On Some Issues of Assessing the Efficiency of Renewable Energy Power Plants and the Share of Renewables in the World＇s Electricity Generation

The paper considers some issues related to the evaluation of power plants using renewable energy sources： energy efficiency, economic efficiency, the share of renewable energy in the world＇s electricity generation. At one time in the world there was a myth that more energy is expended on the construction of wind and photoelectric power stations, than they produce for the service life. Adherents of this myth are still found in Russia. In response to this myth, numerous studies have been carried out for the manufacturers of wind turbines and photovoltaic modules. It was proved that these power plants spent energy on them are produced within a period of less than a year and the energy consumed by them cannot be taken into account, since it is renewable. The author showed that power plants on organic fuel and existing nuclear plants using depleted fuel with a coefficient less than unity fundamentally cannot compensate for the energy used during their construction. In the world, the concept of the LEC （Levelized Energy Cost） produced by any power plant is widely used to estimate economic efficiency. However, the formula for determining it, in the author＇s opinion, contains an inaccuracy, which is proposed to be eliminated. At present, there are different opinions on the role of RES （Renewable Energy Sources） in the production of electricity. A summary indicator is the share of renewable energy in the world＇s electricity generation. The determination of the actual share of RES and the forecast of its growth is of significant importance for the development of the world economy. The author shows the differences in the estimates and suggests an approach for establishing agreed estimates.

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-station status judgement-vehicle relocation”multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study.In stage one,a novel trip demand forecast model based on the long short-term memory network was established to predict users'car-pickup and car-return order volumes at each station.In stage two,a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station.Then vehicle-surplus,vehicleinsufficient,vehicle-normal stations,and the number of surplus or insufficient vehicles for each station were counted.In stage three,setting driving mileage and carbon emission as the optimization objectives,an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi.Setting 43 stations and 187 vehicles in Jiading District,Shanghai,China,as a case study,results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’car-pickup and car-return demands could be fully satisfied without any refusal.

Travel Intention with Shared Electric Vehicles Based on Theory of Multiple Motivations for Urban Governance

Determining the travel intention of residents with shared electric vehicles(EVs)is significant for promoting the development of low-carbon transportation,considering that common problems such as high idle rate and lack of attractiveness still exist.To this end,a structural equation model(SEM)based on the theory of multiple motivations is proposed in this paper.First,the influencing motivations for EV sharing are divided into three categories:consumer-driven,program-driven,and enterprise-driven motivations.Then,the intentions of residents in Shanghai to travel with shared EVs are obtained through a survey questionnaire.Finally,an SEM is constructed to analyze quantitatively the impact of different motivations on the travel intention.The results show that consumer-driven motivations with impact weights from 0.14 to 0.63 have the overwhelming impact on travel intention,compared to program-driven motivations with impact weights from−0.14 to 0.15 and enterprise-driven motivations with impact weights from 0.02 to 0.06.In terms of consumer-driven motivations,the weight of green travel awareness is the highest.The implications of these results on the policy to enable large-scale implementation of shared EVs are discussed from the perspectives of the resident,enterprise,and government.