A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Research Status and Existing Problems. <s> Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programing approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors, and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short-time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer nonlinear programing optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network. <s> BIB001 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Research Status and Existing Problems. <s> This study introduces an efficient energy management system (EMS) for a wind–photovoltaic (PV)–fuel cell (FC)–battery energy scheme with an effective control strategy to integrate with the utility grid. The suggested technique utilises the multi-objective particle swarm optimisation (MOPSO) to minimise the operation cost of the microgrid (MG) and maximise the generated power by each source. The presented MOPSO has the ability to minimise the operation cost of the MG with respect to the renewable penetration, the fluctuation in the generated power, uncertainty in power demand, and continuous change of the utility market price. The proposed optimisation strategy makes the exact choice of sources in right planning and chooses the power that must be created by each source and the power required by the utility network. The price fluctuation data of power and the variance of the renewable energy produced by each unit will be sent to the EMS by a communication network using global positioning system. The dynamic performance and the operation cost of the MG have been tested under different weather conditions and the variance of the power demand through a whole simulation period of 24 h. The stability of the MG, power quality, and voltage regulation is verified by Matlab simulation and experimental results. <s> BIB002 </s> A Survey on Optimal Control and Operation of Integrated Energy Systems <s> Research Status and Existing Problems. <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> BIB003
In view of the current research status of electro-gas coupling system, there are mainly the following di culties. Firstly, the transform of the original system on the basis of the existing equipment of power system or natural gas system, and the correctness and economy of selected scheme considering the coupling relationship need to be further analyzed. Because the existing system construction is di cult to come up with a set of extendable and applicable expansion scheme, it is easy to have problems such as large investment and small income. Secondly, the analysis and calculation of multienergy ow of electro-gas coupling system is still in the initial stage of research, lacking fast, accurate and practical methods. Because the power system and natural gas system are dominated by di erent subjects, whether there are con icts of interest and how to distribute interests need to be studied by game theory and other methods. It also has problems whether di erent subjects will implement scheme according to the scheduled. In addition, the price of hydrogen or methane generated by excess power or renewable energy in power system through P2G technology and whether hydrogen or methane is allowed directly into gas transmission and distribution pipelines by the main body of natural gas still need relevant research and investigation. e reliability of natural gas supply and power system supply still needs further study. e reliability of single natural gas supply is mainly a ected by pipeline capacity and gas storage capacity. e reliability of power system mainly consists of the reliability of power generation system, transmission system, distribution system, and main electrical connection of power plant/substation. Generally, the operation of IES needs to consider the dynamic characteristics of the system, such as the delay of pipeline transmission, the nonlinearity of equipment conversion e ciency, and power ow constraints, which are nonlinear programming problems BIB003 . Traditional algorithms such as sequential quadratic programming (SQP) and primal dual interior point or intelligent algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), evolutionary algorithm (EO), and reinforcement learning (RL), are used to solve nonlinear programming problems BIB001 BIB002 .