Machine Learning and Optimization with Applications of Power System

Dear Colleagues,

This Special Issue is focused on machine learning and optimization techniques that can be applied for power system operation, such as energy data analytics, time series energy forecasting, renewable energy markets, energy storage systems (ESS), microgrids and distribution networks. Modern power systems face new challenges due to the high penetration of renewable generation, and thus prediction and control are essential for grid reliability. Thanks to massively deployed energy IoT sensors and energy big data, machine learning including deep learning is being actively applied to predict renewable generation and electric loads. The accurate forecasting of PV and wind power is also of prime importance for strategic bidding in renewable energy markets. Deep learning techniques including recurrent neural networks (RNN), long short-term memory (LSTM), and convolution neural networks (CNN) are expected to improve the prediction accuracy of time series energy data.

Nevertheless, forecasting errors are unavoidable, and mitigating the variability of the grid requires other techniques. Indeed, ESS plays a key role in controlling the grid under volatile generation and loads, and is widely deployed for peak cut, frequency regulation, bidding in renewable energy markets, demand response, etc. Multiple small scale ESS units can be also aggregated and collectively controlled as one virtual unit. Finally, it is desirable to optimally operate distribution networks and/or microgrids with the aforementioned distributed energy resources; optimal power flow possibly combined with peer-to-peer energy trading is also of great interest.

In this Special Issue, new theoretical and/or practical research results using machine learning and optimization techniques with the application of power systems are solicited. Pilot programs and field tests considering regional requirements are also welcome. The preferred topics include, but are not limited to:

Energy data analytics and forecasting

Deep learning (RNN, LSTM, CNN, etc.) for load and renewable generation prediction

Deep reinforcement learning for stochastic control

ESS operation considering uncertainty, frequency regulation, demand response, and/or battery degradation

Demand response

Energy bidding and game theory in renewable energy markets

Pilot programs and field tests

Microgrid optimization and simulator development

Optimal power flow in distribution networks

Prof. Hongseok Kim
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Energy data analytics and forecasting
• Deep learning (RNN, LSTM, CNN, etc.) for load and renewable generation prediction
• Deep reinforcement learning for stochastic control
• ESS operation considering uncertainty, frequency regulation, demand response, and/or battery degradation
• Demand response
• Energy bidding and game theory in renewable energy markets
• Pilot programs and field tests
• Microgrid optimization and simulator development
• Optimal power flow in distribution networks
• Virtual power plants

• Machine Learning and Optimization with Applications of Power System II in Energies (11 articles)
• Machine Learning and Optimization with Applications of Power System III in Energies (8 articles)