Abstract
Large and complex systems, such as the Smart Grid, are often best understood through the use of modeling and simulation. In particular, the task of assessing a complex system’s risks and testing its tolerance and recovery under various attacks has received considerable attention. However, such tedious tasks still demand a systematic approach to model and evaluate each component in complex systems. In other words, supporting a formal validation and verification without needing to implement the entire system or accessing the existing physical infrastructure is critical since many elements of the Smart Grid are still in the process of becoming standardized for widespread use. In this chapter, we describe our simulation-based approach to understanding and examining the behavior of various components of the Smart Grid in the context of verification and validation. To achieve this goal, we adopt the discrete event system specification (DEVS) modeling methodology, which allows the generalization and specialization of entities in the model and supports a customized simulation with specific variables. In addition, we articulate metrics for supporting our simulation-based verification and validation and demonstrate the feasibility and effectiveness of our approach with a real-world use case.
A preliminary version of this chapter appeared under the title “Simulation-Based Validation for Smart Grid Environments,” in Proceedings of the 14th IEEE International Conference on Information Reuse and Integration, San Francisco, USA, August 14–16, 2013. All correspondences should be addressed to Dr. Gail-Joon Ahn at gahn@asu.edu.
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Notes
- 1.
As of January 2013, 213 use cases are available at http://smartgrid.epri.com/Repository/Repository.aspx.
- 2.
Ericsson et al. [6] suggested four domains: Generation, Transmission, Distribution and Markets, respectively, which is mostly covered in NIST model.
- 3.
Peak usage times may vary for each Energy Service Provider, but are generally weekday afternoons from 2 pm to 6 pm in Arizona. The relevant reference is available at http://www. azenergy.gov/SavingTips/TimeOfUse.aspx.
- 4.
\(\alpha = \frac{RT P_{users}}{All_{users}}, P_{t} = \) real-time price, \(\bar{P} = \) fixed tariff price, \(P_{c} = \) capacity market cost,
\(P_{a} = \) ancillary service cost, \(\eta = \) elasticity of demand variable, \(\epsilon _{t} = \)error fixing variable.
- 5.
MS4 software is available at http://www.ms4systems.com/pages/ms4me.php.
- 6.
The simulation viewer also provides state updates, message exchange animations, as well as a mechanism for advancing time.
- 7.
The information of each energy service provider is available at https://www.srpnet.com and http://www.aps.com/en/residential/Pages/home.aspx, respectively.
- 8.
In order to reduce redundancy, we mainly address compulsive cases from our evaluation results in this chapter.
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This work was partially supported by grants from the National Science Foundation and the Department of Energy.
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Han, W., Mabey, M., Ahn, GJ., Kim, T.S. (2014). Simulation-Based Validation for Smart Grid Environments: Framework and Experimental Results. In: Bouabana-Tebibel, T., Rubin, S. (eds) Integration of Reusable Systems. Advances in Intelligent Systems and Computing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-04717-1_2
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