Controllable load demand response menu pricing method considering user risk preferences
A technology of risk preference and demand response, applied in data processing applications, instruments, business, etc., to achieve multiple economic compensation, increase demand response electricity prices, and reduce power supply costs
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Embodiment 1
[0068] In this embodiment, when the present invention is applied, the specific steps are as follows:
[0069] Step 1, the power dispatcher predicts the power situation of the system based on the load and power plant data in the real-time market, and obtains the power shortage;
[0070] In the real-time electricity market, when the power dispatcher predicts that the demand for electricity will change greatly at a certain moment, and the supply and demand of electricity will be unbalanced, the dispatcher of the power system will issue a demand response signal to the user in advance, and encourage the user to increase or decrease the load power through the price to meet the demand. real-time balance;
[0071] Step 2, in response to the power shortage situation, the power dispatcher issues a demand response signal to the society;
[0072] As electricity is a commodity, users will decide whether to participate in grid dispatching within a limited time according to their own risk t...
Embodiment 2
[0119] The power system single-line diagram of the IEEE-30 node used for simulation calculation in this embodiment is as follows Figure 5 As shown, it is assumed that conventional thermal power units are connected to nodes 1, 2, 5, 8, and 11, and controllable load users are connected to node 3, and the total power of controllable loads is 500MW. The coefficients of generator power generation cost function are shown in Table 3, and the coefficients of generator emission function are shown in Table 4.
[0120] Table 3 Coefficients of cost function of generator power generation
[0121]
[0122] Table 4 Coefficients of Generator Emissions Function
[0123]
[0124] In steps S1 and S2, the power grid company conducts data analysis based on the historical quotations of market users participating in demand response, selects the quotation data of 600 groups of users at 17:30 every night in June 2015 for linear fitting, and performs linear fitting according to the load reducti...
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