To mitigate greenhouse gas emissions from the transportation and electricity sectors, a large-scale adoption of battery electric buses (BEBs) and photovoltaic solar energy is planned in the upcoming decades. Nevertheless, the integration of these technologies may result in a mismatch between electricity demand and supply. This paper addresses these challenges by proposing a generic framework of different technologies that involves calculating the energy consumption of battery electric buses, sizing the photovoltaic charging system, and scheduling bus-to-grid integration. The energy consumption of a large-scale BEB network is calculated by applying well-to-wheel (WTW) assessment and combining the Geographical Information System combined with the longitudinal dynamic model. Particle swarm optimization is also applied to size the charging system. A voltage profile based on a typical residential or commercial load profile is required in scheduling the energy storage of BEBs, support load power balancing, and regulate the voltage and frequency of the power grid. Real-world data on the Rapid Penang bus network of Malaysia, Space Shuttle Radar Topography Mission, and Malaysia Representative Network are used to validate the proposed framework. This framework provides the necessary groundwork for a further examination of charging infrastructure requirements, photovoltaic charging sizes, battery sizes, and bus-to-grid technology scheduling.
Smog has become the most serious climate problem in North China in recent years. Coal burning has been found as the key factor to generate smog. Hebei province has started to stop coal burning in winter, however, there should be an efficient warming way for the millions of people in the cold North China. Heat pump is a high energy-saving technique and could be a good solution for Hebei warming. This paper compared different heating systems, and the result shows that heat pumps can bring the obvious energy-saving as well as the smog-reducing.
The intermittence and site-dependence of renewables requires the efficient systems to storage the surplus wind/solar electricity. Substitute nature gas (SNG) is a promising energy carrier which facilitates existing pipeline transportation. This work proposed a novel efficient SNG production system, in which the high temperature electrolysis combined with oxygen/steam biomass gasification is adopted. In this proposed system, the O2 produced by electrolyzer is sent to the gasifier directly and the air separation can be eliminated. Besides, through the coexistence methanation process, the conventional water gas shift process and CO2 capture consumption will be avoided. The electrochemical model of SOEC is established and the whole SNG production system is simulated by Aspen plus. Parametric and energy/exergy analyses are conducted and the results shown that the thermal efficiency of the novel plant can reach 77.4%, which is 15 percentage points higher than the traditional SNG production pathways. This work provide a new method to realize high energy conversion efficiency and reduce the wind/solar energy curtailment.
In this paper, the thermal performance of an all SiC 650 V, 116 A MOSFET power module has been studied by numerical simulation. Aiming at the heat dissipation problem of the SiC power devices in the inverter, three integrated liquid cooling heat sinks with different channel structures were designed. The results showed that compared with the other two cooling structures, the serpentine flow channel cooling structure can provide the best cooling effect. For the serpentine flow channel cooling structure, the heat source maximum temperature increases as the fin thickness increases, and the pressure drop decreases as the fin thickness increase when the coolant flow rate is constant. The improved heat sink has been numerical tested up to 110 A showing the device maximum temperature of 39.8Â°C, which can well meet the design requirements.
Waste-to-energy is one of the feasible approaches to reduce the dependence of fossil fuel. Sewage sludge and food waste, as main municipal wastes from water and food consumption, have potential to be converted into energy (methane and biochar) to alternative non-renewable energy. In the present study, sewage sludge was hydrothermally treated to improve its dewaterability. The separated filtrate and food waste were applied for mesophilic anaerobic digestion for biogas generation with methane yields of 253 and 510 mL/gVS, respectively, at organic loading of 20 gVS/L. The food waste digestate was used for co-pyrolysis with separated filter sludge cake for improving flammable gas, oil, and biochar generation. Additionally, Cu and Zu in biochar were significantly immobilized after co-pyrolysis, and higher pyrolysis temperature and more digestate addition led to better immobilization performance. In brief, the present work paves the path for friendly treatment of sewage sludge and food waste with energy recovery and improved biochar production, and provides valuable guidance for the operation of our pilot-scale and full-scale reactors to realize the energy self-sufficiency and net energy generation.
