Path anxiety is a major problem for electric vehicles, and charging infrastructure is indispensable to solve this problem. How to guide an electric vehicle to the optimal charging facility is a problem worth studying. This paper proposes a charging user discount rebate and reservation priority strategy for large scale electric vehicles (EV) access. First of all, the response characteristics of EV users to the discounts and reservation strategies of charging stations are analyzed and the user satisfaction decision model including economic satisfaction and reservation satisfaction is established. Secondly, the charging station benefit model with the goal of maximizing the benefits of the charging station is established. Eventually, the effectiveness of the proposed model is verified by a simulation considering two types of charging stations. The simulation is solved by using genetic algorithms (GA) and the simulation results show that the strategy can effectively attract users to charge and improve the interest of the charging station, as well as improve user satisfaction.
Building stock is a key determinant in building energy modelling and policy analysis. However, official statistics on total floor area of urban residential stock in China only exist up to 2006. Previous studies estimating Chinese urban residential stock size and energy use made various questionable methodological assumptions and only produced deterministic results. This paper presents a Bayesian approach to characterise the stock turnover dynamics and estimate stock size uncertainties. Firstly, a probabilistic dynamic model is developed to describe the building aging and demolition process governed by a hazard function specified by a parametric survival model. Secondly, with each of five candidate parametric survival models, the dynamic model is simulated through Markov Chain Monte Carlo (MCMC) to obtain posterior distributions of model-specific parameters, estimate marginal likelihood, and make predictions on stock size. Finally, Bayesian Model Averaging (BMA) is applied to create a model ensemble that combines the model-specific posterior predictive distributions of the stock evolution pathway in proportion to posterior model probabilities. This Bayesian modelling framework, and its results in the form of probability distributions of annual total stock and age-specific substocks, can provide a solid basis for further modelling and analysis of policy trade-offs across embodied-versus-operational energy consumption and carbon emissions of buildings in the context of sectorwide transition towards decarbonising buildings.
This study proposed the operation planning of the battery energy storage systems (BESS) to maximize the economic value in terms of life-cycle cost considering both the electric power self-consumption and peak load reduction. Toward this end, a bi-objective optimization model was developed in consideration of the economic net profit as well as the battery aging. An economic simulation was then conducted to create a configuration of the most cost-effective operation planning. As a result of the case study, the operation with limits on selfsufficiency rate and peak load reduction could raise the self-sufficiency rate by up to 22.1% and reduce the peak load by up to 29%, while the net present value (NPV) of the BESS was US$7,067.9 lower compared to the operation without such limits. The customers of the BESS with the PV systems can maximize their economic profits and the policy makers can establish plans for economic support schemes to improve the environmental performance of the BESS with the PV system.
Many countries have introduced various renewable energy sources to reduce carbon emissions. However, installing the renewable energy system without any energy storage system often fails to consume all of the produced energy, which then ends up being wasted. This study evaluated the economic performance of the surplus electricity trading of energy prosumer through three different business models. The proposed method can help energy prosumers to determine the business model with the highest profits and to evaluate the economic feasibility of peer-to-peer electricity trading for the future improvement.
Windows are a key design element that can affect the building energy performance and occupant psychological satisfaction. While smaller windows can increase building energy performance, they can also lower occupant psychological satisfaction. Despite the importance of determining the optimal window size by considering the building energy performance and occupant psychological satisfaction and their trade-off relationship, few studies have proposed a window size that considers both aspects. To solve this problem, this study proposed the following framework capable of accounting for both aspects in determining the optimal window size: (i) experimental settings for measuring the occupant psychological satisfaction based on the window size; (ii) virtual environment creation using SketchUp, 3dsMax, and the Unreal engine; (iii) measurement of occupant psychological satisfaction using questionnaire survey; (iv) measurement of building energy performance using SketchUp and EnergyPlus; and (v) selection of the optimal window size using the Pareto optimal solution. Using the proposed framework, even non-specialists of virtual reality or energy simulations can easily measure building energy performance and occupant psychological satisfaction by SketchUp modeling. Based on the building energy performance and occupant psychological satisfaction measured as such, the optimal window size can be determined according to building usage and conditions as well as client requirements.
To control global mean temperature at the levels proposed by the Paris Agreement, energy systems need to be net-zero emission systems by mid-century. In 2014, global carbon emissions increased by only 0.5%, and China had contributed to the reduction in emissions. Therefore, a discussion of greenhouse gas emissions and air pollutant emissions in China for 2014 is important, as China is one of the world’s largest emitters. Taking Shanghai, Beijing, Tianjin and Chongqing as examples, we identified 251 power plants with a capacity of greater than 6,000 kW in these four municipalities. In addition, we calculated carbon emissions and air pollutant emissions of five types of power generators in China based on a life cycle assessment. The results illustrate that the thermal power plants and biomass power plants account for most of the emissions (including SO2, CO2, and NOx emissions, 1.87E+09 kg) in the power sector, and the emissions of new energy power generation (2.68E+06 kg) are concentrated in Chongqing. According to the analysis, emissions of CO2 markedly exceed the emissions of SO2 and NOx, with carbon emissions being 156 times that of air pollutants. These results provide new insights into the reduction of carbon emissions and air pollutant emissions for governments and stakeholders.
The city and power sectors are major contributors to global carbon emissions. However, there is insufficient research on carbon emission accounting of the urban power sector, and electricity-related carbon emission flows through regional trade are ignored. Using the IPCC method, the network approach and the multiregional input-output model, our study quantifies Beijing’s production-based, supply-based, and consumption based electricity-related carbon emissions between 2007 and 2012. The results show the following. (1) Both supply-based and consumption-based electricity-related carbon emissions were more than three times that of production-based electricity-related carbon emissions. (2) Beijing’s production-based electricity-related carbon emissions fell by 4.6% (from 1.73E+07 tons in 2007 to 1.65E+07 tons in 2012), while the supply-based and consumption-based electricity-related carbon emissions increased by 29.4% (from 5.95E+07 tons in 2007 to 7.70E+07 tons in 2012) and 7.3% (from 6.88E+07 tons in 2007 to 7.38E+07 tons in 2012), respectively. (3) The electricity produced locally in Beijing became cleaner (the carbon emission intensity of electricity Beijing produced decreased from 0.76 kg/kWh to 0.57 kg/kWh), but the carbon emission intensity of the electricity Beijing actually used after buying electricity from other provinces increased (the supplied-based carbon emission intensity increased from 0.88 kg/kWh to 0.91 kg/kWh). This study provides a framework for accounting for the electricity-related carbon emissions of different aspects of the city, which can help to allocate the environmental responsibilities between regions and improve the efficiency of China’s emission reduction policies.
In order to optimize the operation of the hot water and steam central heating systems in Tianjin Economic-Technological Development Area, China, the energy and exergy efficiencies and exergy losses of overall system have been analyzed by integrating the first and the second law of thermodynamics. The results indicate that the energy efficiencies of the hot water heating system and steam heating system are 57.10% and 89.98%, while the exergy efficiencies are only 13.89% and 41.02%. The heat losses of energy stations account for 56.98% and 89.75% and the exergy losses of energy stations account for 94.84% and 98.64% for the hot water central heating system and steam central heating system, showing that the energy station is the major part of energy and exergy destruction in the heating system. In order to improve the energy and exergy efficiencies, more attentions should be paid to improving the combustion efficiency of the boiler, and some measures should be taken to reduce energy and exergy losses, such as reducing the exhaust gas temperature, recycling condensed water and improving the hot water or steam temperatures. Besides, good thermal insulation materials should be used to reduce the heat losses of heating pipe networks.