Scope
To reduce greenhouse gas emission and to enhance the robustness of the power grid, the development of renewable energy and Smart Grid technologies is indispensable.
Renewable energies in general include wind, photovoltaic, hydroelectric, fuel cell and biomass power generation systems.
They have been getting more attention recently due to cost competitiveness and environment friendly, as compared to fossil fuel and nuclear power generations.
Owing to the relatively higher investment cost of renewable power generation systems, it is important to operate the systems near their maximum power output point, especially for the wind and solar PV generation systems.
In addition, since the wind and solar PV power resources are intermittent, accurate predictions and modeling of wind speed and solar insolation are necessary.
Plus, to have a more reliable power supply, renewable power generation systems are usually interconnected with the power grid.
Smart Grid technology uses information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and users.
Smart Grid emphasizes automation, safety, and the close cooperation between the users and suppliers to improve the operating efficiency of power system, to enhance power quality and to solidify grid reliability.
From this perspective, a crucial issue is how to support the evolution of existing electrical grids from static hierarchical systems to self-organizing, high scalable and pervasive networks. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management system are the key enabling factor toward the Smart City concept.
The use of Smart Grid technology is also aligned with the policy goals of expanding the application of renewable energy, energy conservation, and carbon reduction.
As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automations become very important issues.
Additionally, due to the highly nonlinear and time-varying nature with unmodeled dynamics, effective uses of computational intelligence techniques such as fuzzy systems for the controlling and modeling of renewable power generation in a smart-grid system turn out to be very crucial for successful operations of the systems.
Moreover, the large scale deployment of fuzzy-based technologies in Smart Grids could lead to a more efficient tasks distribution among the distributed energy resources and, consequently, to a sensible improvement of the electrical grid resiliency.
Hence, topics of interest of this Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid” cover the whole range of researches and applications of fuzzy systems in renewable power generations and Smart Grid systems, with particular emphasis also on the emerging technologies and methodologies of Fuzzy logic and Computational Intelligence for resilient and proactive Smart Grids, ranging from methods for balancing resources to various control and security aspects.
This Task Force not only focuses on technological breakthroughs and roadmaps in implementing the technology, but also on the much needed sharing of best practices.
Topics of interest (not limited to):
- Fuzzy modeling of renewable power generation systems
- Fuzzy energy management systems
- Fuzzy control of renewable power generation systems
- Fuzzy distribution systems automation
- Prediction of renewable energy using fuzzy and neuro-fuzzy systems
- Fuzzy power quality, protection and reliability analysis of power system
- Hybrid systems of computational intelligence techniques in renewable power generation systems
- Fuzzy Logic application for Smart Cities
- Probabilistic and non-probabilistic paradigms for smart grids analysis in the presence of data uncertainty
- Meta-heuristic algorithms for fast contingency analysis
- Proactive paradigms for smart grid control and regulation
- Decentralized and cooperative sensor networks for dynamical loading of power equipment
- Fuzziness in optimal power flow analysis
- Fuzziness in system restoration and smart restoration tools
- Fuzzy Inference Systems for renewable power forecasting
- Application of pervasive sensing systems for condition monitoring