Virtual power plant random adaptive robust optimization scheduling method considering central air conditioning system

Abstract

The invention discloses a virtual power plant random adaptive robust optimization scheduling method considering a central air-conditioning system, which comprises the steps of determining a human bodycomfort range based on a predicted average voting number index, deducing a room temperature time-varying equation of a public building by a thermodynamic equation of the central air-conditioning system of the public building, and performing modeling analysis of the central air-conditioning system; constructing a virtual power plant (VPP) deterministic model by taking the maximum VPP profit as a target function according to the original data, and constructing constraint conditions; adopting a stochastic programming method to process market electricity price uncertainty, adopting a self-adaptive robust method to process photovoltaic output uncertainty, establishing a virtual power plant stochastic self-adaptive robust model, and adopting a standard scene algorithm to solve. According to themethod, the VPP can be flexibly dispatched according to the market electricity price, the profit of the VPP is improved, the problem of electricity utilization peak in the summer load peak period canbe relieved, and the effects of peak clipping and valley filling are achieved.

Description

technical field [0001] The invention belongs to the field of power system dispatching, in particular to a random self-adaptive robust optimization dispatching method of a virtual power plant considering a central air-conditioning system. Background technique [0002] A virtual power plant (virtual power plant, VPP) aggregates renewable energy, energy storage, demand response (demand response, DR) and other distributed energy sources through advanced communication, metering and control technologies, and participates in grid operation as a whole, which can reduce The impact of small distributed energy grid connection alone on the public network and improve its market competitiveness. In recent years, the air-conditioning load has accounted for 30% to 40% of the peak load in the summer peak period. Therefore, the air-conditioning load can be used as a DR resource with great potential to participate in VPP optimal scheduling. [0003] In the current research on VPP, DR is only ...

AI Technical Summary

Problems solved by technology

[0003] In the current research on VPP, DR is only regarded as an interruptible / transferable load for modeling, and there is a lack of specific modeling analysis of DR

Images