Rapid development of electric vehicles (EVs) has a negative impact on the operating and planning of the power systems, and it is important to accurately simulate the EV charging load when large amount of EV charging demands are connected to the grid. In this paper, an EV charging load simulation method is proposed based on the users’ daily travel mode. Different from most of the previous works, this paper adds additional consideration of the charging preference, location type, day type and power consumption rate to improve the simulation accuracy. First, probabilistic distribution models for many defined spatialtemporal variables are established under refined conditions to improve the modelling accuracy, and the models include Burr Type XII, lognormal, generalized extreme, Weibull and Stable distribution, etc. Second, considering additional influential factors, daily profile of EV charging load is simulated based on the established distribution models and Monte Carlo algorithm. To take the public data from the US National Household Travel Survey (NHTS) as example, the proposed method is validated. The results show that the proposed method can provide more realistic daily curve of the charging load with no requirements on the historical charging data. And, the consideration of refined modelling conditions and additional influential factors can improve the accuracy of distribution models and the load simulation.
Methane is a short-lived climate pollutant responsible for approximately 20% of anthropogenic greenhouse gas emissions. Reducing methane emissions from the oil and gas (O&G) industry is considered among the most urgent and actionable measures to mitigate climate change. Recent reports suggest a large fraction of upstream O&G methane emissions result from a small number of super-emitter facilities, emphasizing the value of novel methods that inspect O&G facilities with greater frequency than is practical using existing techniques. Here we described an optimized method wherein O&G facilities are inspected for emissions at high frequency and high sensitivity using active laser (LiDAR) sensors mounted to aircraft. The method relies on a hierarchical clustering and routing procedure to establish optimal routes to be flown by aircraft departing from local airports and equipped with LiDAR methane sensors. Routes were optimized to inspect all well sites subject to emissions regulation in three O&G intensive regions: the Permian basin, the state of Colorado, and the state of Pennsylvania. While some cost estimates require additional field data, these modeling results suggest the optimized inspections can be performed with comparable effectiveness and up to a factor of six lower cost per inspection compared to current detection methods. This modeling exercise suggests that optimized routing may enable frequent inspection of upstream O&G facilities at large scale and potentially lead to a significant decrease in both anthropogenic methane emissions and compliance costs borne by industry.
To better account the contribution of various industries around the world to embodied carbon emissions, this study proposes a new accounting method from a global perspective. Unlike traditional accounting methods, our proposed accounting method considers all industries in a country as a whole. The embodied carbon emissions of the country are first accounted for and distributed to each sector within the country according to the contribution of each industry to the country’s inherent carbon emissions. To achieve this goal, we propose a network model and algorithm for the global embodied carbon network. Based on our proposed models and algorithms, we recalculated the contribution of inherent carbon emissions in various industries. Unlike traditional accounting results, we find that supportive industries such as education, health care, and public services contribute a lot to embodied carbon emissions. Not just embodied carbon-related research, our accounting methods, and NoN models provide a model basis for other research, such as international trade and global input and output.
Basedon the detailed mechanismmodel of the methane combustion, the kinetic parameters of the global reaction, such as pre-exponential factors and activation energy, are modeled and solved. The variations of concentration of the radicals and components over time are shown for certain mechanism models, and that for global models as well. The algebraic relationship deduced from elementary reactions to global reactions are also discussed. By the method proposed, the dynamic parameters of the global reaction from essential mechanism model were drawn and compared with those from literature. Correspondingly, this paper proposed an approach to achieve the kinetic parameters of certain global reaction, in application, from availablemechanismmodels.
With rapid urbanization and climate change, urban physical environment and energy demand are drawing greater attention. This paper conducts an empirical study on the new urban core zone of Jinan, China. Under the greater background of replacing old growth drivers with new ones, as the capital of Shandong Province, Jinan constructs a Prior Zone in this regard. This core zone occupies an area of 13km2 and will be built into a people-oriented green new town. In the urban design process, the author conducts a simulation analysis on urban physical environment and proposes optimization suggestions on urban design based on analysis results to eventually ensure construction of a new green and habitable urban area.
With multiple energy sources, diverse energy demands, and heterogeneous socioeconomic factors, energy systems are becoming more and more complex and multifaceted. Therefore, it is also becoming more and more challenging to improve the efficiency, security and sustainability for such complicated systems. To address these challenges, we propose a general optimal dispatch model for integrated energy systems. Two interesting and challenging decisions of any such model is how it takes power curves of equipment into account and how it deals with nonlinearity. We use a Gaussian Process (GP) to estimate the dynamic power curves, and then we linearize the nonlinear program using special ordered sets of type 2 and solve it using CPLEX. To demonstrate the practicality of the proposed approach, we combine real world and simulated scenarios to perform extensive experiments.
This paper presents the Sustainable Campus project at UNICAMP – Brazil, started in January 2017. With a year in progress, the project introduces unexpected results and beyond the initial proposal. The organization structural change and the academic community support contribute to new advances in management, continuous improvement, multi professional participation and new public, private and social partnerships. Thus, the results shown here will serve as a guide for future projects and important points for sustainability development on university campus.
This article approaches the consumption disaggregation methodologies for electricity bill breakdown, that aims to promote the user’s conscious consumption. That is, by accessing information about equipment use and consumption, consumers will become active agents in residential energy efficiency. The electric charges monitoring can be performed in several ways, however, regardless the used model, by analyzing the load curves of each equipment, through direct or disaggregated measurement (NILM), it is possible to understand the consumers and equipment behavior. This is, their electrical signatures, when compared to a standard, may indicate inefficient use or malfunction, by obsolescence or by default. Therefore, this paper concludes that the use of load disaggregation may go beyond its initial proposal to electricity bill breakdown, but to study behaviors of people and equipment in order to improve energy efficiency.
This paper studies a distinct perspective of a nano-grid to provide water needs at a survivable level for people with limited access to both electricity and drinkable water. It is shown that by considering the essential requirements of colonias, a portable treatment facility supported by a few photovoltaic (PV) panels and water storage tanks can provide the bare minimum living standards. This set up is defined as the survivable nanogrid. Given the primitive conditions, the proposed sizing and operation of the nanogrid leverages the flexibility of water filtration energy needs to compensate the fluctuating solar PV generation. The case study based on a water samples and historical weather data for targeted colonias in Texas suggests the unique benefits of joint optimization of both energy and water needs. It is shown that power generated by as little as 20 PV panels in each colonia can provide drinkable water for as many as 200 people in a sustainable and cost-effective manner. This finding is potentially generalizable towards for many other under-developed remote communities.
A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performance and outcomes of different methods. To fill this gap, this paper selects two benchmarking methods— multiple linear regression (MLR) based on Energy Star, and stochastic frontier analysis (SFA) to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency and explanatory ability of two methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency scores obtained from these methods. The results show that even though the ranks of sampled buildings are basically consistent, the energy efficiency scores have significant differences especially for the buildings with low energy efficiency scores. Furthermore, we discuss the explanatory ability of each method. In addition to building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption.