Recent Advances in Smart Grid and Energy Storage Applications

Topic Information

Dear Colleagues,

The evolution of smart grids and energy storage technologies is transforming the energy sector, addressing grid stability, energy efficiency, and renewable energy integration challenges. Recent advances enable enhanced real-time grid monitoring, predictive analytics, and demand–response strategies. Innovations in energy storage systems (ESSs), including batteries, supercapacitors, and hydrogen-based solutions, are revolutionizing energy management and dispatch. Integrating artificial intelligence (AI), the Internet of Things (IoT), and advanced power electronics further strengthens smart grid operations, improving reliability, flexibility, and resilience. Additionally, smart grids have a vital role in smart cities, enabling sustainable urban development through intelligent energy distribution, smart metering, and energy-efficient strategies. Emerging technologies like non-intrusive load monitoring (NILM) play a key role in energy disaggregation, enhancing energy efficiency and enabling personalized energy consumption insights for residential and industrial applications. This Topic explores the latest research, technological breakthroughs, and case studies on smart grids, NILM, energy storage, and their integration into smart city infrastructures. This discussion highlights challenges, emerging trends, and innovations shaping the future of energy management and urban sustainability.

Prof. Dr. Alfredo Alcayde
Prof. Dr. John Alexander Taborda-Giraldo
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Processes processes	2.8	5.5	2013	14.9 Days	CHF 2400	Submit
Smart Cities smartcities	5.5	14.7	2018	28.4 Days	CHF 2000	Submit
Sustainability sustainability	3.3	7.7	2009	19.7 Days	CHF 2400	Submit
Electricity electricity	1.8	5.1	2020	27.9 Days	CHF 1000	Submit
Inventions inventions	1.9	4.9	2016	18.5 Days	CHF 1800	Submit
Batteries batteries	4.8	6.6	2015	19.7 Days	CHF 2700	Submit

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Published Papers (3 papers)

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All Electronics Energies Processes Smart Cities Sustainability Electricity Inventions Batteries

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20 pages, 2045 KiB  

Open AccessArticle

Multi-Objective Optimization of Offshore Wind Farm Configuration for Energy Storage Based on NSGA-II

by Xin Lin, Wenchuan Meng, Ming Yu, Zaimin Yang, Qideng Luo, Zhi Rao, Jingkang Peng and Yingquan Chen

Energies 2025, 18(12), 3061; https://doi.org/10.3390/en18123061 - 10 Jun 2025

Viewed by 274

Abstract

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains [...] Read more.

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains an urgent challenge to be addressed, especially against the backdrop of widespread spot trading in the electricity market. How to achieve effective wind power stabilization at the lowest cost has become a key issue. This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. NSGA-II performed stably in dual-objective scenarios and effectively balanced the relationship between the investment cost of the energy storage system and power fluctuations through the explicit elite strategy. Furthermore, this study analyzed the correlation between the rated power and rated capacity of the energy storage system and the battery life, and corrected the battery life of the Pareto frontier solution obtained by NSGA-II. The research results show that when only considering the investment cost of the energy storage, the optimal configuration was a rated power of 4 MW and a rated capacity of 28 MWh, which could better balance the investment economy and power fluctuation. When further considering the participation of energy storage systems in the electricity spot market, the economic efficiency of the energy storage systems could be significantly improved through the fixed-period electricity price arbitrage method. At this point, the optimal configuration was a rated power of 8 MW and a rated capacity of 37 MWh. The corresponding project investment cost was CNY 242.77 million, and the annual fluctuation rate of the wind power output decreased to 17.84%. Full article

(This article belongs to the Topic Recent Advances in Smart Grid and Energy Storage Applications)

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18 pages, 2731 KiB  

Open AccessArticle

Prediction of Dissolved Gas in Transformer Oil Based on Variational Mode Decomposition Integrated with Long Short-Term Memory

by Guoping Chen, Jianhong Li, Yong Li, Xinming Hu, Jian Wang and Tao Li

Processes 2025, 13(5), 1446; https://doi.org/10.3390/pr13051446 - 9 May 2025

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Abstract

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), [...] Read more.

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH₄ test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data. Full article

(This article belongs to the Topic Recent Advances in Smart Grid and Energy Storage Applications)

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16 pages, 3414 KiB  

Open AccessArticle

Efficiency Improvement in a Crude Oil Heating Furnace Based on Linear Regulation Control Strategies

by Francisco Jacas-Portuondo, Leonardo Peña-Pupo, Miguel R. Forgas-Brioso, Electo E. Silva-Lora, John A. Taborda-Giraldo and José R. Nuñez-Alvarez

Energies 2025, 18(7), 1578; https://doi.org/10.3390/en18071578 - 21 Mar 2025

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Abstract

This paper presents research results to improve energy efficiency in one of the crude oil heating furnaces at the “Hermanos Díaz” refinery in Santiago de Cuba, Cuba. It analyzes the main process’s variables and disturbances, and the multivariate dynamic behavior of the F-101 [...] Read more.

This paper presents research results to improve energy efficiency in one of the crude oil heating furnaces at the “Hermanos Díaz” refinery in Santiago de Cuba, Cuba. It analyzes the main process’s variables and disturbances, and the multivariate dynamic behavior of the F-101 furnace temperature is characterized to evaluate different control strategies. In addition, the design of a linear regulation control law was implemented as a way to solve the limitations of the existing control of the furnace, to control the plant for the first time with a multivariable approach, demonstrating superior performance by guaranteeing decoupling between the variables, decreasing the overruns by 6%, and increasing the response speed of the system by more than 5 min. The comparison with results obtained with other control strategies allowed us to determine the better performance of the furnace by increasing its energy efficiency, evidencing the economic and environmental impact and obtaining as benefits a better dynamic behavior by reducing fuel oil consumption by 5%, equivalent to 0.74 m³/day, which reduces the operating costs of the plant, the temperature of the gasses by 2%, emissions of CO₂ pollutant gas to the environment by between 3 and 5%, and increasing energy efficiency by 1.5%. Full article

(This article belongs to the Topic Recent Advances in Smart Grid and Energy Storage Applications)

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