same way, but only under very specific conditions that are rarely met in practice. While the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. While very much discouraged, customers have an implicit incentive to surreptitiously inflate the administrative baseline in order to receive greater financial compensation. An artificially inflated baseline is shown to result in a higher resource dispatch and higher system costs.
The high resource scheduling due to inflated baseline likely require more control activity in subsequent layers of enterprise control including security constrained economic dispatch and regulation service layer. Future work will continue to explore the technical and economic effects of erroneous industrial baseline.
About the Author:
Bo Jiang conducted this research in collaboration with her Master’s thesis advisor Prof. Amro M. Farid and Prof. Kamal Youcef-Toumi at Massachusetts Institute of Technology. Her research interests include renewable energy integration, power system operations and optimization. Bo is now pursuing her PhD at MIT Mechanical Engineering Department.
A full reference list of Smart Power Grids and Intelligent Energy Systems research at LIINES can be found on the LIINES publication page: http://engineering.dartmouth.edu/liines
Journal Paper Accepted at Springer’s Intelligent Industrial Systems Journal: Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems
The LIINES is pleased to announce the acceptance of the paper: “Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems” in Springer’s Intelligent Industrial Systems Journal. The paper is authored by Amro M. Farid and was published online at May 28th 2015.
Recently, the vision of academia and industry has converged, defining future power system as intelligent, responsive, dynamic, adaptive, and flexible. This vision emphasizes the importance of resilience as a “smart grid” property. It’s implementation remains as a cyber-physical grand challenge.
Power grid resilience allows healthy regions to continue normal operation while disrupted or perturbed regions bring themselves back to normal operation. Previous literature has sought to achieve resilience with microgrids capable of islanded operation enabled by distributed renewable energy resources. These two factors require a holistic approach to managing a power system’s complex dynamics. In our recent work (e.g. link 1 and link 2), we have proposed as means of integrating a power system’s multiple layers of control into a single hierarchical control structure.
In addition to enterprise control, it is important to recognize that resilience requires controllers to be available even if parts of the power grid are disrupted. Therefore, distributed control systems, and more specifically Multi-Agent Systems have often been proposed as the key technology for implementing resilient control systems. Multi-agent systems are commonly used to distribute a specific decision-making algorithm such as those in market negotiation and stability control. However, very few have sought to apply multi-agent systems to achieve a resilient power system.
The purpose of the paper entitled “Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems” is two fold. First, it seeks to identify a set of Multi-Agent System design principles for resilient coordination and control. Second, the paper assesses the adherence of existing Multi-Agent System implementations in the literature with respect to those design principles.
The set of design principles is based on newly developed resilience measures for Large Flexible Engineering Systems. These measures use Axiomatic Design and are directly applicable to the power grid’s many types of functions and its changing structure. These design principles, when followed, guide the conception of a multi-agent system architecture to achieve greater resilience.
About the author: Wester C.H. Schoonenberg completed his B.Sc. in Systems Engineering and Policy Analysis Management at Delft University of Technology in 2014. After his bachelors’ degree, Wester started his graduate work for the LIINES at Masdar Institute, which he continues as a doctoral student at Thayer School of Engineering at Dartmouth College in 2015. Currently, Wester is working on the integrated operation of electrical grids and production systems with a special interest in Zero Carbon Emission Manufacturing Systems.
LIINES Website: http://amfarid.scripts.mit.edu
Journal Paper Accepted at the Journal of Intelligent Manufacturing: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems
The LIINES is pleased to announce that the Journal of Intelligent Manufacturing has accepted our paper entitled: “Measures of reconfigurability and its key characteristics in intelligent manufacturing systems”. The paper is authored by Amro M. Farid and was published in October 2014.
Many manufacturing challenges arise with the global trend of increased competition in the marketplace. Production processes must deal with shorter product lifecycles and mass-customization. Consequently, production systems need to be quickly and incrementally adjusted to meet the ever-changing products. Reconfigurable manufacturing systems have been proposed as a solution that facilitates changing production processes for highly automated production facilities.
Much research has been done in the field of reconfigurable manufacturing systems. Topics include: modular machine tools and material handlers, distributed automation, artificially intelligent paradigms, and holonic manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, there has been comparatively little attention devoted to quantitative design methodologies of these reconfigurable manufacturing systems and their ultimate industrial adoption remains limited.
Measuring reconfigurability of manufacturing systems quantitatively has been a major challenge in the past, since a quantitative reconfigurability measurement process was non-existent. Earlier work developed a measurement method that extracts measurables from the production shop floor. When this was established, basic measures of reconfiguration potential and reconfiguration ease were developed, based on axiomatic design for large flexible engineering systems and the design structure matrix respectively.
Reconfiguration of a production process can be split up in four steps: Decide which configuration, Decouple, Reorganize, and Recouple. The larger the number of elements in the system, the more configurations are made possible. This is measured using the reconfiguration potential measure, based on axiomatic design for large flexible engineering systems.
Production processes contain multiple interfaces within themselves. Multiple layers of control can be distinguished, that have to work together to coordinate the physical components. These interfaces are the main determinants for the reconfiguration ease measure.
This paper combines these techniques to define a quantitative measure for reconfigurability and its key characteristics of integrability, convertibility and customization. The intention behind this research contribution is that it may be integrated in the future into quantitative design methodologies for reconfigurable manufacturing systems, which may be easily adopted by industrial automation and production companies.
About the author: Wester Schoonenberg completed his B.Sc. in Systems Engineering and Policy Analysis Management at Delft University of Technology in 2014. After his bachelors’ degree, Wester started his M.Sc. at Masdar Institute of Science & Technology. Currently, Wester is working on the integrated operation of electrical grids and production systems with a special interest in the demand side management of industrial facilities.
LIINES Website: http://amfarid.scripts.mit.edu
It’s been a long time since 2003 when the concept of the Internet of Things was first proposed by U. of Cambridge Auto-ID Laboratory. At the time, Dr. Amro M. Farid, now head of the Laboratory for Intelligent Integrated Networks of Engineering Systems, was a doctoral student investigating how RFID technology enabled intelligent products within reconfigurable manufacturing systems. The Internet of Things was being applied primarily in the manufacturing and supply chain domain.
Since then, the Internet of Things concept has taken hold not just in manufacturing systems and supply chains but nearly every industrial system domain including energy. Every “thing” or “device” has the potential to be connected via an intelligent sensor so as to make decisions — be they centralized within an operations control center — or distributed amongst artificially intelligent multi-agent systems. The Internet of Things concept has the potential to fundamentally transform industrial systems.
Have a look at Duke Energy’s take on the Internet of Things:
The LIINES is proud to have been working in this area since its inception and continue to do so. More information on our research can be found on the LIINES website.
LIINES Website: http://amfarid.scripts.mit.edu