User-level comprehensive energy system optimization scheduling method based on multiple time scales
A comprehensive energy system and multi-time scale technology, applied in the direction of system integration technology, information technology support system, resources, etc., can solve problems such as unconsidered, low work efficiency, large error, etc., and achieve the effect of reducing operating costs
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Embodiment 1
[0063] This embodiment is applied in an integrated energy system, and the structure of the integrated energy system is as follows figure 1 shown.
[0064] A user-level comprehensive energy system optimization scheduling method based on multiple time scales, comprising the following steps:
[0065] Step S1, obtain the basic operating parameters of the integrated energy system. The basic parameters in this embodiment include renewable energy sources such as photovoltaics and wind power, as well as the intraday forecast output of cooling, heating and electric loads, micro-combustion engines, electric boilers, fuel cells, absorption The capacity and climbing constraints of type refrigerators, electric refrigerators, batteries, heat storage tanks, cold storage tanks and other equipment, the interactive power constraints of the upper power grid connection line, and the purchase and sale price of power grid.
[0066] Step S2, establishing an optimal scheduling model for the day-ahea...
Embodiment 2
[0148] The user-level comprehensive energy system structure diagram of this embodiment is shown in Figure 1. The equipment components in the system include: photovoltaic, wind power, fuel cell, micro-gas turbine, electric boiler, absorption refrigerator, electric refrigerator, electric energy storage, thermal Energy storage, ice storage tanks, etc., and perform power interaction with the upper-level distribution network. The basic data of photovoltaic, wind power and load include the forecast data of the day and the rolling correction data of the day as shown in Figure 2. Figure 2a It is the day-ahead data and intra-day data of electric load, Figure 2b is the day-ahead data and intra-day data of PV, Figure 2c is the day-ahead data and intra-day data of wind power, Figure 2d is the day-ahead data and intra-day data of heat load, Figure 2e is the day-ahead data and intra-day data of the cooling load; image 3 shown in Table 1;
[0149] Table 1 Relevant operating parame...
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