Computational Intelligence Algorithms for the Energy Transition

Dear Colleagues,

The energy system is experiencing a shift towards more sustainable energy sources, also fostered by the increasing energy demand. This phenomenon leads certainly to environmental benefits but also to new challenges as the penetration of Renewable Energy Sources (RES) increases in the grid. In addition, not only the energy supply is becoming more intermittent and chaotic, but due to the higher number of electric vehicles the demand is also becoming more variable and unpredictable.

To properly plan, organize and maintain the energy system, the tools provided by Computational Intelligence (CI) techniques can be exploited. In particular, models based on Evolutionary Optimization (EO) Algorithms, Machine Learning (ML) methods and Fuzzy Logics are suitable to analyse and examine the future energy technologies. Therefore, this Special Issue is focused on Computational Intelligence Algorithms to support the Energy Transition. In fact, Smart Grids and Micro Grids require special algorithms to optimize their design, to efficiently manage the resources, to plan the maintenance, and to forecast the energy production and consumption.

Evolutionary Optimization Algorithms are flexible and have the ability to deal with non-linear, high-dimensional and multi-modal problems. They could be exploited especially in the design phase, when the optimal system configuration must be determined. In addition, they also enable to take into account different objectives, since it is being coped with a multi-objective problem, and determine a trade-off between the different needs, such as system stability, operational and investment cost or even the quality of service. Finally, they could also be employed together with Machine Learning techniques and Fuzzy Logics to actively operate and manage the system

On the other hand, Machine Lerning methods do not have a fixed time horizon for their application and thus can be applied to a variety of decision making activitied. Indeed, they could be employed for the production phase as well as for the load forecast at a range of temporal scales.

Finally, to better define the system management and its maitenance, Fuzzy Logics can be exploited. They allow to specify advanced control logics while handling non-numerical classifications, which are usually provided by qualified operators.

Spatial and spatio-temporal analyses have proved to be beneficial in renewable energy studies. Since renewable energy resources are linked to spatial phenomena, for example solar power is direct derivative of solar radiation that is a spatial phenomenon, GIS can have wide applications in this field. Geospatial analysis coupled with CI and ML methods could be used in variety of research studies, such as location allocation, potential mapping, resource optimization, energy network management and expansion planning, optimal routing, and smart cities.

Potential topics include, but are not limited to:

1. Machine Learning for Variable Renewable Energy Sources forecasting.
2. Machine Learning and Data-driven Load forecasting.
3. Machine learning for Electric Vehicles charging session forecasting.
4. Advanced Electric Vehicles charging strategies.
5. Evolutionary Algorithm for sizing and optimal system design.
6. Fuzzy logics and Evolutionary Algorithms for energy management and control of smart and micro grids.
7. Machine Learning and Computational Intelligence applications for communication systems in smart and micro grids.
8. Machine Learning for trading algorithms in Electricity Markets.
9. GIS and spatial analysis applications in renewable energy studies.

Dr. Alessandro Niccolai
Guest Editor

Silvia Trimarchi
Babak Ranjgar
Guest Editor Assistants

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• optimization of smart and micro grids
• machine Learning for energy forecasting
• computational Intelligence applications to electricity markets
• GIS for renewable energy deployment
• fuzzy logic control algorithms for microgrids
• optimized electric vehicles charging