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Journal Paper Accepted at Applied Energy – Demand Side Management in Power Grid Enterprise Control: A Comparison of Industrial & Social Welfare Approaches
The LIINES is happy to announce that our recent paper entitled: “Demand Side Management in Power Grid Enterprise Control: A Comparison of Industrial & Social Welfare Approaches”, has been accepted to the Applied Energy Journal. This study comes as a result of collaboration between three universities; MIT, Masdar Institute, and Dartmouth. The work is authored by Bo Jiang (MIT), Aramazd Muzhikyan (Masdar Institute), Prof. Amro M. Farid (Dartmouth) and Prof. Kamal Youcef-Toumi (MIT).
Demand response is an integral part of a reliable and cost-effective power grid. As wind and solar energy become two important power generation sources that reduce CO2 emissions and ensure domestic energy security, their intermittent and uncertain nature poses operational challenges on the electrical grid’s reliability. Instead of relying solely on dispatchable generation, power grid operators, called ISOs, are adopting Demand Response (DR) programs to allow customers to adjust electricity consumption in response to market signals. These DR programs are an efficient way to introduce dispatchable demand side resources that mitigate the variable effects of renewable energy, enhance power grid reliability and reduce electricity costs. Fortunately, the U.S. Supreme Court’s recent ruling Federal Energy Regulatory Commission vs. Electric Power Supply Association, has upheld the implementation of Demand Response allowing its role to mature in the coming years.
Despite the recognized importance and potential of DR, the academic and industrial literature have taken divergent approaches to its implementation. The popular approach in the scientific literature uses the concept of “Transactive Energy” which works much like a stock market of energy; where customers provide bids for a certain quantity of electricity that they wish to consume. Meanwhile industrial implementations (such as those described by FERC order 745) compensate customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DR. Such a counter-factual baseline may be erroneous. At the LIINES, we have rigorously compared the two approaches. Our previous journal paper published at Applied Energy “Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches” conducted the comparison in a day-ahead wholesale market context. It showed, both analytically and numerically, that the use of power consumption baselines in demand response introduces power system imbalances and costlier dispatch.
Our recent paper now expands the analysis from a single day-ahead electricity market to the multiple layers of wholesale markets found in many regions of the North American power grid. This holistic analysis includes the day-ahead, real-time, and ancillary service markets. The integration of these multiple layers of power system operations captures the coupling between them and reveals the the impacts of DR implementation over the course of a full-day with a granularity of tens of seconds. The paper quantifies both the technical and economic impacts of industrial baseline errors in the day-ahead and real-time markets, namely their impacts on power system operating reserve requirements, operating costs and market prices.
The paper concludes that the presence of demand baseline errors – present only in the industrial implementaiton – leads to a cascade of additional system imbalances and costs as compared to the Transactive Energy model. A baseline error introduced in the day-ahead market will increase costs not just in the day-ahead market, but will also introduce a greater net load error residual in the real-time market causing additional costs and imbalances. These imbalances if left unmitigated degrade system reliability or otherwise require costly regulating reserves to achieve the same reliability.
Figure 1: Cascading Cost Increase of Demand Response Baseline Errors in Day-Ahead Energy Market
An additional baseline error introduced in the real-time market further compounds this cascading effect with additional costs in the real-time market, amplified downstream imbalances, and further regulation capacity for its mitigation.
Figure 2: Cascading Cost Increase of Demand Response Baseline Errors in Real-Time Energy Market
Based on these results, the potential for baseline inflation should be given attention by federal energy policy-makers. The effects of industrial baseline errors can be mitigated with effective policy. As a first solution, ISOs could calculate demand response baselines using the same methods of load prediction normally used in energy markets. Such an approach leaves less potential for baseline manipulation. A more comprehensive solution to this problem will be the upcoming trend of transactive energy and would eliminate the concept of baselines and their associated uncertainties entirely.
In depth materials on LIINES smart power grid research can be found on the LIINES website.
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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 Commitment to Open-Information
- 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
The LIINES seeks Quantitatively-Minded Dartmouth Undergrad for Smart Grid Research Competition
Interested students may contact Prof. Amro M. Farid for further information and an interview.
LIINES Website: http://amfarid.scripts.mit.edu