12 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2011

EditorialGuest

Digital Object Identifier 10.1109/MCI.2011.941586

Haibo HeUniversity of Rhode Island, USA and Andrew Kusiak University of Iowa, USA

Special Issue on Computational Intelligence in Smart Grid

The growing energy demand and environmental concerns have increased interests of academia, industry, and governments in the develop-ment of a smart electric power grid. While the existing power grid has served us well in the past, innovative technolo-gies and solutions are needed to realize an intelligent smart grid providing affordable, reliable, sustainable, efficient, and secure supply of electricity. Among many efforts toward this goal, computational intelli-gence research offers key technical solu-tions to accomplish this goal.

This special issue aims at presenting the state-of-the-art research with a focus on algorithms, architectures, applications needed for the development of a smart grid. Our goal is not only to introduce readers to the cutting-edge research results, but also to present critical vision-ary discussions and highlights for future computational intelligence research in smart grid. All contributions submitted to this special issue have been subject to a rigorous peer-review process, leading to the selection of four articles covering dif-ferent aspects of computational intelli-gence in smart gr id research and development.

In the opening feature article, Werbos presents a review of the evolution of the four generation of concepts of smart grid with a focus on contributions of the computational intelligence research to smart grid. The article shows that com-putational intelligence has played a criti-

cal role in four generations of smart grid. The first generation focused on the tra-ditional concepts such as automated meters while the second generation focused on global control systems and stability issues. The new third and fourth generations of smart grid target an intel-ligent power grid operating in a sustain-able global energy system. Critical components such as electric vehicles, renewable energy, storage, distributed intelligence and new computational intelligence tools providing key capabili-ties to handle the complexity and sto-chastic challenges of a smart grid have been discussed. This paper reviews the critical technical solutions needed for the smart grid development, and presents important opportunities and visionary discussions for the community for new fundamental research of computational intelligence for smart grid.

In his paper, Venayagamoorthy outlines contributions of computational intelli-gence in developing dynamic, stochastic, and scalable technologies needed for sense-making, situational awareness, con-trol, and optimization of a smart grid. The development of an intelligent power grid has been hampered by its complexity (e.g., spatial and temporal dependencies, nonlinearity, non-stationarity, uncertain-ties) far exceeding those in traditional power systems. The paper indicates that computational intelligence tools ranging from neural networks to swarm intelli-gence could play a critical role in many aspects of a smart grid, such as prediction of system states, stochastic power flow, and system optimization.

In the third paper, Hu et al. presents a belief propagation (BP) based distri-bution system state estimator for smart grid development. Versed in computa-tional intelligence, their approach over-comes the limitations of the traditional power system state estimations by using the BP approach to deal with sparse measurements. The presented probabi-listic inference approach derives first and second order statistics of the state variables given prior distributions and real-time measurements. The article suggests that the BP solution can bridge the gap between the transmission state estimation and real-time feeder analysis leading to a potential usage in a smart micro-grid.

The fourth paper by Felice and Yao presents a comparative study of short-term load forecasting with neural net-work ensembles. Load forecasting has been widely recognized to be a critical component in management, scheduling, and dispatching operations in power sys-tems. In an intelligent power grid, reli-able and accurate forecasting of loads could provide vital information for sys-tem optimization and control, as well as decision-making in energy generation and dynamic pricing. In this article, a neural network ensemble has been inves-tigated for hourly prediction of energy load based on data coming from seven office buildings located in Rome, Italy.

We hope the four selected papers illustrate essential aspects of computa-tional intelligence research in smart

Date of publication: 14 July 2011 (continued on page 64)

64 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2011

* 2012 IEEE Computational Intelligence for Measurement Systems (IEEE CIMSA 2012)September 1921, 2012Place: Tianjin, ChinaGeneral Chairs: Leonid Perlovsky andFabio Scotti http://cimsa2012.ieee-ims.org/

* 2013 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2013)

April 1619, 2013Place: Suntec, SingaporeGeneral Chair: P.N. Suganthanhttp://www.ieee-ssci.org

* 2013 IEEE Congress on Evolutionary Computation (IEEE CEC 2013)July 36, 2013Place: Cancun, MexicoGeneral Chair: Carlos Coello CoelloWeb site: TBD

* 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)July 710, 2013Place: Hyderabad, IndiaGeneral Chair: Nik PalWeb site: TBD

Guest Editorial (continued from page 12)

grid, ranging from the overview and vision of smart grid research involving computational intelligence, to the state-of-the-art techniques and solutions addressing the critical challenges of the smart grid. With the expansion of smart grid research and technologies, we hope these papers will highlight the

importance of computational intelli-gence research in smart grid, and pro-vide stimulus to further research in this important domain.

In closing, we would like to express our deep gratitude to numerous review-ers who have assisted in the peer-review process. Their expertise and profession-

alism guarantee high quality of the selected papers. We would also like to thank the Editor-In-Chief, Kay Chen Tan, for his guidance and suggestions in this special issue and making the issue a reality. Finally, the credit goes to the authors contributing their innovative research results to this special issue.

Innovation doesnt just happen.Read rst-person accounts ofIEEE members who were there.

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