Hybrid Storage System Research Articles

A remote and cost‐optimized voting system using blockchain and smart contract

AbstractTraditional voting procedures are non‐remote, time‐consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters consider this voting system untrustworthy and manipulative, discouraging them from voting, and consequently, an election loses a significant number of participants. Although the inclusion of electronic voting systems (EVS) has increased efficiency; however, it has raised concerns over security, legitimacy, and transparency. To mitigate these problems, blockchain technology has been leveraged and smart contract facilities with a combination of artificial intelligence (AI) to propose a remote voting system that makes the overall voting procedure transparent, semi‐decentralized, and secure. In addition, a system that aids in boosting the number of turnouts in an election through an incentivization policy for the voters have also developed. Through the proposed virtual campaigning feature, the authority can generate a decent amount of revenue, which downsizes the overall cost of an election. To reduce the associated cost of transactions using smart contracts, this system implements a hybrid storage system where only a few cardinal data are stored in the blockchain network.

Control-Oriented Model of an Optimally Designed Hybrid Storage System for a Standalone Microgrid

Control-Oriented Model of an Optimally Designed Hybrid Storage System for a Standalone Microgrid

Metro traction power measurements sizing a hybrid energy storage system utilizing trains regenerative braking

The paper describes the measuring systems and methodology for acquiring traction power measurements on the on-board traction systems of two metro trains and three 750 V DC rectifier substations in the Athens Metro Line 2. Being part of a wider investigation to develop a Hybrid Energy Storage System (HESS), the purpose of the present measurements is to provide traction systems experimental and operational data that would assist in the understanding of the dynamic characteristics of train braking regeneration and quantify the available energy that could be stored in a super-capacitors/batteries hybrid storage system for further use, which would otherwise be wasted and consumed as heat in stations and tunnels through the trains' braking resistors. In turn the stored energy could power upon demand selected stationary electrical loads in Metro stations of a non-safety critical character (such as lighting, ventilation, pumps, etc.) leading to very significant energy savings and to a corresponding reduction of greenhouse gases. Preliminary results confirm the feasibility of the energy saving concept indicating a significant potential for the hybrid energy storage devices and subsequent energy re-use of 4000–6000 kWh/day per rectifier substation of otherwise unused train braking energy, with a typical Metro station stationary loads consumption of 2000 kWh/day.

Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets

More energy flexibility is required to alleviate the stress of power systems caused by intermittent and weather-dependent solar and wind power. The utilization of demand-side flexibility (e.g., flexibility management strategies and distributed storage technologies) can be considered a win-win approach for demand-side users and power grids. However, the economic benefits of distributed energy storage systems in buildings are usually underestimated without considering the full-scale flexibility utilization, which may impede storage investments. Meanwhile, the disposal problem of retired EV batteries is becoming more serious. Repurposing these batteries for stationary applications in buildings seems cost-effective and eco-friendly. Therefore, this study first proposes novel optimal dispatch strategies for different storage systems in buildings to maximize their benefits from providing multiple grid flexibility services simultaneously, and then conducts a comparative life-cycle economic analysis on thermal energy storage, new and second-life batteries. The optimal configuration of hybrid storage systems is also analyzed to facilitate the decision-making of building owners/operators. Test results show that thermal energy storage and electrical energy storage can increase the economic benefits by 13% and 2.6 times, respectively. Battery storage may no longer be an expensive option for building-scale investment due to downward trends in capacity costs and environmental impacts.

Numerical Investigation of a Fuel Cell-Powered Agricultural Tractor

In recent years, growing awareness about environmental issues is pushing humankind to explore innovative technologies to reduce the anthropogenic sources of pollutants. Among these sources, internal combustion engines in non-road mobile machinery (NRMM), such as agricultural tractors, are one of the most important. The aim of this work is to explore the possibility of replacing the conventional diesel engine with an electric powertrain powered by a hybrid storage system, consisting of a small battery pack and a fuel-cell system. The battery pack (BP) is necessary to help the fuel cell manage sudden peaks in power demands. Numerical models of the conventional powertrain and a fuel-cell tractor were carried out. To compare the two powertrains, work cycles derived from data collected during real operative conditions were exploited and simulated. For the fuel-cell tractor, a control strategy to split the electric power between the battery pack and the fuel cell was explored. The powertrains were compared in terms of greenhouse gas emissions (GHG) according to well-to-wheel (WTW) equivalent CO2 emission factors available in the literature. Considering the actual state-of-the-art hydrogen production methods, the simulation results showed that the fuel-cell/battery powertrain was able to accomplish the tasks with a reduction of about 50% of the equivalent CO2 emissions compared to traditional diesel-powered vehicles.

