Federated double DQN based multi-energy microgrid energy management strategy considering carbon emissions

Multi-energy microgrids(MEMG)play an important role in promoting carbon neutrality and achieving sustainable development.This study investigates an effective energy management strategy(EMS)for MEMG.First,an energy management system model that allows for intra-microgrid energy conversion is developed,and the corresponding Markov decision process(MDP)problem is formulated.Subsequently,an improved double deep Q network(iDDQN)algorithm is proposed to enhance the exploration ability by modifying the calculation of the Q value,and a prioritized experience replay(PER)is introduced into the iDDQN to improve the training speed and effectiveness.Finally,taking advantage of the federated learning(FL)and iDDQN algorithms,a federated iDDQN is proposed to design an MEMG energy management strategy to enable each microgrid to share its experiences in the form of local neural network(NN)parameters with the federation layer,thus ensuring the privacy and security of data.The simulation results validate the superior performance of the proposed energy management strategy in minimizing the economic costs of the MEMG while reducing CO_2 emissions and protecting data privacy.

Improvement in the calculation of anti-Stokes energy transfer and experimental justification based on Er_(0.01)Yb_xY_(1 0.01 x)VO_4 crystal

The improvement on the calculation of anti-Stokes energy transfer rate is studied in the present work. The additional proportion coefficient between Stokes and anti-Stokes light intensities of quantum Raman scattering theory as compared with the classical Raman theory is introduced to successfully describe the anti-Stokes energy transfer. The theoretical formula for the improvement on the calculation of anti-Stokes energy transfer rate is derived for the first time in this study. The correctness of introducing coefficient exp{△E/kT} from well-known Raman scatter theory is demonstrated also. Moreover, the experimental lifetime measurement in Er0.01YbxY1-0.01-xVO4 crystal is performed to justify the validity of our important improvement in the original phonon-assisted energy transfer theory for the first time.

Band Edge Emission Improvement by Energy Transfer in HybridⅢ-Nitride/Organic Semiconductor Nanostructure

GaN nanorods are fabricated using inductively coupled plasma etching with Ni nano-island masks. The poly [2- methoxy-5-（2-ethyl）hexoxy-l,4-phenylenevinylene] （MEH-PPV）/GaN-nanorod hybrid structure is fabricated by depositing the MEH-PPV film on the GaN nanorods by using the spin-coating process. In the hybrid structure, the spatial separation is minimized to achieve high-emciency non-radiative resonant energy transfer. Optical properties of a novel device consisting of MEH-PPV/GaN-nanorod hybrid structure is studied by analyzing photoluminescenee （PL） spectra. Compared with the pure GaN nanorods, the PL intensity of the band edge emission of GaN in the MEH-PPV/GaN-nanorods is enhanced as much as three times, and the intensity of the yellow band is suppressed slightly. The obtained results are analyzed by energy transfer between the GaN nanorods and the MEH-PPV. An energy transfer model is proposed to explain the phenomenon.

Analysis on Technical Measures on Energy Saving and Consumption Reducing During Stevia Sugar Production

Based on the analysis of actual energy consumptions during stevia sugar production,practical measures were proposed to save energy（ gas,water and powder） consumption and reduce cost through waste heat recycle and utilization,process improvement,equipment operation efficiency,application of newly energysaving equipment,and some project applications were provided as case study for some techniques and methods adopted in the production.

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach is declined according to two different strategies: first, a setpoint tracking is proposed for the couple crystal mass/concentration, whereas a second way consists in tracking of crystal content and concentration. The controlled variables, unavailable online, are issued from an observer developed in previous works. The performance of these strategies, which application to cane sugar crystallization constitutes a real novelty, are compared with experimental data issued from a PID-controlled industrial plant. The results reveal a significant improvement of energy efficiency, leading to an economy of more than 10% of energy.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in Energy Consumption

This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.

