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The problem that the early capacity planning of microgrid in data center is unreasonable and cannot adapt to the operation characteristics of data center has not been solved. At the same time, in order to improve the power supply economy and renewable energy consumption capacity of the microgrid in data center, a bi-level optimal configuration model of the microgrid capacity in data center taking into account the load characteristics is proposed in this paper. Scenario reduction-based selection method is used to deal with the uncertainty of renewable energy and the computational complexity caused by a large number of scenarios. The corresponding load scheduling strategies are proposed according to the characteristics of different loads together with their flexibility. The problem of capacity planning for different microgrid resources, including energy storage system, photovoltaic and wind turbine, is solved by using non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) and Gurobi. Finally, the conpromise optimal soulation is obtained by the fuzzy multi-attribute decision-making approach. The simulation results show that the proposed approach can reduce the construction costs, operation costs and carbon emissions of data center microgrids. Besides, the satisfaction of data center operators is improved.

The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs' power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations. [ABSTRACT FROM AUTHOR]