A Two-Stage Robust Optimal Scheduling Method for Integrated Energy Systems
An integrated energy system and robust optimization technology, which is applied in the field of two-stage robust optimal scheduling of integrated energy systems, can solve problems such as the inability to guarantee the economic operation of integrated energy systems and the economic impact of operation, so as to reduce adverse effects and save economic costs. , the effect of improving work efficiency
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Embodiment 1
[0068] This embodiment is applied in an integrated energy system, and the structure of the integrated energy system is as follows figure 1 shown.
[0069] A two-stage robust optimal scheduling method for integrated energy systems, such as figure 2 shown, including the following steps:
[0070] S1, establish the objective function of the two-stage robust optimization scheduling model of the integrated energy system:
[0071] The objective function of the established two-stage robust optimal scheduling model for the integrated energy system is:
[0072]
[0073] Among them, t is the scheduling period; y is the decision variable of the first stage; x is the decision variable of the second stage; is the output power value of renewable energy in period t; is the outdoor temperature value in period t; U is the uncertain set of net renewable energy power; W is the uncertain set of outdoor temperature; C on-off is the cost of starting and stopping the equipment; C gas is t...
Embodiment 2
[0123] The integrated energy system of this embodiment includes a 5MW gas turbine, a 5MW gas boiler, a fan with a capacity of 1.5MW, a battery of 1MWh and a heat storage tank of 5MWh, such as image 3 As shown, the heating network of the system consists of 6 nodes, among which node 1 is connected to the CHP system, and nodes 4, 5 and 6 are respectively connected to heat loads. The operation optimization period is 24 hours. 12. The wind power prediction deviation is 0, 0.05, 0.10, 0.15, 0.20 in sequence, and the outdoor temperature prediction deviation is 0, 0.05, 0.10, 0.15, 0.20 in sequence, a total of 25 scenarios.
[0124] Among them, the operating costs of the system in each scenario are shown in Table 1. After running the steps according to the present invention, it can be seen that considering the uncertainty of wind power output and outdoor temperature increases the system operating cost by 1.0% to 8.1%, and the greater the prediction deviation, the higher the operating...
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