Multi-agent-based comprehensive energy system multi-agent joint optimization operation method
A technology of integrated energy system and joint optimization, applied in the field of multi-agent joint optimization operation of integrated energy system, can solve the problems of lack of fast interaction and processing, and unable to fully meet the requirements of high efficiency and flexibility of integrated energy system optimization operation method.
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Embodiment 1
[0099] This embodiment is applied in a typical integrated energy system, and the structure of the typical integrated energy system is as follows figure 1 shown.
[0100] A multi-agent-based multi-agent joint optimization operation method for an integrated energy system includes the following steps:
[0101] Step 11) Build an external energy network proxy model
[0102] Step 111) Create External Grid Proxy
[0103] An external power grid agent is established to have the power price information of each time period in the dispatch cycle, as well as the transmission power limit information of the network tie line.
[0104] The power constraint of the proxy tie line of the external power grid is shown in formula (7):
[0105]
[0106] In the formula, E grid·emax It is the maximum transmission power for the connection line between the external power grid and the integrated energy system.
[0107] Step 112) Set up an external heat network agent
[0108] Establish an external...
Embodiment 2
[0211] The structure diagram of the integrated energy system in this embodiment is as follows: figure 1 As shown, the equipment in the system shown includes: photovoltaic units, wind turbines, combined heat and power units, electric boiler equipment, electric energy storage equipment, and thermal energy storage equipment, etc., and perform power interaction with the external power grid. The output information of photovoltaic and wind turbines and the forecast demand information of electric heating load are shown in Figure 4, where Figure 4a is the day-ahead forecast data of electric load, Figure 4b is the day-ahead forecast data of heat load, Figure 4c Day-ahead forecast data for wind power output, Figure 4d Day-ahead forecast data for photovoltaic output; energy price information from external networks such as Figure 5 shown. Other main parameter settings are shown in Table 2 below:
[0212] Table 2 Main parameter setting of integrated energy system
[0213]
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