Storage Subsystem Research Articles

Seamlessly Safeguarding Data Against Ransomware Attacks

Encryption has become an indispensable technology for preserving confidentiality. Unfortunately, cybercriminals have re-purposed this technology to deny users access to their data. This trend has sparked an onslaught of ransomware attacks, that resulted in several victims being extorted to pay ransoms in return for restoring their maliciously encrypted data. In response to these challenges, we propose a novel runtime solution that seamlessly defends against cryptographic ransomware. A key observation made by this work is that maliciously encrypted data is initially buffered in the OS's page cache before it is flushed to the underlying storage device. Based on this observation, we develop a solution that efficiently manages data synchronization between the memory and storage subsystems to prevent maliciously encrypted data from being permanently committed to the underlying storage. We extensively validate the robustness of this approach against more than one thousand ransomware samples and show that our design reliably restores all encrypted files. Furthermore, our solution is resilient to ransomware that employ techniques including master boot record infection and multi-threaded attacks. Finally, an evaluation of our proof-of-concept implementation shows minimal performance impact while running a mix of compute and I/O bound applications.

A novel peak shaving approach to improving load flexibility of the Allam cycle by integrating cold energy storage

Allam cycle is a promising alternative cycle for power generation, characterized by high net electrical efficiency and near-zero emissions. For efficient and load flexible operation, a novel system integrating Allam cycle and cold energy storage system (CES-Allam) has been proposed. The compression train is employed to increase the pressure of CO2-rich working fluid for both Allam and refrigeration subsystems. The ice slurry is used as a cold energy storage carrier, which is further used to assist in liquefying carbon dioxide (CO2) at a lower evaporating pressure, and leads to higher electrical power output during peak load hours. The thermodynamic and economic models of this integrated system have been developed for comprehensive evaluation. The results show that the proposed integrated system is technically feasible for load flexible operation with a wider range of power generation regulation capacity (28.46%–105.97%) than that of the conventional Allam system (40%–100%). The round trip efficiency is 71.09% for the cold energy storage system, which is equivalent to the adiabatic compressed air energy storage system. The payback period is 4.15 years for the cold energy storage subsystem, which indicates that the CES-Allam system is economically feasible and competitive. Moreover, the sensitivity analyses of electricity prices and natural gas shows that the proposed CES-Allam system is more suitable for areas with higher natural gas prices and greater differences in peak and valley electricity prices. The present study is expected to provide a detailed reference and guidance for the use of cold energy storage to improve the load flexibility of the Allam system.

Multi-objective optimization of a hybrid system based on combined heat and compressed air energy storage and electrical boiler for wind power penetration and heat-power decoupling purposes

Faced with uncertain heating supply task in heating season, the combined heat and power (CHP) unit groups in “Three North Areas” of China with considerable capacity of wind power often operate in heat-controlled mode with limited flexibility, which exacerbates the wind power curtailment. In order to further improve the operation flexibility of CHP unit group and the wind power penetration level, a hybrid system based on CHP unit group embedded with electrical boiler subsystem and combined heat and compressed air energy storage (CH-CAES) subsystem is proposed. A novel operation strategy on power generation side considering device reserve constraints and transient behaviors is put forward. Then, a bi-level optimization algorithm based on multi-objective particle swarm optimization (MOPSO) and technique for order preference by similarity to an ideal solution (TOPSIS) method is utilized. Finally, a completed heat season case is conducted to investigate the dynamic behaviors and the performance of the improved system. The simulation results of optimal plan mainly dominated by ratio of abandoned wind (ROAW) show that energy and exergy efficiencies with remaining stored energy can be improved by 3.52 % and 2.86 % compared with the basic system, respectively. For the economic performance, the levelized cost of heat (LCOH) drops by 43.56 % while levelized cost of electricity (LCOE) increases by 4.99 %. In the environmental aspect, the coal consumption, CO2 and SO2 emission can reduce by 7.35 %, 7.34 % and 7.35 %, respectively. The result shows that the improved system can decrease the curtailed wind power significantly and boost the CHP operation flexibility remarkably. Meanwhile, the dynamic behaviors of system components in reasonable scope mean that the fluctuant renewable energy can be eliminated efficiently, validating the effectiveness of the proposed system in high shared wind region simultaneously.

