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Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the D T distance formula and shape based on the triangular cloud model (DDTSTCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DDTSTCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DDTSTCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DDTSTCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China's island microgrid industry, and the evaluation results based on DDTSTCM are in line with human cognition and have good value for engineering applications. [ABSTRACT FROM AUTHOR]

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

[Objective] Globally, biobanks have become an essential tool for the protection and development of human genetic resources, and their development has played a very pivotal role in promoting basic research in life sciences and clinical diagnosis and treatment of major diseases. High-quality and standardized biobanks have become essential for basic research, translational clinical medicine research, and the implementation of precision medicine strategies. Recently, China has established some national or regional alliance-level biobanks; however, the issue of sharing mechanisms has always been a long-term factor restricting the full utilization of biological resources. There are a few examples of colleges and universities coordinating resources to promote the construction of school-level biobanks. [Methods] Herein, the focus is on constructing biobanks in medical colleges and universities in China, analyzing the difficulties and bottlenecks in sharing, and corresponding optimization policy references are proposed. The research methods employed are literature review, typical case analysis, survey questionnaire, and in-depth interviews, among others, to conduct multichannel and all-around research on the problem. [Results] Based on the literature survey, this paper summarizes the problems hindering biobank sharing into internal construction systems, external institutional management, and ethical issues, such as imperfect sample quality standardization and information access, informed consent, benefit sharing, and other specific issues. Through questionnaires, semi-structured interviews, and in-depth interviews, the results reveal that insufficient willingness to share, incomplete information disclosure, unclear benefit-sharing mechanism, lack of standardization of sample and information quality, and ethical concerns are the prominent problems hindering sharing. The relatively successful mechanism of the international and domestic representative biobanks in promoting sharing includes the formulation and implementation of unified biobank construction standards and norms, establishment of public and open information platforms, standardization of sharing rules and processes, benefit-sharing and distribution mechanism, and full implementation of scientific and reasonable ethical principles. [Conclusion] By referring to international and domestic successful cases while considering the advantages and foundations of medical colleges and universities, this paper proposes the following suggestions for biobank construction in medical colleges and universities. First, it is crucial to strengthen the top-level design and to create a "centralized and disaggregated" biological sample storage platform and network-sharing platform. Second, it is necessary to continuously strengthen the scientific construction of biological sample banks, promote the standardization of biological sample bank technology, boost the strength of professional management teams, and pay great attention to the disciplinary construction of biobanks. Third, strengthening the construction of the ethical governance system and ensuring reasonable informed consent and data security are important measures to promote effective data sharing while ensuring data privacy. Last, drawing on agreements signed by both parties for sample and data sharing, it is essential to establish the life cycle of research result data, constantly improve the sharing management mechanism, and innovate the benefit-sharing model. [ABSTRACT FROM AUTHOR]

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system's key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China. [ABSTRACT FROM AUTHOR]

Increased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents' consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents' consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents' consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents' consumption. Positive shocks reduce the urban residents' consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents' consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

With the implementation of China's carbon-peaking and carbon-neutrality strategy, new energy will achieve leapfrog growth. Due to the good economics of distributed new-energy generation, it can not only save users' own investment, but also help to achieve local consumption of new energy. However, it will also bring about a series of incremental costs to the power grid. This paper first enumerates the concept, development status and scheduling mode of a distributed new-energy storage system. Based on the above, it establishes a new-energy power generation model and an energy storage system charging and discharging model, and proposes a global optimization scheduling model for a distributed new-energy storage system, considering the time-of-use electricity price and taking the lowest total operating cost of the distributed new-energy power generation system as the objective function. Finally, it proposes a distribution network incremental cost analysis model based on the penetration of distributed new energy. The calculation results show that the incremental cost of grid-connected distributed new energy is 1.0849, 1.2585 and 1.3473 yuan/kWh, respectively, which indicates that the global dispatching model can optimize the power consumption structure of a distributed power generation system, and has the function of peak shaving and valley filling, but the incremental cost of the distribution network will also increase. [ABSTRACT FROM AUTHOR]

The construction and development of energy storage are crucial areas in the reform of China's power system. However, one of the key issues hindering energy storage investments is the ambiguity of revenue sources and the inaccurate estimation of returns. In order to facilitate investors' understanding of revenue sources and returns on investment of energy storage in the existing electricity market, this study has established multiple relevant revenue quantification models. The research methodology employed in this paper consists of three main components: Firstly, we established a revenue model and a cost model for energy storage participation in the electricity market. These models focus on arbitrage revenue, subsidy revenue, auxiliary services revenue, investment cost, operational and maintenance cost, and auxiliary service cost of energy storage. Subsequently, we utilized an enhanced Grey Wolf Optimizer algorithm to solve the optimization problem and maximize revenue, thus obtaining the optimal capacity and revenue scale of energy storage in the electricity market. Finally, we compared the whole-lifecycle ROI of different energy storage options in various scenarios. The evaluation results demonstrate that the difference between peak and off-peak loads impacts the investment demand and charging/discharging depth of energy storage. In addition, the discrepancy between peak and off-peak prices affects the arbitrage return of energy storage. These two factors can serve as criteria for energy storage investors to assess their return expectations. When solely considering economic returns and disregarding technical factors, pumped storage energy storage emerges as the most suitable mechanical energy storage option requiring investment. The main contribution of this study lies in the estimation of the lifecycle investment returns for various energy storage technologies in the Chinese electricity market, thus providing valuable insights for the investment and operational practices of market participants. [ABSTRACT FROM AUTHOR]

