Abstract:Green electricity consumption is a crucial pathway to achieve carbon peak and carbon neutrality goals, prompting many countries to introduce policies that promote its adoption. Firstly, the study examines and compares various guiding policies, such as tax incentives, renewable energy quota systems, and green electricity certification. Secondly, from an organizational management perspective, it analyzes the internal and external factors influencing green electricity consumption. Internal factors include leadership, corporate social responsibility, and green branding, while external factors encompass market pricing, policy support, and socio-cultural influences. Finally,the study discusses the challenges facing China’s green electricity consumption research and proposes recommendations to enhance the pricing mechanism, strengthen alignment with international standards, and expand research perspectives. By integrating analysis of policies, markets, and organizational behavior, this study not only provides valuable insights for policymakers to optimize incentive mechanisms for green electricity consumption but also offers practical guidance for industrial and commercial enterprises in formulating green transition strategies. The findings have significant implications for promoting the widespread adoption of green electricity in China and advancing the achievement of the“dual carbon”goals.

Abstract:The accurate assessment of the demand response potential of air conditioning loads is a crucial foundational task for effectively scheduling their participation in demand response. To address the issue of low prediction accuracy caused by the traditional deep learning methods’neglect of the time-sequential distribution differences in real-world air conditioning loads, the concept of transfer learning to the time dimension is extended. The phenomenon of time-sequential distribution drift in air conditioning load time series is analyzed by drawing an analogy to covariate shift in transfer learning. Based on this, two time-sequential migration strategies, time-sequential distribution matching and time-sequential similarity quantification, are proposed. These strategies are integrated into the traditional recurrent neural network（RNN）architecture to build an adaptive RNN air conditioning load prediction model, thereby improving the prediction accuracy in real-world scenarios. Finally, an air conditioning load demand response potential evaluation method is proposed based on the overall approach of predicting the load values before and after the response and the adaptive RNN air conditioning load prediction model. Comparative experimental analysis on real datasets shows that this method can significantly improve the prediction accuracy of demand response potential over existing methods, thus providing effective reference for the demand response scheduling decisions of the grid dispatch center.

Abstract:In order to solve the problem of analysis ductility and large consumption of cloud resources caused by massive non-intrusive load monitoring（NILM）data uploaded to the cloud, a non-intrusive load identification framework based on cloud-edge collaboration is proposed. Firstly, the Markov transition field（MTF）coding method is used to color code the power data, and the load identification with clear characteristics is constructed. Then, a lightweight deep learning model with the same structure is deployed in the cloud service layer and the edge service layer respectively to complete the training and load identification tasks. While reducing the pressure of cloud-edge resources, the cloud-edge coordination of load identification is realized through transfer learning. Finally, based on adaptive synthetic sampling（ADASYN）, the REDD dataset is extended to solve the model learning bias caused by dataset imbalance, and the identification performance of the framework proposed is validated based on the dataset. The results show that the framework can not only meet the requirements of high precision and real-time load identification, but also significantly reduce the pressure of cloud and edge storage and computing resources.

Abstract:With the low-carbon transformation of China’s energy structure, the demand for peak shaving in the power grid is increasing day by day. How to effectively utilize the fixed frequency air conditioning load group to participate in peak shaving has become a major research hotspot. The coordinated control method of fixed frequency air conditioning load group in power grid peak shaving is mainly explored. Firstly, based on the power model of fixed frequency air conditioning load, the traditional priority sequence control strategy has been deeply improved, and an optimized control technology combining start and cut off time has been proposed. On this basis, a comprehensive switch control coordination strategy has been developed for the response and recovery characteristics of air conditioning load regulation. Through detailed empirical analysis, it has been proven that the proposed optimization strategies have significant benefits in pursuing the goals of minimum total load or maximum reduction.

