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The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

With the rise of the global green low-carbon energy revolution and the advanced implementation of the '30·60' carbon peaking and carbon neutrality strategy proposed by China, the development of the offshore wind industry is greatly encouraged in China. The wind energy resources in Fujian sea area are excellent, but due to the geological characteristics in the area, rock-socketed pile foundations are very common. Taking Pile No.13 for Changjiang'ao Offshore Wind Power Project in Pingtan, Fujian, as an example, this paper introduces the key technology involved in the 'Driving-Drilling-Driving' technology for construction of Type I monopile foundations and the five aspects that need to be considered emphatically during the construction, and illustrates the related calculation and evaluation process, which can provide some useful reference for subsequent similar projects. [ABSTRACT FROM AUTHOR]

As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generation and user demand. The smart distribution network contains a wide range of flexible resources, and its flexibility and uncertainty will bring great challenges to grid data acquisition and control feedback. To adapt to the precise control and feedback of smart distribution network access equipment under the high proportion of new energy access and to ensure the safe operation of the system, it is urgent to accelerate the study of the evolution of the future distribution grid based on the existing distribution grid. Hence, a multi-stage planning method for distribution networks based on source–load prediction is proposed in this paper. Firstly, a distribution network source–load prediction method based on the autoregressive integrated moving average model (ARIMA) and error gradient sampling is proposed, using ARIMA to predict the scale of source–load development and error gradient sampling based on the generation of source–load scenarios with error intervals. K-means is further used for scenario reduction, to explore multiple operating scenarios of China's distribution network source–load, and the unit's output forecast interval and load demand from 2021 to 2030 for typical regions are derived using rolling forecasts by combining the unit's output, end-demand and clean energy share over the years. Secondly, the planning model of distribution grid evolution in different stages is constructed to analyze the future evolution form of the distribution grid considering the distribution network's load cross-section, respectively, and to provide a development path reference for the future construction of distribution grid form in China. [ABSTRACT FROM AUTHOR]

In recent years, under the guidance of China's green energy development strategy, a large number of photovoltaic, wind power and other DGs have been connected to the rural distribution network. The current rural distribution network structure, DG grid-connected location and capacity, line transmission capacity and other system conditions are in conflict with the ever-increasing rural power demand. DG output and load demand are continuously changing with time. The large-scale access of DGs and EVs has made the “source-load” side of the rural distribution network present significant uncertainty. The traditional topology of rural distribution network cannot cope with the impact of this “source-load” double uncertainty. Therefore, it is urgent to study a new method of dynamic reconfiguration for rural active distribution network. This paper establishes a dynamic reconfiguration model of active distribution network with DG consumption and line loss as objective functions. Taking into account the time-varying characteristics of “source-load”, this paper proposes a new method of dynamic reconfiguration of rural active distribution network based on regional division, and designs the process of this dynamic reconfiguration method. In order to improve the efficiency of solving the problem of dynamic reconfiguration of active distribution network, a regional division method is proposed for the first time. The regional division method includes two parts: The initial division of regions and the optimized division of regions. The dynamic reconfiguration method of active distribution network based on area division mainly includes the following four steps. Firstly, the distribution network structure is divided into several initial regions which include main line regions and branch line regions based on the regional initial division method. Secondly, with the goal of promoting the flexible and efficient combined application of DGs between regions, the result of regional initial division is optimized dynamically based on the breadth-first traversal algorithm in the graph theory algorithm. Thirdly, based on the obtained results of dynamic regional optimization, the depth-first traversal algorithm is used to test and modify the DNR scheme to meet the topology constraints of the distribution network. At this time, all feasible DNR schemes can be obtained. Finally, the fast non-dominated sorting strategy is adopted to select the best network reconfiguration scheme that meets the constraints such as node voltage. To validate the performance of the proposed method, it is tested on the well-known IEEE 33-node and PG&E 69-node distribution system. The simulation result of 33-node distribution system shows that the loss reduction effect of the proposed method is very good. Especially at 14:00, the loss reduction effect of the distribution network was the most obvious, which was reduced by 71.41%. At this time, the effect of increasing the utilization rate of DG consumption is also obvious. On this basis, the proposed method on the consumption of each DG was deeply analyzed in this article. Result shows that the proposed method can achieve complete consumption of DG. The voltage of each node under the network structure obtained by the regional optimization division method meets the voltage quality requirements. In addition, the average daily DG consumption rate of the PG&E 69-node distribution system was increased by 16.09 percent point, and the daily line loss was reduced by 55.32%. The effectiveness of the proposed method is verified by the simulation of these two case studies. The simulation results show that the proposed method can fully switch and adjust the ability to improve the absorption capacity of the distributed power, reduce the line loss, suppress the fluctuation of the distributed power, and keep the node voltage smooth. [ABSTRACT FROM AUTHOR]