The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems

With the surge in the amount of data in the internal and external environment, the collection, analysis, processing, and storage of the increasing data sources and data volume, as well as the problems of big data management, are the current situation and dilemmas of data management faced by enterprises today. Cognitive science and big data technology can provide good auxiliary support for enterprise management decision-making. This study takes the business information management of cold chain logistics enterprises as an example, aiming at the characteristics of business intelligence data in real applications, based on cognitive science and big data technology, from low-cost and high-performance storage, security management, and big data analysis. This paper is mainly through the research of big data processing theory and key technologies. Based on analyzing the logistics industry’s data access rules and characteristics, this study proposes a hot data prediction model for multilayer hybrid storage systems. It is verified that the prediction model has good accuracy, robustness, and universality. For the application scenario of multitenant distributed data access, a data transparent security management model is proposed. Simulation experiments show that this method can realize data security management when the performance loss is controlled within an acceptable range. Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.

Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm

In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently, provide a comfortable ride, and require fewer energy sources. In this research, energy management between the PV system and the hybrid energy storage system (HESS), including the battery, and UC are discussed. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. The proposed combined ANN–AOA approach takes full advantage of UC while limiting the battery discharge current, since it also mitigates high-speed dynamic battery charging and discharging currents. The responses’ behaviors are depicted and viewed in the MATLAB simulation environment to represent load variations and various road conditions. We also discuss the management among the PV system, battery, and UC to achieve the higher speed of 91 km/h when compared with existing Modified Harmony Search (MHS) and Genetic Algorithm-based Proportional Integral Derivative (GA-PID). The outcomes of this study could aid researchers and professionals from the automotive industry as well as various third parties involved in designing, maintaining, and evaluating a variety of energy sources and storage systems, especially renewable ones.

Electrothermal Multicriteria Comparative Analysis of Two Competitive Powertrains Applied to a Two Front Wheel Driven Electric Vehicle during Extreme Regenerative Braking Operations

The powertrain performance in an electric vehicle is fully dependent on the electrical and thermal constraints of the static converters ensuring the power transfer taking place between the energy storage systems and the electromechanical machines. These constraints depend on the architectures of the power converters, and their control strategies. Particularly, the maximal limits are reached in maneuvers such as hard regenerative braking circumstances. Indeed, braking recovery is a critical phase in the vehicle’s operation, and its duration and intensity may strongly impact the vehicle’s battery behavior or integrated hybrid storage system. The innovative objective of the paper is to propose an electrothermal multicriteria comparative study based on electrical and thermal criteria for two competitive powertrains. These semi-active power configurations (a 3-level DC/DC converter-based, and a Z-source converter-based) are implemented in a two-front wheel driven electric vehicle during extreme regenerative braking conditions. Open-loop and closed-loop controls were implemented in the Z-source using the maximal constant boost control with 3rd harmonic injection modulation technique. We considered two paralleled IGBT modules instead of the single shoot-through structure. Our approach is based on simulation during an extreme braking maneuver leading to heavy repercussions on the overall powertrain system. The aim is to investigate the challenging structure of the Z-source. Results showed that the proposed 3-level DC/DC-based topology has better performances in terms of power losses, efficiency, thermal behavior, and electromagnetic interference.

An Experimental Study of Power Smoothing Methods to Reduce Renewable Sources Fluctuations Using Supercapacitors and Lithium-Ion Batteries

The random nature of renewable sources causes power fluctuations affecting the stability in the utility grid. This problem has motivated the development of new power smoothing techniques using supercapacitors and batteries. However, experimental studies based on multiple renewable sources (photovoltaic, wind, hydrokinetic) that demonstrate the validity of power smoothing techniques under real conditions still require further study. For this reason, this article presents a feasibility study of a renewable grid-connected system, addressing various aspects based on power quality and energy management. The first of them is related to the fluctuations produced by the stochastic characteristics of renewable sources and demand. Two power smoothing algorithms are presented (ramp rate and moving average) combining photovoltaic, wind, and hydrokinetic sources with a hybrid storage system composed of supercapacitors and lithium-ion batteries. Then, the self-consumption for an industrial load is analyzed by studying the energy flows between the hybrid renewable energy sources and the grid. The main novelty of this paper is the operability of the supercapacitor. The experimental results show that when applying the power smoothing ramp rate method, the supercapacitor operates fewer cycles with respect to the moving average method. This result is maintained by varying the capacity of the renewable sources. Moreover, by increasing the capacity of photovoltaic and wind renewable sources, the hybrid storage system requires a greater capacity only of supercapacitors, while by increasing the capacity of hydrokinetic turbines, the battery requirement increases considerably. Finally, the cost of energy and self-consumption reach maximum values by increasing the capacity of the hydrokinetic turbines and batteries.

A Novel Stochastic Mixed-Integer-Linear-Logical Programming Model for Optimal Coordination of Hybrid Storage Systems in Isolated Microgrids Considering Demand Response

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as ‘green’ storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives.

Performance Evaluation and Techno-Economic Analysis of Modular Multilevel STATCOM with Hybrid Storage for Renewable Power Dominated Grids

The increase in renewable power injection into the existing ac network has affected voltage and frequency stability. This creates the need for both active and reactive power support systems at the Common Coupling Point (CCP) of a renewable power generating station. Considering the aforesaid requirements, a distinct configuration using a Modular Multilevel Converter (MMC) with Hybrid Storage System (HSS) has been recommended in this paper. These kinds of systems are termed as “Statcom Embedded with Storage System”(SESS). The SESS helps to pump invariable power into the grid by absorbing fluctuating components of active power generated by the renewable power generating plant. Apart from this, provides ancillary services at CCP like, voltage regulation and compensation for negative sequence currents, and ride through capability during the faults in the ac system are to be provided by SESS. The required control methods to operate the proposed configuration for the aforesaid necessities are discussed in this manuscript. The economic advantage of the proposed configuration in comparison to other multilevel topologies has been identified using a techno-economic study and it is presented. The competency of the recommended configuration for SESS application along with its control is verified through PSCAD/EMTDC simulation and the results show its efficacy.

Comparative analysis of HESS (battery/supercapacitor) for power smoothing of PV/HKT, simulation and experimental analysis

Photovoltaic and hydrokinetic systems are increasing their penetration in electrical distribution systems. This leads to problems of power fluctuations due to the intermittence of renewable sources that could compromise the stability and quality of the power grid. To address this issue, this paper presents a feasibility study of three power smoothing methods for a photovoltaic-hydrokinetic system using laboratory equipment to optimally replicate the real behavior of this type of hybrid system. The proposed algorithms are based on a hybrid storage system with supercapacitors and lithium-ion batteries, several analyzes are presented based on technical and economic parameters.The results demonstrate the feasibility of power smoothing methods for real systems, the comparison between the algorithms highlights the characteristics of the Enhanced Linear Exponential Smoothing Method, reducing the energy cost and regulating the point of common coupling voltage. Moreover, the sensitivity studies show that the energy exchange with the utility grid is affected according to the variations in the capacity of the batteries and the response to power smoothing can decrease or improve depending on the size of the supercapacitors.

