In the context of global resource scarcity, the integrated and coordinated development of urban modernization and low-carbon development is becoming more and more crucial. In order to calculate the degree of coupling coordination between urban modernization and low-carbon growth in 31 Chinese provinces from 2010 to 2021, this paper thoroughly applies the entropy approach and coupling coordination model; the geographical correlation of the degree of coupling coordination of various regions was confirmed using the Moran’s I test method; and by utilizing the gray correlation degree model, we examined the elements that affect the degree of coupling coordination between the two in the various provinces. We found that: (1) there are periodic fluctuations in the coupling coordination between the two during the research period, with a general rising tendency year after year; (2) the degree of coupling and coordination between the two shows the characteristics of HH clustering (eastern region) and LL clustering (western region); and (3) the degree of coupling and coordination between the two is influenced by different factors in different regions. Overall, low-carbon variables have a significant impact on the eastern area, but urban modernization factors have a significant impact on the central, western, and northeastern regions. This study can provide policy recommendations for provincial governments in various regions, help identify favorable factors for coordinated development, and improve the role of some influencing factors in a targeted manner, thereby improving the level of urban modernization and low-carbon coordinated development and promoting urban development and ecological harmony.

To enhance the low-carbon level and economic performance of microgrid systems while considering the impact of renewable energy output uncertainty on system operation stability, this paper presents a robust optimization microgrid model based on carbon-trading mechanisms and demand–response mechanisms. Regarding the carbon-trading mechanism, the baseline allocation method is utilized to provide carbon emission quotas to the system at no cost, and a ladder carbon price model is implemented to construct a carbon transaction cost model. Regarding uncertainty set construction, the correlation of distributed generation in time and space is considered, and a new uncertainty set is constructed based on historical data to reduce the conservative type of robust optimization. Based on the column constraint generation algorithm, the model is solved. The findings indicate that upon considering the carbon-trading mechanism, the microgrid tends to increase the output of low-carbon units and renewable energy units, and the carbon emissions of the microgrid can be effectively reduced. However, due to the increase in power purchase from the distribution network and the increase in carbon transaction costs, the operating costs of the microgrid increase. Secondly, through the utilization of demand–response mechanisms, the microgrid can achieve load transfer between peaks and troughs. It is imperative to establish appropriate compensation costs for demand and response that balances both economic efficiency and system stability. At the same time, due to the time-of-use electricity price, the energy storage equipment can also play a load transfer effect and improve the system’s economy. Finally, sensitivity analysis was conducted on the adjustment parameters of distributed power sources and loads that have uncertain values. A comparison was made between the deterministic scheduling model and the two-stage robust optimization model proposed in this study. It was proved that this model has great advantages in coordinating the economy, stability and low carbon level of microgrid operations.

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China’s power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China’s power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China’s power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed.

High-carbon emission industries are the most important source of carbon emissions in the Zhejiang Province. Due to the differences in the development level of various industries, it is necessary to adjust the carbon emission reduction strategies of various industries. As the first ecological province in China, the promotion of carbon emission reduction in high-carbon industries in the Zhejiang Province plays an important leading role in the development of low-carbon economy in other industries and other provinces in China. Taking eight high-carbon industries in Zhejiang Province as the research object, this paper uses the LMDI factor decomposition model to deconstruct the influencing factors and effects of carbon emissions in eight industries in the Zhejiang Province from 2010 to 2021. On this basis, the Tapio decoupling model is applied to study the reasons and driving factors of the decoupling between economic growth and carbon emissions. The results showed that: (1) During the study period, the total carbon emissions of eight industries in the Zhejiang Province increased by 24,312,200 t, showing an overall upward trend. (2) The effect of economic growth and population size led to the rapid growth of carbon emissions in eight industries in the Zhejiang Province, and the effect of energy intensity on carbon emission reduction was the most significant; the effect of industry structure presented a trend of first promoting and then inhibiting, and the effect of carbon emission coefficient always inhibited carbon emissions. (3) The population size has restricted decoupling efforts; energy intensity has the greatest impact on the realization of industry decoupling; energy structure and industry structure decoupling efforts are small; the carbon emission coefficient has always influenced decoupling efforts. This research paper will provide suggestions and policies for the development of low-carbon economy in Zhejiang Province.