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development. • China's Coastal region is the major geographical unit for the future development of China's industrial. • In high-quality economic growth and climate change, clean energy use is vital for sustainable development. • The utilization of wind energy and it's efficiency is predicted using Deep Belief Network (DBN) model. • Empirical results link economic growth with carbon emissions, showing growth promotes emissions offset. • Green innovation changes the economic growth-carbon link, aiding China's carbon reduction efforts. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy and with the increasing awareness of environmental protection, clean energy will become the ultimate solution of energy in the near future. In order to meet the future demands of clean energy, it is important to accurately predict the consumption of clean energy. This paper is based on the total consumption data of clean energy in China from 2000 to 2019, including natural gas, hydropower, nuclear power, and wind power consumption. The combined model is used to predict the clean energy consumption and achieves the optimal prediction with minimum variance. The results show that compared to the GM(1,1) and ARIMA model, the combined forecasting model has lower relative error when fitting and predicting the consumption demand of clean energy. It is observed from the prediction results that the clean energy consumption would have a rapid growth tendency, and the growth rate will be about 8.6% in the next five years. The consumption would be about 1.7 billion tons standard coal in the year of 2024. [ABSTRACT FROM AUTHOR]

China prioritizes clean energy for power generation in response to climate change and air pollution. By the end of 2021, the installed capacity of wind power has reached 328.48 GW, accounting for 13.8% of China's total installed capacity of 2376.92 GW. However, wind power is inherently unstable and its output depends on resources, which poses challenges for the national grid. Wind curtailment is often encountered when electricity production is plentiful. The main objective of this work is to develop a clean energy generation system for on-demand power supply to improve the utilization of wind resources. The LBE reactor is also a clean energy source with flexible power regulation that helps mitigate the impact of wind variability. Therefore, a hybrid wind-nuclear power system (HWNPS) is proposed. This paper demonstrates the technical feasibility of the LBE reactor and conducts a preliminary analysis of the economics of the HWNPS. The additional cost of HWNPS due to power regulation is about $ 9.04/MWh, which has potential for research and development. [ABSTRACT FROM AUTHOR]

Clean energy plays an essential role in responding to environmental crises. Accurate forecasting of clean energy power generation can provide necessary references for the formulation of energy policy. This paper proposes a novel fractional discrete grey model with a dynamic time delay function (DTDFF-DGM (1,1)) to forecast clean energy power generation. This model introduces the fractional accumulation operator and the dynamic time-delay function into the discrete grey model, which ensures the priority of new information in the original data and improves the model's adaptability to different sample data. The optimal parameters of the model are calculated by the dynamic linkage between the fitting error and the test error, which effectively avoids the overfitting problem. Empirical studies have proved that the model has better prediction accuracy compared to other methods. Finally, the proposed model is employed to forecast clean energy power generation in China. The results show that from 2020 to 2025, the overall growth rate of hydropower, wind power and nuclear power in China will be 9.27%, 119.61% and 36.38%, respectively. Based on the discussion of the forecast results, relevant policy suggestions were made. This paper realizes the transformation from a static model to a dynamic model in methodology and promotes the sustainable development of clean energy power generation in application. [Display omitted] • A novel fractional discrete grey model is established. • The model is the general paradigm of the existing six discrete grey models. • The jump error is avoided in the transition from discrete to continuous. • The dynamic time-delay function is introduced. • China's clean energy power generation is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Transferring energy-intensive industries to regions with abundant clean energy is crucial for achieving low-carbon transformation of energy-intensive sectors during carbon neutrality. By manually collecting the project data from energy-intensive enterprises in China, we construct a three-dimensional panel data with 1.86 million observations to empirically examines the impact of the carbon intensity constraint policy (CICP) on the transfer of high energy-intensive projects (HEP). Our findings indicate that the CICP accelerates the transfer of HEP. The transfer direction shows that the HEP transferred from the eastern region to the western region have increased, but the HEP transferred from the western region to the eastern region have not decreased. To explain the above phenomena, we further discuss the underlying mechanism from the perspective of energy structure and industrial agglomeration. From the standpoint of transfer-out regions, the higher the proportion of thermal power in the energy consumption structure, the more HEP will be transferred out. From the standpoint of transfer-in regions, the industrial agglomeration will attract the transfer of the HEP. However, we do not find evidence that wind power and photovoltaics can attract the HEP. This paper provides project-level empirical evidence for promoting the low-carbon transformation of energy-intensive industries. • The carbon intensity constraint accelerates the transfer of energy-intensive projects. • The dynamic effect shows a fluctuating and decreasing trend. • The transfer direction is the net transfer from the east to the west. • The more thermal power, the more energy-intensive projects will be transferred out. • We don't find the attraction of the clean energy for the energy-intensive projects. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, China vigorously develops clean energy and renewable energy. As a result, offshore wind power projects have grown rapidly. The success of offshore wind power projects depends on the location of offshore wind power station(OWPS), and this paper aims to develop a hybrid decision-making framework to address the location of offshore wind power stations. Firstly, a comprehensive evaluation attribute system of offshore wind power station site selection is innovatively constructed, including wind resources, construction conditions, economy, environment and risks. Secondly, probabilistic linguistic term set(PLTS) is used to describe uncertain information, which improves the reliability of decision information. Thirdly, The Best-worst method (BWM) based on probabilistic language term sets determines the weights of attributes, which ensures the accuracy of the attribute weight. Finally, an extended TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) method is proposed to rank the alternatives. The proposed method is applied to the location selection of offshore wind power station in Jiangsu Province, China. Through sensitivity analysis and comparative analysis, it is verified that the proposed model has good stability and applicability. • A new multi-attribute decision-making model is proposed. • Establish the evaluation attribute system of offshore wind power station location. • Expression of evaluation information by probability language. • Probability Language and Best-worst Method Combined to Calculate Attribute Weight. • An improved Interactive and Multicriteria Decision Making method is proposed. [ABSTRACT FROM AUTHOR]

The government of China has committed to bring carbon dioxide emissions to a peak before 2030 and to achieve carbon neutral before 2060 to tackle climate change. Renewable energy plays a key role in the energy strategy of China in order to achieve the targets for reducing greenhouse gas emissions while ensuring energy security. China has abundant offshore wind resource, distributed along its 18,000 km long coastline and 6000 islands. The first offshore wind farm of China was built in 2008 and put into operation in 2010. It provided green energy for 2010 Shanghai World Expo and then launched an era of offshore wind power development in the coastal areas adjacent to highly populated regions. Over the last decades, many thousands wind turbines have been installed, with an accumulated installed capacity of over 13 GW. This paper reviews the development of offshore wind power and foundation technology used for offshore wind turbines in China using published information, data, and web sources. An ongoing offshore wind farm project is taken as an example to describe the foundation technologies involved. • The development of offshore wind power in China is reviewed. • The foundation technology for offshore wind in China is reviewed. • Foundation technologies of an ongoing offshore wind farm project is described. [ABSTRACT FROM AUTHOR]

China's hydropower, with a total installed capacity of over 390 GW, is currently considered to be the most reliable flexibility resources to support the grid integration of wind and solar power with a total planned capacity of over 1200 GW. Fully exploiting hydropower flexibility is of great practical significance to China. This paper preliminarily evaluates the feasibility of transforming cascade hydropower stations to a large-scale cascade hydropower energy storage system (LCHES) via adding a pumping station between two adjacent upstream and downstream reservoirs. The pumping station can utilize excess electricity to recycle water potential energy between the two linked reservoirs. Taking cascade hydropower stations of a large hydro-wind-solar clean energy base (HWSCEB) in China as the case study, a simulation model is developed to simulate annual operation of LCHES at hourly resolution. The simulation results for the multi-year average representative year (2017) indicate that LCHES can effectively reduce the energy loss of HWSCEB, and store excess electricity of power grid during valley-electricity-price periods to realize price arbitrage; the overall efficiency of LCHES is approx.65%; LCHES is economically feasible only when the pumping station efficiency is greater than 55%. The performances of LCHES are further verified in different typical years. • Energy storage of cascade hydropower stations achieved via a pumping station. • Feasibility of the large-scale cascade hydropower energy storage system is evaluated. • Excess electricity can be effectively utilized to recover water potential energy. • Pumping station efficiency is critical to the economic feasibility. [ABSTRACT FROM AUTHOR]

