Abstract:Regarding the non-stationary and stochastic nature of wind power, wind power generation forecasting plays an essential role in improving the stability and security of the power system when large-scale wind farms are integrated into the whole power grid. Accurate wind power forecasting can make an enormous contribution to the alleviation of the negative impacts on the power system. This study proposes a hybrid wind power generation forecasting model to enhance prediction performance. Ensemble empirical mode decomposition (EEMD) was applied to decompose the original wind power generation series into different sub-series with various frequencies. Principal component analysis (PCA) was employed to reduce the number of inputs without lowering the forecasting accuracy through identifying the variables deemed as significant that maintain most of the comprehensive variability present in the data set. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by bat algorithm (BA) was established to forecast those sub-series extracted from EEMD. The forecasting performances of diverse models were compared, and the findings indicated that there was no accuracy loss when only PCA-selected inputs were utilized. Moreover, the simulation results and grey relational analysis reveal, overall, that the proposed model outperforms the other single or hybrid models.

Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA)-least squares support vector machine (LSSVM) hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.Keywords: wind power grid connected capacity prediction; bat algorithm (BA); least squares support vector machine (LSSVM); Granger causality test Literature ReviewAs one of the most proven forms of environmentally friendly and renewable energy, wind power continues to attract considerable attention throughout the world [1][2][3][4]. In 2014, the new installed capacity of global wind power was 51,477 MW, of which China accounted for 45.4%, explicitly becoming a global leader pertaining to wind power capacity. However, this rapid integration of wind power into the grid has resulted in many operational challenges in the distribution networks since most wind farms are directly connected to the distribution network instead of the transmission network. The benefit of rapid extension of the distribution system is incident to the operational challenges. Although they have started to create impact on the overall power system operation, until recently no support were imperatively offered to the distribution/transmission system operation [5]. For instance, the overwhelming scale and speed of deployment are now embarrassing China's wind power industry due to grid connectivity issues. The problem of "abandoned wind" in China has led to more than 100 billion kWh of power being wasted. The disconnection between the rapid increase of wind power supply and grid-connected consumption hinders the sustainable development of the wind power industry. As the literature on grid-connected capacity prediction only gives scant regard to the disorderly expansion of wind power, there appeared to be a situation of repeated construction and "surplus production". Therefore, the precise forecasting approaches with respect to grid-connected capacity have positive implications on the reduction of the wasted energy and the healthy and stable development of the wind power industry.

Reasonable distribution network planning is an essential prerequisite of the economics and security of the future power grid. The comprehensive benefit evaluation of a distribution network planning project can make significant contributions towards guiding decisions during the planning scheme, the optimization of the distribution network structure, and the rational use of resources. In this paper, in light of the characteristics of the power distribution network, the comprehensive benefit evaluation index system is constructed considering the influencing factors of technical benefit, economic benefit, and social benefit. To eliminate the influence of subjective factors on the evaluation effects and the uncertainty of the influencing factors effectively, the improved interval analytic hierarchy process is employed to calculate the index weights more simply. Moreover, based on the traditional single-factor extension evaluation, this study proposes a multi-level extension assessment model to evaluate the comprehensive benefit of the power distribution network planning project. The model can not only identify the key factors that affect the evaluation effect of the power distribution network planning project, but also can predict the overall development trend of the project. Finally, using a specific urban distribution network planning project as an example, the findings indicate that the comprehensive benefit grade of this power distribution network planning project is "better" due to the benefit grade variable eigenvalue j * ∈ [3.33, 3.418] ∈ [3, 4], and illustrates that the model is credible and practical to achieve the comprehensive benefit evaluation of the power distribution network planning project.

Accurate wind power generation prediction, which has positive implications for making full use of wind energy, seems still a critical issue and a huge challenge. In this paper, a novel hybrid approach has been proposed for wind power generation forecasting in the light of Cloud-Based Evolutionary Algorithm (CBEA) and Least Squares Support Vector Machine (LSSVM). In order to improve the forecasting precision, a two-way comparison approach is conducted to preprocess the original wind power generation data. The pertinent parameters of LSSVM are optimized by using CBEA to verify the learning and generalization abilities of the LSSVM model. The experimental results indicate that the forecasting performance of the proposed model is better than the single LSSVM model and all of the other models for comparison. Moreover, the paired-sample t-test is employed to cast light on the applicability of the developed model.

Abstract:In this paper, the Long-range Energy Alternatives Planning (LEAP) model is constructed to simulate six scenarios for forecasting national electricity demand in China. The results show that in 2020 the total electricity demand will reach 6407.9~7491.0 billion KWh, and will be 6779.9~10,313.5 billion KWh in 2030. Moreover, under the assumption of power production just meeting the social demand and considering the changes in the scale and technical structure of power industry, this paper simulates two scenarios to estimate carbon emissions and carbon intensity till 2030, with 2012 as the baseline year. The results indicate that the emissions intervals are 4074.16~4692.52 million tCO 2 in 2020 and 3948.43~5812.28 million tCO 2 in 2030, respectively. Carbon intensity is 0.63~0.64 kg CO 2 /KWh in 2020 and 0.56~0.58 kg CO 2 /KWh in 2030. In order to accelerate carbon reduction, the future work should focus on making a more stringent criterion on the intensity of industrial power consumption and expanding the proportion of power generation using clean energy, large capacity, and high efficiency units.