Laser induced graphanized microfluidic devices.

With the advent of cyber-physical system-based automation and intelligence, the development of flexible and wearable devices has dramatically enhanced. Evidently, this has led to the thrust to realize standalone and sufficiently-self-powered miniaturized devices for a variety of sensing and monitoring applications. To this end, a range of aspects needs to be carefully and synergistically optimized. These include the choice of material, micro-reservoir to suitably place the analytes, integrable electrodes, detection mechanism, microprocessor/microcontroller architecture, signal-processing, software, etc. In this context, several researchers are working toward developing novel flexible devices having a micro-reservoir, both in flow-through and stationary phases, integrated with graphanized zones created by simple benchtop lasers. Various substrates, like different kinds of cloths, papers, and polymers, have been harnessed to develop laser-ablated graphene regions along with a micro-reservoir to aptly place various analytes to be sensed/monitored. Likewise, similar substrates have been utilized for energy harvesting by fuel cell or solar routes and supercapacitor-based energy storage. Overall, realization of a prototype is envisioned by integrating various sub-systems, including sensory, energy harvesting, energy storage, and IoT sub-systems, on a single mini-platform. In this work, the diversified work toward developing such prototypes will be showcased and current and future commercialization potential will be projected.

MPC Trajectory Feasibility Constraints for Self-Powered Control Systems

A self-powered control system derives the energy to power its operation entirely from the dynamics of the plant in which it is embedded. Each actuator is a transducer capable of injecting energy into, as well as removing energy from, the plant. Removed energy is stored for reuse in rechargeable energy storage subsystems. Control inputs are physically constrained by the requirement that the system does not exhaust these energy storage subsystems. This article analyzes model-predictive control (MPC) trajectory feasibility for self-powered control systems. Specifically, we examine the case in which the MPC algorithm formulates a discrete-time zero-order-hold control trajectory, for which the energy constraints are approximately enforced by using response information only at discrete sample times. It is shown that continuous-time control feasibility can be ensured by conservatively overbounding the transmission loss model. The analysis is conducted both for the case in which future disturbances are known deterministically and the case in which they are uncertain but bounded. The methodology is demonstrated on an example pertaining to vibration control.

Mechanically Improved Zn-Ion Battery Cathodes Based on Branched Aramid Nanofibers

Zinc-ion batteries address common environmental and safety concerns by using nontoxic and abundant metals in conjunction with aqueous electrolytes. Nonetheless, for applications such as structural energy storage, where a multifunctional system aims to replace both the structural and energy storage subsystems of an electric vehicle, safe but mechanically strong components are desired. MnO2, a typical cathode material for Zn-ion batteries, however, exhibits poor mechanical performance. Therefore, a lack of knowledge remains for mechanically strong cathodes for safe, structural Zn-ion batteries. Here, we combine branched aramid nanofibers (BANFs) with MnO2 particles and reduced graphene oxide (rGO) to fabricate mechanically improved cathodes for Zn-ion batteries. The addition of BANFs allows for an increase in the MnO2 loading and significantly improved electrochemical properties (up to 190% increase of capacity). Additionally, hydrogen bonding between BANFs and rGO notably improved the mechanical properties (up to 51% increase in ultimate tensile strength and up to 593% increase in ultimate strain). Electrochemo-mechanical tests were also performed to assess coupling, showing that applied strain decreases the capacity, but no measurable internal stresses develop during electrochemical cycling. This work combines the multifunctionality of structural electrodes with the inherent safety of Zn-ion batteries.

Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources

Under the background of energy internet and low-carbon power, integrated energy system (IES) has become an important carrier of energy conservation and emission reduction. The IES utilizes innovative energy management mode to coordinate various energy sources such as natural gas, electric energy and heat energy. It is composed of energy production, conversion, storage and consumption subsystems, which emphasizes breaking the isolation of energy subsystems through reasonable scheduling, realizing energy cascade utilization and improving energy utilization efficiency. In this paper, an energy management model with two-stage scheduling before day and in real time is proposed aiming at the collaborative optimization of generator-load-storage of IES. Firstly, the first stage is the day-ahead economic dispatch, which aims to realize the power distribution of units in the system. The day-ahead economic dispatching model takes the maximization of economic benefits, the maximization of exergy efficiency and the minimization of carbon emission cost as the optimization objectives, so as to make the day-ahead global optimal dispatching decision. Secondly, the second stage is real-time optimal scheduling, which aims at real-time power adjustment of the previous scheduling plan. The real-time optimal scheduling model takes the minimum interactive power deviation punishment cost, wind abandonment punishment cost and user satisfaction loss cost as optimization objectives, so as to balance the energy supply and load demand deviation between planned output and actual output. Thirdly, according to the characteristics of the model, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the first-stage day-ahead economic scheduling, and YALMIP toolbox is used to solve the real-time optimal scheduling model. Finally, based on the established model, a typical IES is selected for case simulation, which verifies that the proposed method can effectively improve the economy of system operation.

Techno-economic analysis of combined cooling, heating, and power (CCHP) system integrated with multiple renewable energy sources and energy storage units

This study presents the optimal design of a multi-source renewable energy combined cooling, heating, and power (M−RCCHP) system integrated with energy storage units for an apartment community from techno-economic perspectives using response surface methodology (RSM) and dynamic evaluation. The proposed M−RCCHP system is consisted of photovoltaic/thermal (PV/T) collectors, wind turbines (WTs), fuel cell subsystem, heat pump (HP), while the battery banks and hydrogen storage tank are the energy storage subsystems. The behavior of the system is examined by a dynamic approach using the TRNSYS simulation platform. Besides, the main design variables of the M−RCCHP system are selected to find the foremost combination of them using the design of experiments (DOE) technique. The number of PV/T panels, number of WT, fuel cell power, HP cooling capacity and HP heating capacity were considered as decision variables. Overall electricity consumption (OEC) and boiler fuel consumption (BFC) as indicators of primary energy consumption of the system and predicted average vote (PMV) as an index of thermal comfort of the system and life cycle cost (LCC) as economic criteria as 4 objective functions were selected for optimization. The optimization results showed that the M−RCCHP system with 225 PV/T collectors, 15 wt, fuel cells with a power of 74.96 kW, HP cooling capacity of 50.25 kW and HP heating capacity of 20 kW achieved the best techno-economic performance during the period. The results of the system modeling showed that the fuel cell subsystem, PV/T collectors and WTs with the maximum power of 75 kW, 52 kW and 24 kW have the largest contribution in electricity production. The optimization results showed that the system cycle cost (LCC) is 323,004.96 $ per year, the system comfort coefficient (PMV) is 0.115 per year, the boiler fuel consumption is 41,067.88 cubic meters per year, and the total electricity consumption is −317,194.94 kWh per year, as well as the amount The optimal cooling and heating capacities of HP were calculated as 50.25 and 20 kW, respectively. And finally, the error percentage of the optimization method was reported as 6.53 %.

An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System

With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.

