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With the guidance of low carbon policy, renewable energy such as wind energy and photovoltaic energy are heavily connected to distributed energy hubs. Multiple uncertainties bring challenges to the operation and control of the system, which makes it of great significance to formulate the operation scheme of low-carbon economy of energy hub. In this paper, the influence of the uncertainty of cold thermoelectric load on the cost is considered, and the gas-electric cooperative optimization is studied. The Lagrangian two-stage relaxation solution model is constructed by using the minimum-maximum regret criterion and dual optimization theory. The validity of the model is verified by simulation and the influence of prediction accuracy level, energy price level and seasonal characteristics of load on the optimization results is analyzed Ring.

We consider a scenario that device-to-device (D2D) communication is enabled in a cellular network. The licensed spectrum of the macro-cell operator is divided into sub-bands, which are able to be leased to the unlicensed subscribers to perform D2D communication. The licensed subscribers of the macro-cell in the shared sub-bands are protected by the maximum tolerable interference level constraint, while the unlicensed subscribers seek chances to transmit in multiple sub-bands with a limited amount of power. We propose a hierarchical game framework to investigate the distributed solution of the resource allocation problems in this scenario. The hierarchical game contains a Stackelberg game which is imposed to protect the licensed subscribers and an overlapping coalition formation game (OCF-game) to jointly allocate the power and sub-bands to the unlicensed subscribers. Distributed algorithm to achieve the Stackelberg Equilibrium (SE) of the proposed game is provided.

In this paper, the access strategy for spectrum-sharing based two-tier networks is investigated. By exploring the motivations of the home base station (HBS) and the macrocell user (MU), we propose a Stackelberg game based approach, which enables them to improve their performances by establishing direct links. The proposed approach copes with the distributed nature of the user-deployed small cell networks, and requires no inter-cell coordinations. Experimental results show that the proposed approach can guarantee the capacity gain of the small cells, while improving the energy efficiency of the macrocell users. Furthermore, by adjusting the objective function, the benefits of open access can be balanced between the capacity gain of small cells and the energy efficiency improvement of the MUs. Therefore it can be applied flexibly for various design purposes.

The melt index (MI) is an important quality specification in polypropylene (PP) production industries. Accurate online measurement of MI becomes vital in PP quality control. A new MI inferential model is proposed by using macro heat of reaction (MHR) as a key factor in MI correlation. MHR is calculated from a dynamic propylene polymerization model. The proposed MI model is validated by plant data and the prediction error is less than 5%. Simulations are performed by three different MI inferential models. The results show that the proposed model has least errors in various operation conditions. The MI model could be used as online soft sensor model or MI control

A mathematical model is developed to simulate the ethylene polymerization in continuous stirred-tank reactors. The model is based on the mass and energy balances around the reactive system. Polymer molecular properties, including molecular weight distribution, number average molecular weight, weight average molecular weight, and polydispersity index, are calculated based on the method of moment. End-use properties, such as melt index and density, are correlated with polymer molecular properties. The model is validated with actual operation date.

For state feedback model predictive control system design, the disturbance decoupling problem is studied in detail. For the undetectable or immeasurable disturbance, necessary and sufficient conditions are given for the disturbance decoupling problem design of the state feedback model predictive control system. We consider the disturbance decoupling problem and state space model, the design procedures are given step by step for choosing the design parameter-the prediction horizon. A simulation example is given, the simulation result shows that both the control performance and the disturbance rejection ability are improved and the control system design procedures are effective.

The load responses of predictive control systems can be significantly improved by the addition of an observer for load estimation. The observer design is based on the inverse of a simplified process model. Simulation studies demonstrate that the proposed design method results in predictive controllers with load estimation which are superior to both existing predictive controllers and conventional PID control1ers.