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Control methods based on global positioning systems (GPS) are reported in multiple literatures recently, which achieve a fixed frequency operation of the microgrid and therefore has the tremendous benefit that any problems related to frequency instability are eliminated. However, the possible interruption of GPS timing signals may cause current circulating and finally leads to instability of the microgrid, yet it is largely neglected in literatures. This paper presents an angle synchronizing mechanism which utilizes the timing signal from GPS satellites and an auxiliary frequency droop loop to ensure synchronization during GPS offline events. Smooth transfer is guaranteed between primary and auxiliary control loops with discrete control architecture. Also, to allow microgrid using GPS-based control to work in tandem with the bulk power system or another frequency droop microgrid, an extra synchronization algorithm is proposed. The viability and performance of the proposed control structure is validated by case studies.

In grounding fault of DC system, overvoltage will appear on equipment nodes in converter station. Due to different operation mode and equipment type, it is very different from overvoltage in AC system. Based on Shanghaimiao–Linyi​ ±800kV Ultra High Voltage Direct Current (UHVDC) transmission project, this paper studies the converter station overvoltage characteristics under the conditions of DC pole line grounding, DC bus grounding and converter transformer valve side grounding fault, then analyzes the influence of smoothing reactor, filter capacitor and grounding pole line on overvoltage. In grounding fault at DC pole line near the converter station, the maximum overvoltage occurs on the inductance of DC filter, which is 592.75kV; In grounding fault at the top of the high voltage converter valve, the maximum overvoltage occurs on the neutral bus, which is 272.17kV. Increasing the inductance or capacitance of the smoothing reactor and filter capacitor, shortening the length of grounding electrode wire can significantly reduce the amplitude and steepness of overvoltage. The calculation results provide a reference for the internal overvoltage suppression of DC converter station with layered access at the inverter side.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

To alleviate the pressure of energy utilization of buildings, more attention was focused on the utilization of GWSHP (groundwater source heat pump) systems. However, there have been many debates on feasibility. This paper is to study the suitability and feasibility of GWSHP systems in different climate zones. A simulation model of a GWSHP system is established based on TRNSYS software in the severe cold climate zones A and B, cold climate zones, hot summer and cold winter climate zones, and the hot summer and warm winter climate zones. Simultaneously, the reliability and energy-saving benefits of GWSHP systems in typical residential buildings situated in different climate zones are deeply analyzed. Results reveal that the operating performance of GWSHP systems is considered as the best in the climate zones that need both heating and cooling loads for residential buildings. In contrast, the energy-saving benefits of the GWSHP system in typical residential buildings are deemed to be higher in the cold climate zones and severe cold climate zones. Overall, compared with the ASHP, the economy of the system is generally better based on economic analysis.

The power flow calculation is a significant basis for the optimized operation of integrated energy systems. For two-way coupled electricity–gas integrated energy systems, the dynamic characteristics of the slow time scale of the gas subsystem make the steady-state energy flow calculation unable to accurately obtain its real-time operating status. In this paper, for the electricity–gas integrated energy system, propose a time-domain continuous power flow calculation method considering the dynamic process of the gas subsystem, which can obtain continuous change of the state variables. The Taylor series expansion method is applied to the power subsystem, the unified energy path model and matching calculation method is used in the natural gas subsystem. The solution is realized by alternating the input and output variables of the coupling element. At the same time, the input of the power subsystem derivative information and the natural gas subsystems frequency domain information makes this method obtain continuous solutions in the time domain, which can meet the time scale requirements in different scenarios. Finally, a calculation example is carried out, and the results show that compared with traditional discrete method, this method can greatly reduce the calculation time under the condition of ensuring a certain degree of accuracy, which verifies the effectiveness of the proposed method.

To realize on-line monitoring for insulated-gate bipolar transistor (IGBT) modules in the three-level neutral point clamped (NPC) inverters, a reconfiguration modulation strategy is proposed. The saturated voltage drop (VCE) is one of the most common diagnostic signs of IGBTs’ healthy status due to the advantages of monitoring method such as simple theory, mature implementation, and accurate measurement, and the method to extract VCE in the monitoring system has been researched in the previous study. On this basis, considering realizing on-line monitoring process, essential supported hardware and proactive on-line monitoring environment are needed to guarantee the safe and friendly on-line monitoring implementation. Analysis has been made to show that the on-line monitoring system is able to work regularly by just making use of the inherent O state of a three-level NPC inverter in some circumstances. But to guarantee the monitoring process and the safety of the monitored NPC inverter, the novel reconfiguration strategy is a kind of necessary and essential means to create an on-line monitoring environment, and it is proposed and researched in this paper. Through theoretical derivation, the optimization of reconfiguration strategy is obtained. The reconfiguration strategy proposed in this research has been achieved and its validity is proved based on experimental results.

High voltage direct current (HVDC) technology is the ideal solution for long distance bulk power transmission. However, different from AC systems, differential protection of HVDC lines still faces a lot of challenges. We claim that from the perspective of state estimation, the essence of differential protection is to estimate state variables using measured data, of which the validity indicates whether there is a fault. Therefore, the performance of the differential protection is highly related to the model accuracy. Using a detailed distributed parameter line model, this paper developed a novel differential protection method for HVDC lines based on traveling waves with high reliability. The optimal differential point is also discussed to enhance protection rapidity. Experiments demonstrate that the proposed protection could soon detect internal faults with high fault resistance while maintaining reliability when external faults occur.

Abstract The objective of this paper is to structure a data-driven-based workflow to design and optimize alkali-surfactant-polymer flooding (ASP) projects. Various objective functions from the techno-eco perspectives are considered to obtain comprehensive optimum ASP slug formulations and injection operation schemes. Several universal multi-layer neural networks (MLNN) are trained and they act as surrogate models of high-fidelity numerical simulation models to evaluate the objective functions involved in the optimization workflow. The input parameters considered by the machine-learning workflow include the reservoir rock and fluid properties, ASP slug formulation, and project design parameters. A physical equation of state, HLD-NAC equation, is employed to model the microemulsion phase behavior of the crude oil/brine/surfactant system. The validity of the surrogate models is confirmed via extensive blind testing applications with error margins of less than 5%. Thus, the MLNN can be employed as an expert system to assess the response functions of the ASP injection processes, including oil recovery, water cut reduction, pressure responses, etc. A hybrid global optimization workflow is structured by coupling the expert MLNN systems with particle swarm optimizer (PSO). Comprehensive techno-eco-assessments are carried considering the oil recovery, chemical slug utilization ratio (incremental oil production per unit mass of chemical additives), and project economics as objectives. This paper presents case studies to illustrate the robustness of the proposed workflow in optimizing the ASP injection projects considering multiple objective functions. The optimization work focuses on the design of ASP slug formulations and injection patterns. A sensitivity analysis is carried to investigate the trade-off factors between pairs of the objectives. Objective functions exhibiting a strong trade-off relationship can be included to structure Pareto front solutions, which enables the optimization workflow to find various optimized ASP formulations and injection schemes. The outcomes of this work not only provide field operators with various options to implement ASP injection processes but also generate stochastic assessments of the optimized objectives. This work develops a universal proxy model that is competent to generalize the microemulsion phase behavior that occurs in the ASP injection processes. The trade-off relationship amongst the critical objective functions is investigated via a rigorous sensitivity analysis using the proxy models. With the help of the developed model, the decision-makers can design ASP injection projects based on various project preferences. Moreover, the project risks can be understood by observing the stochastic outcomes of the optimized objective functions.