Abstract:Accurate day-ahead load forecasting is essential for optimizing distribution network planning. As the load data available to distribution networks becomes increasingly multidimensional and extensive, efficiently leveraging this data for precise day-ahead load forecasting has become a key research focus. To address this, an end-to-end approach that integrates data preprocessing, data decomposition,and data forecasting is proposed. In the data preprocessing stage, the bisecting K-means（BKM）clustering technique is used to reduce data noise and categorize the data, while combining dynamic and static feature extraction to capture load characteristics. In the data decomposition stage, the variational mode decomposition（VMD）technique is applied to decompose the preprocessed data into frequency components with strong periodicity and randomness. Finally, in the data forecasting stage, a temporal convolutional network（TCN）is employed to predict each mode component, and the predictions are aggregated to produce the final day-ahead load forecast. Case studies demonstrate that the BKM-VMD-TCN method proposed achieves superior forecasting accuracy compared to three other load forecasting methods.

Abstract:Industrial load accounts for a large proportion of social electricity consumption, and the adjustable load resources are abundant,so it is imperative to analyze its adjustable potential. Due to the large change rate of industrial load and many load tips, it is difficult to predict the adjustable potential in real time. Therefore, the silicon carbide industry of a typical industrial enterprise is selected to establish an adjustable potential deduction model. First, the impact of weather characteristics and electricity price factors on the enterprise load is considered through the Person correlation analysis method. At the same time, the Bi-directional long short-term memory（BiLSTM）prediction model processed by convolutional neural network（CNN）and Attention mechanism is established. The adjustable potential of silicon carbide enterprises is explored by using the model prediction results. In order to verify the effectiveness of this method, this algorithm is significantly superior to other comparison algorithms by establishing different algorithms for comparison and the tunable potential results under different strategies. Meanwhile, the tunable potential results of the three strategies can deepen the power grid’s understanding of the load characteristics of such enterprises.

Abstract:The lack of reasonable energy management strategies in building microgrids has led to increasingly prominent issues of low energy utilization and high carbon emissions. Based on the electric demand response technology and the principle of human body comfort. By taking the lowest energy cost of buildings as the optimization objective, and constraining human body comfort, electric demand response,energy balance and equipment operation safety, a building microgrid energy optimization scheduling model is established. Matlab is selected to call the Gurobi solver for solution. The analysis results of the example show that applying the building energy optimization scheduling model can reduce the overall energy consumption cost of buildings while ensuring comfort by optimizing internal energy supply modes, responding to external energy supply prices, and providing smooth output and peak shaving for the power grid.

Abstract:Faced with high upfront investment and low utilization rate of physical energy storage, cloud energy storage has received wide attention as a new type of energy storage investment and operation mode. Considering the high proportion of air-conditioning load in national electricity consumption, the virtual energy storage potential of fixed-frequency air-conditioners is fully exploited to establish an aggregated air- conditioner virtual energy storage model, which can be leased. Based on this, considering wind, photovoltaic and load uncertainty,cloud energy storage operators and users are taken as an alliance, and the research on the optimized operation method of cloud energy storage is carried out with the goal of improving the overall economic model of the alliance. The economic benefits of each user under the fixed pricing and dynamic pricing scenarios are compared and analyzed. Case study shows that the cloud energy storage optimization scheduling method based on the thermal inertia of scaled air conditioners can effectively improve the overall revenue of the operator and users, reduce the expenditure of users’electricity consumption, and promote the consumption of renewable energy on the users’side.

Abstract:Accurate prediction of multi-energy load is crucial for the optimal scheduling and economic operation of integrated energy systems（IES）. Aiming at the strong randomness of regional IES and the coupling relationship between multi-energy sources, a multi-task short-term load prediction model based on SEResNet-BiLSTM network and attention mechanism is proposed. Firstly, the model of squeezeand-excitation networks-residual network（SEResNet）is used as the high-dimensional feature extraction unit to mine the coupling relationship between multiple energy sources. The high-dimensional feature extraction of multi-energy load data is realized. Then, bidirectional long short-term memory（BiLSTM）network is used to capture the time series characteristics between data to realize the prediction of load data. Multi-task load learning is realized by hard weight sharing to realize multivariate load forecasting. Finally, the effectiveness of proposed method is verified by simulation experiments, and the accuracy of the proposed method is significantly improved compared with other models.

Abstract:In order to improve the absorption capacity of high-penetration new energy power generation, promote the optimal allocation of energy and reduce carbon emissions, an economical low-carbon optimal scheduling method of integrated energy system based on heat-electric-gas-hydrogen-carbon coupling and source-charge dual response mechanism is proposed. Firstly, a comprehensive energy system model with heat-electric-gas-hydrogen-carbon coupling is established. Secondly, a flexible dual response mechanism of thermoelectric hydrogencarbon coupling is proposed, that is, on the source side, carbon collecting devices are installed on the source side of carbon emission to reduce carbon emission, heat storage devices are installed to absorb waste heat, waste heat boilers are used to generate electricity or hydrogen, and the surplus energy of new energy is used for electricity storage, heating and hydrogen production during the load valley period.And on the load side, the demand response of transferable load and interruptible load is implemented. Then, the green certificate low-carbon economic scheduling strategy based on carbon paving equipment and cascade carbon trading is constructed. Finally, an integrated energy system in North China is taken as an example to demonstrate the advantages of the proposed method.

Abstract:In order to further promote the development of electric vehicles, a method for estimating the ownership of electric vehicles is pro posed considering the master-slave game of carbon trading among the government, suppliers, and consumers. Firstly, based on the interests of the three parties, a master-slave game model led by the government is established. Secondly, considering the innovation, imitation effect,and price factors of the population, the Bass model is improved to predict the regional electric vehicle ownership. Furthermore, the utility functions of the three parties in the game are optimized as the objective function, in order to obtain the basic data under the Nash equilibrium solution for the parameters of the Bass model. And then, establishes a regional electric vehicle ownership prediction method is established by alternating between ownership prediction and vehicle market game simulation. Finally, with actual vehicle data from Shanghai,the impact of introducing carbon trading into the vehicle market on the number of electric vehicles is analyzed in the coming years.Through scenario comparison, the feasibility and economy of the proposed method are verified.

Abstract:Taking the typical industrial users of Zhejiang Province as the research object, the potential for participating in the demand response of industrial cluster loads is analyzed, and a demand response model has been established. Considering the uncertainty of the aggregate participation in the electrical energy market and the peak adjustment assist service market, including the probability of winning bids,calling calls, calling probability and shortage possibilities, Bayesian neural network is used to predict, a bidding strategy model for aggregates is established to participate in the joint market, and the purchase of the park in the spot market in each period of time is obtained.Through the minimum cost of electricity purchase and maximum demand response income, the power purchase volume of the park in the spot market in each period is obtained. Important references for the formulation of industrial cluster load aggregates to participate in the power market can be provided, selecting the bidding time and capacity, and considering how to minimize the risk of power shortage and maximize the total profit.

Abstract:Air conditioning load is a kind of flexible resources with fast response speed and large adjustable capacity. Through the analysis and modeling of the interaction potential of air conditioning load under multiple demand response time states, it can quickly respond to the grid side dispatch with little impact on user comfort, reduce the power demand in peak hours, and alleviate the contradiction between power supply and demand. In order to better reveal the various factors that affect air-conditioning participation in demand response, firstly,based on the thermodynamic model of air-conditioning, air-conditioning load is decomposed into static load and dynamic load. The concept of data-driven is different from the classical model-driven, which uses massive data acquired by collection or simulation. The deep feature relationships are mined and explored, and the problem architecture and solution ideas under the data-driven algorithm are established.Then, the constrained regression method and the data-driven temporal convolutional network-bidirectional gated recurrent unit-attention （TCN-BiGRU-Attention）neural network are used to estimate the adjustable interaction ability under different time states when the air conditioner participates in the demand response. The simulation results show that there is a significant correlation between the setting temperature and other factors and the static load of air conditioning, and there is a huge difference in the interaction ability between air conditioning load and the grid side under different demand response time scales. This method can effectively reduce the total cost of system calls and greatly improve the efficiency of air conditioning load participating in peak load shaving. The validity and accuracy of the method are verified based on the data of real users.