压缩二氧化碳储能系统膨胀机研究进展

压缩二氧化碳储能系统具有储能密度大、系统设备紧凑、运行寿命长等优点。膨胀机是储能系统的关键设备,对整个储能系统的效率和性能有直接影响。综述了各类膨胀机的研究现状;介绍了不同类型二氧化碳膨胀机的工作原理及特点;提供了可在压缩二氧化碳储能系统中应用的膨胀机类型;分析了应用于储能系统的4种膨胀机存在的问题及其解决方案;指出了二氧化碳膨胀机的研究方向,包括设计方法优化、关键部件结构优化、泄漏、密封、摩擦、润滑等,为压缩二氧化碳储能系统膨胀机的优化设计提供参考。

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

我国航运业绿色低碳发展的需求、路径与展望

为探讨航运业在综合运输体系中的重要性,并针对加速航运业绿色低碳发展对实现交通运输行业双碳战略目标的意义展开分析。通过考察航运业当前的碳排放状况,分析我国在绿色低碳发展方面存在的问题,对国内外绿色航运政策需求及其对航运业的影响进行综合评估。在此基础上,研究我国航运业绿色低碳发展的路径,包括总体思路、基本原则,以及相应的策略与实施措施,展望航运业绿色低碳发展的技术创新方向,提出从能源替代和运营管理两个方面促进航运业绿色低碳发展。

基于改进蝙蝠算法的多目标移动储能调度

在大规模电动汽车的随机充电等因素的影响下,电网峰谷差等问题突出。首先,计算所需多类型移动储能(电动汽车、移动储能车、氢燃料发电车)调度功率,考虑交通能耗,建立各类移动储能模型。其中,针对电动汽车交通能耗,根据电价对用户参与意愿的影响,建立相应的补贴体系。其余类型则只计及交通能耗成本。然后,建立以的配电网负荷峰谷差、新能源利用率、配电网运行成本为目标,利用超平面的概念自适应地确定不同目标函数的权重,将多目标归一化,建立三类移动储能协调调度模型。而后,使用改进蝙蝠算法求解,得到多类型移动储能协同调度的方案。其中,基本蝙蝠算法引入柯西变异逆累积分布函数等改进,得到改进蝙蝠算法,该算法有效提升优化速度、全局搜索能力。最后,在IEEE33节点系统中,进行仿真验证,结果证明了该调度方案的有效性。

基于多源数据和改进链路预测的新能源汽车技术机会研究

[研究目的]在绿色低碳发展的背景下,旨在识别新能源汽车领域的技术机会,为未来新能源汽车的技术研发与商业化方向提供借鉴参考,助力新能源汽车的发展。[研究方法]搜集反映新能源汽车领域基础研究、应用研究和商业化的多源数据,采用三螺旋模型,将基于改进链路预测方法得到的预测结果“节点链”进行融合对比分析,得到最终细化的新能源汽车技术机会。[研究结论]验证了基于多源数据融合思想和改进链路预测的方法识别新能源汽车技术机会的可行性。研究结果表明:新能源汽车未来技术机会主要集中在动态无线充电技术、磁悬浮技术与自动驾驶技术的结合;通过碳化硅材料等改进的驱动技术;通过光催化等改进的电池技术;制氢与车载储氢等。

考虑能耗节约的集装箱码头装卸设备集成调度

针对自动化集装箱码头中岸桥与人工智能运输机器人(ART)的集成调度问题,依据作业阶段将设备能耗划分成多个表现形式,构建以最小化岸桥和ART的总能耗为目标的整数规划模型。为提高求解质量,提出一种具有重组变异和随机扰动的自适应粒子群算法。根据不同时期的搜索需求,对惯性权重实行自适应调整;并引入随机粒子增强个体的交互能力,结合迭代进程对最优粒子实施不定维更新,为其摆脱局部困境提供更多机会。最后,以天津港北疆C段自动化集装箱码头为研究背景设计了不同规模算例,将改进算法与GUROBI求解器以及其他算法进行比较,验证了模型和算法的有效性。结果表明,随着岸桥和ART的配置数量逐渐增加,作业进程加快,码头总能耗分别呈现降低和先降后升的趋势;此外,相比于传统的调度模型,所提方法能够在较短的完工时间里节约更多的作业能耗。

