The energy consumption and CO2 emission of China’s passenger transport have been increasing in recent years. With China’s population, economic development level, passenger volume, public transportation share, private car stock, and new energy vehicle (NEV) policies developing year by year, we need a medium – and longterm model to predict the future energy demand and greenhouse gas emissions of China’s passenger transport. In this paper, we divide the passenger transport sector into inter-city and inner-city, then establish a bottom-up model using the LEAP (long-term energy planning system) platform to estimate China’s provincial passenger transport emissions up to 2050. Four scenarios, namely reference (REF), business as usual (BAU), electric vehicles promoting(EVP) are set to evaluate possible policy alternatives. The results show that the BAU scenario and EVP scenario are efficiently reduce the energy consumption and CO2 emissions. Under the BAU scenario and the EVP scenario will reduce 45% and 53% energy consumption respectively. Under the BAU scenario and the EVP scenario will reduce 78% and 91% CO2 emissions respectively. The results show that promoting the development of electric vehicles will help China to achieve the goal of low-carbon transportation.
Heat pumps will be a major player in the future energy system for their ability to efficiently extract heat from a source at lower temperature and provide it at higher temperature using electrical work. If coupled with a heat storage tank, the system can store heat and provide it at the point in time when the electricity price is more favorable.
In this study, we present a model of the heat pump energy performance using the coefficient of performance (COP) and the Lorentz efficiency. The latter gives an indication of the offset of the actual COP from the theoretical COP. We then compare different refrigerants performance to find out which one provide the best performance for district heating application. Finally, we study the cost-optimal operation of a heat pump with and without a thermal storage tank.
Results show that ammonia is a better candidate in terms of performance compared to other selected refrigerants. The performance analysis shows that the heat pump has a better Lorentz efficiency for lower COPs. Finally, we show that coupling the heat pump to a thermal storage tank can reduce the electricity cost of operation.
A new online fault diagnostic method for photo voltaic array is proposed in this paper, which is based on the Extreme Gradient Boosting (XGBoost)classifier. Firstly, the string current, array voltage, temperature and irradiance are measured by a monitoring system, from which a seven-dimensional fault feature vector is extracted as the input of the fault diagnosis model. Secondly, based on the XGBoost classifier, a new fault diagnosis model is established. Lastly, the feasibility and superiority of the proposed XGBoost based fault diagnosis model are tested by both Simulink based simulation and real fault experiments on a laboratory PV system. The correct rate of fault diagnosis in Simulink simulation is 99.99%, while the correct rate of fault diagnosis in laboratory PV power plant simulation is over 99.90%. Extreme learning machines (ELM) and Random Forests (RF) are tested for comparison. Experimental results demonstrate the superiority of the proposed XGBoost based model.
Beijing has a relative long history in new energy vehicles promotion and achieves series achievements. However, traditional fuel vehicle is still heavily outnumbered. In this paper, we build a system dynamics model to forecast Beijing’s new energy vehicles ownership from 2017 to 2022. The simulation model also helps figure out how the policy, purchase price, psychology and usage affecting private new energy vehicles ownership.
This paper presents a household battery charging and discharging game for a power supply-demand regulation in a peer-to-peer energy sharing, operating in the day-ahead electricity market. The problem is formulated as a noncooperative Nash equilibrium game where the households are considered selfish but rational players whose objectives are to optimize their individual battery state of charge and energy cost. The application of the proposed model to a practical case study of three households shows the potential of the households to regulate the electricity in the smart grid and save their energy costs. Households 1, 2 and 3 operating in the proposed model saved energy costs of up to 59.8%, 58.8% and 58.9%, respectively compared to them operating in a strictly real-time electricity market and household 1, 2 and 3 also had savings of up to 10%, 3.8% and 8.4%, respectively compared to them operating in a strictly day-ahead electricity market.
As a high-efficiency and low-carbon energy supply mode, district energy systems (DESs) have gained rapid development recently. This paper proposes a framework for energy trading among DESs, with the aim of exploiting the synergies and complementary advantages of various energy demand profiles in DESs. Blockchain technology is utilized to facilitate energy trading. The distributed algorithm based stochastic decision-making process is developed to determine the energy trading, including the transaction quantities and prices. A chance-constrained programming model is developed for dealing with the uncertainties. The illustration of the technique is provided based on a test system.