Urban power grids are facing many operational and expansion challenges to meet further demand growth and increased reliability requirements. Advanced transmission technologies have been considered by the electric utilities to effectively increase the utilization of existing infrastructure and operational flexibility. The focus of this paper is on VSC-HVDC technologies for urban power grid enhancement and modernization. First, a potential technical scheme is proposed for converting an existing AC circuit to DC operation, which could boost the power transfer capability of the critical transmission corridor and increase network operational flexibility. Second, this paper proposes three operation modes for the VSC-HVDC interties in urban power grids corresponding to normal, emergency and island operating conditions, respectively. An integrated, adaptive emergency control strategy (AEC) is proposed that can enable adaptive power flow responses of the VSC-HVDC intertie under varying system operating conditions and critical contingencies. The flexibility and effectiveness of the proposed operational principles of urban VSC-HVDC intertie and the corresponding control strategies are verified in PSCAD/EMTDC using a realistic urban power grid in China. [ABSTRACT FROM AUTHOR]

To address the complexity of siting and sizing for the renewable energy and energy storage (ES) of offshore oil–gas platforms, as well as to enhance the utilization of renewable energy and to ensure the power-flow stability of offshore oil–gas platforms, this paper proposes a hierarchical clustering-and-planning method for wind turbine (WT)/photovoltaic (PV) ES. The proposed strategy consists of three stages. First, the WT/PV power generation is forecast by a LightGBM model. The WT/PV siting and sizing at each node of the distribution network is optimized with a particle swarm optimization (PSO) algorithm, with the objectives of economy and stability. In the second stage, the distribution network is partitioned into sub-clusters, based on a voltage and loss-sensitivity index. Finally, the ES siting and sizing is optimized with PSO to minimize the line loss and the voltage fluctuation for each sub-cluster. The relationship between the economic and stability indicators is conducted quantitatively in the joint-planning approach. Considering the 10 kV distribution network of an oil–gas platform in the Bohai Sea of China as an example, our experiments demonstrated that by adjusting the WT/PV ES capacity for different gas-turbine power outputs, line losses can be reduced by 55–66% and voltage fluctuations can be reduced by 30.4–47.5%. [ABSTRACT FROM AUTHOR]

As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

This paper addresses the doubts regarding the spatial characteristics of the commonly used rules for parallel reservoir system operation. The rules based on aggregation-decomposition determine the system total release first and then assign this release to individual reservoirs, without considering the water demand distribution in the river network. In this paper, a conceptual model for parallel reservoir systems with distributed water demands is proposed. Three specific optimality conditions are derived for determining the optimal analytical solution. A rigorous proof shows that the aggregation-decomposition-based rules are a special case of the derived rules. An efficient algorithm is then developed based on the optimality conditions and shortage allocation index (SAI), in which a larger SAI indicates taking a higher percentage of the system water shortage, as release or storage. Unlike traditional algorithms that modify the violated variables empirically, we propose a criterion in terms of relative deviation indicators to determine the crucial priority of variable modification. This criterion can effectively address constraint violations. The optimal rules along with the solution algorithm are then demonstrated by the operation of a parallel reservoir system in the Shiyang River Basin, China. The results show that the proposed rules and algorithm are more efficient and effective than traditional algorithms and aggregation-decomposition-based rules, especially in dry seasons with more binding constraints. [ABSTRACT FROM AUTHOR]

In order to realize the optimal planning of grid-resource-storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning. [ABSTRACT FROM AUTHOR]

Hydrogen is receiving unprecedented momentum and is projected to meet a sizable share of the final energy demand in the future. The coupling between the hydrogen and power systems can help integrate volatile renewable energy, reduce curtailment, and realize long-term energy storage. Different coupling modes are being proposed, yet quantitative comparisons are absent. To fill this gap, this paper firstly summarizes the system composition of potential power–hydrogen coupling modes and analyzes their advantages and disadvantages. Then, a model for power–hydrogen coordinated planning optimization is proposed for quantitative analysis. Southern Xinjiang is chosen as a representative of the northwestern area in China, which has plentiful renewable resources and a relatively small local load at present, for a case study. Through result analysis, it is found that the mode of power–hydrogen coupling at the source side, either for in situ utilization or for long-distance transport via pipelines, shows better economic competitiveness. The discussion provides suggestions and a reference to policymakers for formulating infrastructure or industry plans in advance to better accommodate the rapidly developing renewable energy. [ABSTRACT FROM AUTHOR]