Fusion Fuel Cycle Inventory Reduction Studies Using a Processing-Time–Based Discrete-Time Interval Model

Developing a Fusion Pilot Plant (FPP) design that minimizes risks due to tritium in-process inventory (IPI) is an important concern for the operation of commercial devices. This becomes even more of concern since an FPP will be breeding more tritium than is burned in the reactor for sustainability. The IPI is the tritium moving through the system that is not in the storage and delivery subsystem. A process model that solves time-dependent differential equations based on processing times was used to investigate the reduction of the IPI of a potential fuel cycle design. The impact of new and more efficient technologies such as direct internal recycling (DIR), metal foil pumps, continuous pumping, improved isotope separation, and hydrogen separating continuous pumps on IPI was investigated by adjusting subsystem processing times and material flow streams. It was shown that any of the insertions of DIR studied in this paper caused a reduction in the total IPI of the system and proved to be the optimal way to reduce the IPI in the system. Fuel cycle modifications near the torus, such as a coupled DIR and improved pumping systems, produced the largest reductions in tritium inventory.

Machine Learning Based Modeling for Real-Time Inferencer-in-the-Loop Hardware Emulation of High-Speed Rail Microgrid

The application of artificial intelligence (AI) technology in the field of power systems and power electronic devices is increasingly prevalent. With massive datasets generated by a wide range of equipment, AI-based modeling is promising in the future of hardware-in-the-loop emulation. This article studies and improves the machine learning based modeling approach for power electronic devices, and the inferencer-in-the-loop (IIL) system is proposed together with optimized neural network (NN) models. The high-speed rail microgrid, includes autotransformer rectifier unit subsystems, energy storage subsystems, two-level converter based permanent magnet synchronous motor propulsion subsystems, and modular multilevel converter based induction motor propulsion subsystems, serves as study cases to demonstrate the adaptability of this approach. Finally, to show high accuracy and versatility of the IIL real-time emulation system, the system-level (1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu} \mathbf{s}$</tex-math></inline-formula> timestep) and device-level (50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{ns}}$</tex-math></inline-formula> timestep) results are compared in three domains: the referencer system (C code simulation program in NVIDIA Jetson and offline SaberRD datasets), offline inferencer emulation on Xilinx VCU118 board, and online refined inferencer emulation on Xilinx VCU118 board.

ANALYSIS OF A FLAT PLATE SOLAR COLLECTOR

In the solar-energy industry great emphasis has been placed on the development of "active" solar energy systems which involve the integration of several subsystems: solar energy collectors, heat-storage containers, heat exchangers, fluid transport and distribution systems, and control systems. The major component unique in active systems is the solar collector. This device absorbs the incoming solar radiation, converting it into heat at the absorbing surface, and transfers this heat to a fluid (usually air or water) flowing through the collector. The warmed fluid carries the heat either directly to the hot water or space conditioning equipment or to a storage subsystem from which it can be drawn for use at night and on cloudy days. The thermal analysis of a solar flat plate collector is quite complicated because of the many factors involved. Efforts have been made to combine a number of the most important factors into a single equation and thus formulate a mathematical model which will describe the thermal performance of the collector in a computationally efficient manner.

Transient thermodynamic modeling and economic assessment of cogeneration system based on compressed air energy storage and multi-effect desalination

This study examined the design of a renewable system for producing electricity and freshwater based on the solar cycle and using thermal storage in different cities. The system comprised heliostat, gas turbine, multi-effect desalination, and compressed air energy storage (CAES) subsystems. The Engineering Equation Solver software was used for modeling and obtaining results by system analysis. Eight scenarios were examined by considering the charging time, discharging time, number of sunny hours, and various desalination effects. Finally, the best scenario was selected following a parametric study, which was scenario 5 with seven parameters, and it had the best performance. Specifically, the exergy round trip efficiency, round trip efficiency, power generation, and freshwater generation rate of the system were optimal. The number of heliostats and the turbine and compressor efficiencies influenced the system outputs. Based on an economic analysis of the system, the CAES, solar unit, and gas turbine costs were the highest among the system component costs. Moreover, based on an exergy analysis of the system, most exergy degradation corresponded to the heliostats, receiver, and gas turbine. Moreover, five cities—Esfahan, Dubai, London, Paris, and San Francisco—were compared in terms of the peak consumption time per year. Finally, the performance of the developed system was investigated for ambient temperature changes, solar radiation, and wind speed of the cities considered per year. The outcomes revealed that the system performed the best in the climate conditions of Esfahan during the peak consumption time.

