Abstract: With the increase in the number of Electric Vehicles (EVs), more power will be needed from the grid. An increase in the load demand, losses, and grid operational costs will occur. Furthermore, with the increasing number of EVs, there comes a possibility of user dissatisfaction which can further lower the popularity of EVs. Objective: This article focuses on EV battery charging and discharging control optimization, i.e., optimizing load demand to minimize power grid system losses and reduce charging costs. It also aids in the vehicle to grid (V2G) process while considering the "User Decision Variable" for each prospective EV. Methods: As a load minimization problem, the optimal power flow model has been formulated first, then the problem has been formulated for each EV. The operational cost of the charging station is an objective function. Results: Further investigation of the load minimization of the IEEE33-bus system has been carried out, and Particle Swarm Optimization (PSO) algorithm is proposed. Conclusion: MATLAB results show that the proposed strategy to charge and discharge can decrease losses from the power grid, reduce the running operational cost while considering battery life and the user's sense of security.
To investigate the influence of entraining and separating effect of gas–liquid two-phase on self-priming performance in the flow-ejecting centrifugal pump, three different schemes of adding the baffle plate behind the guide vane were proposed. Experiments on self-priming performance for three different guide vane schemes were carried out, and numerical calculations on entraining property in the ejector and separating property in the chamber were analyzed by means of the Eulerian–Eulerian multiphase flow model. Meanwhile, the frequency domain properties of pressure pulsation and the pump performance curves were obtained to further verify the feasibility of the scheme in practical application. The results show that the simulation analysis agreed well with the test results. The area and magnitude of high velocity region and vorticities in the ejector of scheme 2 are remarkably larger than those of other schemes. Gas–liquid separation efficiency in a pump chamber also significantly improves when the baffle plate behind the guide vane is mounted at an appropriate position. Furthermore, different guide vane schemes have certain impact on the characteristics of internal and unsteady flow pulsation phenomena in model pump but are within the acceptable operation range. The head and efficiency of scheme 2 are also slightly higher than those of the prototype in the full operation range.
Fluid-dynamic noise induced by the unsteady fluid phenomena usually causes a negative influence on the hydraulic circuit system during the pump operation, especially at off-design flow rates. The spectrum of the pressure signals measured directly in the pipeline of the pump is usually employed to reflect the acoustic characteristic parameters of the fluid-dynamic noise of the pump itself. However, there exists a large difference between the spectrum characteristics directly measured and the actual characteristics of the acoustic source inside the pump due to the effects of the acoustic properties of the piping. Therefore, in order to verify the effect of the discharge piping on the pressure fluctuations of a laboratory pump, three different discharge piping schemes connected to the pump outlet were studied by opening and closing different valves. The results showed that the amplitude of the pressure pulsations in a constant monitor point changed with the shaft frequency and blade passing frequency. The variation range of the pressure pulsation magnitudes for the points monitored at the pump outlet is evidently larger than that for the points close to the cutwater of the volute.
This research studies finite element (FE) model updating through sum of squares (SOS) optimization to minimize modal dynamic residuals. In the past few decades, many FE model updating algorithms have been studied to improve the similitude between a numerical model and the as-built structure. FE model updating usually requires solving nonconvex optimization problems, while most off-the-shelf optimization solvers can only find local optima. To improve the model updating performance, this paper proposes the SOS global optimization method for minimizing modal dynamic residuals of the generalized eigenvalue equations in structural dynamics. The proposed method is validated through both numerical simulation and experimental study of a four-story shear frame structure.
Silicon steel strip is the major magnetism material made of electrical machines, but the cost of electrical machines is higher because of the Silicon steel strip’s higher price and low avail. In view of carbon steel having rich resource and low price, the study about the technics principles and measures by decarburization and forming blue oxidation film in order to substitute the Silicon steel strip is validated. It analyzed the magnetism of Q195 and Silicon steel strip before and after the heat treatment and also compared with the sample machines and customary machines. It is drawn that all of the performances of sample machines can meet the requirement.
To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.
To mitigate the impact of wind forecasting error uncertainty, a Chance-Constrained Goal Programming (CCGP) based day-ahead scheduling model is proposed in this paper. Compared with the traditional Chance Constrained Programming (CCP) method, the CCGP based model is more flexible, which allows higher violation probability than the predefined probability in necessary situations. In this way, the day-ahead scheduling and the uncertain range covered by reserves can be both optimized. Therefore, the reliability and economy of the system with wind power uncertainty can be considered in details with more flexibilities. In addition, because slack variables in CCGP model have corresponding physical meanings, they can provide more information to system operators. Furthermore, numerical tests are performed with the IEEE 118 bus system with wind power input. Results indicate that the proposed method achieves a good balance of cost and risk. And the total operation cost, especially the unit commitment cost, is reduced by the proposed method. Comparative evaluations of the proposed CCGP method and CCP method are presented in the paper.
The large-scale integration of renewable energy sources leads to a reduction in power system inertia, while frequent extreme weather events pose threats to the safe and stable operation of the power system. In response, this paper proposes a resilience enhancement strategy for power systems considering inertia participation during extreme weather events. Focusing on the system resistance phase, this strategy integrates the derivation of critical inertia demand formulas under extreme weather events, establishing a linearized inertia assessment model. Additionally, considering the vulnerability of power lines to extreme weather events, we propose the Resilience Reserve Factor (RRF). It employs three resilience assessment indexes to delineate the system's demand for inertia supply, efficiently targeting vulnerable areas for inertia reinforcement, thereby comprehensively enhancing the resilience of the power grid. Lastly, based on the critical inertia demand constraint criterion, we establish a two-stage pre-scheduling strategy incorporating both day-ahead planning and real-time correction while considering assessment accuracy. This approach transforms the inertia assessment problem into a resilience optimization problem, yielding the scheduling status of each generator unit and inertia replenishment results during extreme weather after iteration. The optimized strategy is validated through simulations on the improved IEEE39 buses system. The results demonstrate its capability to rationally plan scheduling resources before and after disasters during extreme weather events. This strategy not only improves the economic efficiency of the power system but also effectively boosts the support of system during the disaster resistance phase, thereby enhancing the overall resilience of the power system.
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