As distributed generations and flexible loads are widely connected to distribution networks, traditional distribution networks become “active”, and the response of the active distribution network (ADN) to the transmission network is receiving increasing attention. In this paper, the congestion problem of transmission network is studied from a demand-side perspective, and a multi-layer scheduling framework considering capabilities of ADNs is developed to reduce unnecessary load shedding operations in transmission network. The proposed framework consists of three layers. In the first layer, ADN’s power flow is linearized by the second-order cone programming, based on which a reactive power optimization scheduling model is constructed to minimize network loss in ADNs. When the network loss reduction in ADN cannot meet the requirements for congestion mitigation, a bi-level self-cycle model based on analytical target cascading is constructed in the second layer, which contributes to dispatch distributed generators’ outputs and further curtail load from the ADN side. When the second layer fails to satisfy the required load curtailment, a demand response model considering user acceptance and rejection is proposed to obtain the optimal shedding strategy for interruptible loads, and ultimately alleviate the overload problem. A case study with varying degrees of overload in the transmission network is conducted to demonstrate the proposed framework.

Data security and operating efficiency are common problems in traditional electricity Carbon emission quota (CEQ) trading systems, in view of these unsolved issues, this study presents a new multi-connected blockchain electricity CEQ trading model based on reputation value. In this paper, combining with blockchain's characteristics of decentralisation, smart contracts, and transparency, a safe and reliable multi-connected blockchain CEQ mechanism was established; a reputation value-based scoring mechanism was adopted to effectively improve the initiative of participant nodes. Through three highly-coupled subsystems of CEQ allocation, matching, and rewards and punishments, a CEQ trading decision model was constructed to realise the goal of carbon emission reduction. Then on this basis, the CEQ reputation value was used to promote connections among the subsystems of the CEQ trading decision model, thereby realising reliable transactions.

Existing energy-saving and eco-friendly dispatching models for microgrid have a few shortcomings such as large load-prediction errors, low transaction efficiency, and high system security risks, to overcome these defects, this paper presents a novel Energy-saving and Eco-friendly Dispatching (EED) model for microgrid based on energy blockchain. After revealing blockchain's several features of transparent information, decentralised, safe, and credible, at first, this paper established a microgrid electricity transaction architecture. Then, a new energy blockchain consensus mechanism constructed based on environmental trust value was adopted to embed the environmental trust value into the basic attributes of prosumers, in this way, the probability of prosumers successfully digging up blocks could be effectively improved, further, the transaction behaviours of prosumers within the scope of the microgrid could be regularised. After that, with the help of modified objective functions, the probability of transactions between prosumers and external grids could be reduced to realise EED optimisation inside the microgrid. At last, the results of a calculation example proved that, the proposed model could realise the efficient use of clean energy in the microgrid, and ensure the security of electricity transactions and the credibility of data storage in the microgrid. Research in this paper provided a useful theoretical support for the decision-making and optimisation of the EED of microgrid.

The traditional microgrid electricity transactions face a low security, poor economic benefits, and weak stability. To solve these problems, this paper proposes a multimicrogrid cross-chain transaction model based on quantum blockchain. Specifically, a bidding strategy was developed for the noncooperative dynamic game of aggregator-multimicrogrid alliance, aiming to balance and optimize the benefits of all parties and effectively enhance the consumption rate of electric energy. To improve the transaction efficiency between aggregators, a consensus mechanism was designed for multimicrogrid cross-chain communication, realizing consistent self-adaptation to cross-chain information. In addition, the quantum threshold signature technology was adopted to ensure the reliability of transaction data and create an unconditional secure communication environment. Case analysis shows that our model proposed in this paper not only ensures the security and stability of transactions but also enhances the economic benefits, providing theoretical support and decision support for the optimization of multimicronetwork cross-chain transaction model.

Aiming at the problems of single traditional index, fuzzy evaluation result and subjective index weight in evaluating the investment benefit of substation project, a substation project evaluation model based on coefficient of variation and fuzzy comprehensive evaluation is constructed. First of all, this paper constructs a more perfect evaluation substation project investment index system from four dimensions: project maturity, project rationality, project effectiveness and investment construction environment. Furthermore, a combination weighting method based on coefficient of variation is proposed and introduced into the calculation of fuzzy comprehensive score, and the necessity model of power grid project investment is established. Finally, an example analysis is carried out for the 35kv project put into production in a certain province, and it is verified that the weighting method and evaluation model proposed in this paper can reasonably and effectively quantify the investment benefit and necessity of the evaluated substation project, which is practical in promoting the lean investment of the substation project.

