Distributed energy systems (DESs) exhibit potential to promote the energy market reform worldwide. In this study, a bi-level optimization model is proposed to analyze the operation of a DES that purchases high-voltage electricity and natural gas from utility companies, and supplies low-voltage electricity and heat to multiple users. To simplify the resolution process, the bi-level optimization is transformed into a single-level mixed integer linear programming model using the Karush-Kuhn-Tucker approach and Big M method. The results indicate that (i) the time-sensitive energy prices offered by the DES could smoothen the load profiles of users; and (ii) the tiered pricing scheme set up by the utility companies could maximize the utility of the ESS integrated into the DES, but the capacity of the ESS should be accurately designed to fit the corresponding pricing scheme.
Cross Laminated Timber (CLT) is attracting worldwide attention, due to its durability, usability, and many other advantages. However, since CLT is made of wood, analysis of the hygrothermal performance is essential. In this study, the various conditions that affect the thermal moisture behavior were applied to the simulation for getting stable hygrothermal results. As a result, the standard of Passive house and Domestic wooden house, the climate condition, the presence of breathable water proofing paper, and the insulation alternatives of Expended Polystyrene (EPS) and Extruded Polystyrene (XPS) were applied. It was concluded that breathable waterproofing paper should be installed inside, and the applications of both XPS and EPS made no difference to moisture but the application of Mineral Wool was adversely affect to hygrothermal performance of the CLT wall system. The thickness of insulation should be designed according to the Passive house standard (0.15 W/m 2 K) rather than the Domestic (Korea) Wooden house standard (0.21 W/m 2 K).
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.