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.
aking methane as fuel gas, nickel oxide as oxygen carrier and calcium oxide as carbon dioxide adsorbent, a solar-driven chemical-looping hydrogen production system was established and the hydrogen production performance of the system under different reaction conditions was explored. The results showed that, if the concentrating solar energy was used as the heat source of the fuel reactor, the ratio of nickel to methane was 2.25, the ratio of water to methane was 1, the temperature of the fuel reactor was between 450 and 600 oC, the pressure of the fuel reactor was at atmospheric pressure, and the ratio of calcium oxide to methane was 1, a better system performance could be achieved. The key process experiments are carried out in the honeycomb reactor. The results show that the separation of CO2 generated in the reaction process is and significant way to improve the efficiency of chemical looping hydrogen production. Using CaO to capture and separate CO2 in the reaction process could improve the effect of hydrogen production. Also, the huge energy consumption caused by gas separation could be avoided. This study presents a method for efficiently using low-temperature solar energy for hydrogen production.
Cryo-compressed vessels have many advantages in storing hydrogen for automotive applications because of large storing density and thermal endurance. However, the cooling power of venting hydrogen in the processes of dormancy and discharge is not fully utilized. A throttling valve can be used to recycle the cooling power. A thermodynamic model is established to analyze the behavior of hydrogen in the insulated pressure vessel with a throttling valve. Different initial pressures and release pressures of hydrogen in the vessel are studied in the processes of dormancy, discharge and driving. The dormancy can be extended 55% with a throttling valve in the vessels of 2 MPa release pressure. The cooling capacity of the throttling valve decreases with the increase of the initial pressure. Simulations of hydrogen storage during the actual driving are performed at different initial pressures. The throttling valve in the lowinitial- pressure vessel can reduce the upper pressure limit of the vessel by 50% which reduces the manufacturing costs obviously. This work introduces the great potential of the throttling valve in the vessel for automotive applications.
In this paper, we develop a residential grid-level optimization model, which incorporates both electrical consumption scheduling (ECS) systems and energy storage devices (ESDs), so as to lower the peak-to- average ratio (PAR) of electricity demand and reduce the costs of electricity supply and consumption. This model consists of three levels: household consumption optimization (solo opt), grid consumption optimization (base opt) and ESD allocation optimization (ESD opt). To evaluate this model, a realistic residential population of 180 households subdivided into subpopulations by household sizes and income levels was simulated using a bottom-up randomization approach, with electricity supply from conventional thermal generation (CTG). The results show that PAR can ideally be reduced to 1 with an optimal allocation of ESDs among households with positive bills savings.
Modelling tools are frequently used to study China’s carbon policies while central and local governments initiate and implement carbon targets. Reviews of these low-carbon-planning tools and their usefulness, however, are not sufficient. In this regard, we review eight often used tools in China and suggest their applications and limitations. Tools are classified into four categories: computable general equilibrium, cost- optimization, benchmarking, and accounting tools. For China, application cases are recognized in addressing three research questions, i.e., emission scenario building, policy optimization, and carbon-policy impact analysis. From these studies, it is found that while tools usually require significant assumptions, the disclosure of them are in shortage and lack standardization and comparability. Tools also exclusively focus on policy- planning phase without attention to policy- implementation and policy-evaluation phases. Since the Chinese government has initiated three rounds of low- carbon-pilot-city programs, therefore, it is recommended that tool developers learn from some empirical evidence to integrate real policy outcomes into tools, e.g., policy effectiveness, expected implementation barriers, and required administrative power. Hence, analysts can complete a more holistic, evidence-based, and local-oriented policy suggestion. Standardization of model disclosure rules and evidence- based assumption-making are suggested to enhance comparability and mutual learning. Finally, modules to track progress in policy implementation and evaluation process can be added into tools for policy iteration and evidence collection.