Evolutionary game-based optimization of green certificate-carbon emission right-electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.

A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

Electricity is essential for keeping power networks balanced between supply and demand,especially since it costs a lot to store.The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce.The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand.There is a new deep learning model called the Green-electrical Production Ensemble(GP-Ensemble).It combines three types of neural networks:convolutional neural networks(CNNs),gated recurrent units(GRUs),and feedforward neural networks(FNNs).The model promises to improve prediction accuracy.The 1965–2023 dataset covers green energy generation statistics from ten Asian countries.Due to the rising energy supply-demand mismatch,the primary goal is to develop the best model for predicting future power production.The GP-Ensemble deep learning model outperforms individual models(GRU,FNN,and CNN)and alternative approaches such as fully convolutional networks(FCN)and other ensemble models in mean squared error(MSE),mean absolute error(MAE)and root mean squared error(RMSE)metrics.This study enhances our ability to predict green electricity production over time,with MSE of 0.0631,MAE of 0.1754,and RMSE of 0.2383.It may influence laws and enhance energy management.

Challenges of Renewable Forest Fuels for Green Electricity Market

In this study, strategic electricity market scenarios are considered in a grid of Scandinavia. This multiple-objective decision environment includes the allocation of a number of renewable forest fuel procurement chains to a combined heat and power plant in Finland. The decision environment includes also electricity procurement from Sweden and Russia. The environment is further complicated by sequence-dependent operations of the local procurement chains during different periods. Due to the complex nature of the environment, multiple-objective methods cannot be directly used to solve the electricity production problem in a manner that is techno-economically relevant to the forest energy industry. Therefore, local and time-varying parameters were measured in local wood procurement conditions to improve the solution method. Using these measurements the smart decision-support system automatically adjusted the multiple-objective methodology to better describe the combinatorial complexity of the production sector. The properties of this methodology are discussed and three scenarios of how the system works based on local real-world data and optional feed-in tariff of green electricity are presented. The Finnish electricity market is subject to policy decisions regarding green energy production regulations. These decisions should be made on the basis of local techno-economic analysis presented in this study accounting for the effects of forest operations on the electricity production and import.

Carbon Abatement Cost-Sharing Strategy for Electric Power Sector Based on Incentive and Subsidy Mechanisms

The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction,to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain.Against this background,this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users,and studies the optimal decision-making of each subject in the electricity supply chain,so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time.Firstly,taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side,we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties.Subsequently,considering government low-carbon subsidies and user incentive subsidies,the optimal decisionmaking model is established under two scenarios of decentralized and centralized cooperative games in the supply chain,respectively,with the objective of maximizing profits and carbon reduction rates.Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium.Finally,we analyze the role of the government’s low-carbon subsidies,users’incentive subsidies,and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions.

The Effectiveness of Animal Dungs and Leaf Mold for Bioelectricity Generation Using Microbial Fuel Cell with Soils

A resource recovery from waste material is an important topic. Microbial fuel cell (MFC) is getting interest nowadays due to its superiority to generate electricity from a wide range of substrates by utilizing organic wastes. This study was aimed to investigate the potential electricity generation by utilizing animal dung and leaf mold as a very prospective substrate for bioelectricity generation using MFC. MFC as a direct current source (DC), in this experiment we focus to prove the potential difference (voltage) generated by the material. The best result is 10980 ± 11.32 mV/m2 for a mixed sample of Test V (leaf mold and soil), while for the pure samples showed at 10880 ± 4.784 mV/m2 for Test IV (chicken dropping). The advancements in this environmentally friendly study have expanded that cow dung, chicken droppings and leaf mold as a variety of organic wastes which contains large amounts of nutrients and various other minerals, as a microbial fuel cell proved to generate sustainable green and safe electricity moreover as an efficient, eco-friendly solution for organic waste management.