Clean energy, represented by hydropower, wind power, and nuclear power, plays a crucial role in addressing the issue of power shortage and promoting the transition of the energy structure. Scientific and accurate forecasting of clean energy generation volume can provide the necessary reference for relevant authorities to formulate reasonable energy policies. This paper proposes a novel fractional time-delay polynomial discrete grey model, FTDP-DGM(1,1), for forecasting clean energy generation volume in China. Firstly, both the structural parameters and the time response function are derived directly from the discrete equation. Secondly, the genetic algorithm is employed to determine the optimal value of the nonlinear parameter in the model, with the optimization objective of minimizing the mean absolute percentage error. Then, the proposed model is applied to the simulation and prediction of hydro, wind, and nuclear energy generation volume in China. The results show the validity and superiority of the model. Finally, out-of-sample forecasts are performed, and the forecasting results show that the proposed model has good performance with values of 1.29%, 2.94%, and 2.32% for mean absolute percentage error(MAPE), respectively. It is expected that in 2021–2025, China's hydro, wind, and nuclear power generation volume will continue to grow at a rate of 1.45%, 10.74%, and 10.66% respectively. Based on the forecast results, policy recommendations are suggested. [ABSTRACT FROM AUTHOR]

• Analysis of the techno-economic pathways to net-zero emission attainment in China. • Analysis of peak carbon emission and carbon neutrality by year 2030 and 2050. • Solar PV, biomass, hydro, and wind power are considered for 100 % decarbonization. • River hydro is constructed considering dry, normal, and wet years. • Carbon emission is reduced by 13.26% to 15.3% between 2030 and 2050. With the imminent acute problems and the harsh reality of climate change, many countries/groups of countries have developed policies to reduce their carbon emission. At the end of 2020, the Chinese government announced a revised policy that will see the country attain carbon neutrality by 2060 and peak emission by 2030. This study aims to determine the techno-economic requirements for net-zero emission attainment in China. The models created are used to explicate the 100% integration of renewable energy into the energy systems in a Province in China considering the years 2030 and 2050. This study is novel as it aggregates the detailed requirements of all the energy sectors and the government policies for the case study. While the approach presented in this paper is applied specifically to Sichuan Province, China, the application of this method is limitless as it is vast and viable for other countries. This study can also serve as a template for many high carbon-emitting countries with sizeable RE potential. The total energy demand in the case study is summarized under three sectors; electricity, industry, and transport. Following the government policies, renewable energy sources such as wind power, solar PV, river hydro, and biomass are considered for 100% decarbonization of the case study. River hydro being the predominant renewable energy source in this region is constructed considering three levels of hydro (dry, normal, and wet) years. The 2017 energy demand and supply data are first used to develop/validate the model on the EnergyPLAN simulation platform, then the 2030 proposed pathway to peak carbon emission by the government is analyzed. Three innovative pathways to net-zero emission attainment by 2050 are proposed in this study considering the additional use of biomass (BIO model), pumped hydro storage (EES model), and the import of clean electricity from neighboring provinces (IMP model). Based on the analyses in this study, the proposed pathway by the government cannot achieve net-zero emission by 2050, however, the three optimized strategies/models presented in this study show a clearer and faster path to decarbonization. Carbon emission will reduce by 13.26 %, 14.77%, and 15.3 % between 2030 and 2050 reference models for the dry, normal, and wet year scenarios respectively. From the three optimization models, the total cost of the import model was the lowest under different scenarios. Therefore, the economic feasibility of this approach proves the superiority of the import model in terms of economic benefits. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) trade disputes have been rising in response to certain green industrial policies, i.e., policy mechanisms used to develop domestic RE industries and accrue associated local economic benefits. Since 2011, countries have been increasingly filing trade litigations to counter the use of green industrial policies by their trading partners so that they can expand RE industry competitiveness and the associated manufacturing jobs within their own countries. However, in the absence of systematic studies on the impacts of RE trade duties, evidence regarding their success in achieving these economic goals remains limited. In this paper, the effectiveness of RE trade duties in supporting employment, especially in manufacturing, is assessed. First, the literature on the motivations behind RE trade duties is reviewed, and then the case of trade duties imposed by the US on the imports of solar and wind energy components from China between 2011 and 2019 is examined. Although several factors may drive employment, no evidence is found for the increase in jobs in solar and wind manufacturing in the years after the trade duties were imposed. Meanwhile, downstream jobs in installation, operation and maintenance continued to grow, corresponding to increases in annual installation of solar and wind capacity in the US. These were driven by policy incentives and the declining costs of solar and wind technologies. Overall, this case study highlights the broader implication that trade duties alone are unlikely to promote local manufacturing industries, and countries would benefit from other well-designed policies. • Renewable energy (RE) related trade disputes rising but studies examining effectiveness of trade duties limited. • Literature on the goals behind trade duties on RE components reviewed. • A framework developed to assess the relationship between RE trade duties and manufacturing employment. • Framework applied to examine the case of trade duties by the US on the imports of solar and wind energy components from China. • Trade duties not effective to develop local RE industries; other well-designed policies needed to increase local RE manufacturing jobs. [ABSTRACT FROM AUTHOR]

