This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the ''dimension disaster'' caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.
Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.
Although combinatorial testing has been widely studied and used, there are still some situations and requirements that combinatorial testing does not apply to well, such as a system under test whose test cases need to be performed contiguously. For thorough testing, the testing requirements are not only to cover all the interactions among factors but also to cover all the value sequences of every factor. Generally, systems under test always involve constraints and dependencies in or among test cases. The constraints among test cases have not been effectively specified. First, we introduce extended covering arrays that can achieve both t-way combinatorial coverage and t-wise sequence coverage, and propose a clocked computation tree logic-based formal specification method for specifying constraints. Then, Particle Swarm Optimization (PSO) based Extended covering array Generator (PEG) is elaborated. To evaluate the constructed test suites, a method for verifying the constraints' validity is presented, and kernel functions for measuring the coverage are also introduced. Finally, the performance of the proposed PEG is evaluated using several sets of benchmark experiments for some common constraints, and the feasibility and usefulness of PEG is validated.
Adaptive beamforming is widely used in the fields such as radar, sonar, wireless communication to estimate the parameters of the signal of interest (SOI) at the output of a sensor array by data-adaptive spatial filtering and interference suppression. The standard Capon beamformer (SCB) is a typical adaptive beamforming approach which provides a superior performance by minimizing the array output power while simultaneously maintaining the array response under the assumption of distortionless direction of arrival (DOA). However, the advantages in performance of SCB are obtainable only when the number of snapshots available for the sample covariance matrix estimation is large enough and the direction of the SOI is known accurately. When applied to practical situations where the aforementioned two requirements are not satisfied, SCB will suffer high sidelobe levels and performance degradation in the parameter estimates due to lack of measurements and mismatch in the steering vector.A sparsity-constrained Capon beamformer (SCCB) arises to alleviate these problems. Unlike SCB, the constraint in SCCB is composed of two parts: the original array output power constraint part and the sparse constraint part (?1 norm constraint, encouraging sparse distribution in the array responses). However, if the sparse constraint in SCCB is set too large compared with the array output power constraint part, the responses in the directions of interferences will be influenced, and a tradeoff between the ability to reduce the sidelobe levels and the ability to reject the interferences must be made. Thus, based on the SCCB, a new robust Capon beamformer utilizing a weighted sparse constraint is proposed in this paper. In the proposed method, the sparse constraint part is replaced by a weighted sparse constraint, which is applied only to the sidelobe regions of the beampattern. By doing so, the number of the non-zero elements in the sidelobe response is minimized, resulting in an enhanced mainlobe region and suppressed sidelobe ones.In sparse recovery, the sparse constraint (the l1 norm constraint) does not necessarily enforce democratic penalization, which means that larger coefficients are penalized more heavily than smaller coefficients. Based on such a consideration, a weighting matrix can be constructed to put larger weights in the interferences directions to discourage their responses, and put smaller weights to maintain the responses in the remaining parts of the sidelobe regions. In this paper, the weighting matrix is obtained by utilizing the orthogonality between the signal subspace and the noise subspace. Since the steering vectors corresponding to the interferences and the SOI span the same space as the signal subspace, the inner products between the steering vectors in the interference directions and the noise subspace will produce zeroes ideally. By taking the reciprocals of these inner products, large values will yield in the interference directions while small values are obtained in other directions in the sidelobe regions. Using these values as the weights to the sparse constraint, a beampattern with deeper nulls, lower sidelobes, and better robustness to steering vector mismatch is obtainable as compared with SCB and SCCB. Besides, the output SINR is also effectively improved. Numerical simulations and a water-tank experiment are conducted to demonstrate the effectiveness of the proposed method.
Land use/land cover change (LUCC) caused by human beings is the main source of the increases of CO² in the atmosphere. Land resource is not only the natural carrier of carbon emission of land ecosystems, but also the spatial carrier of carbon emission from human society. Human activity and its carbon emission intension have a very close relationship with the land use pattern, exploration on the low-carbon oriented land use scope, and land use structure can effectively reduce the rate of carbon emission, and also provides consults to low-carbon oriented land use planning. This paper presents a multi-objective land use optimization model based on low-carbon development. Carbon emission, economic benefit objectives, and constraint conditions are integrated into the multi-objectives optimization model of land use, and the model was solved with non-dominated sorting genetic algorithm-II (NSGA-II). And through providing weight coefficients to the objective functions, land use patterns in three scenarios (low-carbon, mid-carbon, high-carbon) were obtained.
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