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From a spatial perspective, this paper constructs a spatial Durbin model based on the adjacent weight matrix to analyze the impact of environmental regulation on the development of renewable energy in 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2007 to 2020. The results show that (1) both environmental regulation and renewable energy development have positive spatial autocorrelation and form relatively similar spatial agglomeration areas, specifically showing the characteristics of "high-high" agglomeration in resource-rich areas and "low-low" agglomeration in resource-scarce areas. (2) Environmental regulation significantly promotes the development of local renewable energy and has a significant spatial spillover effect on the development of renewable energy in neighboring provinces. (3) The moderating effect results show that renewable energy installed capacity and electricity demand play an inverse moderating role between environmental regulation and renewable energy development. (4) From the perspective of different regions, the impact of environmental regulation on the development of renewable energy is heterogeneous. Based on the research conclusions, this paper finally provides corresponding policy suggestions from three aspects: regional cooperation, technical support, and differentiation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China's power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system. [ABSTRACT FROM AUTHOR]

The world faces several problems related to natural gas resource rents and energy production from renewable sources. One of the main problems is the influence of energy imports, manufacturing exports, and alternative energy sources on natural gas and electricity production from renewable sources. Energy imports, manufacturing exports, and alternative energy sources can impact natural gas and electricity production. This paper examines natural gas resource rents and electricity production from renewable sources nexus from 1971 to 2021, using energy imports, manufacturer's exports, and alternative energy sources in China. Electricity production from renewable sources and manufacturing exports are negatively associated with natural gas resource rents. Energy imports and alternative energy sources positively relate to natural gas resource rents in China. These results suggest that the energy sector in China is highly interconnected and that policies that seek to promote renewable energy sources and other alternatives can positively affect natural gas resource rents. China needs to develop an energy policy considering the policy implications of energy imports and natural gas resource rents. Such a policy should focus on increasing domestic production, reducing energy imports, and ensuring adequate revenue from natural gas resource rents. Additionally, regulations could be implemented that support the development of alternative energy sources, such as requiring utilities to purchase a certain percentage of their power from renewable sources., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

The concept of time-of-use (TOU) electricity pricing is widely recognized as a key strategy to bridge the gap between electricity availability and consumption, enhance the efficiency of electricity, and refine the patterns of electricity usage. Nonetheless, the existing policy on pricing electricity based on TOU electricity pricing is missing a theoretical approach that evaluates the load properties and the advantages of investing in the power grid. Consequently, the article suggests a method for optimizing electricity prices based on TOU electricity pricing to reduce the costs associated with investing in power grids. Initially, a model for optimizing electricity prices based on TOU electricity pricing is developed, offering support for the pricing strategy of the power grid; Subsequently, a method for dividing TOU electricity pricing using the Gaussian Mixture Module (GMM) clustering algorithm is introduced, offering theoretical backing for the creation of such pricing strategies; Following this, a detailed optimization approach for electricity pricing of electricity TOU electricity pricing is suggested, along with taking into account the benefits of grid investments and the power grid's load properties, the formulation of the electricity pricing strategy for TOU electricity pricing; Ultimately, this approach is corroborated by the Chongqing power system in China, aiming to minimize disparities in peak load valleys and enhance the advantages of grid investments, thereby offering technical assistance for the scientific determination of TOU electricity pricing. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head. • Long-term operation of cascade energy storage system was modelled. • Operating rules of cascade energy storage system were derived. • 81.6 % renewable curtailment was converted to hydroelectricity. • Upper plant accounted for 96.5 % of hydroelectricity increment. • Water saving and renewable accommodation could be coordinated. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

The factors that influence residents' willingness to accept long-term compensation are largely ignored, which leads to coal-to-electricity compensation failures. The core of Internet use is a viable solution to the inefficient coal-to-electricity transition for clean energy. This study employs the Heckman two-stage model to show that the compensation standard for coal-to-electricity transition should range from 25.61 to 174.07 yuan per person. Internet use has significantly promoted the compensation amount for the coal-to-electricity transition among residents. The conclusion is consistent with the theory of "information transmission", indicating that netizens are particularly concerned about the positive information that interferes with the clean energy subsidy standards. The propensity score match method and replacement variables ensure the stability of the research conclusion. Secondly, a subdivision of the sample has shown a significant positive impact of internet use on women in rural areas with lower education levels in northern China. The paper also found that the increase in the importance of different Internet use promotes the amount of compensation for residents' coal-to-electricity transition. The driving mechanisms behind the increase include the learning effect, transaction cost effect and marketization effect, and the family life cycle was found to exhibit an inverted "U" regulation function in the relationship. Therefore, this study provides new policies for the determinants of compensation standards for the coal-to-electricity transition with Internet use. [ABSTRACT FROM AUTHOR]

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises. [ABSTRACT FROM AUTHOR]

The effective load prediction capability of Energy Aggregation Service Providers is fundamental to their participation in the electricity market and a crucial factor in maximizing their market profits. However, a major challenge we face is that the data required for prediction is typically scattered across individual energy entities, and due to concerns regarding data security and privacy, these entities are often unwilling to share their data. This issue hampers the operational efficiency of Energy Aggregation Service Providers and also impedes the learning and analysis of relevant energy supplier operational patterns. To address this problem, this paper proposes a Federated Learning-based load prediction method for Energy Aggregation Service Providers, allowing them to share information without accessing the actual datasets, thereby obtaining accurate and effective load prediction capabilities. Firstly, an artificial neural network with multidimensional environmental feature selection is established based on task requirements. Then, a weighted average strategy is employed to jointly train the artificial neural network, bridging the gap between individual energy entities and environmental features. Finally, the trained model features are sent to the server of the energy aggregation service provider for fitting. Through a case study using partial data from a large-scale energy aggregation service provider in a southern city of China, the results demonstrate the effectiveness of the proposed method in improving overall operational efficiency and average prediction accuracy. [ABSTRACT FROM AUTHOR]

Driven by global targets to reduce greenhouse gas emissions, energy systems are expected to undergo fundamental changes. In light of carbon neutrality policies, China is expected to significantly increase the proportion of hydrogen and electricity in its energy system in the future. Nevertheless, the future trajectory remains shrouded in uncertainty. To explore the potential ramifications of varying growth scenarios pertaining to hydrogen and electricity on the energy landscape, this study employs a meticulously designed bottom-up model. Through comprehensive scenario calculations, the research aims to unravel the implications of such expansions and provide a nuanced analysis of their effects on the energy system. Results show that with an increase in electrification rates, cumulative carbon dioxide emissions over a certain planning horizon could be reduced, at the price of increased unit reduction costs. By increasing the share of end-use electricity and hydrogen from 71% to 80% in 2060, the unit carbon reduction cost will rise by 17%. Increasing shares of hydrogen could shorten the carbon emission peak time by approximately five years, but it also brings an increase in peak shaving demand. [ABSTRACT FROM AUTHOR]