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Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031. [ABSTRACT FROM AUTHOR]

Wind power curve (WPC) is an important index of wind turbines, and it plays an important role in wind power prediction and condition monitoring of wind turbines. Motivated by model parameter estimation of logistic function in WPC modelling, aimed at the problem of selecting initial value of model parameter estimation and local optimum result, based on the combination of genetic algorithm and least square estimation method, a genetic least square estimation (GLSE) method of parameter estimation is proposed, and the global optimum estimation result can be obtained. Six evaluation indices including the root mean square error, the coefficient of determination R2, the mean absolute error, the mean absolute percentage error, the improved Akaike information criterion and the Bayesian information criterion are used to select the optimal power curve model in the different candidate models, and avoid the model's over-fitting. Finally, to predict the annual energy production and output power of wind turbines, a two-component Weibull mixture distribution wind speed model and five-parameter logistic function power curve model are applied in a wind farm of Jiangsu Province, China. The results show that the GLSE approach proposed in this paper is feasible and effective in WPC modelling and wind power prediction, which can improve the accuracy of model parameter estimation, and five-parameter logistic function can be preferred compared with high-order polynomial and four-parameter logistic function when the fitting accuracy is close. [ABSTRACT FROM AUTHOR]

Fast insecurity early warning is the key technique to resist the dynamic insecurity risk, which becomes intractable due to the strong nonlinearity of hybrid AC/DC grids and the high uncertainty of wind generation. Considering dynamic security constraints and wind power uncertainty, this paper presents an insecurity early warning method based on decision tree (DT) and semi-supervised deep learning. First, semi-supervised deep learning is deployed to estimate the dynamic security limit of the critical interface of hybrid AC/DC grids. The system security is assessed by comparing the actual power transfer of the critical interface with the security limit. Then, operating conditions (OCs) are ranked into different insecure levels according to the type of preventive control actions that is needed to ensure the system security. Finally, oblique DT is utilized to identify insecurity classification boundaries in the wind power injection space. Insecure OC sets are constructed based on these classification boundaries. Simulation results of the real-life Jiangsu-Shanghai interconnected grid in east China demonstrate that the proposed method can fast construct the insecure OC sets corresponding to different insecure levels. [ABSTRACT FROM AUTHOR]

To efficiently utilize renewable energy sources primarily consisting of wind and solar power, large-scale compressed air energy storage (CAES) can be implemented using underground salt caverns. In this paper, the abundant wind and solar energy resources and the geological conditions of the three major salt mines in Jiangsu Province are introduced. A geomechanical model is established based on the Huai'an rock salt formation. The simulation results show that as the internal air pressure (IAP) increases, the safety factor increases, and the displacement, plastic zone, volume shrinkage, and equivalent strain decrease. Based on the above results, the minimum IAPs of caverns AN 13 and AN 21 are recommended to be 10–12 MPa. According to estimates, to store 15% of wind and solar power in Jiangsu Province through CAES by 2050, a 9894.0 × 104 m3 salt cavern volume is needed, of which at least 77.8 million m3 can be provided by salt mining enterprises. The cost of reconstructing an underground salt cavern is 12.1 billion yuan, while the investment in surface equipment is 746.7 billion yuan. We provide an economical and feasible solution for achieving carbon neutrality in Jiangsu Province in this study. • A CAES salt cavern is utilized to achieve a 100% renewable energy power system in Jiangsu Province. • The non-salt interlayers significantly reinforce the stability of CAES salt caverns. • The minimum internal air pressure of the CAES salt cavern is recommended to be 10–12 MPa in Huai'an. • A preliminary evaluation of the investment cost of CAES salt caverns is analyzed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel strategy to forecast the short-term wind power using model structure selection in combined with data fusion technique. The available inputs are usually treated as an integrated one for the predictive modeling, which ignores the fusion of the different types of inputs. This results the relationship between the multi-inputs and desired outputs are not effectively reflected in forecasting procedure. Moreover, the performances of the various types of data fusion methods are susceptible to the wind power distribution and critical factors such as smoothness, overlapping, the intrinsic amount of information evaluation and the number of neighbor points. These outlined factors can result the lower generality ability of the proposed methods due to the insufficient optimization of the model structure. So this paper presents short-term wind power forecasting method using model structure selection technique in combined with four representative fusions of dimensionality reduction methods to optimize the model structure, promote the computational efficiency and improve the forecasting accuracy. Experimental evaluation based on the real data from the wind farm in Jiangsu province is given to verify the effectiveness of the proposed method by comparing the traditional techniques. [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]

