This paper uses the three-stage DEA method to measure the input–output efficiency of China’s 23 listed power generation companies (mainly thermal power generation) in 2019, and uses the SFA regression model to eliminate environmental elements and random disturbances. The results show that in a non-homogeneous environment, the scale efficiencies of most power generation companies are greater than or equal to their pure technical efficiencies. These companies should first improve management and technical levels, and then optimize the scale of investment. Furthermore, after removing environmental variables, half of the companies should turn to increasing economies of scale instead of diminishing economies of scale. It can be seen that environmental factors, such as the degree of regional development and IPO time, have reduced the economies of scale of enterprises, so they should strengthen the communication between different regions, and the government should provide assistance to companies that are listed late.

The introduction of the new round of electricity reform policies has made the electricity sales companies’ trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China’s current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market.

In the context of China’s new power system, various regions have implemented policies mandating the integration of new energy sources with energy storage, while also introducing subsidies to alleviate project cost pressures. Currently, there is a lack of subsidy analysis for photovoltaic energy storage integration projects. In order to systematically assess the economic viability of photovoltaic energy storage integration projects after considering energy storage subsidies, this paper reviews relevant policies in the Chinese photovoltaic energy storage market. It analyzes the cost and revenue composition of photovoltaic energy storage integration projects, and constructs a system dynamics model for the levelized cost of electricity (LCOE) of such projects. Taking a specific photovoltaic energy storage project as an example, this paper measures the levelized cost of electricity and the investment return rate under different energy storage scenarios. Combining energy storage allocation ratios and internal rate of return indicators, this paper analyzes the net present value of photovoltaic energy storage integration projects under different subsidy standards. The results indicate that, while the current energy storage subsidy policies positively stimulate photovoltaic energy storage integration projects, they exhibit a limited capacity to cover energy storage investment costs, thereby failing to incentivize capital market participation in the construction of such projects. Rational allocation of energy storage capacity and optimization of corresponding subsidy policies are crucial prerequisites for enhancing the economic viability and widespread adoption of photovoltaic energy storage integration projects. This study not only aids in investment decision making for photovoltaic power stations but also contributes to the formulation of energy storage subsidy policies.

With the rapid development of the world economy and the progress of modern science and technology, e-commerce has gradually spread to the public. For the online shopping platform, the number of online stores has increased rapidly, especially so in recent years. Mastering the rules of customers’ shopping behavior will help the stores to stand out amidst such a fiercely competitive environment. Taking the cosmetics industry in online shopping as an example, this paper studies the purchase behavior of online platform customers. Through the analysis of order data, it is found that the number of customers’ repurchase times and the corresponding number of people conform to the law of power-law distribution. On this basis, the customer attributes of repurchase behavior are analyzed and demonstrated, and the influences of different factors, such as region, postage, and usage of clients, on the customer repurchase rate and the relationship between the number of orders and the number of days between repurchase are revealed. The analysis results can provide better sustainable operation decision support for online platform operators and improve the overall repurchase rate and benefits of stores.

In the context of China’s electricity market reform, green certificate trading and carbon trading, as important policy tools to promote the development of renewable energy and energy conservation and emission reduction in the power industry, will inevitably be coupled with the electricity market. In order to study whether the coupled market can successfully achieve the goals of power supply structure adjustment and carbon emission reduction, this paper establishes a system dynamics (SD) model, analyzes the correlation and coordination mechanism among the green certificate market (TGC), carbon market (ET) and electricity market, including generation right trading, and simulates the changes of market price and power supply structure. The results show that (1) the power price under the coupling of three markets includes the TGC price and the ET price, so it is influenced by the ratio of renewable portfolio standards (RPS) and carbon reduction policy; (2) the combination of the TGC mechanism and the ET mechanism will be conducive to the optimization of long-term market power supply structure, so as to promote the realization of emission reduction targets; and (3) power generation rights trading, as a carbon reduction policy, will reduce the power generation of fossil energy in the short-term market, but in the long run, it will lead to the loss of momentum for the development of renewable energy. Therefore, regulators need to reasonably adjust different policies in order to give full play to the comprehensive regulatory role and help the energy and power industry and the low-carbon transformation of society.