This article presents an optimized prediction model of building dynamic HVAC system load, which simplifies the input parameters of the model while meeting the accuracy requirements of the prediction results. The model was established using the open-source Modelica-based building library, and the linear aggregation method was used to establish the model. A reduced-order model was developed, and the accuracy of the simplified and reduced-order models was verified. A control strategy was constructed using the indoor mean radiant temperature (MRT) aggregated from a simplified prediction model of HVAC system load as the target feedback parameter, and its feasibility was verified experimentally. It was found that the MRT adopted by the new control strategy can reflect the changes in outdoor air temperature and load in a timely manner; moreover, using this as a control parameter can significantly reduce the influence of load changes to maintain a stable indoor temperature. The control system is further simplified by the predictive model, which improves the engineering practicability by maintaining the control accuracy.

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 emissions trading is regarded as an effective method that can consider both power economy and low-carbon environmental protection, bringing new features to the conventional multi-microgrid (MMG) day-ahead dispatch. In this paper, a robust decentralized energy management framework for monitoring a collaborative structure of gas turbines (GT), gas boilers (GB), ground source heat pumps (GSHP), energy storage (ES), and electrolyzers for a microgrid (MG) in the presence of power-to-gas (P2G) and carbon capture systems is proposed (CCS). Demand response (DR) is presented as a means of improving the system’s flexibility to multiple energy demands. As MMG are sensitive to price fluctuations, the robust optimization model can manage uncertainty by considering the worst-case scenario for trading prices at varying conservative levels. The Kalman filter (KF) distributed algorithm based on iteration is utilized to decompose the dispatch problem to minimize the total daily overhead of the MMG system while protecting the privacy of MG data. In conclusion, the simulation results demonstrate the ability of the proposed decentralized model to meet the demand for electricity and heat under the worst-case scenario of electricity pricing.

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.