Given the pillar role of renewable energy in the low-carbon energy transition and the balancing role of energy storage, many supporting policies have been promulgated worldwide to promote their development. To achieve the ambitious goal of no less than 1200 GW of wind and solar by 2030, China has also introduced policies to encourage the deployment of energy storage for the grid integration of renewable energy. The national policy is conducive to enhancing system flexibility for renewable integration, but it will also add the costs of renewable energy, especially when counting the regional differences. This paper adopts an improved levelized cost of electricity model to examine the total costs of renewable power co-deployed with energy storage in different provinces of China. The results show that the nationally unified energy storage co-deployment requirement, namely, 15% capacity ratio of renewable installation and 4 h duration, will negatively affect the economics of renewable generation, leading to an average cost increase in 15% and 21% for wind and photovoltaic generation, respectively. The economics of co-deploying energy storage under current market mechanism is inferior, but it can be effectively improved when energy storage participates in ancillary services market. With the revenue of frequency regulation, the cost of renewable co-deployed with energy storage can be even less than that without co-deployment in most provinces, except for Hebei, Jiangxi, and Gansu. An independent market entity status is thus conducive to encourage the co-deployment. Our finding also implies for a province-specific energy storage co-deployment policy. [ABSTRACT FROM AUTHOR]

Efficient allocation of resources and transition towards cleaner sources of energy are among the key factors in minimizing the negative externalities arising from the growing dependency on fossil fuels. Moreover, environmentally benign trade policies are crucial due to the rapid expansion in global trade and comparative advantage. To explore the relationship between global trade and Consumption‐Based Carbon Emissions (CBCE) in China in the context of renewable energy consumption and energy efficiency from 1990 Q1 to 2020 Q4, this paper employs the dynamic Autoregressive Distributed Lags (Dynamic ARDL) model to evaluate the connection between international trade, renewable energy usage, energy efficiency, and CBCE, with the Kernel‐Based Regularized Least Squares analysis being used to determine the causal association. The findings reveal that GDP and imports are directly linked with CBCE in the long run, while exports, energy efficiency, and renewable energy consumption have a negative significant long‐run and short‐run influence on CBCE. In addition, exports help in the reduction of CBCE by sharing and adapting environmentally friendly technologies adopted in, for example, renewable energy and ecological advancements. Renewable energy promotes the quality of the environment, which is attributable to the performance of renewable energy and environmentally friendly technologies that directly stimulate the reduction of CBCE emissions. By cutting CBCE, energy efficiency tends to boost environmental quality. The research findings have noteworthy policy implications; trade enhances the quality of environment by introducing lower energy intensive production methods and technology that support sustainable development. As a result, this study advocated quite stringent policies in terms of aligning international trade orientation with environmental quality policy in China. Furthermore, energy efficiency policies are required to lower CBCE by focusing on high‐energy‐using industries and providing alternative clean energy sources. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

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With the increasing penetration of renewable sources, the cogeneration integrated energy system (CIES) is much more efficient and flexible to achieve various types of energy supply for remote loads. Due to uncertainties in both sources and loads, the CIES should be carefully planned and optimised to keep it working at the suitable operation condition. Therefore, this paper proposes a multi-objective capacity allocation optimisation method for the islanded CIES, which is composed of renewable energy generation unit, electric-thermal conversion unit, electric storage, thermal storage and various loads. Firstly, three optimisation objectives are selected as typical indexes, including economic cost, solar energy utilisation, and life cycle carbon emission. An improved radar chart evaluation (RCE) solution is subsequently utilised for calculating a fitness function and building the multi-objective optimisation model. Besides, several compulsory constraints are supplemented to ensure the overall CIES performance satisfy energy supply requirement. As a mature and widely accepted algorithm, a genetic algorithm is chosen as an example to obtain the optimal capacity allocation scheme. Case study of an islanded CIES is provided on the basis of real data from the remote region of northwestern China, which successfully verifies the effectiveness of the proposed optimal capacity allocation method. [ABSTRACT FROM AUTHOR]

It is an effective way to regard the electric vehicles as the demand response for reducing the negative impact of large-scale introduction on the power system. Aiming at the microgrid with demand response, the adaptive uncertainty sets-based two-stage robust optimisation method is established in this study. The coordination of micro-gas turbine, energy storage, and demand response etc. are considered in the economic dispatch model. To effectively consider the uncertain variable contained in the microgrid, the concept of adaptive uncertainty sets is proposed in this study. The uncertainty sets are achieved by the long short-term memory network and modified fuzzy information granulation. To handle the adaptive uncertainty sets-based robust optimisation model, the column and constraint generation algorithm and strong duality theory are introduced to decompose the model into a master problem and a subproblem with mixed-integer linear structure. To verify the performance of the proposed adaptive uncertainty sets-based two-stage robust optimisation method, measured data from a plateau city of China are introduced in the simulation test. The simulation results demonstrate the effectiveness of the model and solution strategy. [ABSTRACT FROM AUTHOR]

