Search

This paper takes Jiangsu as an example to measure the carbon emissions from the electric power industry from 2002 to 2017, builds an extended STIRPAT model to quantify its driving factors, and uses the Monte Carlo method to simulate the evolution of carbon emissions in multiple scenarios from 2018 to 2030. The results show that: (1)Population scale, urbanization level, GDP per capita, industrial added value, and electricity consumption intensity promote the increase of carbon emissions in the electric power industry. (2)Trade openness and the transmission level of other provinces play a role in reducing carbon emissions. (3)Under the baseline scenario and the green development scenario, the carbon emissions of the electric power industry have shown a continuous growth trend, but the growth rate of carbon emissions has slowed down significantly under the green development scenario. [ABSTRACT FROM AUTHOR]

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises' decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making. [ABSTRACT FROM AUTHOR]

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

In this paper, GM(1,N r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm; (2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables; and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions. • Propose a new model to improve the traditional GM(1,N) model. • Adopt a new method to find the best value to satisfy the modeling conditions. • Use the grey incidence analysis with stepwise examination to identify factors. • Combine the improved GM(1,1) model to improve the performance of GM(1,N). • Apply this new model to analysis electricity consumption in Jiangsu Province. [ABSTRACT FROM AUTHOR]

• A grey multivariate model with dynamic sinusoidal term is proposed. • The Moore-Penrose generalized inverse matrix is used in the least square method. • The validation set is introduced to the training process of model parameters. • The relationship between sample size and model accuracy is discussed. To solve the problem that the grey multivariate prediction model cannot well simulate systems with periodic oscillations, a novel multivariate grey model called the GM(1,N|sin) power model is proposed. The power exponential term and dynamic sinusoidal function are developed to represent the nonlinear relationship and periodic oscillations of the independent and dependent variables in the proposed model, respectively. First, the discrete time response formula for generating the simulated values at each time point is given. Second, the nonlinear programming model based on the particle swarm optimization algorithm is established to solve the power exponential and periodic coefficients. In addition, to enhance the generalization ability of the parameters, an improved nonlinear programming model considering the accuracy of the validation set is constructed. Finally, in the case studies, the quarterly electricity consumption of Jiangsu Province and PM 2.5 concentrations in Nanjing are adopted to test the effectiveness of this model, and the results are obtained respectively through the GM(1,N|sin) power model and alternative models. The results indicate that the accuracy of GM(1,N|sin) power model in the time series with periodic oscillations outperforms the other six models in this paper. [ABSTRACT FROM AUTHOR]

Under the background of carbon peaking and carbon neutrality goals, with the expansion of the scope of power market-oriented reform and the increase in uncertainty of social, political, and economic factors outside the market, the market environment is becoming more and more complex, and it is more difficult to analyze and control the market situation. From a macro perspective, overall modeling for the power transmission link between the generation side and the consumption side of the provincial power market is built, in which a variety of real-time elements or scenarios such as renewable energy power generation growth, carbon emission quota, coal shortage, and dual control system of total energy consumption and energy intensity are considered. The system dynamics tools are used for simulation, and the medium and long-term operation trends of the market are analyzed. In the case studies, the actual situation of the Jiangsu power market is used as background. The trend of the power market situation under the basic scenario is analyzed, and the sensitivity analysis is carried out for three factors: coal supply growth rate, carbon emission caps, and electricity demand growth rate. It is shown that diversified macro factors inside and outside the market will affect the balance of the market operation situation to varying degrees, making the key operation indicators of the market deviate from the existing stable state. [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]

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu's electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu's electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers. • A self-adaptive fractional weighted grey model is developed for electricity demand forecast. • The novel initial condition having dynamic fractional order weighted coefficients is proposed. • The PSO algorithm is applied to estimate the weighted coefficients and time parameter. • Jiangsu's electricity consumption is predicted from 2018 to 2020. [ABSTRACT FROM AUTHOR]