考虑电-热储能协同的综合能源系统规划优化研究

针对传统电化学储能造价高导致我国可再生能源消纳困难问题,提出一种考虑电-热储能协同的综合能源系统最优化规划方法。分析铅酸电池和蓄热罐的物理特性,建立其状态表征模型;设计电-热储能系统融合平抑可再生能源出力波动、保障供需平衡、峰谷套利三种功能场景的协同运行策略,建立考虑电-热协同的规划优化策略;基于最优化理论,以年总成本最小为优化目标,提出一种考虑电-热储能协同的综合能源系统规划优化模型,并以改进粒子群算法对模型进行求解。算例结果表明,所提出的模型能够有效提升能源系统的可再生能源消费占比。

基于改进KNN算法的新能源发电单元运行状态识别

目前识别发电单元运行状态的研究较少,数据来源以数据采集与监控系统为主,采集速度较慢。为此,提出了一种基于发电单元机端电气量数据并融合改进k近邻(KNN)算法的新能源发电单元状态识别方法,直接采集机端电气量数据用于快速判断发电单元状态。提出KNN算法的改进策略,克服了传统KNN算法准确度低、识别速度慢的缺点。利用电力系统分析综合程序获取用于状态识别的发电单元机端电气量数据,利用改进策略对数据进行预处理,并对比传统KNN算法、逐条使用改进策略的KNN算法对新能源发电单元状态识别的耗时与准确度。结果表明所提算法较传统算法的识别准确度和速度明显提升,能满足稳定控制过程中对新能源发电单元的状态感知需求。

基于数字孪生技术的云储能点对点交易双层优化策略

为解决云储能(Cloud energy storage, CES)日前交易模型预测精度低、信息传输不及时、交易机制可靠性差等问题,提出一种基于数字孪生技术的云储能点对点交易双层优化策略。首先,针对环境因素的时变性,采用数字孪生(Digital Twins, DT)技术实现新能源电厂出力与负荷的超短期预测;其次,考虑到用户规模不断增大,构建以云储能供应商-社区-用户为体系的改进分布式点对点(Peer-to-peer, P2P)交易策略,实现交易的去中心化;再次,为同时满足CES供应商与用户两大主体的利益需求,建立基于日内滚动优化的双层优化配置模型,上层求解储能电站的优化配置,下层求解用户用电策略,并采用Karush-Kuhn-Tucher(KKT)条件与Big-M法将多目标双层非线性优化问题转化为单层线性优化问题;最后,通过两次使用shapley值计算社区与用户成本分摊。算例仿真结果表明,所提策略能够实现新能源电厂出力与负荷的高精度预测,降低用户群与CES供应商交易的复杂度,在用户购电成本最小的基础上最大化CES供应商的收益,保证交易市场的长期稳定运行。

考虑综合经济性的风光配电网储能优化配置

在配电网中配置储能系统可以有效缓解风光接入对配电网产生的影响,提升对风光的消纳能力、减少电压偏差等,而储能接入的位置和容量不同会对配电网产生不同的影响。首先分析储能建设过程中的主要费用,考虑基于分时电价下的网损成本和峰谷差收益,以年综合成本、配电网的日节点电压偏差和最小为目标,建立配电网储能选址定容模型,采用改进多目标遗传二代算法(improved multi-objective nondominated sorting genetic Ⅱ algorithm, INSGA-Ⅱ)对模型进行求解,利用改进理想点决策法(improved ideal point based decision, IIPBD)来选取最优配置方案,最后在IEEE-33节点模型中对方案进行验证。仿真结果表明:使用INSGA-Ⅱ算法进行求解时最终决策出的储能配置方案实现了成本最小,并且显著提升了电压的稳定性,验证了改进后算法的优良性和所提出的储能优化配置方案的有效性。