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This paper presents a collaborative planning method of an electricity-gas-storage regional integrated energy system based on LSTM neural network and demand response. First, the LSTM Neural network is used for load forecasting, and the energy hub structure of the electric gas storage system is established. Then, the mathematical models of power storage, gas storage, electric network topology, gas network topology, and P2G are established to minimize the expansion cost of the electricity-gas-storage system, and the collaborative planning of energy storage, power lines, and natural gas pipelines is proposed based on the existing electric gas coupling integrated energy system. The original model which is difficult to solve is transformed into a mixed-integer linear programming model by introducing auxiliary variables, and the CPLEX solver is called to solve it. Finally, the economic advantages of collaborative planning of electricity-gas-storage system are verified by an example, and the connection of power storage and gas storage can reduce system pressure and optimize equipment selection.

A feasible method in evaluating the protection effect and corrosion state of marine cathodic protection (CP) systems is collecting sufficient electric potential data around a submarine pipeline and then establishing the mapping relations between these data and corrosion states of pipelines. However, it is difficult for scientists and researchers to obtain those data accurately due to the harsh marine environments and absence of dedicated potential measurement device. In this paper, to alleviate these two problems, firstly, the theory of dimension and conductivity scaling (DACS) physical scale modeling of marine impressed current cathodic protection (ICCP) systems is generalized to marine CP systems, secondly, a potential measurement device is developed specially and analogue experiment is designed according to DACS physical scale modeling to verify the feasibility of the measuring system. The experimental results show that 92 percent of the measurement errors are less than 0.25mv, thereby providing an economical and feasible measuring system to get electric potential data around an actual submarine pipeline under CP.

To monitoring the subsea pipeline in real-time, a special potentiometric sensor array and a potential prediction model are presented in this paper. Firstly, to measure the potential of seawater, a special potentiometric sensor array with Ag/AgCl all-solid-state reference electrodes is first developed in this paper. Secondly, according to the obtained distribution law of electric field intensities a prediction model of the pipeline potentials is developed. Finally, the potentiometric sensor array is applied in sink experiment and the prediction model is validated by the sink measurements. The maximum error for pipeline potential prediction model is 1.1 mV. The proposed non-contact monitoring method for subsea pipeline can predict the potential of sea pipeline in real-time, thus providing important information for further subsea pipeline maintenance.