Since the concept of carbon asset is still new to most power enterprises, they generally lack the knowledge and experience of carbon asset management, to help them cope with the related works, this paper proposes a carbon footprint tracking and quantitative analysis model for power enterprises based on thermodynamics. At first, by employing thermodynamic theories, this paper established an inventory model through the measurement and calculation of carbon concentration and carbon emissions, which can fix defective items, improve inventory efficiency, and save inventory cost. Then, the paper used thermo-economics and the TOPSIS method to construct a carbon inventory index system containing indexes such as carbon exergy ratio and regulation interval, which can reduce the error of carbon emission intensity and update the carbon account information in a timely manner. After that, the carbon footprint was calculated by coupling the carbon thermal map with the multi-dimensional evaluation mechanism, thereby achieving accurate control of carbon emissions and realizing the energy-saving and emission-reduction goals. At last, the attained experimental results showed that, the proposed carbon inventory measurement model established based on thermodynamics can effectively regulate the emission behavior of power enterprises, help achieve the ultimate purposes of reducing cost, increasing efficiency, and optimizing environment. The work done in this paper could provide theoretical support for making decisions for the smooth development of carbon rights market in China.

The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations.

With the continuous expansion of power demand, the scale of distribution network investment and construction is also growing rapidly. In order to improve the accuracy and balance of distribution network investment and further maximize the economic benefits of investment, this paper creatively proposes a distribution network investment allocation model that considers the input-output benefits and development level quality. Firstly, the comprehensive evaluation index system of distribution network is constructed from two dimensions of technology and benefit, and the index calculation model is established to realize the horizontal comparison of the development status of distribution network. Then, by introducing the two factor theory and taking into account the development status and future development needs of the distribution network, an investment allocation model is built to determine the proportion of investment allocation. Finally, the empirical analysis is carried out based on the development status of Sichuan distribution network, and the results show that the model can better meet the future development needs of various cities, and achieve accurate and efficient investment in Sichuan distribution network.

In the Industrial Internet of Things (IIoT), peer-to-peer (P2P) distributed energy (DE) transactions exist in various scenarios. This paper attempts to improve the intelligence, real-timeliness, and security of the direct transaction between DE generation companies (DEGCs) and users, and reduce the default frequency of distributed power (DP) transactions. For these purposes, a P2P DE transaction model for the IIoT was proposed based on blockchain. Firstly, a blockchain-based distributed energy peer-to-peer transaction framework is constructed, which is more suitable for generalized energy transactions based on typical transaction scenarios of the IIoT. Using credit value evaluation and smart contracts to ensure the transparency, openness, and non-tampering of credit scores. On this basis, the energy currency reward mechanism is used to promote the trustworthiness of transaction nodes and maintain transaction security. Finally, the P2P direct transaction based on credit value was designed to improve the transaction efficiency and security. Through case analysis, the DE transaction model for the IIoT, which is based on the credit value of the blockchain, supports fast and frequent energy transactions, as it overcomes the confirmation delays of energy blockchain transactions. The proposed model improves the efficiency of DE transactions in the IIoT, effectively suppresses default frequency, and maintains the order of DE market in the IIoT.

In view of the problems such as high carbon emissions and low return on investment in conventional power planning, this paper proposed a low-carbon thermal energy power planning model based on a green certificate allocation mechanism. First, considering the uneven distribution of power generation resources in the power sector and the significant differences in power generation technologies in various regions of China, a green certificate allocation mechanism was established based on regional comparison to ensure a fair allocation of carbon quotas; then, based on the idea of full life cycle, an in-depth and comprehensive analysis was conducted on the low-carbon thermal energy power technologies, and a new low-carbon thermal energy power planning mode was established; on this basis, with investment costs, operation and maintenance costs, power generation costs and low-carbon benefits as the objective functions, a low-carbon power planning model with the maximum net benefit was established; after that, the proposed low-carbon thermal energy power planning model was optimized and solved with the discrete bacterial colony chemotaxis algorithm, and the results were compared with the optimized results of conventional power planning, and the roles of various low-carbon power elements in different low-carbon scenarios were analyzed. The results of the example analysis show that the proposed model can effectively reduce the carbon emissions of the power sector and increase the power generation benefits of the units, so it will provide a useful reference for the low-carbon power planning work in the future.

