Power Energy Storage Research Articles

Construction of Ti3C2Tx MXene with Hopping Migration Mechanism via Deep Eutectic Supramolecular Polymers Anode for Sodium Ion Batteries

AbstractThe potential applications of high‐power sodium‐ion batteries are numerous, including use in distributed energy storage power plants and electric vehicles. In order to develop anode materials with fast sodium‐ion reaction kinetics and high reversibility, the use of deep eutectic supramolecular polymers to electrode materials is pioneered. The LA‐DESP is in situ polymerized on the Ti3C2Tx MXene surface, resulting in electrode materials that exhibit rapid reaction kinetics and high‐capacity retention. The supramolecular polymer network constructs channels for high‐speed transport of ions and electrons, and also provides more Na+ pseudocapacitance storage sites. In addition, the superior mechanical properties of the polymer network help protect MXene structure from damage during cycling, resulting in ultrastable Na+ storage. The hopping migration mechanism of Na+ through the polymer‐constructed ion transport channels is proposed. As the anode of SIBs half‐cells, it exhibits remarkable cycling performance of 114.4 mAh g−1 and a capacity loss of only 5.1% at 1000 mA g−1 after 1500 cycles. This study opens a new way to rationally design the structure of MXene polymers and promotes the application of deep eutectic supramolecular polymers in electrodes.

Smart Flexible Fabrics for Energy Storage, Self‐Heating, Energy Harvesting, and Self‐Powered Motion Sensing at Low Temperatures

AbstractEnergy harvesting and storage at extreme temperatures are significant challenges for flexible wearable devices. This study innovatively developed a dynamic‐bond‐cross–linked spinnable azopolymer‐based smart fabric (PAzo‐M/PVA, M = Mg, Ca, Zn) capable of photothermal energy storage, light‐induced self‐heating, mechanical energy harvesting, and self‐powered motion sensing under cold conditions, overcoming issues like low energy density and poor structural stability when azopolymers are combined with other fabrics via impregnation or spraying. PAzo‐Mg, operating without solvents, demonstrated high energy density (264.8 J g−1) and long‐term stability (14 days). Upon light excitation at −20 °C, this fabric achieved the highest temperature increase (9.3 °C) and sustained self‐heating for 45 minutes. A triboelectric nanogenerator based on this fabric achieved a maximum output power density of 3.43 W m−2 and demonstrated excellent durability (≈10 000 cycles) at −20 °C, with light‐induced trans/cis isomerization and dynamic bond formation/dissociation affected the electrical output, a phenomenon not previously reported. Moreover, a self‐powered motion sensor embedded with this fabric successfully detected subtle pulse variations during outdoor human activities at −18 to −21 °C. This smart fabric combines energy storage, self‐heating, and triboelectric power generation at low temperatures, providing a feasible solution for creating flexible wearable devices for complex environments.

A Bi-Level Optimization Scheme for Energy Storage Configuration in High PV-Penetrated Distribution Networks Based on Improved Voltage Sensitivity Strategy

This study proposes a bi-level optimization strategy to determine the optimal location and capacity for energy storage systems. Before applying the proposed bi-level optimization scheme, the PV output data are clustered by the K-means algorithm, and an active power–voltage sensitivity strategy is presented to identify nodes with high sensitivity in the clustered PV output data. Based on the data of the clustered nodes, a bi-level optimization model is established to determine the optimal location, rated power, and capacity of energy storage using the particle swarm optimization (PSO) algorithm. Finally, compared with traditional voltage sensitivity methods, some case studies with the proposed scheme are executed in the IEEE-33 bus system, which reveals better performance in reducing voltage deviation, network losses, and economic costs.

Risk‐Based Expansion Planning of Regional Energy Networks

ABSTRACTReliable power supply provision for customers is very important for electrical energy providers. The emergence of regional energy networks (RENs), and electricity market developments add to the above challenges. RENs consist of natural gas and electricity as inputs and energy storage systems (ESSs), combined heat and power (CHP) systems, thermal energy storages (TESs), boilers, photovoltaics (PVs) and wind turbines (WTs) as resources. In this paper, a risk‐based expansion planning model is developed for RENs considering operation and investment costs. PVs and WTs power generation, demand response (DR), and failures of tie switches between REN and distribution networks are defined as RENs operation uncertainties. Reliability index is formulated and Probability‐Tree tool is used for scenario generation. The mentioned optimization problem is solved using a genetic algorithm (GA). We use 25‐Bus IEEE standard distribution network to verify the efficiency and performance of the proposed model. Two RENs are connected to the test system and simulation results are obtained for two case studies. Simulation results show that if the probability of failure of tie switches is considered, the investment and operation costs of RENs are increased by about 92% in the planning horizon.

