Modification of the immediate flow field by means of an upstream circular body has been effective in the energy harvesting improvement of circular oscillators. In this empirical study, the influence of square wake on the harnessed power and efficiency of circular oscillators is investigated. The subject has not been addressed by other researchers. The results show that the square wake has Improving effects on the mechanical power of circular oscillators but deteriorates the hydroelastic efficiency. The overall energy harvesting performance of circular oscillators under nearby square wake is slightly higher than that in single configuration but is almost half of that under circular wake.
The energy consumption of carbon-based fuels production can be decrease through direct electrolytic (bi)carbonate conversion due to its lack of the energyextensive process (CO2 release, CO2 compression and production separation), compared to electrolytic gaseous CO2 conversion. In this study, life-cycle and economic assessments are performed to evaluate the energy conversion characteristics, environment impacts and economic benefits of CO production via the two pathways. The results show that the net energy input, greenhouse gas emissions and net present value of electrolytic (bi)carbonate conversion are 10.4905 GJ/(t CO2 gas), -0.6287 t CO2-eq/(t CO2 gas) and $ 42,264,560, respectively, whereas in CO production through electrolytic gaseous CO2 conversion, the corresponding values are 32.5314 GJ/(t CO2 gas), -0.2949 t CO2-eq/(t CO2 gas) and $52,917,640, respectively. Additionally, according to the sensitivity analysis, the cell voltage and Faradaic efficiency have the maximum effects on the net energy input and net present value. The greenhouse gas emissions are affected mainly by the efficiency capture. This study demonstrates the prospect and provides a theoretical direction to promote the technical and economic benefits of carbon-based fuels production via electrolytic conversion from (bi)carbonate
The multi-energy ship, which is composed of renewable energy and hybrid ship, has the advantages of energy saving and emission reduction under the background of increasing global oil resourcesâ€™ tense. At the same time, structure improvement is an effective way to improve the effectiveness of energy management strategy. To the above end, based on the wind and solar power data generated by HOMER, we establish and apply the extreme scenarios of wind and solar output power. Secondly, we propose a main and auxiliary generator set structure in which the main generator bears the baseload and the auxiliary generator bears the fluctuating load. Based on the structure and dynamic programming algorithm, we establish an energy management model of multi-energy ship with the battery and generator power as the control variables and the minimum operating cost as the optimization goal. The simulation results show that the application of dynamic programming algorithm, wind-solar energy, and main and auxiliary generator set structure combined with dynamic programming algorithm can respectively reduce the ship energy consumption by 0.54%, 5.07%, and 0.80%. The results show that supposed method can effectively reduce the energy consumption of the ship.
In order to solve the problem of renewable energy consumption, this paper focuses on the study of dynamic adjustment methods of maximum transmission power capacity for the key transmission sections. Firstly, based on the current power grid company’s simulation ideas for solving cross-sectional quotas, a step-size search simulation sample generation method is proposed. Then based on the BP neural network optimized by the LM algorithm, a model that can quickly determine the transmission section quota is established. Finally, the effectiveness of the model is verified through the operating data of the Western China Power Grid. The results show that the model can fit the non-linear relationship between the generator output combination and the section transmission quota well, and has great practical value.
For the adoption of renewable energy to buildings that consume a lot of fossil fuel-based energy, the government, environmental service company, and consumers have organized for the Private-Public-Partnership (PPP) project encouraging private buildings to install renewable energy generators. However, the majority of the building owner is unwilling to install renewable energy generators to their buildings because of high initial investment cost, low rate of return, and long payback period. Most of the previous studies analyzed the economic benefits of PPP projects for adopting renewable energy generators in residential buildings, but they rarely present any decision-making model to support choosing an appropriate strategy and the optimal incentive and penalty rate. To fill the gap, this study aims to construct the decision-making model for implementing PPP projects from a three-participant perspective through evolutionary game theory. This study firstly collected background costs and benefits information related to renewable energy adoption PPP projects. Secondly, this study analyzed the evolutionarily stable strategy of each participant. Finally, the decision-making model was proposed to support choosing an appropriate strategy for accomplishing a win-win solution from each stakeholder.