Antiferroelectricity in ZrO₂and Ferroelectricity in Zr, Al, La Doped HfO₂Nanoparticles

The dependence of the polarization P in Hf1-xZrxO2 nanoparticles on electric field, dopant concentration x, size and temperature are studied using the transverse Ising model and the Green’s function method. Pure ZrO2 shows at high electric fields an antiferroelectric behavior. Pure HfO2 is a linear dielectric in the monoclinic phase. With increasing ZrO2 content the of HZO shows a ferroelectric behavior. The composition dependence x of the remanent polarization Pr(x)?has a maximum for x = 0.5. For x = 0, pure HfO2, and x = 1, pure ZrO2, Pr=0.?P increases with decreasing HZO nanoparticle size. The influence of Al and La doping on Pr?in HfO2 nanoparticles is also studied. The exhibiting of the ferroelectricity in ion doped HfO2 is due to a phase transformation and to an internal strain effect. The observed results are in good qualitative agreement with the experimental data.

ELECTRO-ELASTIC GREEN'S FUNCTIONS FOR APIEZOELECTRIC HALF-SPACE ANDTHEIR APPLICATION

In this paper, as is studied are the electro-elastic solutions for a piezoelectric halfspace subjected Io a line force, a line charge and a line dislocation, i. e.. Green sfunclions on the basis of Stroh formalism and the concept of analytical continuation,explicit expressions for Green's functions are derived. As a direct application of theresults obtained, an infinite piezoelectric solid containing a semi-infinite crack isexammed. Attention iffocused on the stress and electric displacement fields of a cracktip. The stress and electric displacement intensity .factors are given explicitly.

半平面压电体的Green函数及其应用

本文研究半平面压电体在线力、电荷和位错作用下的弹性场和电场，即Green函数．基于各向异性弹性力学中的Stroh方法和解析延拓理论，推导了Green函数的封闭形式的解．作为解的应用，分析了含半无限裂纹的无限大压电介质的机电耦合场，给出了应力和电位移强度因子的解析表达式．

Poisson方程边值问题的Green函数法

归纳出在求解Poisson方程边值问题时引入Green函数的物理意义,讨论了电像法、级数法和保角变换法等三种方法求解Green函数的适用范围.

保角变换在求解曲面边界Green函数中的应用

介绍了用保角变换,将曲面化为平面,从而根据对称性方便地求出镜像电荷Green函数的方法。与相似形不同的是,此方法可以求复杂曲面的Green函数,并不局限于柱面和球面。由于所使用的变换涉及到复平面,因此该方法仅限于求解2-D问题和可以化为2-D问题的3-D问题(在与真实电荷<位置任意>与感应电荷形成的电偶极子垂直的面内保持轮换对称性)

Surface Modification Process by Electrical Discharge Machining with Ti Powder Green Compact Electrode

This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface with Ti or other compressed powder electrode in a certain condition. This new revolutionary method is called Electrical Discharge Coating (EDC). The process of EDC begins with electrode wear during EDM,then a kind of hard carbide is created through the thermal and chemical reaction between the worn electrode material and the carbon particle decomposed from kerosene fluid under high temperature. The carbide is piled up on a workpiece quickly and becomes a hard layer of ceramic about 20 μm in several minutes. This paper studies the principle and process of EDC systemically by using Ti powder green compact electrode. In order to obtain a layer of compact ceramic film, it is very important to select proper electric pulse parameters, such as pulse width, pulse interval, peak current. Meantime, the electrode materials and its forming mode will effect the machining surface quality greatly. This paper presents a series of experiment results to study the EDC process by adopt different technology parameters. Experiments and analyses show that a compact TiC ceramic layer can be created on the surface of metal workpiece. The hardness of ceramic layer is more 3 times higher than the base body, and the hardness changes gradiently from surface to base body. The method will have a great future because many materials can be easily added to the electrode and then be coated on the workpiece surface. Gearing the parameters ceramic can be created with different thickness. The switch between deposition and removal process is carried out easily by changing the polarity, thus the gear to the thickness and shape of the composite ceramic layer is carried out easily. This kind of composite ceramic layer will be used to deal with the surface of the cutting tools or molds possibly, in order to lengthen their life. It also can be found wide application in the fields of surface repairing and strengthening of the ship or aircraft.

Evaluation of cold resistance of ornamental species for planting as urban rooftop greening

Rooftop gardening or green roof establishment, one of the methods of afforestation for urban greening, has many benefits for the environment, the economy and urban landscapes. Various environmental stresses including heat, strong wind, sunshine and cold prevent most plants from growing well in extensive green roof systems. For the establishment of urban rooftop gardening in Beijing, we evaluated 13 ornamental plant species [Berberis thunbergii ‘Atropurea Nana’, Euonymus fortune （Turcz.） Hand., Viburnum dilatatum Thunb., Cotinus coggygria Scop., Swida alba Opiz., Weigela florida （Bunge.） A. DC., Jasminum nudiflorum Lindl., Euonymus japonicus Thunb., Euonymus japonicus ‘Cuzhi’, Buddleja davidii Franch., Pyracantha fortuneana （Maxim.） Li., Ligustrum lucidum Ait. and Spiraea salicifolia L.] for greening of flat rooftops. We obtained three indices concerning cold resistance, i.e., electrical conductivity of leaves, amounts of soluble sugars and amounts of free proline, providing a theoretical basis for selection of plants fit for roof and balcony greening. The results show that the six species P. fortuneana （Maxim.） Li., B. thunbergii ‘Atropurea Nana’, J. nudiflorum Lindl., E. fortune （Turcz.） Hand., E. japonicus Thunb. and E. japonicus ‘Cuzhi’ are excellent plants for rooftop gardening, judging from their strong ability in cold resistance. Plants such as L. lucidum Ait., C. coggygria Scop. and W. florida （Bunge） A. DC. showed a medium ability in cold resistance. However, these species can be used in roof and balcony greening after intensive management or acclimatization of variety. The ability to resist cold of plants such as W. florida （Bunge） A. DC., S. salicifolia L., V. rhytidophyllum and S. alba Opiz. （Cornus alba L.） was found to be rather weak. Therefore, cold-proof measures are recommended when using these species.

How will tradable green certificates affect electricity trading markets under renewable portfolio standards?A China perspective

Renewable portfolio standards(RPS)are important guarantees to promote renewable energy(RE)consumption.The tradable green certificate(TGC)trading mechanism is a supporting mechanism of RPS,but the rate of TGC trading is low and there is a double-metering problem of RE consumption.With the introduction of new policies in China,we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation(BEG)projects are considered to participate in TGC trading.To explore the interaction between the TGC market and the electricity market,this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model.The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed.The main conclusions are as follows.(i)The established market structure and model effectively solve the double-metering problem of RE consumption,making the TGC turnover rate reach 82.97%,greatly improving the market efficiency.(ii)Increased demand for TGC will increase demand for RE electricity.The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers(BEPs),reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity.This will help promote the rapid development of China’s RE,especially the BEG industry.(iii)Among the influencing factors,the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs.The decline in TGC price and subsidy price will reduce the profit of BEPs.Finally,we put forward policy recommendations for China’s RPS and TGC trading mechanism.This study can provide a reference for the construction of China’s TGC market and electricity market and the development of RE.

Green Demonstrated Analysis on Structure of Our Electric Power Resource

According to green view and history data, the demonstrated method is applied to analyze the green rationality on the present structure of thermal, hydraulic and nuclear electric power resource; the green trend of large-scale on thermal power generating unit in our history; the distribution of between water and coal resource with the space structure of electric power installed capacity, and recommending the premise, dominance and direction of building our green electric power structure in the future.

Photoluminescence of green nanophosphors Sr_(2)MgSi_(2)O_(7)doped with Tb^(3+)under 374-nm excitation

A series of Sr_(2)MgSi_(2)O_(7):Tb^(3+)nanophosphors is prepared using a high-temperature solid-state reaction.The x-ray diffraction patterns show that the crystal structure of the sample is not significantly affected by Tb^(3+)ions.However,the images of the scanning electron microscope illustrate that the average size of nanoparticles becomes larger with the increase of Tb^(3+)concentration.Unlike earlier investigations on down-conversion emission of Tb^(3+)ion excited by deep ultraviolet light,in this work,the photoluminescence characteristics of Sr_(2)MgSi_(2)O_(7)nanophosphors doped with different Tb^(3+)concentrations are analyzed under 374-nm excitations.The intense green emission at 545 nm is observed at an optimal doping concentration of 1.6 mol%.The main reason for the concentration quenching is due to the electric dipole-electric dipole interaction among Tb^(3+)ions.

