Electric Power Material Logistics Distribution Based on Improved Multi-Population Particle Swarms

In view of the problem that the information requirements of electric power supplies logistics distribution are increasingly high, this study proposes the research of electric power supplies logistics distribution based on improved multi-population particle swarm. Firstly, the simulated annealing algorithm is introduced in detail, and then the particle swarm optimization (PSO) is improved by using the domain extension method. Then the multi-population particle swarm optimization was carried out through the optimization of coding system, the updating speed formula and the algorithm convergence control. Finally, the optimization effect was verified. Final results show that, this study put forward based on improved particle swarm more population of electricity supplies logistics distribution model has feasibility and suitability, through the optimized parameters and algorithm of the model, to identify the logistics network of electric power supply company the best optimization model, thus reducing power cost of inventory and logistics distribution time, so as to improve the efficiency of logistics distribution.

Energy, Exergy, Exergoeconomic and Exergoenvironmental Impact Analyses and Optimization of Various Geothermal Power Cycle Configurations

Energy, exergy, and exergoeconomic evaluations of various geothermal configurations are reported. The main operational and economic parameters of the cycles are evaluated and compared. Multi-objective optimization of the cycles is conducted using the artificial bee colony algorithm. A sensitivity assessment is carried out on the effect of production well temperature variation on system performance from energy and economic perspectives. The results show that the flash-binary cycle has the highest thermal and exergy efficiencies, at 15.6% and 64.3%, respectively. The highest generated power cost and pay-back period are attributable to the simple organic Rankine cycle (ORC). Raising the well-temperature can increase the exergy destruction rate in all configurations. However, the electricity cost and pay-back period decrease. Based on the results, in all cases, the exergoenvironmental impact improvement factor decreases, and the temperature rises. The exergy destruction ratio and efficiency of all components for each configuration are calculated and compared. It is found that, at the optimum state, the exergy efficiencies of the simple organic Rankine cycle, single flash, double flash, and flash-binary cycles respectively are 14.7%, 14.4%, 12.6%, and 14.1% higher than their relevant base cases, while the pay-back periods are 10.6%, 1.5% 1.4%, and 0.6% lower than the base cases.

Improvement of Working Conditions and Opinions of Mine Workers When Battery Electric Vehicles (BEVs) Are Used Instead of Diesel Machines — Results of Field Trial at the Kittilä Mine, Finland

A major part of the European Union’s (EU) project Sustainable Intelligent Mining System (SIMS) is investigating the development of diesel-free/carbon–neutral underground mines in order to ensure sustainable underground mining in the future. Replacing diesel machines with electric vehicles in underground hard rock mines has been widely acknowledged by the mining industry worldwide as a critical step to improve working conditions by reducing diesel exhaust–related contaminants, to reduce mine ventilation electrical power cost by reducing mine airflow quantity, and to reduce mine greenhouse gas emissions. All of these are major requirements to achieve sustainable future underground mining practices. A field trial of Epiroc’s 2nd generation of Battery Electric Vehicles (BEVs) at Agnico Eagle Finland’s Kittilä mine was conducted during 2019–2020. Vehicles tested were MT42 mine truck, ST14 Load-Haul-Dump (LHD), and Boomer E2 jumbo drill rig. This paper outlines the improvement of the working conditions observed in the field trial, and the opinions of the mine personnel at Kittilä mine on using BEVs instead of diesel machines. Measurements of atmospheric contaminants and air temperatures taken during the field trial clearly demonstrated a significant improvement of working conditions when BEVs were operating as opposed to diesel machines. This field observation was supported by the opinion of the majority of the Kittilä mine workers. However, some remaining concerns must be addressed before BEVs can replace diesel machines.

Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network

In this paper, an optimal sizing and placement framework (OSPF) is performed for electric parking lots integrated with wind turbines in a 33-bus distribution network. The total objective function is defined as minimizing the total cost including the cost of grid power, cost of power losses, cost of charge and discharge of parking lots, cost of wind turbines as well as voltage deviations reduction. In the OSPF, optimization variables are selected as electric parking size and wind turbines, which have been determined optimally using an intelligent method named arithmetic optimization algorithm (AOA) inspired by arithmetic operators in mathematics. The load following strategy (LFS) is used for energy management in the OSPF. The OSPF is evaluated in three cases of the objective function such as minimizing the cost of power losses, minimizing the network voltage deviations, and minimizing the total objective function using the AOA. The capability of the AOA is compared with the well-known particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms for solving the OSPF in the last case. The findings show that the power loss, voltage deviations, and power purchased from the grid are reduced considerably based on the OSPF using the AOA. The results show the lowest total cost of energy and also minimum network voltage deviation (third case) by the AOA in comparison with the PSO and ABC with a higher convergence rate, which confirms the better capability of the proposed method. The results of the first and second cases show the high cost of power purchased from the main grid as well as the high total cost. Therefore, the comparison of different cases confirms that considering the cost index along with losses and voltage deviations causes a compromise between different objectives, and thus the cost of purchasing power from the main network is significantly reduced. Moreover, the voltage profile of the network improves, and also the minimum voltage of the network is also enhanced using the OSPF via the AOA.

A Review on Drive Train Technologies for Passenger Electric Vehicles

Transportation is the second-largest sector contributing to greenhouse gas emissions due to CO2 gas generation from the combustion of fossil fuels. Electric vehicles (EVs) are believed to be a great solution to overcome this issue. EVs can reduce CO2 emissions because the vehicles use an electric motor as a propeller instead of an internal combustion engine. Combined with sustainable energy resources, EVs may become zero-emission transportation. This paper presents an overview of the EV drive train types, including their architecture with the benefits and drawbacks of each type. The aim is to summarize the recent progress of EV technology that always continues to be updated. Furthermore, a comparative investigation on energy density and efficiency, specific energy and power, cost, and application is carried out for batteries as the main energy storage. This discussion provides an understanding of the current development of battery technology, especially the batteries used in EVs. Moreover, the electric motor efficiency, power density, fault tolerance, reliability, and cost are also presented, including the most effective electric motor to use in EVs. The challenges and opportunities of EV deployment in the future are then discussed comprehensively. The government regulation for EVs is still a major non-technical challenge, whereas the charging time and battery performance are the challenges for the technical aspect.

An energy-efficient pathway to turbulent drag reduction

Simulations and experiments at low Reynolds numbers have suggested that skin-friction drag generated by turbulent fluid flow over a surface can be decreased by oscillatory motion in the surface, with the amount of drag reduction predicted to decline with increasing Reynolds number. Here, we report direct measurements of substantial drag reduction achieved by using spanwise surface oscillations at high friction Reynolds numbers ($${{{\mathrm{Re}}}_{\tau }}$$ Re τ ) up to 12,800. The drag reduction occurs via two distinct physical pathways. The first pathway, as studied previously, involves actuating the surface at frequencies comparable to those of the small-scale eddies that dominate turbulence near the surface. We show that this strategy leads to drag reduction levels up to 25% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 6,000, but with a power cost that exceeds any drag-reduction savings. The second pathway is new, and it involves actuation at frequencies comparable to those of the large-scale eddies farther from the surface. This alternate pathway produces drag reduction of 13% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 12,800. It requires significantly less power and the drag reduction grows with Reynolds number, thereby opening up potential new avenues for reducing fuel consumption by transport vehicles and increasing power generation by wind turbines.