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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.

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.

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.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

The reclosing time of a transmission line has a distinct influence on system stability. A new method of calculating the optimal reclosing time based on the transient energy function (TEF) is presented in this paper. It has been shown that an optimal reclosing time can be used to set the autoreclosing device so that the system stability or power-transfer capability can be significantly improved. Though power-system TEF is based on classical models, the results from simulation studies of a real system (46 generators and 230 buses) with complex models show that the optimal reclosing time calculated using TEF can be used effectively.

Inter-area transfer power is a key factor affecting the damping of electromechanical oscillation. This paper presents an output-only based inter-area transfer capability (ITC) assessment method considering small signal stability constraint. Based on the construction of an equivalent two-machine system (ETmS) of an interconnected power grid, the measurements-based approach for estimating the parameters of the ETmS was studied. Then, we developed an optimization model with a maximum ITC target and damping as constraint. Under the premise of using recursive adaptive stochastic subspace identification (RASSI) to extract the oscillation modes (frequency and damping) from the ambient data, the optimization model was solved by employing the particle swarm optimization (PSO) algorithm. The effectiveness of the proposed approach was demonstrated using numerical simulations on the IEEE 16-machine 68-bus test system and measured data from a real system.

This paper proposes a method to obtain the optimal placement of wind turbines (WTs) in an offshore wind farm (WF). The optimization objective is to minimize the levelized average cost per net electric power generated by a WF with a fixed number of WTs while the distance between WTs is not less than the allowed minimal distance in the far wake region. The WT wake losses have been taken into account, with the Frandsen-Gaussian (F-G) wake model and the optimization problem is subsequently solved by the hybrid grey wolf optimization (HGWO) algorithm. Synthesis methods that contain a special WT ranking strategy for multiple WTs are described in detail. Both the F-G model and Jensen's model are applied in the offshore WF optimization simulation platform for comparison. Simulation results demonstrate that the F-G model is more consistent with real wakes and thus the optimization result is more accurate than the commonly used Jensen's model.

In microgrid, photovoltaic (PV) and storage are always combined as a droop-controlled ideal source, which is not very practical. Alternatively, this paper introduces a PV-storage independent system via allocating the PV-storage separately. For this structure, a novel quasi-master-slave control frame is proposed without communication. Storages work as master voltage sources, and PVs operate as current controlled voltage sources (CCVS). For the slave PVs, a MPPT-based power droop control and an adaptive reactive power control are proposed. Thus, PVs can simultaneously achieve maximum energy utilization in real-time and the voltage/frequency regulation. Compared with the conventional master-slave frame, this quasi-master-slave frame is more applicable for allowing a high PV penetration and for unifying islanded and grid-connected modes. Furthermore, the small signal stability of entire system is analyzed to design the physical and control parameters, such as, the minimum capacitance value of DC side, droop coefficients. Finally, simulation and experimental results are presented to verify the system effectiveness.