Advanced Air Electrodes for Reversible Protonic Ceramic Electrochemical Cells: A Comprehensive Review

AbstractReversible protonic ceramic electrochemical cells (R‐PCECs) have great potential for efficient and clean power generation, energy storage, and sustainable synthesis of high‐value chemicals. However, the sluggish and unstable kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in the air electrode hinder the R‐PCEC development. Durable H+/e−/O2− triple‐conducting air electrode materials are promising for enhancing reaction kinetics and improving catalytical stability. This review synthesizes the recent progress in triple‐conducting air electrodes, focusing on their working mechanisms, including electrode kinetics, lattice and its defect structure in oxides, and the generation and transport processes of H+, O2−, and e−. It also examines the required physicochemical properties and their influencing factors. By synthesizing and critically analyzing the latest theoretical frameworks, advanced materials, and regulation strategies, this review outlines the challenges and prospects shaping the future of R‐PCEC technology and air electrode development. Based on these theories and multiple strategies about the bulk triple conducting properties and surface chemical states, this review provides practical guidance for the rational design and development of efficient and stable air electrode materials for R‐PCECs and related electrocatalytic materials.

Stochastic Optimal Scheduling of Flexible Traction Power Supply System for Heavy Haul Railway Considering the Online Degradation of Energy Storage

The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system.

Energy storage configuration considering user-shared costs in peak shaving auxiliary services with improved multi-objective particle swarm optimization

To enhance peak-shaving and valley-filling performance in residential microgrids while reducing the costs associated with energy storage systems, this paper selects retired power batteries as the storage solution, breaking through existing optimization models. This research incorporates the simultaneous participation of multiple stakeholders, including consumers and energy storage providers, into the peak shaving process. It introduces an optimized configuration method for microgrid energy storage using retired power batteries, which also accounts for the equitable distribution of peak shaving auxiliary service costs among users. Moreover, an improved particle swarm optimization algorithm, specifically adapted for this model, is developed. The methodology begins with the introduction of mathematical models for power generation output, load calculations, and energy storage charging and discharging dynamics. The objective functions are designed to minimize both the cost of implementing the retired power battery storage system and the distribution of peak shaving auxiliary service costs among users. These functions are addressed using the improved multi-objective particle swarm optimization algorithm. This paper concludes with a case study of a residential microgrid, comparing and analyzing the economic and operational effectiveness of the proposed method. The simulation results corroborate the validity and efficacy of incorporating user cost-sharing for auxiliary peak shaving into the optimization of microgrid retired power battery storage configurations.

Analysis of the parameters of local power system devices with solar power plants and energy storage facilities and determination of operating modes during various periods of power outages

Analysis of the parameters of local power system devices with solar power plants and energy storage facilities and determination of operating modes during various periods of power outages

Technical viability of 136 MWh PV-biogas-battery energy storage power plant: Policy guidelines for BESS based-VRE integration deployment in Thailand

Technical viability of 136 MWh PV-biogas-battery energy storage power plant: Policy guidelines for BESS based-VRE integration deployment in Thailand

A novel LSTPA methodology for managing energy in electrical/thermal microgrids through CHP, battery resources, thermal storage, and demand-side strategies

This paper presents a stochastic optimization model for integrated energy management in electrical and thermal microgrids, addressing uncertainties in renewable energy resources. The model optimizes the placement of combined heat and power (CHP) systems, energy storage, and demand-side management for both islanded and grid-connected operations. A multi-objective function is formulated to minimize energy losses, voltage deviations, costs, and renewable supply interruptions. The Large-Scale Two-Population Algorithm (LSTPA) is employed to solve the problem, with the IEEE 69-bus network as a case study. Results indicate that the proposed approach reduces energy losses to 3634 kWh, improves voltage stability to 0.9828 p.u., and lowers operational costs to $2845 in islanded mode. The findings demonstrate that increasing CHP units enhances system performance, reducing losses from 4280 kWh to 3634 kWh. This study offers valuable insights for policymakers and system operators in optimizing microgrid energy management while balancing efficiency, cost, and reliability. Future work will explore grid integration challenges and advanced control techniques to further optimize microgrid performance.

