Guest Editorial: Special Section on Data Analytics for Energy, Water, and Environment

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In 2008, the U.S. National Academy of Engineering proposed 14 grand challenges for engineering, of which five were related to energy, water, and environment. The world population has exceeded 7.7 billion in 2020, up from 6.7 billion in 2008. A common goal of the human beings is to meet the global need of energy and water without sacrificing the environment.

A growing interest in the recent decade is data analytics. Many business sectors have embarked analytics to extract actionable insights from the data and make informed business decisions. This Special Section aims at highlighting the state of the art in data analytics applications in energy, water, and environment.

The Guest Editorial Board solicited a wide range of topics covering various aspects of energy, water, and environment. More than a hundred researchers responded to the Call for Papers. Through a careful peer-review process, the Guest Editorial Board collected 15 high-quality articles for this Special Section.

While 14 of the 15 articles are more or less related to energy, one focuses on environmental policy—in [A1], Zhou et al. perform panel data analysis to assess the municipal-level environmental policy mixes. The case study was on the textile industry in 13 Chinese cities. They show that these environmental policy mixes could significantly promote green performance without compromising productivity growth.

Four articles focus on problems related to power grids. In [A2], Pourahmadi et al. propose a new analytical framework to address several operational flexibility concerns due to the increase in generation variability. In [A3], Zhang et al. propose a two-stage bootstrap sampling method for probabilistic load forecasting. In [A4], Hong and Hofmann propose a new research problem, data integrity attacks against outage management systems. In [A5], Lin et al. apply data analytics to synchrophasor data in order to estimate transmission line parameters in real time.

Energy storage systems are often required to compensate the variability of the wind and solar generation. In [A6], Li and Wu formulate as a mixed integer nonlinear programming problem to identify the optimal size of solar panels, the optimal capacity of battery energy storage system (BESS), and optimal scheduling of BESS charging and discharging that can minimize the long-term overall cost. They transform the nonlinear formulation into mixed integer linear programming, which can be solved by a branch-and-bound algorithm. In [A7], Jin et al. focus on the distributed BESSs and the power dispatch. They introduce a consensus control algorithm to dispatch the power output and track the load in a decentralized manner.

Understanding end users is crucial to making informed business decisions. Four articles present investigations on the behaviors of end users. In [A8], Sen and Qiu use nationally representative household survey data to analyze the aggregate energy-saving behavior of households. In [A9], Liu et al. conduct experiment on college student dormitories to study the psychological mechanisms of the comparative feedback effect on consumers’ energy-saving behaviors. In [A10], Zhang et al. leverage the concepts from dynamic cooperative game theory to study the optimal pricing strategies of manufacturers and retailers. In [A11], Malinowski and Povinelli apply cluster analysis to segment the customers of a water utility company.

Four other articles concern water and sustainability issues. In [A12], Castillo-Calzadilla et al. propose a design of a water network with a solar-powered pumping system. In [A13], Zuloaga et al. present new metrics and a new methodology to determine system infrastructural–operational resilience for the water distribution and electric power systems under critical conditions. In [A14], Bai and Sarkis introduce a multistakeholder transdisciplinary method to support water, energy, food, and sustainability nexus decisions. In [A15], Lee et al. evaluate the economic benefits of five biomass types and propose a stochastic programming model for the optimal design of the global biodiesel supply chain under the demand and price uncertainty.

Several topics in the original solicitation have not been included in the final collection of the 15 Special Section articles, such as air quality, sustainable agriculture, and alternative transportation technologies. We hope that the readers recognize future research opportunities from these important topics.

Last but not least, the Guest Editorial Board would like to thank the authors for their valuable contributions to this Special Section, and the reviewers for their timely and constructive comments. Special thanks to Dr. Turgul Daim, the Editor-in-Chief of the IEEE Transactions on Engineering Management, for his diligent support throughout the publication process.

Guest Editors

Tao Hong, Guest Editor Department of Systems Engineering and Engineering Management University of North Carolina at Charlotte Charlotte, NC 28223, USA

Hua Cai, Guest Editor School of Industrial Engineering Purdue University West Lafayette, IN 47907, USA

Gang He, Guest Editor Department of Technology and Society Stony Brook University Stony Brook, NY 11794, USA

Guiping Hu, Guest Editor Department of Industrial and Manufacturing Systems Engineering Iowa State University Ames, IA 50011, USA

Yueming Qiu, Guest Editor School of Public Policy University of Maryland College Park, MD 20742, USA

Ekundayo Shittu, Guest Editor Department of Engineering Management and Systems Engineering George Washington University Washington, DC 20052, USA

Hongliang Zhang, Guest Editor Department of Environmental Science and Engineering Fudan University Shanghai, 200437, China

Ning Zhang, Guest Editor Department of Electrical Engineering Tsinghua University Beijing, 100084, China

Appendix: Related Article

  1. Y. Zhou, R. Zhou, L. Chen, Y. Zhao, and Q. Zhang, “Environmental policy mixes and green industrial development: An empirical study of the Chinese textile industry from 1998 to 2012,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.3009282.

  2. F. Pourahmadi, S. H. Hosseini, P. Dehghanian, E. Shittu, and M. Fotuhi-Firuzabad, “Uncertainty cost of stochastic producers: Metrics and impacts on power grid flexibility,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.2970729.

  3. J. Zhang, Y. Wang, M. Sun, and N. Zhang, “Two-stage bootstrap sampling for probabilistic load forecasting,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.2967352.

  4. T. Hong and A. Hofmann, “Data integrity attacks against outage management systems,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2021.3055139.

  5. J. Lin, J. Song, and C. Lu, “Synchrophasor data analytics: Transmission line parameters online estimation for energy management,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2939173.

  6. Y. Li and J. Wu, “Optimum integration of solar energy with battery energy storage systems,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.2971246.

  7. R. Jin, C. Lu, and J. Song, “Manage distributed energy storage charging and discharging strategy: Models and algorithms,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.3003306.

  8. A. Sen and Y. Qiu, “Aggregate household behavior in heating and cooling control strategy and energy-efficient appliance adoption,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.2974377.

  9. M. Liu et al., “Effect of comparative feedback on consumers’ energy-saving behavior: A college dormitory example,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.3007872.

  10. T. Zhang, Y. Qu, and G. He, “Pricing strategy for green products based on disparities in energy consumption,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2907872.

  11. M. R. B. Malinowski and R. J. Povinelli, “Using smart meters to learn water customer behavior,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2020.2995529.

  12. T. Castillo-Calzadilla, C. M. Andonegui, M. Gómez-Goiri, A. M. Macarulla, and C. E. Borges, “Systematic analysis and design of water networks with solar photovoltaic energy,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2940340.

  13. S. Zuloaga, P. Khatavkar, L. Mays, and V. Vittal, “Resilience of cyber-enabled electrical energy and water distribution systems considering infrastructural robustness under conditions of limited water and/or energy availability,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2937728.

  14. C. Bai and J. Sarkis, “The water, energy, food, and sustainability nexus decision environment: A multistakeholder transdisciplinary approach,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2946756.

  15. C. Lee, W. Sun, and Y. Li, “Biodiesel economic evaluation and biomass planting allocation optimization in global supply chain,” IEEE Trans. Eng. Manage., doi: 10.1109/TEM.2019.2900033.