Abstract:Vehicle-to-grid（V2G）technology can fully realize the potential of electric vehicles（EVs）to reduce carbon emissions and increase efficiency in transportation and power systems. However, the effective implementation of V2G technology depends on the public’s willingness to accept the technology, and there are still some research gaps on how to effectively promote the deployment and operation of V2G technology on demand side. In order to comprehensively analyze users’acceptance of V2G technology, considering charging and discharging scenarios, a conceptual framework on the basis of the unified theory of acceptance and use technology model is proposed, and hypotheses are proposed correspondingly. Through questionnaire survey, 855 valid questionnaires are collected, including 428 EV owners and 427 potential EV owners interested in purchasing EV. Based on the structural equation model, the total sample is analyzed, and the results show that perceived utility had the greatest impact on respondents’acceptance, and community had a significant influence on respondents perceived altruism. Then, the differences between the two types of respondents are compared and analyzed. The results show that perceived utility and altruism important for EV users to participate in charging and discharging. While potential EV owners are more concerned about incentive conditions and technical reliability. Finally, suggestions are put forward to improve the acceptance of EV users to participate in orderly charge-discharge scheduling policies, which can provide references for the transportation and power systems to deploy and evaluate the impact of V2G technology.

Abstract:With the advancement of electricity market reforms, the rapid development of intra-provincial and inter-provincial electricity transactions has placed higher demands on transmission section planning. Traditional models often overlook the complexity of market gaming behavior, making it difficult to accurately reflect the economic value of transmission investments. In response, a method for evaluating the value of transmission sections is proposed, considering both intra-provincial and inter-provincial electricity spot market transactions.By constructing an inter-provincial equilibrium constraint model and an intra-provincial centralized clearing model, the method comprehensively accounts for market clearing rules and price signals to assess the market value of transmission expansion. A case study based on the Jiangsu power grid topology shows that insufficient inter-provincial transmission capacity can lead to increased price volatility and market failures. Transmission expansion effectively mitigates these issues and promotes rational bidding, while the evaluation of intra-provincial sections demonstrates that market value indicators can significantly enhance resource allocation efficiency. Adjusting the expansion revenue distribution coefficient and investment ratio can further optimize grid companies’investment decisions across different energy structures.

Abstract:With the IES characteristics and carbon reduction potential, taking the block-chain energy trading platform, starting from the GCT, CET, DR and multi-market linkage, based on the EC-Token interactive mechanism, a comprehensive energy market emission reduction value measurement and trading strategy is proposed. Firstly, with the renewable energy, multi-energy, load, storage and electric-gas coupling equipment, built the IES topology structure and full-process carbon emission model；Secondly, the emission reduction value and carbon elimination technologies in the IES market is designed；Then, the EC-Token with carbon reduction as the core concept is created.With the help of the smart contract and block-chain technology, the EC-Token trading platform is built through the hyperledger Fabric,and the market vitality is stimulated by“quantifying”the low-carbon technology and green action of IES. Finally, a typical IES is used for numerical simulation, and the results verify the rationality of the proposed strategy and the effectiveness of the platform.

Abstract:With the continuous development of information technology and the rise of blockchain technology, distributed balanced trading has attracted widespread attention as an innovative trading method. Flexible resources participate in distributed balanced trading, realize local power complementarity, improve efficiency, and reduce line operation pressure. In order to ensure the interests of producers and consumers, a distributed balanced trading strategy considering flexible resources is proposed. Firstly, for the relationship between photovoltaic （PV）power generation producers, photovoltaic power generation producers with energy storage systems（ES）, power consumers with flexible resources, power consumers and power balance service providers, a distributed transaction framework is proposed; Secondly, based on the model predictive control（MPC）theory, a minimum cost rolling optimization model is proposed to optimize the number of bids for flexible resources；Then according to the market demand and supply situation, put forward the bidding price strategy；Finally, the simulation results show that this strategy can improve the consumer’s income, enhance the local balance ability, and improve the transaction rate.

Abstract:With the integration of distributed energy into the distribution network, its structure has become increasingly complex and the failure rate has also increased. Fault restoration of distribution networks helps to restore power supply, improve the stability and reliability of power systems. The extensive installation of distribution transformer supervisory terminal units（TTU）provides data support for network fault restoration. The real-time topology identification technology of distribution networks considering TTU data measurement errors is focused on. Firstly, based on the analysis of the TTU data, an optimization equation with the target of minimizing the error is constructed.Secondly, considering the mixed-integer programming problem and the nonlinear item in the optimization equation, the equation is converted into a quadratic programming problem to make it possible to solve it with the standard method. Finally, case studies are conducted to verify the effectiveness of the proposed algorithm.