Carbon trading is a market-based mechanism towards low-carbon electric power systems. A hy-brid game optimization model is established for deriving the optimal trading price between mi-crogrids (MGs) as well as providing the optimal pricing scheme for trading between the microgrid cluster(MC) and the upper-layer service provider (SP). At first, we propose a robust optimization model of microgrid clusters from the perspective of risk aversion, in which the uncertainty of wind and photovoltaic (PV) output is modeled with resort to the information gap decision theo-ry(IGDT). Finally, based on the Nash bargaining theory, the electric power transaction payment model between MGs is established, and the alternating direction multiplier method (ADMM) is used to solve it, thus effectively protecting the privacy of each subject. It shows that the proposed strategy is able to quantify the uncertainty of wind and PV factors on dispatching operations. At the same time, carbon emission could be effectively reduced by following the tiered carbon price scheme.

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.

Energy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi-lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation-based bi-lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi-lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG.