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Modeling and Optimization of Energy Storage in Power Systems
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Modeling and Optimization of Energy Storage in Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 10489

Special Issue Editor

Prof. Dr. Pengfei Hu

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: grid integration of renewable energy generation, flexible power transmission and distribution technologies; large-scale energy storage technologies and their applications in power systems; energy digitalization and smart grid IoT technologies

Special Issue Information

Dear Colleagues,

Energy storage systems (ESSs) have emerged as a critical technology in the global transition toward sustainable and carbon-neutral power systems. As renewable energy sources like solar and wind continue to dominate the energy landscape, traditional power systems—designed for centralized generation and predictable demand—face significant challenges in managing intermittency, grid stability, and system flexibility. These challenges necessitate innovative solutions to ensure reliable and efficient operation in modern power grids. Energy storage systems offer a dynamic and versatile approach to addressing these issues by enabling real-time power balancing, load leveling, and enhanced grid resilience. However, the effective deployment and optimization of ESSs require advanced modeling frameworks, control strategies, and integration methodologies that balance technical performance with economic viability.

This Special Issue aims to advance the practical implementation of energy storage in power systems and explore cutting-edge developments in ESS technologies, system-level modeling, control strategies, and optimization frameworks.

Topics of interest for publication include, but are not limited to, the following:

  • Advanced energy materials for electrodes, electrolytes, and separators to enhance electrical performance and efficiency.
  • Innovations in lithium-ion, solid-state flow, and other advanced battery technologies.
  • The design of control strategies for integrating energy storage systems into power grids to achieve efficient energy management, load leveling, and improved grid stability.
  • The development of modeling frameworks for energy storage systems within power systems.
  • The development of advanced energy management systems (EMSs) for the optimal control and operation of BESSs within the electrical grid.
  • Research on the optimization and coordinated control of hybrid energy storage systems to enhance overall performance and cost-effectiveness.
  • The investigation of economic dispatch models that incorporate energy storage systems to optimize operational costs and maximize economic benefits.
  • The lifecycle assessment (LCA) of battery technologies to evaluate their environmental and economic impact on electrical applications.
  • Grid-scale applications of BESSs for distributed energy resource management, demand response, frequency regulation, voltage support, and islanded operation scenarios.
  • Research on energy storage systems to enhance grid reliability and resilience during extreme weather and emergencies.
  • Analysis of policy frameworks and market mechanisms that support the deployment of energy storage systems.

Prof. Dr. Pengfei Hu
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • energy storage systems
  • hybrid storage systems
  • system-level modeling
  • optimization strategies
  • renewable energy integration
  • economic dispatch models

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Published Papers (4 papers)

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Research

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30 pages, 1536 KB  
Article
Behaviorally Aware Pricing of Energy Storage as a Service Platform: A Prospect Theory-Based Bi-Level Framework
by Seyed Shahin Parvar, Nima Amjady and Hamidreza Zareipour
Energies 2026, 19(11), 2493; https://doi.org/10.3390/en19112493 - 22 May 2026
Viewed by 77
Abstract
The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, [...] Read more.
The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article
(This article belongs to the Special Issue Modeling and Optimization of Energy Storage in Power Systems)
22 pages, 9175 KB  
Article
Bi-Level Optimization-Based Bidding Strategy for Energy Storage Using Two-Stage Stochastic Programming
by Kui Hua, Qingshan Xu, Lele Fang and Xin Xu
Energies 2025, 18(16), 4447; https://doi.org/10.3390/en18164447 - 21 Aug 2025
Cited by 3 | Viewed by 2274
Abstract
Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in [...] Read more.
Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article
(This article belongs to the Special Issue Modeling and Optimization of Energy Storage in Power Systems)
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Review

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53 pages, 4686 KB  
Review
Aggregation of Distributed Energy Resources and Energy Storage Systems in Active Distribution Networks: A Critical Review
by Pranta Dash Gupta, Najma Habeeb, Rakibuzzaman Shah and Nima Amjady
Energies 2026, 19(6), 1579; https://doi.org/10.3390/en19061579 - 23 Mar 2026
Viewed by 992
Abstract
The transition of modern power systems is going through the challenges of uncertainties originating from equipment unavailability, forecasting errors, market fluctuations, prosumer behaviors, regulatory and policy changes, and extreme weather conditions. These uncertainties can cause deviations from the planned operating points leading to [...] Read more.
The transition of modern power systems is going through the challenges of uncertainties originating from equipment unavailability, forecasting errors, market fluctuations, prosumer behaviors, regulatory and policy changes, and extreme weather conditions. These uncertainties can cause deviations from the planned operating points leading to non-optimal and even infeasible operation conditions. Energy storage systems (ESSs) can address these challenges in active distribution networks by compensating deviations caused by uncertainties. Consequently, aggregating distributed energy resources (DERs) and ESSs in active distribution networks is a key research area. This paper first introduces these uncertainties and their imposed challenges on aggregated systems. Moreover, correlations and interdependencies among uncertainties and their impacts on aggregating DERs and ESSs are thoroughly investigated. Subsequently, a critical review of the state-of-the-art aggregation optimization approaches is presented, and the comparison is made between static and dynamic DER-ESS aggregation processes. Next, the practical requirements and applications of DER-ESS aggregation are investigated. Finally, conclusions and future research directions in the area of DER-ESS aggregation are presented. Full article
(This article belongs to the Special Issue Modeling and Optimization of Energy Storage in Power Systems)
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28 pages, 4886 KB  
Review
Energy Storage Systems for AI Data Centers: A Review of Technologies, Characteristics, and Applicability
by Saifur Rahman and Tafsir Ahmed Khan
Energies 2026, 19(3), 634; https://doi.org/10.3390/en19030634 - 26 Jan 2026
Cited by 1 | Viewed by 6388
Abstract
The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand [...] Read more.
The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article
(This article belongs to the Special Issue Modeling and Optimization of Energy Storage in Power Systems)
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