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Nowadays, the electric power system and natural gas network are becoming increasingly coupled and interdependent. A harmonized integration of natural gas and electricity network with bi-directional energy conversion is expected to accommodate high penetration levels of renewables in terms of system flexibility. This work focuses on the steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion. A unified energy flow formulation is developed to describe the nodal balance and branch flow in both systems and it is solved with the Newton–Raphson method. Both the unification of units and the per-unit system are proposed to simplify the system description and to enhance the computation efficiency. The applicability of the proposed method is demonstrated by analyzing an IEEE-9 test system integrated with a 7-node natural gas network. Later, time series of wind power and power load are used to investigate the mitigation effect of the integrated energy system. At last, the effect of wind power and power demand on the output of Power to Gas (P2G) and gas-fired power generation (GPG) has also been investigated.

The strong coupling between the power grid and communication systems may contribute to failure propagation, which may easily lead to cascading failures or blackouts. In this paper, in order to quantitatively analyse the impact of interdependency on power system vulnerability, we put forward a “degree–electrical degree” independent model of cyber-physical power systems (CPPS), a new type of assortative link, through identifying the important nodes in a power grid based on the proposed index–electrical degree, and coupling them with the nodes in a communication system with a high degree, based on one-to-one correspondence. Using the double-star communication system and the IEEE 118-bus power grid to form an artificial interdependent network, we evaluated and compare the holistic vulnerability of CPPS under random attack and malicious attack, separately based on three kinds of interdependent models: “degree–betweenness”, “degree–electrical degree” and “random link”. The simulation results demonstrated that different link patterns, coupling degrees and attack types all can influence the vulnerability of CPPS. The CPPS with a “degree–electrical degree” interdependent model proposed in this paper presented a higher robustness in the face of random attack, and moreover performed better than the degree–betweenness interdependent model in the face of malicious attack.

In this paper, a nonlinear, bounded, distributed secondary control (DSC) method is proposed to coordinate all the distributed generators (DGs) in islanded AC microgrids (MGs). This proposed consensus-based DSC strategy can not only guarantee the restoration control of frequency and voltage but also realize an accurate active power sharing control. Through introducing a nonlinear dynamic from beta cumulative distribution function (CDF), the convergence speed of DSC is accelerated, the asymptotical convergence of DSC is ensured, and the transient overshoot of DSC is diminished compared with traditional DSC. Moreover, by ensuring the Lipchitz continuity characteristic of the control algorithm, the common chattering phenomenon in non-Lipchitz DSC scheme is eliminated. The stability and performance of the proposed DSC are also analyzed in this paper. An islanded AC microgrid test system with four inverter-based DGs is built in MATLAB/SIMULINK to further validate the effeteness of the proposed DSC strategy.

Under the development of information and communication technologies, this paper discusses a cloud-based user-side integrated energy management to improve the energy utilization efficiency in a smart community, for which a two-level model is proposed that relies on an energy hub and load aggregators. Furthermore, to address the issue of preserving confidentiality for the cloud service, an information-masking mechanism is designed based on linear mapping functions, whose information-masking requirements and processes are specified accordingly. Numerical results demonstrate the effectiveness of the proposed model and of the method for cloud computing.

This paper presents a microgrid stability controller (MSC) in order to provide existing distributed generation units (DGs) the additional functionality of working in islanding mode without changing their control strategies in grid-connected mode and to enhance the stability of the microgrid. Microgrid operating characteristics and mathematical models of the MSC indicate that the system is inherently nonlinear and time-variable. Therefore, this paper proposes an adaptive robust total sliding-mode control (ARTSMC) system for the MSC. It is proved that the ARTSMC system is insensitive to parametric uncertainties and external disturbances. The MSC provides fast dynamic response and robustness to the microgrid. When the system is operating in grid-connected mode, it is able to improve the controllability of the exchanged power between the microgrid and the utility grid, while smoothing the DGs’ output power. When the microgrid is operating in islanded mode, it provides voltage and frequency support, while guaranteeing seamless transition between the two operation modes. Simulation and experimental results show the effectiveness of the proposed approach.

Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps’ monitoring and control technology without flow sensors.

This paper proposes a novel fast open circuit voltage prediction approach for Lithium-ion battery, which is potential to facilitate a convenient battery modeling and states estimation in the energy storage system. Open circuit voltage measurement suffers from a long relaxation time (several hours, even days) to reach the thermodynamic equilibrium of the battery. On the basis of the feedback control theory, the proposed multiple correction approach utilizes the constrained nonlinear optimization of the power function in each curve fitting step. The voltage measurement in a short period is divided into several segments to correct the voltage prediction multiple times with the feedback errors after each curve fitting. The similarity between the shape of the power function and the variation of the terminal voltage during the relaxation time is utilized. The proposed method can speed up the time-consuming open circuit voltage measurement and predict the open circuit voltage with high accuracy. Experimental tests on a LiFePO4 battery prove the validation and effectiveness of the proposed method in accurately predicting the open circuit voltage within a very short relaxation time (less than 15 min).