China has built the world's largest power infrastructure. Those upgrading power grid facilities not only contribute to providing enough end-used energy for the world's factories, but also offering a basic guarantee for the clean strategy of Building a Beautiful China proposed by the Chinese government. The national grid system supported by extra-high voltage and ultra-high voltage grids as the backbone makes it possible for a non-dispatchable renewable energy source to be connected to the national grid and transmitted to terminal consumers in load centers. The aim of this paper is to test whether China's advanced power grids have played a positive role in promoting power generation of intermittent renewable energy source. A novel nonlinear estimation named panel smoothing transition regression is introduced to capture heterogeneous effects of grid-connecting renewable energy source across regions. The empirical results show that whereas power grid infrastructure generally enhances power generation of renewable energy source and consumption in energy bases and load centers, the effects change across different voltage levels of power grids. The extra-high voltage power grids show strong support for grid-connecting renewable energy source, while the effect of ultra-high voltage power grids is unexpectedly insignificant. The extra-high voltage power grids have not yet become the backbone of the national grid, which is the main reason for the inadequate grid-connected renewable energy source to the ultra-high voltage power grids, indicating the importance of upgrading the power grid infrastructure. [ABSTRACT FROM AUTHOR]

With the dynamic development of renewable energies, energy storage devices, and electric vehicles, microgrids have been playing an increasingly vital role in smart power grids. Under the recent development of carbon neutralisation, microgrid systems containing multiple clean energy sources have become significant modules for energy conservation and emission reduction. Considering technological and environmental elements, we investigated the economic operation of microgrids with the integration of electric vehicles. In this paper, carbon trading mechanisms and operation scheduling strategies are analysed in the simulation models. Then, transaction costs and power balance are discussed. Industrial applications and policy implications are also presented. [ABSTRACT FROM AUTHOR]

The high environmental pollution load caused by the massive pollutant emissions and the accumulation of endogenous and cross-regional pollution has become an important obstacle to the current ecological civilization construction in the Yangtze River Economic Belt (YREB) in China. Taking the YREB as an example, by using four environmental pollutant emission indicators, including chemical oxygen demand (COD), ammonia nitrogen (NH3-N), sulfur dioxide (SO2), and nitrogen oxides (NOx), this paper established an environmental pollution load index (EPLI) based on the entropy-based measurement. Moreover, the Spatial Durbin Model was used to quantitatively analyze the drivers and spatial effects of environmental pollution load. Finally, specific scientific references were provided for formulating environmental regulations of pollution source control in the YREB. The results showed that: 1) During 2011–2015, the EPLI in the YREB was reduced significantly and the environmental pollution load increased from upstream to downstream. Among them, the pollution load levels in the Upper Mainstream subbasin, Taihu Lake subbasin, and Lower Mainstream subbasin were the most prominent. 2) The environmental pollution load situation in the YREB was generally stable and partially improved. High load level areas were mainly concentrated in the Yangtze River Delta Region and the provincial borders in upstream, midstream, and downstream areas. The high load level areas already formed in Chengdu and Chongqing were also the key regulatory points in the future. 3) The degree of local environmental pollution load was apparently affected by the adjacent cities. The population size, industrialization level, and the fiscal decentralization not only drove the increase of the local environmental pollution load level, but also affected the adjacent areas through the spatial spillover effects. The land development intensity mainly drove the increase in the local EPLI in the YREB. While factors such as economic development level and agricultural economic share could only act on the environmental pollution load process in adjacent cities. 4) According to the differentiation characteristics of drivers of each city, the YREB was divided into seven zones based on k-medoids cluster method, and targeted zoning control policy recommendations for alleviating environmental pollution load in the YREB were proposed. [ABSTRACT FROM AUTHOR]

Urban energy supply includes more renewable energy, but renewable energy, such as wind and photovoltaic, is intermittent and connected to the grid and impacts the safe operation of the power system. For urban multi-type energy dispatching, this paper proposed a day-ahead multi-energy robust optimization dispatching method for an urban power grid with a high proportion of renewable power. Firstly, the power supply and load operation scenarios of the urban energy supply system are constructed. Multiple typical scenarios are formed based on fully considering the uncertainties caused by renewable energy and load prediction. A robust optimal scheduling model with system constraints was constructed based on the tuning capacity of adjustable generator sets and energy storage devices in the power grid, with multi-system functional cost optimization as the objective function. Extending the augmented Lagrange multiplier Method, the Alternating Direction Method of Multipliers (ADMM) is proposed to solve the constructed model. The effectiveness of the proposed method is verified by some examples based on the data of an actual power grid in China. [ABSTRACT FROM AUTHOR]

Improving water resources utilization efficiency is conducive to achieving the sustainable development of water resources. It is essential to explore the regional differences and dynamic evolution of agricultural water resources utilization efficiency in China to promote high-quality development of agriculture. In this study, based on the unexpected output, we build a super slack-based measure model to measure agricultural water resources utilization efficiency in China's provinces from 2007 to 2018. In addition, we use the Dagum Gini coefficient to analyze the source of regional differences. Finally, we construct the distributed dynamics model to explore the distribution of the dynamic evolution trend of China's agricultural water resources utilization efficiency. The results reveal that regional difference is the main source of the overall difference in agricultural water resources utilization efficiency in China. Spatial imbalance exists in the development of agricultural water resources utilization efficiency in China. The agricultural water resources utilization efficiency in various provinces and regions of China is relatively stable, having the characteristics of club convergence. The probability of maintaining the initial state is high, and the internal mobility is low. However, with time, the degree of club convergence decreases. [ABSTRACT FROM AUTHOR]

Clean energy has become one of the more broadly acceptable options for the future transformation of existing energy systems. The energy transformation also requires comprehensive preparatory actions. Organizing a multi-chain clean energy value chain into a complex clean energy network system is likely to be critical to the successful utilization of clean energy in the future. This article proposes a novel concept for a clean energy value co-creation system covering the entire clean energy value chain with division on power generation, energy storage, and end utilization sections. This article mainly analyzes the self-organizing evolution mechanism and value co-creation of the clean energy value co-creation system (CEVCS), which is therefore critical to the further development of the renewable energy industry. First, this article analyzes the connotation and basic structure of CEVCS and introduces the self-organizing characteristics of CEVCS. Second, the self-organizing evolution mechanism of CEVCS is discussed, which is analyzed from four aspects: evolutionary power, evolutionary path, evolutionary form, and evolutionary process. Third, this paper analyzes the information cooperative evolution process of CEVCS, defines the value order parameter, and studies how the constituent elements of CEVCS realize value creation through a synergy mechanism based on the theory of synergy. Then the dissipative structure model and information resource collaborative model of CEVCS are presented. Finally, the conclusions are given in the end. The proposed CEVCS concept would support the effective operation of the clean energy value chain within municipalities by establishing virtual energy cooperatives aimed at meeting energy needs and supporting community development. [ABSTRACT FROM AUTHOR]

Summary: This paper considers the co‐optimization operation problem of the multiple micro energy grids (MEGs), and proposes a novel concept of the flexible boundary. Then, a three‐level co‐optimization model of reconfiguration, dispatching, and reserve is designed considering different time scales. Furthermore, an improved ant colony algorithm based on the information entropy and the chaotic search is proposed. The Shenzhen international low‐carbon park in China is selected as an example. The results show that: (a) The capacity configuration scheme considering the flexible boundary conditions could decrease the reserve capacity of power, heating, and cooling from the upper energy grid, namely, 75%, 50%, and 75%, and delay 537 × 104¥ the investment cost of the new capacity by utilizing the complementary characteristics between different MEGs. (b) The proposed three‐level co‐optimization model could establish the multi‐level optimal cooperative operation schemes of multi‐MEGs and decrease the reserve power from the upper energy grid and incentive‐based demand response. Among them, UPG provides MEG 1, MEG 2, and MEG 3 with cooling load reserve capacity reduction of 53.95, 85.44, and 38.3 kw h. The positive reserve provided by IBDR for MEG 1, MEG 2, and MEG 3 decreased by 30.53, 20.21, and 52 kW h. (c) The proposed improved ant colony algorithm can consider the operational characteristics of each energy entity in different stages of multi‐MEGs and obtain the global optimal operation scheme. Compared the traditional ant colony algorithm, the average convergence times decrease by 18% and the optimization degree of the objective value increase by 12.38%. (d) The sensitivity analysis shows the conditional value at risk method can formulate the optimal dispatching schemes of multi‐MEGs according to the risk attitudes of decision makers. When confidence degree is between 0.3 and 0.8, the decision‐makers have a high‐risk sensitivity and are more willing to avoid uncertain risks of wind power plant and photovoltaic power. Overall, the proposed model and solution algorithm could achieve the optimal operation strategy of the micro energy grids. [ABSTRACT FROM AUTHOR]

Many rural communities in western China use renewable energy-based clean energy supply methods, and the community microgrid system of "photovoltaic + energy storage + electric heating" has been widely used. However, the energy management effect of such a typical rural community microgrid system is not very satisfactory. Aiming at the problem of the comprehensive economic operation of the microgrid system in rural communities, a method for optimizing the operation strategy parameters of typical operating scenarios is proposed, and critical parameters of the real-time energy management strategy of the microgrid system are optimized. Through the evaluation of the annual time-scale simulation results, it is proved that the proposed strategy can improve the comprehensive economic benefits of the operation of the rural community microgrid system (comprehensive economic benefits include electricity bills and energy storage system (ESS) life loss). Relying on the actual community microgrid demonstration project system in western China, a community-level microgrid energy management monitoring system is built. The control strategy proposed in this paper is applied to the demonstration system for experimental verification. The operational data of the demonstration system shows that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

The paper introduced the concept of a multilevel microgrid (MG), which starts at the building level and creates an MG at this level. The development process of MG in China was described in detail for the first time, including the key issues and the research direction. The prediction error and uncertainty analysis of wind power, photovoltaic power generation, load, and controllable generator components are given, respectively. The uncertainty distribution model is helpful to construct the objective function of minimizing the operating cost of MG. First, an hypothetical apartment building's suitability for installation of solar panels, wind turbines, and solar thermal devices in Shanghai was analyzed, including the principles and characteristics of their electrical generation and energy collection. Compared to the analysis of a single building, the results are more comprehensive for simulating the loading of different buildings in a variety of situations. The mathematical model of peak load shaving in an MG was constructed, and the interior-point method was used to solve it. By simulation analysis, the variation of load curve and related parameters before and after peak load shaving was obtained. First, the average loading of an office building with and without the MG was compared, and the reduction rate in peak loading of a building was used to analyze whether there was additional storage and found the change rule. A neighborhood with the additional MG technologies and additional storage units is better than other existing literature in showing the effectiveness of peak- load reduction. [ABSTRACT FROM AUTHOR]

During the '13th Five-Year Plan period' (2016-2020), one of the main targets for China's energy strategy is to develop a new generation of power system, integrating high shares of renewable energy sources. This implies that the technology industrialisation of microgrid powered by distributed generation of renewable energy is becoming crucial. This paper carries out a comprehensive study of the status and challenges of developing microgrid, based on case studies of demonstration projects of microgrid in China during different developmental stages. The focus is on the effects of government policies, technical innovations and changes in the business model of electricity market on the promotion of microgrid in China and the reformation of electricity market in China. The ultimate target for the reformation of power system in China is to finish the transformation from a linear business model of centralised power system. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

Microgrid is a promising way to integrate renewable distributed generation into the electric power sector, which has received widespread attention. In this paper, a uniform capacity-constrained optimization model under different power purchase and sale price contracts is developed to explore the optimal green investment strategy for grid-connected microgrid (GCM) with three configuration types. The impact of renewable energy source endowment and incentive policy is focused when macrogrid's generators emit more pollution than GCM's non-renewable generators, such as in China and India. The results show that: (1) GCM with renewable generation can only be invested in a site with relatively abundant renewable endowment; (2) carbon tax effectively promotes the deployment of GCM with renewable generation and achieves carbon emission abatement, but excessive carbon tax cannot incentive investment in the most desired GCM type; (3) a subsidy trap is found under feed-in tariff (FIT) subsidy, which implies that the fade-out or exit of FIT subsidy is beneficial for microgrid's healthy development; (4) and the comparisons under different price contracts emphasize the importance of macrogrid providing non-discriminatory access to feed-in and feed-out services to microgrid. • Investment strategy is explored for grid-connected microgrid (GCM) with three types. • Renewable source endowment and incentive policy determine the investment for GCM. • Excessive carbon tax cannot incentive investment in the most desired renewable GCM. • The fade-out or exit of FIT subsidy is beneficial for healthy GCM deployment due to the subsidy trap. • Emphasizing the importance of macrogrid providing non-discriminatory services to GCM. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

• The operational strategy and pricing mechanism applicable to microgrids and users. • Most microgrids tend to be connected to the grid and use real-time pricing mode. • The internal factors affecting the game: initial price and generation proportion. • The external factor affecting the game: tariff of the external grid. As an effective supplement to the power grid, microgrid systems can make full use of renewable energy and solve the problem of grid connection and absorption of renewable energy. However, microgrid faces the challenges of benefit fluctuation caused by fixed pricing mode with unstable renewable energy supply and changing user's demand. A well-designed pricing mode is necessary while yet to be fully discussed. To solve this problem, this paper introduces flexible operational strategies and pricing mechanism due to the regional differences in resource endowment, microgrid technology mix, user demand and electricity price of external grid. The evolutionary game method is used to explore the operational strategy and pricing mechanism applicable to microgrids with different characteristics and users with different demands, so as to provide valuable reference for pricing standardization of microgrids in China. Based on the theoretical model, we deduce the equilibrium strategy of microgrid facing stable users and flexible users, and discusses the operational strategy and pricing mechanism of microgrid facing different user demands by using numerical simulation method. The results indicate that: (1) Considering the technical characteristics and economic performance, most of the microgrids tend to be connected to the grid and use real-time pricing mode. (2) The initial price between the microgrid and the user, the proportion of independent power generation when the microgrid is connected to the grid, and the tariff of the external grid are the key factors affecting the equilibrium point of the game between the microgrid and the users. [ABSTRACT FROM AUTHOR]

With the gradual attention of global climate change, a series of carbon emission plans have been formally implemented in the field of energy. There is a consistent relationship between the carbon market and the electricity market in promoting clean energy development and improving carbon dioxide emission reduction targets. The market design of a low‐carbon power system should be considered as a whole to promote the coordinated development of carbon and power markets. Taking an industrial park in China as the research object, an internal market trading model of an industrial park considering carbon dioxide emission reduction targets is proposed. The proposed trading mechanism considers both carbon dioxide emission reduction targets and social welfare maximization. The proposed market trading model connects day‐ahead and intraday electricity markets, which deal with uncertainty about renewable energy sources. [ABSTRACT FROM AUTHOR]