“双碳”目标下基于系统动力学的发电行业碳减排政策研究

能源结构转型和碳减排政策是实现“双碳”目标的重要举措。本研究旨在探讨“双碳”背景下发电行业碳减排政策与市场交易机制的耦合作用。通过研究绿色证书市场交易机制、碳排放市场交易机制与电力市场机制间的耦合协同效应,考虑了价格影响市场间机制、能源结构分析和技术创新等因素,建立系统动力学模型并进行多情景仿真,研究不同政策组合对市场均衡及电力行业碳减排的影响。通过对模型的敏感性分析,评估了各种政策措施对系统行为的潜在影响,对政策体系的作用机理及其协同机制进行分析,明确不同政策间可能存在的冗余激励和效应冲抵等现象。研究得出:有效的碳减排政策组合使得未来十年火电装机占比下降至45%,可再生能源占比突破50%与传统能源持平甚至超过传统能源。建议碳减排政策的成功实施需要综合考虑市场监管、激励机制、技术创新等多方面因素。

不同碳减排政策交互影响下发电企业交易策略

在双碳目标和电力系统转型背景下,发电企业的交易策略会发生重要转变。基于可再生能源配额制的实施,综合考虑碳排放权市场和绿证市场,探究了发电企业参与电力市场的最优决策。首先搭建了在多政策情形下市场主体参与交易的市场架构,理清各市场中的供求关系。随后建立发电企业利润函数,以发电企业利润最大化为目标,并分别在非合作博弈和合作博弈两种情况下,建立发电企业的最优生产交易模型。最后通过算例分析检验该交易模型,为发电企业在多种政策影响下参加电力市场交易提供依据。

基于绿电和自愿减排量交易的园区低碳电力运营优化

为保证园区电力运营经济性,提高清洁电能的使用比例,文章提出了一种基于绿电和自愿减排量交易的园区低碳电力运营优化模型。该模型综合考虑了绿电和火电的购电成本、光伏发电成本及自愿减排量收益,以总运营成本最小为目标函数,结合随机概率约束,分中长期、日前和实时三阶段对绿电、火电电量以及光伏发电量进行优化。以南京市某企业园区的数据为基础,在自愿减排量价格合理的前提下,该优化模型可小幅降低园区总运营成本,并且显著提高了清洁电能的使用比例和园区应对扰动的能力。另外,就自愿减排量价格以及置信度取值对优化结果的影响进行了分析。

基于绿色能源的分布式混合电推进系统性能分析

基于绿色能源的电推进飞行器是是航空运输业实现碳中和的重要技术途径。本文旨在利用理论分析和数值计算的手段,研究基于绿色能源的分布式混合电推进系统的性能,重点关注锂电池和固体氧化物燃料电池(SOFC)在航程和载荷方面的影响。本研究构建了一个包含多种能源形式的混合电推进系统架构,并基于能量流建立了功率传递模型,推导出综合考虑部件特征参数、能量分配参数、飞行气动参数的电推进系统航程方程和载荷方程。研究结果显示,在航程方面,锂电池的能量分配系数和能量密度对飞机航程有显著影响,特别是在能量密度阈值之上,增加锂电池的能量分配系数对提升航程有正收益。此外,SOFC的能量分配系数和效率的增加也提高了飞机航程,特别是在其高值区域。在载荷方面,上述锂电池和SOFC参数对飞机零燃料质量(MZF)和有效载荷具有重要影响,尤其在高能量密度、高效率和高能量分配系数的情况下飞机的载荷性能改善最佳。参数敏感性分析揭示了升阻比、边界层吸入(BLI)风扇效率、锂电池荷电状态(αsoc)和氢燃料剩余状态(αsoh)对航程的显著影响,其中在以往研究中较少出现的αsoc和αsoh的敏感性分析不容忽视,对其进行更为精确和可靠的状态估计有助于优化航程和载荷的性能表现。

面向灵活性挖掘的电-氢混联综合能源系统机组组合研究

随着电制氢技术的逐步成熟,利用新能源制氢与管道输氢有望为能源系统的深度脱碳提供重要支撑。该文提出了电-氢耦合综合能源系统机组组合模型,考虑了发电机组与电解制氢系统协同提供灵活运行备用。首先通过高斯混合模型得到新能源预测误差分布,获取其95%置信区间,确定新能源电力系统灵活备用需求;其次提出了考虑灵活备用的电-氢混联综合能源系统机组组合模型,计及了电解制氢系统三状态(开机、关机、热备用)灵活运行模型;算例分析验证了所提电制氢三状态机组组合模型的有效性,分析了新能源渗透率对制氢量与节点电价的影响。