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Smart Grids and Batteries for Sustainable Power Energy System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1987

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Interests: power system analysis; reliability; resilience; value-based reliability investment; probabilistic modeling of energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to disseminate novel concepts, methodologies, and findings from research on advanced battery systems and other smart grid technology integration in power systems that are transforming towards a net-zero emission structure. Power systems are exposed to increasing variability and uncertainty in supply and demand due to large-scale deployment of renewable energy sources like wind and solar generation, restrictions to coal and other fossil-fired generation, and rapid growth of electric vehicles with random charging/discharging characteristics. Advanced battery storage systems can be strategically developed and exploited to manage excess energy, mitigate variability, alter demand characteristics, and balance supply and demand to enhance the security and adequacy of power systems. Smart grid technologies, in general, employ digital communication, automation, and real-time data analytics to maintain the optimal balance between the efficiency, reliability, and resilience of power systems. It is widely anticipated that these technologies will be increasingly integrated into the power transmission and distribution systems to enhance operating decisions. We believe that the articles published in this Special Issue will provide valuable insight, methods, and tools to assist in a smooth transition of sustainable power systems to meet the global net-zero emission objectives.

The research areas covered in this issue include:

  1. Technological Innovations: Investigating the latest smart grid infrastructure and battery technology advancements.
  2. Integration Strategies: Exploring methods for seamlessly integrating batteries with smart grids to optimize energy management.
  3. Renewable Energy Integration: Evaluating how smart grids and batteries can support higher penetration of renewable energy sources such as solar and wind.
  4. Energy Efficiency and Management: Enhancing energy efficiency and reducing wastage through smart grid technologies and effective battery utilization.
  5. Reliability and Resilience: Developing evaluation frameworks to quantify system reliability/resilience and formulate mitigation strategies using batteries and smart grids.
  6. Policy and Economic Impact: Analyzing regulatory frameworks, economic benefits, and challenges associated with deploying these technologies.

The ultimate goal is to develop a reliable, resilient, and efficient low-carbon energy system that can meet future energy demands sustainably.

Prof. Dr. Rajesh Karki
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • smart grids
  • battery storage
  • sustainable energy
  • renewable integration
  • energy efficiency
  • grid stability
  • system reliability
  • infrastructural and operational resilience
  • digital communication
  • automation in energy systems

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Published Papers (1 paper)

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Research

21 pages, 3770 KiB  
Article
A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets
by Oğuz Kırat, Alper Çiçek and Tarık Yerlikaya
Appl. Sci. 2024, 14(21), 10032; https://doi.org/10.3390/app142110032 - 3 Nov 2024
Cited by 2 | Viewed by 1288
Abstract
Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls [...] Read more.
Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets. Full article
(This article belongs to the Special Issue Smart Grids and Batteries for Sustainable Power Energy System)
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