Design and Implementation of Real-Time Image Acquisition Chip Based on Triple-Hybrid Encryption System

With the improved hardware storage capabilities and the rapid development of artificial intelligence image recognition technology, information is becoming image-oriented. Increasingly sensitive image data needs to be processed. When facing a large amount of real-time sensitive image data encryption and decryption, ensuring both the speed and the security is an urgent demand. This paper proposes an original triple-hybrid encryption system for a real-time sensitive image acquisition chip. This encryption system optimizes the symmetric encryption algorithm AES, asymmetric encryption algorithm ECC, and chip authentication algorithm PUF in pursuit of security, calculation speed, and to ensure that it is lightweight. The three optimized algorithms are further mixed and reused on the circuit level, to ensure mutual protection while making full use of their advantages. Apart from sensitive image protection at the algorithm level, the image chip itself is also protected by an innovative PUF chip authentication method that prevents it from being tampered with and copied. Triple-hybrid encryption system hardware implementation achieves a frequency of 132.5 MHz under the Virtex-5 FPGA with an area of 2834 Slices; with Virtex-7 FPGA, it reaches a frequency of 137.6 MHz with an area of 2716 Slices. The system is also implemented on SMIC 40 nm ASIC, and the clock frequency reaches 480 MHz and the area is 94,812.4 μm2. In terms of computing speed, the peak image encryption speed is 6.15 Gb/s, which meets the real-time image encryption requirement. In terms of hardware resource usage, AES reduced the hardware area by 60.1% compared with the results in other literature, ECC reduced the hardware area by 43.4%, and the PUF hardware area decreased exponentially with the increase in information entropy. The implementation of the three algorithms is reasonable and cost-effective, and the mixture of algorithms does not increase the required capacity of the hardware resource. The triple-hybrid encryption system cooperates with the image acquisition subsystem, storage subsystem, and asynchronous clock subsystem through software control to realize a complete triple-hybrid encryption SoC chip solution, and was successfully taped-out under the SMIC 40 nm process with all constraints passed and a total area of 10.59 mm2.

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.

Bivariate active power control of energy storage hydraulic wind turbine

With the increasing proportion of wind turbines in power system, high-precision control of power generation directly affects the proportion of wind turbines connected to the grid. This paper takes the energy storage hydraulic wind turbines (ESHWTs) as the research object, the mathematical model of the hydraulic main transmission system and the hydraulic energy storage subsystem are established, and the energy transmission and control mechanism is obtained. The corresponding relationship between the output power of the hydraulic main drive system and the hydraulic energy storage subsystem and the variable motor speed is analyzed, based on the small signal linearization method, and the power transmission state is obtained with the variable motor speed fluctuation, and a double closed-loop power control strategy is proposed under the motor speed fluctuation, with the power control requirements after grid connection, Based on MATLAB software and 24 kW semi-physical simulation test platform of energy storage hydraulic wind turbine, the output power is accurately controlled and the power output problem is solved under the fluctuation of motor speed with the proposed control strategy.

Evaluation of discharging performance of molten salt/ceramic foam composite phase change material in a shell-and-tube latent heat thermal energy storage unit

Molten salts are widely used energy storage media in integrated solar power systems, however, due to their high corrosivity and the extreme high-temperature environment, many current methods of heat transfer enhancement do not apply. Herein, we proposed to use ceramic foam to enhance the discharging performance of molten salt to efficiently supply heat for power generation. The ceramic foam was prepared and its corrosion resistance was confirmed experimentally. The discharging performance in a shell-and-tube latent heat thermal energy storage unit was numerically studied. It is found that compared to the configuration without enhancement, the solidification time of the ceramic foam-enhanced unit is shortened by up to 52.0%. The unit with the upper foam insert shows better discharging performance than the one with the lower foam insert. Different foam filling height is also considered and results indicate that the thermal energy release rate always increases with the foam filling height and that of the fully foam-inserted unit is 118.1% higher than that of the none foam-inserted unit. For the first time, the discharging performance of ceramic foam-enhanced molten salt is quantitatively evaluated. This study provides guidance on designing thermal energy storage sub-system with excellent heat supply performance for solar power generation.