Distributed energy planning is a complex issue, and the non-dominated sorting genetic algorithm (NSGA-II) is widely employed for the system multi-objective optimization. This algorithm screens the intermediate population based on the fixed crowding distance principle, it does not consider the dynamic crowding change and cannot satisfy the diverse search requirements of solution space in different evolutionary periods. In this paper, an improved NSGA-II method based on dynamic crowding distance and information entropy is proposed. Then a case study of a wind-solar integrated microgrid system is implemented, by refereeing the local meteorological conditions and power loads in Yunnan of China, the renewable energy system under study is optimized in terms of the system energy efficiency, energy volatility and net present cost. Results indicate that the energy system achieves a lower cost after optimization by the improved NSGA-II method, and the matching degree of renewable energy generation and electricity demand is evidently enhanced which means a better system operation stability. Comparing to the general NSGA-II, the improved algorithm also has superiority in convergence speed, and the research findings provide an alternative method for optimizing the renewable microgrid system.
This paper constructs a new directed limited penetrable interdependent network (DLPIN) for the thermal coal price (TCP) between the opening series and closing series based on the criterion of the visibility graph, which is better than the traditional method of the visibility graph to mine the price information for steam coal. According to the DLPIN by analyzing, the mechanism of the price fluctuation and information transmission for the thermal coal can be obtained, some references can be provided for the investors to reduce risk investment and increase revenue.
The promotion of new energy vehicles (NEVs) is in line with China’s eco-civilization strategy and can help China realize the transformation from a big automobile country to a powerful automobile country. For the support policies of the NEV industry, the means of policy suggestions by which previous studies put forward lack pertinence to different time stages and effective objects. In this study, we present a two-dimensional framework of “policy instruments and value chain”, that divides policies instruments into three types of supply, demand and environment and characterizes the industrial value chain as technology research, enterprise development and sales volume of enterprises, and apply the framework to analyze the effect of NEV industrial policies on a typical NEV enterprise in China. Some conclusions are drawn as follows: (1) although the support policies are criticized, the core aim of them is to build a healthy market; (2) policies have a great role in promoting the development of the enterprise, which is indicated by the consistency of the turning point of data between the enterprise and policies; and (3) the correlation between enterprise data and policy data is not obvious as there are many stakeholders, which makes policies have both direct and indirect effects on enterprises.
Solar energy is a significant and fast-growing source of low-carbon electricity. The usual means of utility-scale solar farm condition monitoring is limited by poor measurement accuracy and low-resolution data collection rates. A micro-synchrophasor measurement unit (ÂµPMU) has been adapted and integrated with a power quality monitor (PQM). This apparatus provides the high-resolution, high-precision, time-stamped data needed by analysts to make solar farms more cost-effective and to better understand decentralize grid behaviour. The resulting big data necessitates applying machine learning (ML) for automatic event forecasting, fault detection, and site maintenance. The limited availability of data knowledge, data volume, and performance issues drives the exploration of data-driven based unsupervised ML methods on this occasion. Clustering Large Applications based upon RANdomized Search (CLARANS) algorithm has been employed owing to its suitability to categorise events from the big data. CLARANS has been performed to recognize inefficient voltage phase unbalance. The voltage and current waveforms and related issues such as, voltage dip or voltage sag and phase imbalance events have been considered among multiple data streams and various power distribution issues for this investigation. Ten consecutive days of empirical data have enabled this research. Altogether, 250.92 million power quality data points have been tested and validated.
Electricity constitutes an indispensable source of secondary energy in modern society. Accurate and robust short-term load forecasting is essential for more effective scheduling of load generation, minimizing the gap between generation and demand, and reducing electricity waste. This study proposes a theory guided deep-learning load forecasting (TgDLF) framework to predict the future load through load ratio decomposition, in which dimensionless trends are obtained based on domain knowledge, and the local fluctuations are estimated via data-driven models. The historical load, weather forecast and calendar effect are considered in the model, and the modelâ€™s robustness to inaccurate weather forecast data is improved by adding synthetic disturbance during the training process. Experiments demonstrate that TgDLF is 23% more accurate than LSTM, and the TgDLF with enhanced robustness can effectively extract information from weather forecast data with up to 40% noise.