• Two micro energy grid optimization models combined with regional energy characteristics are proposed. • An on-line adaptive optimization model and strategy for multiple MEG access to DN is proposed. • An effective measure of using distributed clean energy in mountainous area is provided. • The problem of various forms of energy supply in Southwest China has been solved. The rapid growth of distributed photovoltaic and wind power installed capacity in Southwest China has reduced the operation safety margin of the power grid. Power grid dispatchers face the dilemma of ensuring security of the power grid and the output curtailment of renewable energy. To achieve the full consumption of renewable energy, it is an effective way to make use of the space-time complementary characteristics of different energies by forming micro energy grids. By connecting to the distribution network, the energy among the micro energy grids can be transferred and distributed in the form of electricity. Therefore, this study builds collaborative operation optimisation models of distribution networks with multi-micro energy grids and proposes an adaptive dynamic real-time optimisation algorithm based on pre-training and online deep learning techniques for the joint system. A test system was constructed based on the actual equipment parameters and data from the Guizhou power grid. Through the calculation and simulation in the test system, the overload of the distribution network equipment and the network loss were effectively reduced while ensuring the full consumption of wind and photovoltaic power. In addition, the accuracy and effectiveness of the model and algorithm were verified. The results obtained in the case studies suggest that the method proposed in this paper can be applied to southwest China and similar regions. [ABSTRACT FROM AUTHOR]

As a new clean and sustainable energy that can reduce carbon emissions, offshore wind energy has received much more attention in recent years. In response to the complexity and flexibility issues in the decision-making process of offshore wind power investment, this paper proposes an investment decision framework based on a bidimensional binominal lattice that embeds deferral options, aiming to derive the appropriate development timing and investment potential for corporate investors in uncertain environment of market and technological development. And the investment model embedding revenue from carbon emission reduction highlights the advantages of clean energy. Furthermore, we creatively compare the policy effects of three subsidy schemes using the model as a policy analysis tool and use scenario analysis to simulate the impacts of dynamic adjustments in subsidies on investment decisions. For the case of an offshore wind power plant in Guangdong province, China, we find that the local government has to provide a subsidy of 0.11 CNY/kWh to allow the project to be profitable. In the absence of subsidies, however, it is preferable to defer the decision of investment with a wait-and-see attitude. Given the same total government financial input, we find that it will be more beneficial for the government to provide R&D subsidy than electricity tariff subsidy and operation subsidy in terms of long-term sustainable development. Our findings also demonstrate that considerations in the carbon trading market will also contribute to the development of offshore wind power. The conclusions of this article can provide valuable guidelines for corporate investors to adjust the appropriate development timing and for policymakers to optimize subsidy allocation, so as to promote the healthy development of offshore wind power and low-carbon transition. [ABSTRACT FROM AUTHOR]

• The mechanisms for WPO, PVO, and ESO to participate in the DAEM are proposed; • The trading optimization model under risk neutral scenario is constructed; • The trading optimization model under risk non-neutral scenario is constructed. Affected by uncertainty, clean energy power generators need reserve units to alleviate the problems caused by its uncertainty. With the development of energy storage technology, the characteristics of flexibly charging and discharging can reduce the deviations generated by clean energy power generators participating in the electricity market. Firstly, this paper constructs mechanisms for wind power operators, photovoltaic power operators, and energy storage operators in independent and cooperation modes. Secondly, uncertainty analysis models for wind power and photovoltaic power are built, and a solution for processing the uncertainty of clean energy power generation is proposed based on the Latin hypercube scenario generation and synchronous back generation reduction method. Thirdly, day-ahead trading optimization model for wind power, photovoltaic power, and energy storage operators in independent mode and cooperative mode are constructed under risk-neutral situation and CVaR-based risk non-neutral situation, respectively. Finally, a northern area in China is selected as research background to set multiple cases, and CPLEX solver is applied to solve the optimization model, thus verifying the proposed strategy and model's effectiveness. [ABSTRACT FROM AUTHOR]