Home » Posts tagged 'Research & Development' (Page 2)
Tag Archives: Research & Development
On April 27th, 2016, Prof. Amro M. Farid gave an invited lecture at the Utility Variable-Generation Integration Group (UVIG) Spring Technical Workshop held in Sacramento, CA. The presentation entitled: “Enterprise Control as a Holistic Assessment Method for Variable Generation & Demand Response Integration” featured many of the LIINES’ research on renewable energy integration assessment methodologies.
The presentation advocated the concept of “Power Grid Enterprise Control” which has been the subject of several recent blogposts. (See here and here). Traditionally, power system operation & control methods are conducted individually. In contrast, “Power Grid Enterprise Control” integrates these methods into a single simulation of how a power system enterprise behaves as a physical power grid tied to multiple layers of control, optimization and market behavior. Such an integrated approach provides techno-economic performance results of the power grid. Furthermore, it highlights trade-off decisions between technical reliability and cost performance. The presentation showed how enterprise control simulation can be used to study renewable energy, energy storage, and demand-side energy resources.
In depth materials on LIINES smart grid research can be found on the LIINES website.
Journal Paper accepted at IEEE Transactions on Industrial Informatics – An Axiomatic Design of a Multi-Agent Reconfigurable Mechatronic System Architecture
The LIINES is pleased to announce the acceptance of the paper “An Axiomatic Design of a Multi-Agent Reconfigurable Mechatronic System Architecture” to the IEEE Transactions on Industrial Informatics. The paper is authored by Prof. Amro M. Farid and Prof. Luis Ribeiro.
Recent trends in manufacturing require production facilities to produce a wide variety of products with an increasingly shorter product lifecycle. These trends force production facilities to adjust and redesign production lines on a more regular basis.
Reconfigurable manufacturing systems are designed for rapid change in structure; in both hardware and software components to address the required changes in production capacity and functionality.
Qualitative methods have recently been successful in achieving reconfigurability through multi-agent systems (MAS). However, their implementation remains limited, as an unambiguous quantitative reference architecture for reconfigurability has not yet been developed.
A design methodology based on quantitative reconfigurability measurement would facilitate a logical, and seamless transition between the five stages of the MAS design methodology, as shown below.
Previous work on the reconfigurability of automated manufacturing systems has shown that reconfigurability depends primarily on architectural decisions made in stages 1, 2, 3, and 5. Operational performance of the manufacturing system after the reconfiguration is also important, but is often overlooked by the existing literature. As a result, it’s not clear:
- The degree to which existing designs have achieved their intended level of reconfigurability.
- Which systems are quantitatively more reconfigurable.
- How these designs may overcome their inherent design limitations to achieve greater reconfigurability in subsequent design iterations.
In order to address the previously mentioned issues with existing design methodologies, this paper develops a multi-agent system reference architecture for reconfigurable manufacturing systems driven by a quantitative and formal design approach, directly in line with the above Figure.
The paper uses Axiomatic Design for Large Flexible Engineering Systems to support a well-conceptualized architecture, which is necessary for excellent production system performance. Additionally, Axiomatic Design highlights potential design flaws at an early conceptual stage. This results in the first formal and quantitative reference architecture based on rigorous mathematics.
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.
The LIINES wants to congratulate Deema F. Allan with a successful defense of her master thesis entitled: “Enhance Electric Vehicle Adoption Scenarios for Abu Dhabi Road Transportation”. Deema joined the LIINES in 2014 to work on transportation electrification. The past two and a half years Deema has progressed the research in the lab incredibly as a result of her admirable dedication and perseverance. We wish Deema all the best in her future work and we are confident that her passion will lead to great achievements.
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
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