Distributed energy systems (DESs) exhibit potential to promote the energy market reform worldwide. In this study, a bi-level optimization model is proposed to analyze the operation of a DES that purchases high-voltage electricity and natural gas from utility companies, and supplies low-voltage electricity and heat to multiple users. To simplify the resolution process, the bi-level optimization is transformed into a single-level mixed integer linear programming model using the Karush-Kuhn-Tucker approach and Big M method. The results indicate that (i) the time-sensitive energy prices offered by the DES could smoothen the load profiles of users; and (ii) the tiered pricing scheme set up by the utility companies could maximize the utility of the ESS integrated into the DES, but the capacity of the ESS should be accurately designed to fit the corresponding pricing scheme.
Cross Laminated Timber (CLT) is attracting worldwide attention, due to its durability, usability, and many other advantages. However, since CLT is made of wood, analysis of the hygrothermal performance is essential. In this study, the various conditions that affect the thermal moisture behavior were applied to the simulation for getting stable hygrothermal results. As a result, the standard of Passive house and Domestic wooden house, the climate condition, the presence of breathable water proofing paper, and the insulation alternatives of Expended Polystyrene (EPS) and Extruded Polystyrene (XPS) were applied. It was concluded that breathable waterproofing paper should be installed inside, and the applications of both XPS and EPS made no difference to moisture but the application of Mineral Wool was adversely affect to hygrothermal performance of the CLT wall system. The thickness of insulation should be designed according to the Passive house standard (0.15 W/m 2 K) rather than the Domestic (Korea) Wooden house standard (0.21 W/m 2 K).
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.