Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Solar cell-based hybrid energy harvesters towards sustainability

Energy harvesting plays a crucial role in modern society.In the past years,solar energy,owing to its renewable,green,and infinite attributes,has attracted increasing attention across a broad range of applications from small-scale wearable electronics to large-scale energy powering.However,the utility of solar cells in providing a stable power supply for vari-ous electrical appliances in practical applications is restricted by weather conditions.To address this issue,researchers have made many efforts to integrate solar cells with other types of energy harvesters,thus developing hybrid energy har-vesters(HEHs),which can harvest energy from the ambient environment via different working mechanisms.In this re-view,four categories of energy harvesters including solar cells,triboelectric nanogenerators(TENGs),piezoelectric nanogenerators(PENGs),and thermoelectric generators(TEGs)are introduced.In addition,we systematically summar-ize the recent progress in solar cell-based hybrid energy harvesters(SCHEHs)with a focus on their structure designs and the corresponding applications.Three hybridization designs through unique combinations of TENG,PENG,and TEG with solar cells are elaborated in detail.Finally,the main challenges and perspectives for the future development of SCHEHs are discussed.

Hybrid Energy Systems and the Logic of Their Service-Dominant Implementation: Screening the Pathway to Improve Results

The transition of the global economy to a low-carbon development path has led to dramatic changes in the organization and functioning of energy markets around the world,where hybrid energy systems(HESs)are one of the decisive active agents.At the same time,a number of problems facing the modern HESs are primarily due to the stochastic nature of the renewable energy they use,require further profound changes not only in the technologies they use and how they manage them,necessary to meet the needs of end consumers and interact with the unified energy system,but also to preserve the ability of the environment to self-heal.In order to make the process of changes more efficient and eco-deep,the article proposes to use and discusses the approach based on service dominant(SD)logic,which opens up new opportunities for solving the problems of HESs.First of all through:the implementation of closer service interaction with other participants in the energy markets,as well as with the environment;a systemically organized process of transforming the“product”economic activity of HESs into a service-dominant one;developing the generalized and engineering models for solving the problems of optimizing the technical and economic indicators of HESs,operation in steady-state and transient modes.The calculations confirm the effectiveness of the proposed approach and its ability to reduce the average daily costs for the system as a whole by 14.7%compared to the costs with a uniform distribution of power between the modules.

Research on Distributed Cooperative Control Strategy of Microgrid Hybrid Energy Storage Based on Adaptive Event Triggering

Distributed collaborative control strategies for microgrids often use periodic time to trigger communication,which is likely to enhance the burden of communication and increase the frequency of controller updates,leading to greater waste of communication resources.In response to this problem,a distributed cooperative control strategy triggered by an adaptive event is proposed.By introducing an adaptive event triggering mechanism in the distributed controller,the triggering parameters are dynamically adjusted so that the distributed controller can communicate only at a certain time,the communication pressure is reduced,and the DC bus voltage deviation is effectively reduced,at the same time,the accuracy of power distribution is improved.The MATLAB/Simulink modeling and simulation results prove the correctness and effectiveness of the proposed control strategy.

Design and Optimization of a Hybrid Energy System for Decentralized Heating

The performances of a hybrid energy system for decentralized heating are investigated.The proposed energy system consists of a solar collector,an air-source heat pump,a gas-fired boiler and a hot water tank.A mathematical model is developed to predict the operating characteristics of the system.The simulation results are compared with experimental data.Such a comparison indicates that the model accuracy is sufficient.The influence of the flat plate solar collector area on the economic and energy efficiency of such system is also evaluated through numerical simulations.Finally,this system is optimized using the method of orthogonal design.The results clearly demonstrate that the solar-heat pump-gas combined system is more convenient and efficient than the simple gas system and the heat pump-gas combined system,whereas it is less convenient but more efficient than the solarassisted gas system.

Sparse identification method of extracting hybrid energy harvesting system from observed data

Hybrid energy harvesters under external excitation have complex dynamical behavior and the superiority of promoting energy harvesting efficiency.Sometimes,it is difficult to model the governing equations of the hybrid energy harvesting system precisely,especially under external excitation.Accompanied with machine learning,data-driven methods play an important role in discovering the governing equations from massive datasets.Recently,there are many studies of datadriven models done in aspect of ordinary differential equations and stochastic differential equations(SDEs).However,few studies discover the governing equations for the hybrid energy harvesting system under harmonic excitation and Gaussian white noise(GWN).Thus,in this paper,a data-driven approach,with least square and sparse constraint,is devised to discover the governing equations of the systems from observed data.Firstly,the algorithm processing and pseudo code are given.Then,the effectiveness and accuracy of the method are verified by taking two examples with harmonic excitation and GWN,respectively.For harmonic excitation,all coefficients of the system can be simultaneously learned.For GWN,we approximate the drift term and diffusion term by using the Kramers-Moyal formulas,and separately learn the coefficients of the drift term and diffusion term.Cross-validation(CV)and mean-square error(MSE)are utilized to obtain the optimal number of iterations.Finally,the comparisons between true values and learned values are depicted to demonstrate that the approach is well utilized to obtain the governing equations for the hybrid energy harvester under harmonic excitation and GWN.

Nonequilibrium reservoir engineering of a biased coherent conductor for hybrid energy transport in nanojunctions

We show that a current-carrying coherent electron conductor can be treated as an effective bosonic energy reservoir involving different types of electron–hole pair excitations.For weak electron–boson coupling,hybrid energy transport between nonequilibrium electrons and bosons can be described by a Landauer-like formula.This allows for unified account of a variety of heat transport problems in hybrid electron–boson systems.As applications,we study the non-reciprocal heat transport between electrons and bosons,thermoelectric current from a cold-spot,and electronic cooling of the bosons.Our unified framework provides an intuitive way of understanding hybrid energy transport between electrons and bosons in their weak coupling limit.It opens the way of nonequilibrium reservoir engineering for efficient energy control between different quasi-particles at the nanoscale.

New developments in the multiscale hybrid energy density computational method

Further developments in the hybrid multiscale energy density method are proposed on the basis of our previous papers. The key points are as follows. （i） The theoretical method for the determination of the weight parameter in the energy coupling equation of transition region in multiscale model is given via constructing underdetermined equations. （ii） By applying the developed mathematical method, the weight parameters have been given and used to treat some problems in homogeneous charge density systems, which ,＇ire directly related with multiscale science. （iii） A theoretical algorithm has also been presented for treating non-homogeneous systems of charge density. The key to the theoretical computational methods is the decomposition of the electrostatic energy in the total energy of density functional theory for probing the spanning characteristic at atomic scale, layer by layer, by which the choice of chemical elements and the defect complex effect can be understood deeply. （iv） The＇numerical computational program and design have also been presented.

Fuzzy Adaptive Filtering-Based Energy Management for Hybrid Energy Storage System

Regarding the problem of the short driving distance of pure electric vehicles,a battery,super-capacitor,and DC/DC converter are combined to form a hybrid energy storage system(HESS).A fuzzy adaptive filtering-based energy management strategy(FAFBEMS)is proposed to allocate the required power of the vehicle.Firstly,the state of charge(SOC)of the super-capacitor is limited according to the driving/braking mode of the vehicle to ensure that it is in a suitable working state,and fuzzy rules are designed to adaptively adjust the filtering time constant,to realize reasonable power allocation.Then,the positive and negative power are determined,and the average power of driving/braking is calculated so as to limit the power amplitude to protect the battery.To verify the proposed FAFBEMS strategy for HESS,simulations are performed under the UDDS(Urban Dynamometer Driving Schedule)driving cycle.The results show that the FAFBEMS strategy can effectively reduce the current amplitude of the battery,and the final SOC of the battery and super-capacitor is optimized to varying degrees.The energy consumption is 7.8%less than that of the rule-based energy management strategy,10.9%less than that of the fuzzy control energy management strategy,and 13.1%less than that of the filtering-based energy management strategy,which verifies the effectiveness of the FAFBEMS strategy.

Real-time Energy Management Method for Electric-hydrogen Hybrid Energy Storage Microgrids Based on DP-MPC

With the increasing presence of intermittent energy resources in microgrids,it is difficult to precisely predict the output of renewable resources and their load demand.In order to realize the economical operations of the system,an energy management method based on a model predictive control(MPC)and dynamic programming(DP)algorithm is proposed.This method can reasonably distribute the energy of the battery,fuel cell,electrolyzer and external grid,and maximize the output of the distributed power supply while ensuring the power balance and cost optimization of the system.Based on an ultra-shortterm forecast,the output power of the photovoltaic array and the demand power of the system load are predicted.The offline global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtain power values of each unit in the energy storage system.Compared with the traditional DP,MILP-MPC and the logic based real-time management method,the proposed energy management method is proved to be feasible and effective.

High Na-ion conductivity andmechanical integrity of anion-exchanged polymeric hydrogel electrolytes for flexible sodium ion hybrid energy storage

The polymeric gel electrolytes are attractive owing to their higher ionic conductivities than those of dry polymer electrolytes and lowered water activity for enlarged potential window.However,the ionic conductivity and mechanical strength of the Na-ion conducting polymeric gel electrolytes are limited by below 20 mS cm−1 and 2.2 MPa.Herein,we demonstrate Na-ion conducting and flexible polymeric hydrogel electrolytes of the chemically coupled poly(diallyldimethylammonium chloride)-dextrin-N,N′-methylene-bisacrylamide film immersed in NaClO_(4) solution(ex-DDA-Dex+NaClO_(4))for flexible sodium-ion hybrid capacitors(f-NIHC).In particular,the anion exchange reaction and synergistic interaction of ex-DDA-Dex with the optimum ClO_(4)−enable to greatly improve the ionic conductivity up to 27.63 mS cm−1 at 25◦C and electrochemical stability window up to 2.6 V,whereas the double networking structure leads to achieve both the mechanical strength(7.48 MPa)and softness of hydrogel electrolytes.Therefore,the f-NIHCs with the ex-DDA-Dex+NaClO_(4) achieved high specific and high-rate capacities of 192.04 F g^(−1)at 500 mA g^(−1)and 116.06 F g^(−1)at 10000 mA g^(−1),respectively,delivering a large energy density of 120.03Wh kg^(−1)at 906Wkg^(−1)and long cyclability of 70%over 500 cycles as well as demonstrating functional operation under mechanical stresses.

Characteristics of Bi-2223 HTS Tapes Under Short-Circuit Current Impacts in Hybrid Energy Transmission Pipelines

A hybrid energy transmission pipeline is proposed with the aim of long-distance cooperative transmission of electricity and chemical fuels, which is composed of an inner high-temperature superconducting (HTS) power cable and outer liquefied natural gas (LNG) pipeline. The flowing LNG could maintain the operating temperature of the inner HTS power cable within the range of 85 K-90 K, thus the Bi-2223 superconductors in the HTS power cable produce little Joule loss with the transmission current below the critical current. Owing to the advantages of high power density, low transmission losses and economical manufacturing costs, the hybrid energy transmission pipeline is expected to be widely utilized in the near future. In order to ensure the safety of the HTS power cable and explosive LNG in case of short-circuit faults, this paper tests and analyzes the characteristics of Bi-2223 HTS tapes of the Type HT-CA, Type HT-SS and Type H models under short-circuit current impacts at the LNG cooling temperature (85 K-90 K). An experimental platform is designed and established for the ampacity tests of HTS tapes above LN2 cooling temperature (77 K). The AC over-current impact tests at 85 K-90 K are carried out on each sample of Bi-2223 tapes respectively, and the experimental results are analyzed and compared to evaluate their performances under different operating conditions. The results indicate that the Type HT-CA tape can withstand 50 Hz short-circuit current impact with the amplitude of 1108 A (10 times of critical current Ic ) for 100 ms at 90 K, and its resistance is the smallest of the three tested samples under similar current impacts. Therefore, the Type HT-CA Bi-2223 tape is the optimal superconductor of the HTS power cable in the hybrid energy transmission pipeline.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)devices.This study investigates task offloading scheduling in a dynamic MEC system.By integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply model.We jointly optimize local computing,offloading duration,and edge computing decisions to minimize system cost.On the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME.DTOME can make task offloading decisions by weighing system cost and queue stability.We quote dynamic programming theory to obtain the optimal task offloading strategy.Simulation results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids

Among hybrid energy storage systems(HESSs),battery-ultracapacitor systems in active topology use DC/DC power converters for their operations.HESSs are part of the solutions designed to improve the operation of power systems in different applications.In the residential microgrid applications,a multilevel control system is required to manage the available energy and interactions among the microgrid components.For this purpose,a rule-based power management system is designed,whose operation is validated in the simulation,and the performances of different controllers are compared to select the best strategy for the DC/DC converters.The average current control with internal model control and real-time frequency decoupling is proposed as the most suitable controller according to the contemplated performance parameters,allowing voltage regulation values close to 1%.The results are validated using real-time hardware-in-the-loop(HIL).These systems can be easily adjusted for other applications such as electric vehicles.

Recent advances in zinc-ion hybrid energy storage: Coloring high-power capacitors with battery-level energy

Zinc-ion hybrid capacitors(ZICs) are considered as newly-emerging and competitive candidates for energy storage devices due to the integration of characteristic capacitor-level power and complementary battery-level energy. The practical application of rising ZICs still faces the specific capacity and dynamics mismatch between the two electrodes with different energy storage mechanisms, which cannot meet the ever-growing indicator demand for portable electronic displays and public traffic facilities. Focusing on these unresolved issues, this mini-review presents recent advances in ZICs referring to the hybrid energy storage mechanism, design strategies of both capacitor-type and battery-type electrode materials, and electrolyte research toward advanced performances(e.g., high operational potential, wide adaptive temperature). Finally, current challenges and future outlook have been proposed to guide further exploration of next-generation ZICs with a combination of high-power delivery, high-energy output and high-quality service durability.

RED WoLF hybrid energy storage system: Algorithm case study and green competition between storage heaters and heat pump

Green house gases reduction is critical in current climate emergency and was declared as major target by United Nations.This manuscript proposes the progressive adaptive recursive multi threshold control strategy for hybrid energy storage system that combines thermal storage reservoirs,heat pumps,storage heaters,photovoltaic array and a battery.The newest control strategy is tested in numerical experiment against primal dual simplex optimisation method as benchmark and previous iterations of RED WoLF threshold approaches.The proposed algorithm allows improvement in reduction of CO_(2) emissions by 9%comparatively to RED WoLF double threshold approach and by 26%comparatively to RED WoLF single threshold approach.Besides,the proposed technique is at least 100 times faster than linear optimisation,making the algorithm applicable to edge systems.The proposed method is later tested in numerical experiment on two measured datasets from Luxembourg school and office,equipped with batteries and ground source heat pumps.The system allows the reduction of CO_(2) emission and improvement of self-consumption,size reduction of the photovoltaic array installed at the facilities by at least by half as well as substituting battery storage by thermal storage,reducing the initial investment to the system.Intriguingly,despite 3.6 times difference in efficiency between heat pumps and storage heaters,the system equipped with latter have potential to achieve similar performance in carbon reduction,suggesting that energy storage have more prominent carbon reduction effect,than the power consumption,making cheaper systems with storage heaters a possible alternative to heat pumps.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.