To improve data center thermal management, a water cooling system based on the cooling tower of data center with thermal power of 4.8 kW is introduced. A fin-type water-cooled heat sink is taken to cool chip in the server cabinet. Aiming to realize the minimum energy consumption of the water cooling system, and the optimization analysis on the cooling water working condition is carried under different safe chip temperature with aid of TRNSYS software. The results show that when taking safe chip temperature of 70Â°C, the minimum power usage effectiveness (PUE) value of approximately 1.097 is yielded at these optimal case: inlet temperature of secondary cooling water of 20Â°C, and the primary and secondary cooling water flow rates of 9.207 L/min and 6.108 L/min, respectively. Finally, the fitting correlation equations of optimal parameters for different safe chip temperature are given to guide cooling system design.
In order to more accurately analyze electrical energy loss for the low-voltage distribution network, the feature of “virtual line resistance” is calculated based on the measured data when the topology of the distribution network and the data of a single user’s meter are unknown. Then, combined with the measured power supply and sale, current, and statistical line loss as the features, the isolated forest algorithm is used to detect the data outliers. After removing the abnormal values of the data, the regression analysis method in machine learning is adopted for a large amount of measured data to establish an actual line loss estimation model. The results show that the fitting effect for the actual line loss estimation model based on the features of the measured data is better than based on the non-measured data such as “transformer district information” in previous studies, and provides a reference of the electrical energy loss management for the low-voltage distribution network.
This paper presents a novel approach to integrating a thermal energy storage (TES) system in coal-fired power plant. Which can improve the load flexibility of coal-fired power plant(CPP) through reheat steam extraction(RSE) for increasing the power output of renewable resources into the grid. The novel system and its operation mode were described. The load flexibility was increased in charging and discharging of CPP. The 50%THA of 600MW coal-fired power plant was select as basic condition. The results show that the operating range of the CPP was widened from 34.78%THAï½ž 54.66%THA through integrating TES system and thermal energy release(TER) system. The proposal of this problem can provide some guiding ideology for peak shaving of CPP.
To decarbonise air transport sector, all-electric and hybrid-electric aircraft have advanced rapidly, particularly for small or regional electric aircraft (EA). However, the airport energy infrastructure for EA charging remains a key challenge owing to the high-power charging demand with highly-scheduled charging patterns. This paper develops an optimal airport charging infrastructure for EA. Battery swap and plug-in charging systems are proposed and compared in terms of charging schedule flexibility, costs and revenue. The novel mechanism â€œAviation to Gridâ€ is proposed to enable the bi-directional power flow interaction between power grid and EA charging system. The two alternative charging systems are implemented with different penetration levels of electric domestic flights in five case studies of London Gatwick airport. The optimal EA charging schedules with hourly generation dispatch and EA charging demand are developed. A conclusion is made that the battery swap is more economic when the EA penetration level is lower than 10%. The plug-in charge becomes a cost-effective option when the EA penetration level increases above 10%.
Private electric vehicle (â€œPEVâ€) is an environmentally friendly transportation for household, which should be further popularized in the future together with electric vehicle (â€œEVâ€). Aiming at the strategy on promoting PEV, the impact of social propaganda and subsidy policy is explored by applying Bass model to forecast the number of PEV, which is non-periodic and annual. Bass model is effective to deal with the network externalities by considering the maximum market potential, the innovation coefficient, the imitation coefficient, and the adjustment coefficient. Combining actual data of the number of EV from Annual Report of Guangzhou, China, we demonstrate that social propaganda and subsidy policy will respectively affect the increasement of PEV in the long and short term, which means proper strategy should be adopted by authority to popularize EV.