To define the complicated driving mechanisms of virtual water flow (VWF) in China's main grains production, this study firstly uses the entropy method and a K-means clustering approach to classify the driving types of VWF based on the data from 31 provinces in the mainland of China, and then applies the PLS structural equation model to analyze the driving mechanisms of VWF in four representative provinces or municipalities(Zhejiang, Heilongjiang, Xinjiang and Shanghai)selected by the classified driving types. The results show that the driving types of VWF can be divided into four types, namely, ecology-economy, ecology-environment, ecology-water use, and comprehensive type. In the four typical regions at provincial level, the social factors directly affect the VWF, and reversely it has a direct impact on the nature, environment and economy of the above four regions. In particular, the water use has a direct impact on the VWF in Heilongjiang attached to the ecologic-environmental type. The findings of this paper manifest that the social indicators rather than the indicators in water use play an important role in the VWF, that is, our findings further testify the status quo of China's VWF: the regions with rich water resources import the virtual water while the regions with poor water resources export the virtual water. Finally, for promoting the rational flow of virtual water we propose some corresponding adjustment countermeasures and suggestions that provide a theoretical basis for the rational allocation and management of water resources. • The application of PLS structural equation model reveals the complex driving mechanism of virtual water flow. • Reveals the social indicators play an important role in China's virtual water flow. • Proposed targeted countermeasures and suggestions to promote the rational flow of virtual water. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources. However, the lack of a well-set operational framework and a cost-sharing model has hindered its widespread implementation and large-scale development. This paper introduces three distinct cost-sharing methods, namely the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method, which are laterly applied to develop a combined operational and cost distribution model. The objective is to improve the efficiency of the power generation system by incorporating shared energy storage assistance and allocating the associated costs based on the use of various renewable energy stations. Furthermore, the viability of the suggested operational framework for shared energy storage and the methods for allocating costs are confirmed through numerical simulation. The findings indicate that the uniform allocation method represents the most straightforward approach for distributing costs evenly. In contrast, the second model introduces a cost allocation method that addresses the issue of fairness by considering the predicted generation level of each renewable energy plant. However, the third model takes into account the actual generation level of the plants when determining their respective costs. Sensitivity analysis is further conducted to offer valuable insights into cost-saving policies for four representative regions in China. The proposed operation and cost-sharing model is anticipated to serve as a useful reference for the widespread implementation of shared energy storage in power generation side. • Design three cost-sharing methods to allocate costs among various entities. • Formulate a combined operational and cost allocation model. • Optimize operational and cost allocation strategies for various entities. • Reduce total costs by up to 36% through the dynamic weighted allocation method. [ABSTRACT FROM AUTHOR]

The self-generation and self-use attributes of distributed photovoltaic increase the line costs of grid enterprises, and these costs are mainly borne by regional power users and grid enterprises. With the expansion of distributed photovoltaic scales and the rise of profit rates, distributed photovoltaic operators should also become one of the main contributors to the grid connection costs. Distributed photovoltaic grid connection cost related researches focus on its impact on the regional users of transmission and distribution prices. Alternative apportionment of grid connection cost on distributed photovoltaic related subjects of the impact of the research is less. Based on the theory of evolutionary game, this paper constructs a four-party game model among roof owners, grid enterprises, PV enterprises and the government, and takes 30 regions in China as examples to measure the impacts of different grid connection cost sharing schemes on the decision-making of the stakeholders. At the same time, it considers the impacts of the DPV cost, the grid connection cost and the proportion of self-consumption of DPV on the results of the evolutionary game. The results of this study are used to propose policy recommendations on the sharing of grid connection costs for distributed photovoltaic in China. • The composition and channeling status of the cost of grid-connection of DPV in China was sorted out. • The economic and social benefits brought by DPV were comprehensively considered in the payment matrix. • DPV development under different cost sharing schemes of the cost of grid-connection of DPV were compared. • The effectiveness of different cost sharing schemes of the cost of grid-connection in different regions of China was measured. • The impact of cost changes on the effectiveness of sharing schemes was analyzed. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control. [ABSTRACT FROM AUTHOR]

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011. • Efficiencies and economics of demand response are verified in an off-grid microgrid. • A improve Pareto optimum is proposed to solving the demand response problem. • Electricity price model based on different season meteorological is proposed. • Reasonable control of flexible load to improve the efficiency of demand response. • The economic benefits of retailers and consumers are improved under demand response. [ABSTRACT FROM AUTHOR]

Research into public participation in urban regeneration holds a significant position in global urban research, with a considerable body of literature describing the practices of multiple stakeholders' participation in diverse institutional contexts. In the centralised dynamic context for China's urban regeneration, the institutional arrangements and practices of public participation and the evolution processes between them still remain "black boxes". This paper aims to clarify the institutionalisation process of public participation in China's urban regeneration. Using the historical institutionalism perspective, this research adapts the institutionalisation framework proposed by Burns and Scapens (2000) to the analysis of participatory governance in China's planning context. It analyses three phases of urban regeneration in Guangzhou and provides corresponding case studies for an in-depth analysis. The findings reveal that the success of public participation in urban regeneration varies across phases, mainly due to the degree of government power sharing and the inclusion of non-governmental actors in decision-making through genuinely participative interactions. This research develops an institutionalisation analysis framework and notes that the proposed institutionalisation path only partially applies to public participation in China's urban regeneration. The institutionalisation of public participation from the institutional realm to the realm of action in China's regeneration practices departs from hysteresis, which is the opposite of synchronisation in historical institutionalism. The root cause of this inefficient institutionalisation and its separation from practice is the imbalance in the allocation of power resources. These findings provide institutionalisation insights into the interrelationships between the evolution of policy and practices of public participation. [ABSTRACT FROM AUTHOR]

