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Additional file 1: Table S1. Primer sets used for protein expression and RNA interference experiments.

Additional file 2: Table S2. Identification of fatty acid-/retinol-binding protein coding genes in Haemonchus contortus.

With the liberalization of China’s electricity sales side, a large number of power sales companies emerge and occupy the market, forming a pattern that many power sales companies are competing for the market. Based on the analysis of the connotation, type and purchasing and selling electricity flow of the power selling company, this paper first proposes the power user response model based on peak and valley time-sharing electricity price, then constructs the purchasing and selling electricity model of the power selling company considering differentiated time-sharing electricity price, and finally analyzes the purchasing and selling electricity model through the example. The results show that the model can optimize the load curve of power users and reduce the cost of electricity while maximizing the profit of power selling companies.

In order to better manage demand response resources of user side and reduce short-term load forecasting error, a short-term load forecasting method considering demand response in virtual power plant mode is proposed. Firstly, the demand response mechanism of the virtual power plant is analyzed. Taking the maximum profit of the virtual power plant as the goal, considering the user’s energy consumption habits, self built photovoltaic, energy storage behavior and thermal electric coupling, the optimization model is established for each type of demand response resources. The CPLEX solver is called to solve the mixed integer linear programming problem after the model transformation, and the sub signals of each resource participating in the demand response are obtained. Then, based on this model, a long-term and short-term memory network model considering demand response signals is established to predict load power iteratively. At the same time, the long-term and short-term memory network model considering the demand response signals effectively makes up for the shortcomings of the traditional forecasting model without considering the demand response, and is more accurate in predicting the future trend of load change.

Under New Strategy of Electric Power System Reformation (NSEPSR), an electricity retailer decides its level of involvement in the contract market and in the spot market as well as the selling price offered to its clients with the goal of maximizing the expected profit at its acceptable risk level. Under time-of-use pricing, users will adjust their needs according to the price changes. How to determine the time-of-use electricity price and the power purchase strategy in the contract market and in the spot market while accounting for the profit of the retailer and users becomes an important topic. Based on the above reasons, this paper formulates the optimal pricing and dispatch problem of an electricity retailer as a bilevel programming model, in which the upper level maximizes the retailer’s benefit, while the lower level minimizes the cost of each electricity user. The model is transformed into a mixed integer linear program by jointly using the Karush- Kuhn-Tucker (KKT) optimality condition as well as the duality theorem of linear programming. Finally, a case is studied to analyze effect of risk preference and market price volatility on electricity retailer profit. The result can provide reference for the electricity retailer to make the purchase and sale strategy.