Advanced Energy Storage and Load Forecasting Solutions in Modern Distribution Networks
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".
Deadline for manuscript submissions: 25 February 2026 | Viewed by 29
Special Issue Editor
Interests: renewable energy; energy storages; microgrids; electrical machines and drives; hybrid electric systems; condition monitoring; instrumentation and control
Special Issue Information
Dear Colleagues,
Energy storage in a distribution network involves the use of technologies, including batteries and pumped hydro, to store excess electricity from sources such as renewables and discharge it when demand is high or supply is low, which helps stabilize the grid, improve power quality, manage load, and facilitate the integration of renewable energy. These systems, known as Distributed Energy Storage (DES), enhance the grid's reliability and adaptability by acting like a "savings account" for electricity, smoothing out the variability in renewable sources and reducing costs for consumers and operators. Some of these energy storage options open up additional revenue streams, such as Frequency Control Ancillary Services (FCAS), for the operators, in addition to simple energy arbitrage.
Dispatching energy in a distribution network involves intelligently managing the flow of electricity from substations to end-users, often incorporating distributed energy resources (DERs) such as solar panels and batteries. Key tasks include balancing supply and demand, ensuring safety and reliability, minimizing outages, optimizing economic efficiency, and integrating new renewable energy sources. Load forecasting becomes a key aspect of realising effective and profitable dispatching of energy from DERs.
Advanced approaches to modern load forecasting techniques rely heavily on machine learning (ML) and deep learning (DL) models, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNNs), and variants of neural networks, to capture complex patterns and improve accuracy. These techniques incorporate external factors like weather data and handle probabilistic forecasting to predict not just a single load value but also the potential range of demand. For energy storage, advanced methods use these load forecasts to optimize system sizing, placement, and charge/discharge schedules, aiming to mitigate issues like voltage fluctuations, improve grid stability, and reduce energy losses and costs, often within a robust planning framework.
Considering the significance of energy storage and its close association with load forecasting, this Special Issue will provide an excellent platform for leading researchers in these areas to share their findings with the wider scientific and industrial communities.
Topics of interest for publication include, but are not limited to, the following:
- Novel energy storage solutions and applications in modern distribution networks
- Innovative applications of energy storage solutions in demand management, grid-stabilisation, power quality improvement, load management, and renewable energy integration
- Frequency Control Ancillary Services (FCAS) and other economic benefits of energy storage.
- Dispatching energy in a distribution network
- Advanced approaches to modern load forecasting
- Optimised use of energy storage combined with load forecasting
Dr. Sanath Alahakoon
Guest Editor
Manuscript Submission Information
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Keywords
- distributed energy storage (DES)
- frequency control ancillary services (FCAS)
- grid-stabilisation
- power quality improvement
- demand management
- renewable energy integration
- load forecasting
- machine learning (ML)
- artificial intelligence (AI)
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