Rational planning for remote integrated energy systems is of great importance for demand guarantee and sustainable development of remote areas. A critical challenge is that comprehensive factors, e.g., technical, economic, environmental, and social, which are inherently intertwined, should be properly evaluated and balanced in terms of social benefits. In addition, the feasibility of each planning scheme can only be verified based on their local electrical conditions by employing multi-energy flow calculations. Thus, this paper develops a comprehensive methodology for the optimal planning of remote integrated energy systems taking into account the uncertainty of renewable energy, where a multi-criteria decision-making procedure is designed, including the candidate set construction, remote integrated energy systems modeling, multi-energy flow calculation of the candidates, index definition and selection, and the optimal scheme determination. The methodology is applied to a case study in Lankao County, China. The optimal scheme, selected by the proposed multi-criteria decision-making procedure from 432 candidates, shows the best comprehensive performance, which is verified by different ranking methods. The optimal scheme can rank in the top 13% of each aspect on average, with economic and environmental aspects ranking in the top 2%. The methodology can help decision-makers find reasonable solutions for energy system planning in remote areas. • A comprehensive methodology for planning remote integrated energy systems is proposed. • Refined multi-energy flow calculation fitting with remote area characteristics is presented. • Multi-criteria decision-making procedure realizes high social benefits. • A real case in Lankao is performed to verify the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

• A multiple criteria analysis method is proposed to compare renewable energy and traditional solutions for off-grid island power supply. • Life cycle cost is used to build the economic model. • The fuzzy analytic hierarchy process is used to quantify the risk level. • A simulation platform is built to obtain the energy utilization. • Applied to the actual project to verify the feasibility of the method. Renewable energy microgrids are a new option for powering remote islands. To explore the feasibility of constructing island microgrid in China, based on the failed Dongfushan Island Demonstration Microgrid Project caused by equipment failure, low economic benefits, low residents' satisfaction, and poor power supply stability, this paper compares two power supply methods, microgrid and submarine cable. A systematic multiple criteria analysis method is established to compare these two methods, including economic analysis, risk assessment, energy spilled and energy shortage. Net present value and the life-cycle cost method are used to establish the economic cost model. Fuzzy analytic hierarchy process is used to establish the risk assessment model. Additionally, the simulation using PVsyst software is used to calculate the energy spilled and energy shortage. Furthermore, future trends are predicted with data to provide suggestions for decision-makers. Multiple criteria analysis results showed that energy spilled and shortages are inevitable for the use of microgrid on seasonally tourist islands like Dongfushan Island, with energy spilled and shortages rate as high as 50.11% and 37.57% in the worst case. The microgrid risk level is relatively low risk, higher than the lowest risk level for submarine cables. At full capacity, the cost of microgrid (34.03 million yuan) is comparable to submarine cables (30.31 million yuan), making the latter more suitable for power supply due to its better reliability. [Display omitted] [ABSTRACT FROM AUTHOR]

To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China's isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, to analyze battery-swapping station operation and isolated microgrid operation schedules. Considering the needs of potential future communities that might depend on electric vehicle battery-swapping stations, two scenarios were analyzed, and the most effective integer planning method was adopted. The international algorithm software program YALMIP + CPLEX was used to address the problem, and simulation results proved that the proposed model and its solution method could effectively affect the safe operation and scheduling of islanded microgrid battery-swapping stations and reduce the cost of islanded microgrid operation, with significant advantages. [ABSTRACT FROM AUTHOR]

Rural distributed photovoltaic (PV) is conducive to low-carbon development of China. Nevertheless, the development is hampered by multiple barriers and therefore lags behind expectations. Some studies have been devoted to this, but there are still problems including incomplete identification of obstacles and non-objective analysis methods. Thus, this paper is dedicated to establishing a complete and objective research framework to effectively identify obstacles and promote high level development of rural distributed PV. Firstly, a two-stage process is adopted to extract obstacles from the entire life cycle of rural distributed PV, and 10 obstacles are identified. Then the K-means clustering algorithm are introduced to improve the DEMATEL-ISM method for threshold determination and barrier analysis. Interactions between obstacles are divided into three levels and a network hierarchy that reflects these interactions is created. The results show that imperfect policies and regulations, diminished subsidy, high initial investment and aging equipment and poor quality are critical barriers, and corresponding suggestions on the collaboration between enterprises and the government are put forward. This paper comprehensively studies the obstacles to the development of rural distributed PV by objective methods and proposes suggestions for improvement, which is beneficial to the development of rural distributed PV in China. • A comprehensive barrier system of rural distributed PV projects is established. • K-means clustering algorithm is introduced to improve DEMATEL-ISM method. • The mechanism of barriers affecting the rural distributed PV are determined. • A series of recommendations for rural distributed PV are proposed. [ABSTRACT FROM AUTHOR]