The electricity transactions of microgrids face several problems: the high platform management cost, the low security, and the untimely consumption of scattered electricity. To solve these problems, this paper presents a multi-microgrid thermal game model based on quantum blockchain. Specifically, a dynamic model was established for the noncooperative game between aggregators, microgrids, and large users to maximize the benefit of each party, and to realize the timely consumption of scattered electricity. Next, a transaction platform was constructed based on the two-round password based authenticated key exchange (PAKE) protocol, which eliminates non-interactive zero-knowledge (NIZK), aiming to substantially enhance the post-quantum security of transactions. Then, the quantum signature using two-particle entangled Bell states was adopted to safeguard the quantum communication of electricity transactions, and authenticate the nodes. Example analysis shows that our model can realize the timely consumption of scattered electricity and thermal energy, improve the security of transaction data and users, and achieve Pareto optimality. The research provides theoretical support and decision-making basis for electricity transactions in the post-quantum age.

Users of wearable services are different in age, occupation, income, education, personality, values, and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named trend-following users, fashion-leading users, and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters and draws some conclusions.

This paper designs a direct transaction model for energy blockchain mobile information system based on hybrid quotation strategy, aiming to improve the smartness, real-time property and information security of the direct transaction between distributed power generation companies (DPGCs) and users. Specifically, the continuous auction mechanism was adopted to improve the matching efficiency between the transaction parties, injecting fresh impetus to the power market. In the blockchain system, the serial number of the transaction script was marked with a special label to prove the power quantity being transacted, such that the two parties can exchange the digital proof of transaction power and the transaction fee. Next, the hybrid quotation strategy was introduced to minimize the impacts of frequent fluctuations in the electricity transaction price of the continuous auction market. In this way, the two parties can flexibly adjust their quotations according to the changes of market information. The case study shows that our model outperformed the traditional centralized transaction model in efficiency and mobile power information security. The research findings provides a reference for further research on the application of block chain technology in distributed energy mobile power information transaction.

The central node of traditional mobile transactions is vulnerable to attack and causes user privacy disclosure. To solve this problem, blockchain and mobile transactions are combined to construct a decentralised mobile trading platform. This paper designs a Byzantine consensus algorithm based on spanning tree and minimal gossip sequence (BCA-TG) ensuring the accuracy and reliability of the transaction information. First, the gossip protocol was introduced to the Byzantine consensus algorithm, requiring the nodes to communicate in the push-pull mode. In this way, every two nodes can sync their information within one cycle. Next, the minimal gossip sequence was constructed by spanning tree, and used as the basis for the communication and consensus-making between the nodes. On this basis, the node views in the network were transformed by the gossip protocol, which further enhances the scalability and fault-tolerance of the algorithm. The research findings provide a good reference for problem-solving in blockchain-based mobile transactions.

为解决输配电系统检测设备供电难题,设计了能够从输电线路架空地线取电系统电源电路.根据输电线路地线感应电压不稳定的特点,采用不可控整流桥和单端正激变换电路,结合闭环控制技术,设计并搭建了电路模型.通过分析在不同输入电压情况下电路的输出特性,验证了电路可行性,该电源电路实现了将9 V～100 V可变交流输入转换为12 V稳定直流输出,并且通过拟合不同负载情况下电路输出功率特性曲线,分析得出电源电路输出功率稳定.

The energy power supply and demand network (EPSDN) is difficult to be scheduled in a coordinated manner, due to the fluctuations in intraday power price.To solve the problem, this paper puts forward a blockchain-based spot market transaction model for the EPSDN, with the aim to enhance the intelligence, real-time performance and security of spot power market transactions.Specifically, intraday time-of-use (TOU) pricing mechanisms were introduced to minimize the negative impacts of intraday power price variation on the spot market; the leading influencing factors of spot power market were identified effectively among various factors through factor analysis; multiple purchase plans were optimized by the multi-objective search algorithm based on the particle swarm optimization (PSO), enabling the seller to optimize the purchase plan when multiple suppliers are available under the relaxation of control over direct power trading.On this basis, the real-time property of the transaction information was guaranteed through EPSDN-based information exchange.The case analysis shows that our transaction model outperformed the traditional centralized transaction model in transaction efficiency and security.The research findings shed new light on the operation of spot power market under partial decentralization.