Abstract: This paper presents the influence of different regulation strategies on wind energy integration into the existing energy system of Jiangsu. The ability of wind integration is defined in terms of the ability to avoid excess electricity production, to conserve primary energy consumption and to reduce CO2 emissions in the system. Firstly, a reference model of Jiangsu''s energy system is built using the energy system analysis tool EnergyPLAN based on the year 2009. The model results are then compared to actual values from 2009 to validate their accuracy. Based on the reference model, different regulations of Jiangsu''s energy system are compared and analyzed in the range of a wind input from 0% to 42% of the total electricity demand. It is concluded that operating power plants in a flexible way facilitates the promotion of more intermittent wind integration. [Copyright &y& Elsevier]

As the goal of carbon neutrality has been proposed consecutively, renewable energy sources such as wind, solar, and bioenergy are gaining close attention in national strategic and sustainable development plans. Along with the development of renewable energy generation technologies, it is necessary to assess the environmental burden of renewable energy generation. This study analyzed the economic and environmental benefits of wind power, solar power, and biomass power generation methods over their life cycle. This paper comprehensively assessed and compared their environmental impacts based on a life cycle assessment (LCA) framework combined with ecological footprint analysis for the three renewable energy generation in China, and compared the results with those of thermal power generation to make strategic suggestions for the renewable energy power generation industry. The results show that: (1) wind power consumed the least energy, only 6.80 kJ/kW·h, and had the lowest emissions of CO 2 , SO 2 , NOx and CO and environmental impact potential. (2) Based on the ecological footprint analysis on the provincial level, only Anhui, Shandong and Jiangsu provinces showed ecological deficits, while the other provinces were in ecological surplus, mostly above 50% of their ecological areas. (3) Compared to thermal power generation, which consumed 1170.911 kJ/kW·h, wind power was the only one of the three renewable energy achieved a reduction in energy consumption. Wind power is currently the most efficient way of generating renewable energy. [Display omitted] • The environmental and economic benefits of wind power, solar photovoltaic power, and biomass power generation were assessed. • Life cycle assessment and ecological footprint were used as analytical methods. • Wind power generation is preferred to solar photovoltaic and biomass power generation. • Recommendations for the future development of the renewable energy generation industry are presented. [ABSTRACT FROM AUTHOR]

• A corrective real-time redispatch model incorporating IL programs is proposed. • Considering the probabilistic nature of wind power and IL response in a corrective manner. • Using an adaptive probabilistic collocation method for uncertainty quantification. • Analyzing the effects of wind correlation, IL capacity and pricing on the expected redispatch cost and decisions. Due to the high variability and intermittency of wind power generation, operators tend to have a corrective stage to adjust their predictive dispatch schedules for re-balancing the system following a contingency in the real-time market. This paper presents a new probabilistic real-time re-dispatch model integrating interruptible load (IL) programs with the aim to minimize the total operational cost. The problem is formulated in a probabilistic optimal power flow (P-OPF) framework, accounting for uncertainties on both IL response and wind power forecasts. To address this problem, we develop a probabilistic collocation method (PCM) combining techniques of sparse grids and correlation control, which enables efficient and accurate propagation of correlated uncertain variables with relatively low computational cost. The effectiveness of the proposed model and computational strategy are demonstrated on the modified IEEE RTS 24-bus test case and a simplified regional Jiangsu power grid, where the impacts of IL pricing and capacity on the re-dispatch results are further explored. [ABSTRACT FROM AUTHOR]

It is of great importance for power grids to have black-start capability for rapid recovery, and there is great theoretical significance and practical application value in studying how to use wind farms as the black-start power supply source for power grids with large-scale renewable energy generation. In this paper, a black-start scheme using a permanent-magnet synchronous generator (PMSG)-based wind farm as black-start power supply source is formulated. First, a diesel generator is used as an external supporting power supply for the self-start of a wind power unit (WPU). Then, after all the planned WPUs operate normally, the wind farm with the diesel generator and static var generator (SVG) is used to black start the simulated auxiliary load of a thermal power plant. A field test of the proposed black-start scheme is carried out on an actual wind farm in Jiangsu Province (China). The results of the field test show that wind farms can act as a black-start power supply source for the grid after appropriate technological transformation. [ABSTRACT FROM AUTHOR]