Operation optimization of distributed multi-energy multi-microgrid considering system robustness

Abstract

Carbon emission trading is regarded as an effective way to combine energy economy with green and low-carbon, which brings new vitality to the traditional multi-micro grid day-ahead dispatch. In this paper, a robust decentralized energy management framework is proposed for monitoring a collaborative structure of gas turbines, gas boilers, ground source heat pumps, energy storage and electrolyzers, etc for microgrid in the presence of power to gas and carbon capture systems. The price sensitivity of the power market results in the fluctuations of multi-micro grid dispatching. The worst-scenario uncertainty of multi-micro grid is managed by adopting trading prices at different conservativeness levels. The kalman filter distributed algorithm based on iteration is used to decompose the dispatch problem to minimize the total daily overhead of the multi-micro grid system while protecting microgrid data privacy. Finally, the simulation results represent the effectiveness of the proposed decentralized model of trading prices to meet the demand for electricity and heat. At the same time, the kalman filter distributed algorithm is compared with the alternating direction multiplier method algorithm to ensure accuracy and speed.