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This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Over the past several decades, there has been an increasing trend towards inter-city cooperation, which is an efficient policy option to deal with the challenges from globalization, regionalization and the externalities resulting from urban entrepreneurialism. Specific to China, the city governments, which mainly refer to prefecture-level and county-level governments, have also made many attempts to cooperate with respect to their local economic development and public affairs. Nevertheless, the results of these initiations to cooperate vary to a great extent. Based on a review of regional pollution governance in the Xiaoqing River area, tourism cooperation initiatives at Weishan Lake and transport integration between Jinan and Laiwu. The findings demonstrate that China's idiosyncratic institutional background has a significant impact on the shaping of inter-city cooperation. For the most part, leading small groups (LSGs) and their leadership property tend to determine the effectiveness of inter-city cooperation.

To examine the effect of the LSGs, we categorize them into three types, groups with strong leadership, weak leadership and self-forming leadership. Through reviewing regional pollution governance in the Xiaoqing River area, tourism cooperation initiatives at Weishan Lake and transport integration between Jinan and Laiwu, we try to probe the role of leading groups in the settlement of cross-administrative border issues.

Based on these three cases, the conclusion can be drawn that the leadership type of the LSG can exert an important influence on the efficiency of inter-city cooperation. If there is a leader with a higher administrative rank or authority, the cooperation can be quite efficient. Otherwise, the cooperative ending might be very negative. In terms of the operation principle, we can infer that even though the cities are always self-development oriented, the leader with higher authority or a strong coordinating capacity can convince and persuade the city leaders to overcome their self-centered behavior template and boost the cooperation to March on smoothly. Also, it means that the LSG is constrained by its personalistic characteristic. Key command derives from the person who chairs the LSG other than specific rules and norms. If the lead of the LSG leaves his position, the cooperation might just become paralyzed. From this point of view, the lack of legal basis remains to be the LSGs' significant deficiency and the future reform should attach more importance to the legalization of the LSGs so the operation of LSGs can be more standard and stable.

Many scholars have proposed their own theoretical models to explain the reason some cities successfully and effectively form cooperative relations, while the other cities do not. However, their models do not consider the idiosyncratic context of China or, how and to which extent LSGs can promote cooperation. Therefore, this paper seeks to probe which path in the context of China cities usually follows in the formation of joint efforts, and what role LSGs play in enabling cities to cooperate.

The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.

The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed.

The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly.

A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.