Carbon Felt/Nickel Oxide/Polyaniline Nanocomposite as a Bifunctional Anode for Simultaneous Power Generation and Energy Storage in a Dual-Chamber MFC

Microbial fuel cell (MFC) technology has become a novel and attractive method for generating renewable energy during wastewater treatment. In this study, researchers combined carbon felt (CF), metal oxide (NiO), and polyaniline (PANI) to prepare CF/NiO/PANI multilayer capacitive bioelectrodes. The MFC equipped with a CF/NiO/PANI bioanode has a peak power density of 1988.31 ± 50.96 mW/m2, which is 3.8 times higher than that of the MFC with a bare CF electrode, having a peak power density of 518.29 ± 27.07 mW/m2. Charge–discharge cycle tests show that the storage charge capacity of the CF/NiO/PANI bioanode is 3304.64 C/m2, which is 10.5 times greater than that of the bare CF anode. The electrochemical, morphological, and chemical properties of the prepared anodes are characterized using techniques such as SEM, EDS, FTIR, XPS, and XRD. Notably, high-throughput sequencing reveals that electrogenic bacteria account for 79.2% of the total microbial population on the CF/NiO/PANI multilayer capacitive bioelectrode. The synergistic effects of the composite materials result in the formation of a richer biofilm on the electrode surface, providing more active sites and enhancing capacitive characteristics. This innovative approach significantly improves the output power and peak current of MFCs, while also endowing the electrode with dual functions of simultaneous power generation and energy storage.

Graphene going green—Today’s energy scenarios

Nowadays, mainstream research on graphene trends towards employing environmentally friendly or green synthesis routes and precursors, and ensuing green graphene nanomaterials are shown to be highly beneficial for a range of scientific applications, from energy/electronics to engineering to biomedical arenas. Specifically, graphene has emerged as a leading contender for designing green or ecological energy conversion (solar cells, fuel cells) and energy storage (supercapacitors, batteries) devices/systems. In this perspective article, we basically aim to highlight state-of-the-art advancements of green-sourced graphene and related nanomaterials in today’s energy sectors. According to scientific endeavors so far on green graphene, its successful design, real-world energy conversion/storage device application, and commercialization depend upon resolving underlying challenges of synthesis/performance. We observe notable applications of green graphene in the fields of photovoltaics, fuel cells, capacitors, and batteries. Herein, we suggest comprehensive future surveys for advanced fabrication techniques and sustainable sources/techniques to develop next-generation green graphene-derived energy systems with superior energy storage capacities and power outputs.

Modification of Lithium-Rich Layered Material Li1.5Ni0.17Co0.16Mn0.67O2.5 Coated with Solid Electrolyte (Li2ZrO3)

With the rising popularity of electric vehicles and the widespread deployment of energy storage power stations. The demand for high-energy-density lithium-ion batteries is increasing day by day. Lithium-rich layered materials are among the most promising candidates for the cathode of next-generation lithium-ion batteries due to their high energy density, cost-effectiveness, and advantages in safety and environmental protection. However, the occurrence of side reactions between lithium-rich layered materials and electrolytes has led to poor performance in later stages, posing challenges to their commercial viability. In this study, we enhance the electrochemical performance of lithium-rich layered cathode materials by applying varying amounts of solid electrolyte Li2ZrO3 as a coating on their surfaces. By precipitating ZrO2 onto the surface of the precursor, we successfully sinter both the lithium-rich layered material and the coated material simultaneously, thereby reducing processing costs. The experimental results show that the coated material has more excellent electrochemical performance, specifically, when the coating amount is 1%, compared with the uncoated sample, the first Coulombic efficiency is improved from 56.9% to 63%, and after 500 charge/discharge cycles, the coated sample still has a capacity retention rate of more than 60%; Additionally, the Li2ZrO3 coating significantly improves the rate performance of the material, at a rate of 5 C, the specific discharge capacity improved from 102.2 mAh·g−1 for the uncoated material to 137.3 mAh·g−1. The reaction mechanism was investigated by cyclic voltammetry and AC impedance test, and the results showed that the appropriate amount of Li2ZrO3 coating can effectively reduce the side reaction between the material and the electrolyte, improve the transport performance of lithium ions in the material, and then enhance the overall electrochemical performance of the material.

Principle of MXene Supercapacitors and Their Applications in Power Electronics

This paper systematically explores the fundamental properties of MXene materials and their potential applications in the field of power electronics. As an emerging two-dimensional material, MXene exhibits excellent conductivity, good thermal conductivity, and highly tunable structures, thereby demonstrating significant advantages in applications such as energy storage, heat dissipation, and flexible electronics. The paper first introduces the structural characteristics and preparation methods of MXene, followed by a detailed analysis of its applications in various power electronic devices, including supercapacitors, batteries, and energy storage systems. Finally, the challenges MXene faces in practical applications are discussed, along with possible future development directions. Through this analysis, the paper aims to provide theoretical support and reference for the further research and application of MXene materials in the field of power electronics.

