Dear Colleague,
I warmly invite you to contribute to the Handbook of Smart Energy Systems project https://meteor.springer.com/hses.
Considering the importance of energy production, transmission, and distribution systems on the current and future of human living, to adjust to the new normal life of post-covid and cope with the climate change, this book focuses on enabling solutions for energy access to industry development and the general world population in more reliable and sustainable ways, focusing on improving the performance measures of the energy systems. The proposed book consists of four parts.
Reliability enhancement of energy systems
This part describes the reliability developments for energy systems. For instance, the reliability of the players in an energy distribution system, reliability of data collection procedures, and reliability of the logistics.
The major topics may cover (not limited) are as follows:
- Importance of data reliability in energy demand forecast
- Influences of data collection and analysis reliability in energy systems
- Reliability analysis of offshore renewable energy facilities
- Influences of mobile data on energy systems reliability
- Big data applications in improving the reliability of the energy systems
- Applications of data analytics in reliable energy logistics processes
Intelligent development of energy systems
Intelligent energy systems for fast and rapid decision making, and online monitoring are needed for the development of the distributed complex smart grids with multiple resources planning. This part, introduces avdanced development and usage of new technologies in smart energy system and the application of smart energy management for industrial development.
The major topics may cover (not limited) are as follows:
- Smart grids
- Future energy system analyses
- Energy data management and utilization
- Energy efficiency for smart manufacturing
- Green Production
- Smart energy aware systems
- Integration of renewable energy sources
- Smart network control of distributed system
Simulation and Optimization of energy systems
Operations research methods and search algorithms can beused to optimize the performance of energy systems, and how energy can be distributed among consumers. Methodologies such as Lagrangian relaxation, game theory, stochastic programming, multi-objective optimization, heuristics, metaheuristics are among the methods that can be used to optimize the performance measures of an energy system, such as cost, customer satisfaction, delivery time, and balanced supply-demand. As it is not always possible to fed the real system data or test a system to observe its performance in certain conditions, simulation is a common methodology to observe the anticipated act of a system in predefined conditions. Applying simulation to observe and optimize the performance of an energy system is presented in this part.
The major topics may cover (not limited) are as follows:
- Data analysis applications in optimizing the smart grid systems
- Data analysis applications in optimal integration of energy supply chains
- Optimizing the energy mobile data collection networks
- Developing energy demand forecasting methods
- Data-driven energy waste minimization at energy distribution networks
- Applications of Data Envelopment Analysis (DEA) for optimizing energy consumptions
- Multiple-Criteria Decision Making (MCDM) applications in optimizing multi-objectives energy system performance
- Using simulation to analyze the performance of an energy distribution center
- Novel applications of data simulation to optimize an energy system
- Simulation applications in analyzing the trade-off between climate change and energy consumption
- Simulation applications in analyzing the reliability of an energy system
Sustainable development of energy systems
Global warming and negative environmental experiences have become a major concern of the 21st century. Most of the negative impacts can be mitigated by changing energy consumption practices. This part describes the methods which can reduce the production of emissions by transforming the industrial and urban consumption of fossil fuels to renewable energies consumption
The major topics may cover (not limited) are as follows:
- Cost-environment trade-off in using renewable energy in an energy system
- Data analytics applications in reducing the emission footprint of an energy system
- Logistics processes optimization regards to sustainability concerns
- Influences of waste management practices on energy emission footprints
- Data-driven techniques for optimizing the renewable energy systems operations
- Applying optimization techniques to develop a renewable energy supply map
- Novel applications of data-driven techniques for moving from using traditional energy to renewable energy
- Constructing renewable energy systems using big data applications
Please consider this opportunity and go to the project webpage https://meteor.springer.com/hses for further information and instructions. Also, you can send me your abstract at your earliest convenience.
Kind regards,
Enrico Zio, Panos Pardalos, Mahdi Fathi
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It’s All Good!!
Mahdi Fathi, Ph.D.
Assistant Professor
Department of Information Technology & Decision Sciences
G. Brint Ryan College of Business, University of North Texas
Carnegie Tier One Research University