The integrated energy system is considered to beintroduced in buildings, which proposes a new effectiveapproach to improve energy structure in urban areas.The optimal design problem of building integratedenergy system is normally presented as mixed-integernonlinear programming model with deterministic anduncertainty parameters. Moreover, the uncertaintyproblem results in a more complex problem at a highcomputational cost. In present work, a two-stage multi-objective stochastic programming model underuncertainty is presented. The proposed model dependson clustering method to create different scenarios interms of solar radiation, wind speed and energy demand.In addition, the MINLP models of building integratedenergy system with stochastic scenarios anddeterministic scenarios is investigated to conduct trade-off Pareto optimization with cost-optimal andenvironment-optimal. The results indicate that thedeterministic programming model underestimates thecost and carbon emission of building integrated energysystem, while the result of stochastic programmingmodel is closer to the realistic design.
In CO2 reforming of methane solar thermochemical energy storage, the endothermic methane reforming with CO2 reaction is utilized to absorb solar energy. Although a lot of research has been done to enhance the thermochemical performance of the solar driven CO2 reforming of methane reactor, there is little research conducted investigating the geometrical effect of reactor on the reactor thermochemical performance. Moreover, the catalyst cost is anticipated to be large. Minimizing the required catalyst volume is the key to reduce the capital cost of the CO2 reforming of methane solar thermochemical energy storage system. But there is not much research investigating the geometrical effect on the catalyst volume. In this paper, a pseudohomogeneous computational model is used to simulate methane reforming with CO2 reaction in a tubular packed bed reactor. A parametric study is performed to investigate the geometrical effects of reactor on the reactor performance. The results show that methane conversion as well as outlet gas temperature increase with reactor diameter and/or reactor length increasing while the energy efficiency decreases with reactor diameter and/or reactor length increasing. There is a trade-off between increasing methane conversion and decreasing energy efficiency. As the required catalyst volume increases with reactor size increasing, there is a trade-off between increasing methane conversion and increasing catalyst volume. Another parametric study has been conducted to study the effects of reactor geometries on the required catalyst volume. The results show that the required catalyst volume can be saved by decreasing the reactor diameter due to enhanced heat transfer.
This paper proposes an investment model to analyze the economic feasibility of WtE projects in the Philippines. Applying the real options approach (ROA) under uncertainty, we compare the option values of investing in WtE technologies over continue dumping waste into the landfill. The optimization results find that incineration is the best option followed by gasification and pyrolysis considering the energy production, investment costs, and emission rates. At the current price of electricity, it is more optimal to postpone investment in pyrolysis, otherwise, the tipping should be increased to make pyrolysis a more viable option than continue the landfill. On the other hand, it is a more optimal decision to invest immediately in either incineration or gasification as waiting to invest incurs opportunity losses from generating electricity from these technologies. The paper suggests that the government must support WtE program as it will significantly contribute in solving the problems of the environment, particularly air quality, waste management, and energy security and sustainability.
In order to address the various challenges and well utilize the opportunities brought by the increasing penetration of distributed energy resources at the demand side of power systems, a new paradigm, peerto-peer (P2P) energy trading, has emerged in recent years, where prosumers and consumers are able to directly trade energy with each other. Besides the inherent potential benefits such as facilitating local power and energy balancing, a P2P energy trading community as a whole also has the potential to provide ancillary services to power systems to create additional value. In this paper, a price-based mechanism was proposed, in which the customers of a P2P energy trading community can further respond to the price signals issued by power utilities to provide ancillary services such as demand reduction and generation curtailment. A continuous double auction with a residual balancing mechanism was proposed as the P2P energy trading mechanism. Simulation results verify that the proposed mechanisms are able to increase the social welfare of the whole P2P energy trading community without compromising any individual’s interests, and at the same time incentivize customers to provide ancillary services to power utilities.