A Survey on: E-Commerce Data Analysis and Security Platform in the Era of Bigdata

With the rapid development of e-commerce and mobile communication, e-commerce platform has been widely used in various industries. How much e-commerce stores, whether it can guarantee the security of transaction information, whether it can analyze and study structured and unstructured data, and whether it can guarantee the security of stored data are all the key factors we need to consider. In this paper, the data and e-commerce security together, and to analyze the security system of e-commerce and discuss the prevention of hidden security policy. When the emergence of e-commerce big data technology can effectively solve the problems existing in e-commerce security, the Hadoop structure is introduced through Apache Hadoop and the Hadoop product Yarn is analyzed with emphasis. From the perspective of electronic security data, the hidden dangers of e-commerce can be effectively analyzed, and the security system of e-commerce can be effectively improved. This article starts with the analysis of the existing electronic commerce system, summarizes its characteristics, and analyzes and solves its existing problems. Firstly, the characteristics of the relational database My Structured Query Language (MySQL) and the distributed database HBase are analyzed, their respective advantages and disadvantages are summarized, and the advantages and disadvantages of each are taken into account when storing data. My SQL is used to store structured business data in the system, while HBase is used to store unstructured data such as pictures. These two storage mechanisms together constitute a data storage subsystem. Secondly, considering the large amount of data in the e-commerce system and the complex calculation of the data mining algorithm, this paper uses Map Reduce to realize the parallelization of the data mining algorithm and builds a Hadoop-based commodity recommendation subsystem on this basis. We use JavaEE technology to design a full-featured web mall system. Finally, based on the impact of cloud computing, mobile e-commerce is analyzed, including relevant theories, service mode, architecture, core technology, and the application in e-commerce, which can realize e-commerce precision marketing, find the optimal path of logistics, and take effective security measures to avoid transaction risks. This method can avoid the disadvantages of the traditional e-commerce, where large-scale data cannot be processed in a timely manner, realize the value of mining data behind, and realize the precision marketing of e-commerce enterprises.

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.

Flexible Operation of Concentrating Solar Power Plant with Thermal Energy Storage Based on a Coordinated Control Strategy

With the ambition of achieving carbon neutrality worldwide, renewable energy is flourishing. However, due to the inherent uncertainties and intermittence, operation flexibility of controllable systems is critical to accommodate renewables. Existing studies mainly focus on improving the flexibility of conventional plants, while no attention has been paid to the flexible operation of concentrating solar power with thermal energy storage (CSP-TES) systems. To this end, the ultimate goal of this work is to investigate the potentiality and realization of CSP-TES systems to flexibly operate in grid system regulation. With this goal, the dynamic characteristics of a 50 MW parabolic trough collector CSP plant with molten-salt-based TES is analyzed, and its dominant control characteristics are concluded to demonstrate the possibility of the ideal. After that, a coordinated control strategy is proposed. Specifically, a disturbance observer-based feedforward–feedback control scheme and a feedforward–feedback controller are designed, respectively, for the solar field and the energy storage subsystems, while the power block subsystem is regulated by a two-input and two-output decoupled controller. Based on the decentralized structure, three simulation cases are, respectively, performed to testify the capacity of the CSP-TES system to wide-range load variation tracking, strong disturbance rejection, or both. The results show that the CSP-TES system can adequately track the grid commands based on the proposed coordinated control strategy, even under strong fluctuation of irradiation, demonstrating the flexibility of CSP-TES participating in grid regulation. In the context of continuous penetration of renewable energy into the grid system, research on the role transition of the CSP-TES system from its own optimization to grid regulator is of great importance.