Incentive-driven transition to high ride-sharing adoption

Ride-sharing—the combination of multiple trips into one—may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people’s willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and another one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that even a moderate increase in the financial incentives may have a disproportionately large effect on the ride-sharing adoption of individual user groups.

Optimization of Demand-Response-Based Intelligent Home Energy Management System with Binary Backtracking Search Algorithm

In many nations, limited power from providers and an increase in demand for electricity have created new opportunities that can be used by home energy management systems (HEMSs) systems to enforce proper use of energy. This paper presents a virtual intelligent home with demand response (DR) model home appliances that have an inverter air conditioner, water pump, washing machine, and inverter refrigerator. A binary backtracking search algorithm (BBSA) is proposed to introduce the optimal schedule controller. With the proposed BBSA schedule controller, the highest energy consumption during DR can be reduced by 33.84% during the weekends and by 30.4% daily during the weekdays. The results indicate the effectiveness of the proposed HEMS. Additionally, the model can control the appliances and maintain total residential energy consumption below the defined demand limit.

Peak Time Load Management in Domestic Sector

The electricity demand is increasing day by day and leads do increase in generation and power transmission. India consumes about 3.4% of world energy consumption. The energy demand has grown at the average of 3.6% per annum over the past 30 years. Usually the demand for the electricity increases abruptly during peak hours. So it is necessary to monitor the loads during peak hours in order to provide energy sufficiently. The government has taken steps to reduce power consumption in industrial sector. Demand reduction can also possible in domestic sector. This Paper presents a load management model for domestic houses. Every house has a maximum demand limit for essential loads. During peak hours, if the consumer consumes power more than the preset limit,, based on the priority of load the power supply to the load will be turned off automatically and by using the cloud, the data can be visualized and analyzed. The power consumption during peak time is updated in cloud using NODEMCU and Thingspeak. The information can also be used for load forecasting

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Rule Base Home Energy Management System Considering Residential Demand Response Application

The increasing number of consumer and household appliances causes the rise in home energy use. Therefore, home energy management (HEM) technology is essential to manage and reduce electricity consumption. The objective of this paper is to present an intelligent algorithm for HEM using rule base technique to manage the power consumption with demand response (DR) feature. The scheduling algorithm considers household loads according to the comfort level, customer preference setting and priority of appliance that can be managed at a given time. The algorithm guarantees the total power consumption to be below the electrical demand limit. To exhibit the performance of the proposed HEM, a number of simulations are carried out including DR signal from the network operator. The results show that the algorithm can effectively respond to DR signal, comfort level, customer preference setting and priority of appliance. Furthermore, the algorithm is simple to implement and has flexibility to control the appliances.

Influence of prey species and concentration on egg production efficiency and hatching success in Acartia tonsa Dana (Copepoda, Calanoida)

We measured ingestion rate (IR), egg production rate (EPR) and egg hatching success (EHS) at increasing prey concentrations and calculated egg production efficiency (EPE) and maintenance rate (MR) in the estuarine copepod Acartia tonsa fed three different algal diets. EPR and EHS were relatively more affected by prey species than by prey concentration. EPEs were constant among carbon concentrations (C) on a diet of Rhodomonas baltica (0.202 ± 0.055, mean ± SD) and Dunaliella tertiolecta (0.034 ± 0.015), but decreased significantly from 0.371 ± 0.062 (mean of two lowest prey concentrations) to 0.200 ± 0.019 at the highest concentration of Thalassiosira weissflogii. In general it seems that other requirements than C demand limit EPE and EHS in A. tonsa. The MR (IR when EPR = 0) was significantly higher on D. tertiolecta, which also yielded the lowest EPEs, and it seems that variations in maintenance requirements may have been instrumental in evoking EPE variations as well.