Solid oxide fuel cell (SOFC) was integrated with internal combustion engine (ICE) due to its high operating temperature for improving the energy conversion efficiency in this work. In the SOFC-ICE hybrid energy conversion system, the SOFC anode off-gas with high temperature and combustible fuels is used as the ICE fuel for additional power generation. To evaluate the thermo-economic performance, the thermo-economic model of the hybrid system is developed for the economic analysis. The results showed that the specific electric energy cost (SEEC) of the hybrid system is 3.75 ￠/kWh, which is lower than that of a standard power plant. Through the further analysis, the capital investment cost of the system with the net output power of 470 kW is 50.5 k$, which includes the capital cost of auxiliary devices such as heat exchanger and compressors. In addition, the SOFC accounts for about 35% of the system’s capital cost. The annual cost of the system under the cycle life of 10 years is calculated to be approximately 15.6 k$. Moreover, the influence of operating parameters on thermo-economic performance of the hybrid system is also investigated to optimize the thermo-economic performance. Finally, the corresponding payback period of this system is approximately 6.8 years and the annual return on investment is 4.6%. These results reveal that the proposed NG-fueled SOFC-HCCI engine hybrid system presents a broad market prospect in the practical applications.
Based on the high-potential 5G network and ubiquitous power Internet of things, the concept of smart energy has been proposed to solve the problem of optimal utilization of the power system’s generation, transmission, distribution, consumption, and the related services. Therefore, innovation and reformation will be doomed and embraced by smart energy in China’s energy system, which includes technological progress and system mechanism reform. In the meanwhile, new solutions and challenges would be provided by smart energy for the State Grid Corporation of China’s business model. Focused on the analysis of smart energy marketing strategy and profit model, this paper lists the business model and scenarios under the developing tendency of smart energy and provides a detailed sorting and analysis for the smart operation and management of user service and ecological platform. This article aims to analyze the value of smart energy and provides possible business model choices for integrated energy suppliers.
Faced with ever-increasing environmental pollution and public concerns about energy security, urban energy systems (UESs) need to be constructed to improve the efficiency and reliability of energy utilization. However, due to significant impacts of extreme weather on the operation of UESs, it is important to develop a model to evaluate the resilience of UESs. In this paper, a synthetic model is proposed to quantify the impacts of windstorms on the resilience of UESs. Firstly, the optimization model of UESs under contingency states is developed to determine the generation re-dispatch and load shedding. Moreover, considering the effects of high winds, the line fragility model is utilized to calculate the unavailability of power lines according to the surrounding wind speeds. On the basis, the simulation framework for the resilience evaluation of UESs is developed utilizing Monte Carlo Simulation (MCS) technique to quantify the impacts of high winds on the UESs. Finally, the proposed methods are validated using the urban energy test systems.
A reliable prediction of energy consumption is crucial for a reasonable building energy management. Considering the uncertain principles of annual electricity consumption with limited datasets, a modified grey interval prediction model abbreviated as BOGIM(1,1) is proposed in this paper. Firstly, the changing patterns of annual series were detected, in order to lower the uncertainty. Afterwards, the predicted intervals were obtained with modified BOGIM(1,1), in which various weakening and enhancing buffer operators were added simulate different future operation scenarios. Finally, the adaptability of this model is summarized based on recognized patterns and predicted accuracy. Specifically, 92 office buildings in Beijing of China were adopted to test the BOGIM(1,1) model. Results show that this proposed model outperforms the traditional GM(1,1) by improving the prediction accuracy for almost 90% of the buildings up to 18.45%, and it is more applicable for target-oriented energy policies.
Bioenergy with carbon capture and storage (BECCS), as a negative emissions technology, plays an increasingly vital role in the low-carbon energy systems. Urban wastes are the fastest-growing bioenergy resources in recent years. This study aims to provide a high-resolution spatial assessment of GHG mitigation potentials for using urban wastes as the resource for BECCS in China towards 2030. For this evaluation, the domestic urban wastes potential in a 2017 baseline year and three waste to energy (WTE) processes are calculated and proposed. Results show that the collectable potential of urban wastes in 2017 was 1026.53 PJ and its utilizable potential would reach 2191.98 PJ in 2030. If this utilizable potential would be fully realized to displace fossil energy, approximately 151.82 Mt CO 2 e of GHG emissions could be reduced. Moreover, WTE process coupled with carbon capture and storage (WTE-CCS) would result in extra negative emissions of 1.83 Mt CO 2 e in 2030. Spatially, higher urban wastes potential leads to larger GHG mitigation potentials of WTE-CCS. Compared with less developed regions (e.g. Tibet, Qinghai and Ningxia), the regions with higher densities of population and economy activity (e.g. Guangdong, Jiangsu and Shandong) would have larger GHG mitigation potentials. Our study could provide geographically targeted information on the deployment of WTE-CCS in China.
Waste to energy is a promising way to ease the urban burden of waste treatment and hydrothermal carbonization (HC) can dewater the municipal wastes with high moisture efficiently with hydrochar left. The hydrochar with outstanding fuel characteristics can be used as fuel for incineration to generate power. To predict the fuel characteristics of hydrochar including the yield, higher heating value (HHV) and carbon content (C_char) based on the information of the wet municipal waste, machine learning methods have been explored in this work. Results show that the optimized Random Forest (80 trees with 10 maximum depths) has good multi-task prediction capability of fuel characteristics. The R 2 for the predictions of the yield, HHV and C_char are 0.80, 0.91 and 0.95, respectively. Moreover, according to the feature importance analysis, the yield of hydrochar is mainly determined by the temperature and water content of HC, while the HHV and C_char are dominated by the carbon and ash content of the feedstock, respectively.
In order to reduce the impact of fouling on the efficiency of central air conditioning chillers, and thus improve the energy efficiency of buildings, we propose a new type of special online water treatment technology (SOWTT). The effects of SOWTT were evaluated by tracking and calculating the operating efficiency, annual electricity consumption, annual electricity expenditure, and carbon dioxide emission reduction of dozens of chillers in Xiamen. The results show that compared with the mechanical cleaning technology, the SOWTT not only reduces the annual power consumption of the refrigeration unit, but also increases the emission reduction of carbon dioxide. At the same time, the COP of the chiller is also greatly improved, and the comprehensive benefits are remarkable.
To predict the energy performance of a chilled water system more accurately, the hydraulic resistances of its water pipe network should be calibrated before simulation. However, it is a challenge to calibrate the hydraulic resistance of such a complex pipe network that are compose of chillers, terminal units, variable-speed pumps, valves and many pipes installed in different floors of a high-rise building. In this study, a new calibration method is proposed elaborately to adapt the vertical structure of the water pipe network in a high-rise building. The proposed calibration method utilized an optimization model and a general pipe network hydraulic solver. To overcome the severe nonlinear characteristic of the pipe network, Genetic Algorithm (GA) is used to solve the optimization model. Then, the proposed calibration method is validated in a real-life chilled water system in a high-rise building. With the hourly measured data from the chilled water system in operation in a typical summer day, the hydraulic resistances of 200 terminal units, 46 valves and 912 pipes are calibrated in detail. The calibrated hydraulic resistances are used to predict pressures and flow rates of the chilled water system in the next day. Compared with the uncalibrated simulation results, the average pressure error between the calibrated simulation results and measured data from the 42 onsite pressure meters is reduced from 2.2% to 0.6%. The average flow rate error between the calibrated simulation results and measured data from the 3 onsite flow rate meters is reduced from 5.3% to 0.9%.
In this study, we aim to find an optimally sized battery that can be installed to an existing grid-tied solar home system without a prior energy storage system, in order to maximize the user’s financial benefits while maintaining reliable power supply to the home. To solve this optimization problem, we formulate the objective function as the net present value of the investment on the battery. Solution to the optimization problem returns the optimal battery size, power flows and battery age status during a 10-year evaluation period. In order to identify the most favorable solution to the user, we apply the proposed optimization algorithm to five typical photovoltaic (PV) generation and home load levels, and find that the optimal battery size is very sensitive to the level of PV power generation and the home load. In addition, it is more financially viable to have the battery when the daily PV power generation is less than the home load.
This study aims at exploring the relationship between renewable energy consumption and carbon dioxide emissions in China, and through the significance of renewable energy consumption, the hypothesis of environmental Kuznets curve at individual country level is tested as well as. Autoregressive distributes lag bounds testing approach is employed for empirical analysis. The results show that a quadratic relationship between renewable energy and CO2 emission has been found for the period support EKC relationship, and there exists a negative causality from renewable energy consumption to CO2 emissions.