Distributed power supply, energy storage and flexible load combined scheduling method and device
A distributed power supply and flexible load technology, applied in the direction of circuit devices, resources, electrical components, etc., can solve problems such as long computing time and insufficient scheduling capabilities
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Embodiment 1
[0095] In the embodiment of the present invention, the operation mode of a resource aggregator is taken as an example to describe the model and algorithm of the present invention.
[0096] (1) Operation mode of resource aggregator
[0097] The power grid company issues scheduling instructions to the resource aggregator for the purpose of grid regulation. The resource aggregator jointly dispatches various distributed resources to complete the scheduling instruction through internal optimization. The main scheduling methods are direct scheduling and indirect scheduling in response to electricity prices. The operation mode is as follows: figure 2 Shown:
[0098] 1) The aggregator accepts the power grid dispatching model: the power grid company directly issues scheduling instructions to the resource aggregator for the purpose of peak regulation, frequency regulation, voltage regulation, and network congestion relief, and gives corresponding economic incentives according to the in...
Embodiment 2
[0164] The improved IEEE33 node power distribution range is selected as the resource aggregation area, and the line structure is as follows Figure 4 shown. The rated voltage of the system is 10kV, the reference apparent power is 10MVA, node 1 is a balanced node, and the voltage of the bus is 10.5∠0°kV. The output prediction curve is as Figure 5 shown. Table 2 shows the maximum power of small-capacity resources of each node and node parameters. Set K 1 and K 2 800 and 1600 yuan / MWh respectively, f RA It is 1000 yuan, the confidence degree α=0.98, and the reserve power ε=0.2MW. The solution algorithm adopts the improved particle swarm optimization method, and the algorithm parameters are set as follows: the number of particles is 50, the number of iterations is 100, and the acceleration coefficient c 1 =c 2 = 2, the value range of ω is [0.5, 1.1] through multiple experiments, the algorithm has a strong global search ability, so that no PSO and N PSO are the current...
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