Lei Pu

With the rapid decline in the levelized cost, offshore wind power offers a new option for the clean energy transition of the power sector in China’s coastal areas. Here, we develop a power system capacity expansion and operation optimization model to simulate the penetration of offshore wind power in China and quantify the associated health effects. We find that offshore wind power has great potential in mitigating the negative impacts of existing coal-fired power emissions. By deploying cost-competitive offshore wind power, it is projected that by 2050, offshore wind power would contribute 2% to 5% (306–654 TWh) of China’s total electricity generation, cumulatively reducing pollutants from the electricity sector by 3% and preventing 165,000 premature deaths. Notably, with the implementation of incentive policies, the scale of offshore wind power deployment expands two to three times, yielding public health benefits that far outweigh the costs of policy implementation.

With the increasing coupling of the power system and the natural gas system, the electric–gas interconnection system has become a typical form of comprehensive energy utilization. Through the energy conversion function of the coupling unit, the system can flexibly participate in the bidding for purchasing and selling energy in a power market and a natural gas market on the premise of meeting the internal demand of multiple loads. To solve the internal coordination and optimization problem and the external flexible bidding problem in the multi-energy market, this paper proposes a robust optimization model of energy purchase and sale for the electric–gas interconnection system in a multi-energy market. Firstly, the basic structure of the electric–gas interconnection system is introduced, and the steady-state model of energy flow in the system is built based on the energy hub model. Secondly, considering the uncertainty of energy prices and the output power of renewable energy units in the system, a bidding model for energy purchase and sale of the electric–gas interconnection system in multi-energy market based on the idea of robust optimization is constructed in the framework of the Nordic energy market. Finally, empirical analysis based on the actual data is carried out, and the results prove the validity and superiority of the model. In this paper, aiming at the uncertainty of energy price, a large number of scenes are generated by Latin hypercube sampling (LHS), and then a k-means algorithm is used to reduce the scenes, so as to simulate typical scenes. Aiming at the uncertainty of the output power of the renewable energy unit in the system, a cardinal uncertainty set is used to control deviation between the actual output power and predicted output power, so that the overall robustness of the model can be controlled. The proposed model can make decision-making independent of the accurate probability distribution of uncertainty factors, and is suitable for complex multi energy systems. Meanwhile, the model possesses excellent robustness, which can effectively reduce the risk of bidding loss in the process of energy purchase and sale.

To achieve sustainable social development, the Chinese government conducts electric power substitution strategy as a green move. Traditional fuels such as coal and oil could be replaced by electric power to achieve fundamental transformation of energy consumption structure. In order to forecast and analyze the developing potential of electric power substitution, a forecasting model based on a correlation test, the cuckoo search optimization (CSO) algorithm and extreme learning machine (ELM) method is constructed. Besides, China’s present situation of electric power substitution is analyzed as well and important influencing factors are selected and transmitted to the CSO-ELM model to carry out the fitting analysis. The results showed that the CSO-ELM model has great forecasting accuracy. Finally, combining with the cost, policy supports, subsidy mechanism and China’s power consumption data in the past 21 years, four forecasting scenarios are designed and the forecasting results of 2019–2030 are calculated, respectively. Results under multiple scenarios may give suggestions for future sustainable development.

Recently, various Chinese provinces have greatly reduced their coal consumption due to new environmental protection policies. Because of these policies, the orderly development of the clean energy heating mode has been effectively promoted. As the problem of air pollution in the northern part of China is particularly prominent, adopting clean heating in winter is an important solution to control air pollution for those regions. However, there is a tricky balance to be struck between the government and the heating companies when it comes to using clean heating during winter. Therefore, it is crucial for the government and heating enterprises to research new strategies. Consequently, this paper carries out a comprehensive study on the multiple factors influencing the game relationship between the government and heating enterprises, and tries to set up a more general model for the theoretical analysis of mechanisms of clean heating promotion, as well as their numerical simulation. The research results show: (1) The initial possibilities available to government and heating enterprises have a significant impact on the final strategy choice for the heating system; (2) due to advantages such as increases in social benefits, subsidies, fines, and clean heating profits, as well as the lessening of traditional heating costs, and regardless of the decrease in traditional heating profits, it is possible for the government to adopt the promotion strategy; and (3) there are more opportunities for heating companies to pursue in order to implement clean heating strategies. In conclusion, this paper proposes valuable suggestions for the government and heating companies concerning clean heating in China.