A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimal Control and Operation of IES. <s> The advances in distributed renewable generation technologies in recent years have started to cause load balancing issues in power networks. Distributed energy storage (ES) systems, although seen as a tool to mitigate the stress on local networks, tend to be operated only to minimize the energy cost of their direct owner. In this paper, cooperative game theory is used to construct an energy grand coalition, in which ES system operations are optimized to minimize the coalitional energy cost. Case studies then show that forming energy coalitions is effective in reducing the variability of a local network load profile. The resulting cooperative game is mathematically proven to be balanced, which means the energy cost savings from the cooperative ES operation can be allocated to the players within the energy grand coalition in a manner that disincentivizes them from exiting from the grand coalition to form smaller coalitions. <s> BIB001 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Optimal Control and Operation of IES. <s> Smart districts can provide flexibility from emerging distributed multi-energy technologies, thus bringing benefits to the district and the wider energy system. However, due to nonlinearity and modeling complexity, constraints associated with the internal energy network (e.g., electricity, heat, and gas) and operational uncertainties (for example, in energy demand) are often overlooked. For this purpose, a robust operational optimization framework for smart districts with multi-energy devices and integrated energy networks is proposed. The framework is based on two-stage iterative modeling that involves mixed integer linear programming (MILP) and linear approximations of the nonlinear network equations. In the MILP optimization stage, the time-ahead set points of all controllable devices (e.g., electrical and thermal storage) are optimized considering uncertainty and a linear approximation of the integrated electricity, heat, and gas networks. The accuracy of the linear model is then improved at a second stage by using a detailed nonlinear integrated network model, and through iterations between the models in the two stages. To efficiently model uncertainty and improve computational efficiency, multi-dimensional linked lists are also used. The proposed approach is illustrated with a real U.K. district; the results demonstrate the model’s ability to capture network limits and uncertainty, which is critical to assess flexibility under stressed conditions. <s> BIB002
Power ow calculation of multienergy ow in IES is to determine the power ow distribution of each subsystem, which is an important prerequisite for exploring coupling characteristics and optimizing scheduling. Power ow calculation of multienergy ow in IES is expressed as followers: where , , ℎ , and ℎ are variables of power system, natural gas system, thermal system, and energy hub; (⋅) is the equation of power ow; (⋅) is algebraic equation of natural gas system, including equations of gas pipeline and compressor; (⋅) is the equation of thermal system, including equations of heat ow continuity, pressure loop, node ow and node mixed temperature; is the input-output conversion equations of energy hub. e gas distribution system mainly includes two parts: node and branch. Nodes are divided into pressure-known nodes and ow-known nodes BIB001 , in which the pressure-known node is generally the gas source point with the pressure known and the ow to be calculated; the ow-known node is generally the load node with the pressure to be calculated. Because of the pressure drop at both ends of the pipeline, especially in the medium and high pressure gas transmission network with long distance and large capacity, it is necessary to con gure a compressor to increase the pressure of the gas transmission pipeline BIB002 . e steady-state model of IES mainly includes AC power ow model of power system, natural gas pipeline model with compressor and steady-state model of thermodynamic system.