Biomass fueled chemical looping hydrogen generation, high temperature solar thermal and thermochemical energy storage hybrid system

Over the past decades, environmental and atmospheric pollution have become a significant global issue. Considering the non-renewable resources in the energy supply sectors that are responsible for a major part of these pollutions, makes the situation only worse. Therefore, switching to power generation systems operating on renewable resources with little to none environmental contamination seems more urgent and attractive than ever. In this study, an integrated multi-generation system utilizing two types of clean and renewable fuels is presented. A novel system design comprising a concentrated solar thermal subsystem integrated with a thermochemical energy storage subsystem and a zero-emission subsystem using chemical looping technology for cogeneration of electricity, hydrogen, and heat is investigated. The produced syngas is divided into two parts to fuel the solid oxide fuel cell subsystem for power generation and the chemical looping subsystem for hydrogen production. An extensive performance analysis is conducted to evaluate the effects of various parameters on the system's performance in order to determine the optimal values which are reported. The solar subsystem simulated using Engineering Equation Solver software resulted in 77.69 % and 79 % for the receiver thermal efficiency and field optical efficiency, respectively. Also, the storage subsystem simulated using Aspen Plus software resulted in a maximum round-trip efficiency of 79.34 %. More, the produced power in the SOFC subsystem and the Lower heating value of the generated hydrogen are found to be 5099 kW and 6124.45 kW, respectively. The values of the SOFC, electrical, hydrogen production, total, net total, and CO2 separation efficiency rates are realized as 71.2 %, 51.21 %, 92.19 %, 60.57 %, 54.72 %, and 100 % respectively.

Two-layer robust optimization framework for resilience enhancement of microgrids considering hydrogen and electrical energy storage systems

This paper presents a two-layer framework for improving the resilience of a 118-bus active distribution network consisting of four microgrids, which includes hybrid storage systems, electric buses (EBs), and the direct load control (DLC) program. In the proposed model, the uncertainties of RESs generation, demand, and EBs’ mobility are considered, and the robust optimization approach is used to tackle them. In the first layer, the planning of each microgrid is done separately and the energy purchase/sale request is sent to the control center. Then in the second layer, the control center performs the planning of the main network according to the requested program of the microgrids. Note that in this layer, the control center is able to rearrange the distribution feeder and send EBs to vital points of the network. Finally, the validity of the proposed model is evaluated through the implementation on seven case studies and the results show that the presence of hydrogen and electrical storage devices reduces forced load shedding (FLS) by 45.03% and 12.19%, respectively, during emergency situations. In addition, the results indicate that robust planning and the use of EBs for network recovery increase the resilience index by 3.35% and 3.98%, respectively.

Coordinated multi-objective capacity optimization of wind-photovoltaic-pumped storage hybrid system

Complementary planning and operation of intermittent renewable energy with energy storage technology is a promising option to accommodate renewable energy generation. To improve the utilization rate of wind and photovoltaic generation, this paper proposes a multi-objective capacity optimization model of wind-solar-pumped storage hybrid system. The objectives of the proposed model include minimizing total investment cost, maximizing system economic revenue and maximizing the improvement of system operation. The transmission line capacity constraints are involved as well. The proposed multi-objective model is transformed into a mixed integer linear programming (MILP) problem using weighted factors. Case study is conducted to illustrate the capacity optimization results with different targets by adjusting weight coefficients.

Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks

The unbalance between the renewable energy sources and user loads reduces the performance improvement of regional integrated energy systems (RIES), in which the multi-energy storage system with battery and heat tank is necessarily integrated. This paper aims to optimize the sites and capacities of multi-energy storage systems in the RIES. A RIES model including renewable wind power, power distribution network, district heating network, multi-energy storage system, and heat pump to convert electricity to heat is constructed. An optimization method combining a mixed-integer nonlinear programming optimization model is proposed to minimize the comprehensive cost of RIES. The second-order cone relaxation method performs convex relaxation of the power flow constraints, and the district heat network is operated under a constant-flow-variable-temperature strategy. The original optimization model is transformed into a mixed-integer second-order cone programming problem to solve. Three solution methods, including enumeration method, improved enumeration method and genetic algorithm, are employed to determine the optimum installation locations, and they are compared in computation time and efficiency. The results demonstrate that the distributed hybrid integration of electricity and thermal energy storages improves the RIES economy by collaborating the power and heat balances and reducing the purchased electricity from the grid. By integrating the hybrid storage system, the curtailed wind is reduced by 53.9%, and the economy is improved by 13.4%.

Combination of Batteries and Hydrogen Tanks in a Flexible Hybrid Storage System

Combination of Batteries and Hydrogen Tanks in a Flexible Hybrid Storage System

A new predictive control strategy for improving operating performance of a permanent magnet synchronous generator-based wind energy and superconducting magnetic energy storage hybrid system integrated with grid

This research work proposes an unscented Kalman filter (UKF) as an observer for predictive current control (PCC) of a permanent magnetic synchronous generator (PMSG)-based wind energy conversion system (WECS) connected with a superconducting magnetic energy storage (SMES) system and the main power grid. The proposed UKF observer estimates the rotor angle, rotor angular speed, stator currents, and electromagnetic torque of PMSG connected in the proposed hybrid WECS-SMES configuration. The PCCs designed for both the rotor- and grid- side converters of the proposed hybrid configuration use estimated parameters instead of the measured quantities. Furthermore, due to the intermittent nature of wind energy, control of an energy storage device (which is SMES in this research) is necessary to mitigate the fluctuations in the DC-link voltage and output power being sent to the main power grid. Therefore, a novel control configuration based on fractional order-PI (FOPI) controllers has also been proposed for the SMES system to control the output power and the DC-link voltage. An artificial bee colony optimization algorithm has been used to tune the gains and integration operators of two FOPI controllers. For UKF estimation performance comparison, three popular observers i.e., Luenberger, sliding mode, and extended Kalman filter, have also been designed and implemented in MATLAB. Mean absolute percentage error and root mean squared error performance metrics are used to measure the estimation performance. The simulated results have demonstrated the superior performance of proposed the UKF observer for PCC and FOPI controllers for SMES under different realistic wind speed conditions and noisy measurements. The proposed UKF observer has improved estimation by up to 99.9 %. Lyapunov stability criteria are used to prove the stability of the proposed UKF based PCC control of hybrid WECS-SMES configuration.

Thermodynamic, economic and environmental investigations of a novel solar heat enhancing compressed air energy storage hybrid system and its energy release strategies

In this paper, a novel solar heat enhancing compressed air energy storage hybrid system is proposed, which mainly consist of three subsections: wind power sub-system, compressed air energy storage sub-system, and solar heat enhancing sub-system. The hybrid system driven by the excess electricity of wind power sub-system storages compressed air in an air storage tank and reserves compression heat with thermal storage medium from cylinder liner heat exchangers and inter-stage heat exchangers, and improves the thermal storage medium to a higher temperature by solar heat enhancing sub-system for a greater power output of the system. Three energy release strategies of the systems are proposed. And the thermodynamic performance of three release strategies is compared. The results show that Strategy 2 has a largest energy storage efficiency and energy storage density, which is 56.52%, 19.8 MJ/m3, respectively. In addition, energy, economic and environmental analyses are conducted. The payback period of system is 3.5 years. Compared with systems which are not combined with solar energy, the energy conversion efficiency of the system designed in this paper enhances by 25%, and the carbon emission reduces approximately by 42%. The payback period of system is also reduced. The influence of important parameters on system performance is explored using parametric analysis. The system can achieve a maximum energy storage efficiency when wind speed is 11 m/s. Meanwhile, energy storage efficiency and energy storage density all improve with the increase in environment pressure and solar irradiance, whereas these characteristics show divergent trends as the ambient temperature changes.

Thermodynamic analysis for a novel steam injection adiabatic compressed air energy storage hybrid system

This paper proposes a new steam injection adiabatic compressed air energy storage hybrid system (SI-ACAES) for the purpose of enhancing the installed capacity and output of ACAES systems. The proposed system preheats and humidifies the inlet working medium of the turbine by adding a saturator and recovering the heat of compression. For better evaluation of the performance of the system, this paper first performs an energy analysis and an advanced exergy analysis on the SI-ACAES system, followed by a parametric analysis. The results suggest that the SI-ACAES system has increased turbine power by 6.63 MW, round trip efficiency by 3.7%, compression heat utilization efficiency by 9.3%, and exergy round trip efficiency by 2.1% compared to the traditional ACAES system. The exergy efficiency increases with throttle valve outlet pressure, but when inlet water flowrate to the saturator (SAT) and external inlet air flowrate to heat exchanger 8 (HEX8) increase, the exergy efficiency begins to increase and then appropriately decreases.