Coordinated Optimization Configuration of Wind-PV-Storage in Park Microgrids

Park microgrids integrate wind power, photovoltaic (PV) power, and the main power grid to meet load demands. To improve the utilization of wind and solar power, energy storage systems are configured to address the mismatch between load demand and generation schedules, thereby reducing energy curtailment. Based on actual generation and consumption data from different parks, this study establishes a mathematical model to optimize energy storage configuration and power purchasing strategies and analyzes their impact on economic performance. By conducting comparative analyses of independent and collaborative park operation models, this study investigates the economic benefits of coordinated optimization of wind, solar, and storage systems and proposes the optimal energy storage configuration strategy, offering significant reference value for park microgrid planning and operation. Initially, the data from Appendix 2 were processed to derive the actual wind and PV power generation of individual parks and the collaborative park at each time period. These figures were compared against the corresponding load demand to calculate the power purchase volume for each time period. Without considering energy storage configurations, a mathematical model and associated formulas were developed to define boundary power regulation and calculate the power purchase volume, total purchase volume, total power supply cost, and energy curtailment for each park at different times. Using traditional optimization methods, the economic performance was evaluated by maximizing the utilization rate of wind and solar power and solving for the unit power supply cost and energy curtailment. Finally, the models and data established in this study were analyzed and summarized to determine the optimal configuration scheme for wind-PV-storage systems.

Reliability enhancement with coordinated operation of wind power and battery energy storage using machine learning based unit commitment decision

Reliability enhancement with coordinated operation of wind power and battery energy storage using machine learning based unit commitment decision

Ecological power of energy storage, clean fuel innovation, and energy-related research and development technologies

Ecological power of energy storage, clean fuel innovation, and energy-related research and development technologies

Adaptive current differential protection principle for transmission line connected to energy storage power station based on phase and amplitude compensation

Adaptive current differential protection principle for transmission line connected to energy storage power station based on phase and amplitude compensation

RUL Prediction Method for Lithium‐Ion Batteries Based on the SOA‐ELM Algorithm

ABSTRACTWith the rapid advancement of electrochemical energy storage power stations and electric vehicles, lithium‐ion batteries have gained widespread adoption due to their high specific energy and superior power performance. However, the increasing frequency of safety incidents in electrochemical energy storage facilities in recent years has raised significant concerns. Effective monitoring of lithium‐ion battery conditions is crucial to ensure the safety of power systems and support the sustainable growth of the electrochemical energy storage industry. Among the key challenges, accurate prediction of the remaining useful life (RUL) of lithium‐ion batteries is essential for maintaining the safe and reliable operation of battery management systems. This paper proposes an advanced RUL prediction model that combines the seagull optimization algorithm (SOA) with the extreme learning machine (ELM) to enhance prediction accuracy. The proposed SOA‐ELM model is validated using the NASA dataset, and the results demonstrate its effectiveness and potential in improving RUL prediction for lithium‐ion batteries. This study contributes to the development of more reliable and efficient battery management systems, paving the way for safer and more sustainable energy storage solutions.

Research on the Construction Process Scheme of Artificial Chamber of Compressed Air Energy Storage Power Station

AbstractThe introduction of a new power system centered on renewable energy presents significant opportunities for compressed air energy storage (CAES), which boasts noteworthy advantages such as scalability, cost‐effectiveness, longevity, and rapid construction timelines. However, a considerable constraint on the advancement of affordable air energy storage is the need for substantial gas storage capacity. For instance, a single compressed air project with a capacity of 300 MW over 5 hours necessitates more than 500,000 cubic meters of gas storage space. Due to the extensive gas storage requirements of large‐scale CAES facilities, surface storage solutions are typically only viable for smaller power stations and are largely confined to experimental phases. Furthermore, the capital investment for these above‐ground facilities tends to be higher than that for underground alternatives. Consequently, to optimize both economic viability and storage capacity, underground gas storage solutions are predominantly utilized in projects currently under development or in the planning stage. Gas storage infrastructure represents a crucial component of a CAES power station, serving as a key determinant for both construction costs and site selection as well as being pivotal to the technical efficiency and safety of energy operations. This paper integrates hydropower and extraction construction methodologies, thoroughly evaluates the economic implications and periodic nature of construction, and analyzes the strengths and weaknesses of various construction techniques in relation to specific projects. This analysis aims to facilitate and inform the large‐scale implementation of forthcoming compressed air energy storage initiatives.