Electric hot water multi-energy-flow cooperative scheduling method based on convex optimization
A collaborative scheduling, electric hot water technology, applied in design optimization/simulation, electrical digital data processing, instrumentation, etc., can solve problems such as increasing model complexity, complex thermal and hydraulic physical characteristics, and threats to the safe and stable operation of the system
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Embodiment 1
[0106] This embodiment is applied in a multi-energy flow system, and the structure of the multi-energy flow system is as follows figure 1 shown.
[0107] Coordinated dispatching method of multi-energy flow of electric hot water based on convex optimization, such as figure 2 shown, including the following steps:
[0108] S1. Establish a non-convex programming model for coordinated scheduling of electric and hot water multi-energy flows:
[0109] S11, establish the objective function for the coordinated scheduling of electric and hot water multi-energy flows:
[0110]
[0111] Among them, t is the scheduling period; T is the set of scheduling periods; is the fuel cost in period t; is the interaction cost between the system and the power grid during the t period. is the system operation and maintenance cost in period t.
[0112] The calculation formula is as follows:
[0113]
[0114] Among them, i is the grid node number; Ι bus is the grid node label set; Δt ...
Embodiment 2
[0307] The multi-energy flow system of this embodiment consists of a 9-node power distribution system and an 8-node heating system, such as image 3 shown. The system includes a 2MW gas turbine, a 2MW electric boiler, a 1MW fan, a 200kWh battery, a 1MWh heat storage tank, and a 10kW circulating water pump. Heat network nodes 5, 6, 7 and 8 are connected to heat loads respectively, the operation optimization period is 24h, and the scheduling time interval is 1h. The parameter is set to ρ (0) = ρ max =10 4 ,δ 1 =10 -4 ,δ 2 =10 -2 .
[0308] Cooperative scheduling is carried out according to the steps of the present invention, and the convergence index value in the algorithm iteration process is as follows Figure 4 shown. gaps 1 and GAP 2 The convergence conditions are satisfied at the 17th and 13th times respectively, so the number of iterations required by the algorithm is 13 times. The total solution time is 31.23s. However, none of the existing nonlinear solvers c...
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