Abstract

For the purpose of effective utilization of hot dry rock resource, a double-stage Organic Rankine Cycle (ORC) power generation system is established in present work. Six kinds of organic working fluids are selected for thermodynamic calculation. The results indicate that the evaporation temperature of double-stage ORC system affects performance parameters of system. Compared with single-stage ORC system, double-stage ORC system can improve net output power by 8.44% to 12.20%. The thermal efficiency of double-stage ORC system reduces maximally by 0.81%. The results also show that exergy efficiency is related to the type of organic working fluids. The organic working fluid flow in double-stage ORC is higher than that in single-stage ORC. When R601, R601a and R600 are used as working fluids, the required mass flow rate is small. The double-stage ORC system can effectively reduce the outlet temperature of heat source in the evaporator, ranging from 12.51 to 14.07℃.

Abstract

Collective intelligence (CI) is a form of distributed intelligence emerging from collaborative problem solving and decision making. It has the advantages of simple communication and less need of data transfer and computationally extensive central decision making systems. This work implements CI in demand side management (DSM) of a hypothetical urban area in Stockholm, created based on the representative residential buildings in the city. A simple platform and algorithm are developed for modelling CI-DSM, considering the timescales of 15min for communication and applying or disapplying adaptation measures. According to the results, CI increases the autonomy of the system and decreases the heating demand of buildings effectively, consequently increasing the demand flexibility based on climate conditions. CI results in decreasing the energy demand considerably, decreasing the total heating demand over a year by around 50%.

Abstract

In the context of the expansion of the scale of electric vehicles and the increase in renewable energy generation, smart orderly charging of electric vehicles is an effective means to improve economic and environmental benefits. Smart orderly charging of electric vehicles can not only reduce the impact of electric vehicle charging on the grid but also provide new dispatching resources for power system regulation, thereby avoiding the waste of large amounts of renewable resources, and reducing users’ charging costs and related investments in the power sector. Therefore, this paper focuses on the analysis of the basic elements of the implementation of smart orderly charging of electric vehicles, the research composition of smart orderly charging, and the smart orderly charging incentive measures based on demand-side management. The result that first identifies the user groups and characteristics of smart orderly charging of electric vehicles, and divides its business model into two stages. Secondly, the important components of the research on smart orderly charging are highlighted, such as the determination of influencing factors and the selection of charging load forecasting methods. Thirdly, smart orderly charging incentive measures based on demand-side management is proposed in terms of technology and economy, etc. And the last section summarizes the paper and looks forward to future research directions.

Abstract

A numerical study on effects of the injection direction of the pilot diesel fuel on combustion and emissions under two-stroke dual-fuel marine engine-like conditions is presented in this paper. It is found that the injection direction of the pilot fuel has significant effects on the methane start of combustion as well as flame propagation direction which leads to different heat transfer trends to combustion chamber walls and flame-wall interaction. Furthermore, the injection direction of the pilot fuel changes the methane combustion intensity which leads to different trends for emission formation.

Abstract

Private electric vehicle (“PEV”) is an environmentally friendly transportation for household, which should be further popularized in the future together with electric vehicle (“EV”). 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.

Abstract

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 “Aviation to Grid” 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%.

Abstract

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%THA～ 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.

Abstract

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.

Abstract

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 70°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 20°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.

Abstract

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.