Based on the principle of electromagnetic induction, this paper proposes a new sleeve structure of electromagnetic induction heating energy storage system, which converts the electrical energy that cannot be consumed by wind power, solar power and other power grids into heat energy. The electromagnetic induction heating model of the eddy current field is established by Ansoft/Maxwell, and the magnetic induction intensity, current penetration depth and current frequency are analyzed. The three-dimensional fluid temperature field and its corresponding temperature characteristics of the system are analyzed by Ansys/Fluent. The temperature field cloud diagram of the sleeve is analyzed. It is concluded that with the increase of air thickness, the loss of the internal iron pipe increases, and the loss of the middle iron pipe decreases. The characteristic curve of the resonant circuit of the electromagnetic induction heating power supply is simulated and analyzed to determine the optimal parameters of the resonant circuit of the induction heating. A 100 kW electromagnetic energy storage system is developed, and the effectiveness and practicability of the method are verified, which can be applied to high power thermal energy storage.
Cogeneration units use heat and electricity as the main operation mode in the heating season. It suffers the lack of peak shaving ability, resulting in intense wind curtailment. This study presents a coordinated scheduling strategy of heat storage thermoelectric units considering the uncertainty of wind power. The developed method performs a wind power data measurement and process system based on edge computing and deep learning to acquire operation data quickly and independently and adopts a wind power prediction method based on interval scenario. Furthermore, the coordinated control model of the thermal storage unit is established after the analysis of the characteristics of coordinated scheduling strategy of heat storage thermoelectric units based on wind heat complementation, and then the improved particle swarm optimization (PSO) is adopted to the solving process. The proposed method is verified by applying it to two typical thermal power units.
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