Peak-Hour Pricing Under Negative Externality: Impact of Customer Flexibility and Competitive Asymmetry

Several industries that provide services to customers (e.g., public utility and transportation) charge higher prices during peak hours to smooth demand. With technologies (e.g., electronic shelf labels) enabling retailers to change prices easily within each day, should supermarkets use peak-hour pricing? To examine this question formally, we introduce a stylized duopoly model in the presence of “negative externality,” where firms compete for congestion-averse customers. We characterize how customers endogenously segment themselves regarding when and where to shop, and then use the equilibrium outcomes to examine whether the firms should implement peak-hour pricing for varying types of customer flexibility and competitive asymmetry. Our analysis shows that, if customers are not flexible in their store choice, then both firms would always use peak-hour pricing. However, if store choice flexibility is present, then firms’ decisions depend on the competitive asymmetry as follows. If one firm has a clear competitive advantage (in terms of value or price) over the other firm, then the dominant firm will use peak-hour pricing, whereas the other firm will not. Otherwise, both firms will use peak-hour pricing if they engage in symmetric competition (in terms of similar value and price), or neither firm will use it if they engage in differentiated competition (high value versus low cost). Through our analysis of different extensions, we find that a firm’s ability to set its regular price would dampen the effect of peak-period pricing. Also, we obtain consistent results when there is heterogeneity in customer valuation and customer congestion aversion level

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances

Effect of Water-Cement Ratio on Pore Structure and Strength of Foam Concrete

Foam concrete with different dry densities (400, 500, 600, 700, and 800 kg/m3) was prepared from ordinary Portland cement (P.O.42.5R) and vegetable protein foaming agent by adjusting the water-cement ratio through the physical foaming method. The performance of the cement paste adopted, as well as the structure and distribution of air pores, was characterized by a rheometer, scanning electron microscope, vacuum water saturation instrument, and image analysis software. Effects of the water-cement ratio on the relative viscosity of the cement paste, as well as pore structure and strength of the hardened foam concrete, were discussed. Results showed that water-cement ratio can influence the size, distribution, and connectivity of pores in foam concrete. The compressive strength of the foam concrete showed an inverted V-shaped variation law with the increase in water-cement ratio

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness