Optimal Bidding Strategy for Power Market Based on Improved World Cup Optimization Algorithm

Razmjooy, Navid; Deshpande, Anand; Khalilpour, Mohsen; Estrela, Vania V.; Padilha, Reinaldo; Monteiro, Ana Carolina Borges

doi:10.1007/978-3-030-56689-0_7

Cited by 3 publications

(1 citation statement)

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“…SNNI algorithm is employed to refine the resolution of the battery model table, so as to improve the precision of given voltage. The key processing of SNNI is iterative operation, which is widely used in many fields, such as optimal bidding strategy for power market, 22 configuration of autonomous CHP system, 23 model parameter estimation of the PEMFCs, 24 and robust control. 25,26 In an iterative way, a large extraction ratio can be adopted for the standard power battery model table to reduce the capacity of the model table; thus, the storage capacity requirement of the battery simulation system is reduced.…”

Section: Introductionmentioning

confidence: 99%

Method of voltage setting for power battery simulator using successive nearest-neighbor interpolation

Tan

Zhang

et al. 2022

Measurement and Control

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The power battery simulator is important equipment in new energy vehicle test platform and other industrial fields, and the battery model is the key to emulate the battery characteristics accurately. A novel method of given voltage for the power battery simulator is proposed in the paper. According to the state of charge (SOC) of the power battery, three sub-tables with different resolutions are established corresponding to the initial, stationary, and final stage of SOC. To ensure the accuracy of a given voltage under small data capacity, a successive nearest-neighbor interpolation (SNNI) algorithm is proposed for processing battery model data and the iterative calculation is carried out for the data obtained by looking up the table until it approaches the required value. Simulations and experiments are done on various capacity data tables, which are obtained by employed different sampling ratios to reduce the data capacity, and the influence of iteration number selection on the accuracy of the algorithm is discussed. The results show that, after 4–5 iterations, the given voltage accuracy is controlled within 0.03 V by the SNNI algorithm with only 1.6% capacity of the standard model table, which is greatly improved compared with the traditional method, and the feasibility is verified by executing the proposed algorism in a DSP system.

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Section: Introductionmentioning

confidence: 99%