The changing carbon content of coal consumed in China between 2002 and 2012 is quantified using information from the power sector. The carbon content decreased by 7.7% over this interval, the decrease particularly pronounced between 2007 and 2009. Inferences with respect to the changing carbon content of coal and the oxidation rate for its consumption, combined with the recent information on coal use in China, are employed to evaluate the trend in emissions of CO2. Emissions are estimated to have increased by 158% between 2002 and 2012, from 3.9 Gt y-1 to 9.2 Gt y-1. Our estimated emissions for 2005 are notably consistent with data reported by China in its “Second National Communication” to the UN (NDRC, 2012) and significantly higher than the estimation published recently in Nature. The difference is attributed, among other factors, to the assumption of a constant carbon content of coal in the latter study. The results indicate that CO2 emissions of China in 2005 reported by Second National Communication are more reliable to serve as the baseline for China's future carbon commitments (e.g. those in Paris Agreement of the UNFCCC). Discrepancies between national and provincial statistics on coal production and consumption are investigated and attributed primarily to anomalous reporting on interprovincial trade in four heavily industrialized provinces.
This paper proposes a comprehensive scheme for day-ahead congestion management of distribution networks with high penetration of distributed energy resources (DERs). In the proposed scheme, the dynamic tariff (DT), network reconfiguration and re-profiling products are integrated, which combines the advantages of these methods. In addition, the previously proposed DT model is relaxed in order to handle possible infeasibility of the DT problem and set a limit for the DT. With the utilization of the flexibilities from various types of DERs and the advantages of the three congestion management methods, the proposed comprehensive scheme can solve the congestion more effectively and at the same time ensures that the congestion management prices are within an acceptable level. Three case studies were conducted with the modified Roy Billinton Test System (RBTS) to validate the effectiveness and advantages of the proposed comprehensive scheme.
Among biomass energy technologies which are treated as the promising way to mitigate critical energy crisis and global climate change, biomass gasification plays a key role given to its gaseous fuels especially syngas for distributed power plant. However, a system analysis for the energy saving and greenhouse gas emissions abatement potentials of gasification system has been directed few attentions. This study presents a system analysis that combines process and input-output analyses of GHG emissions and energy costs throughout the full chain of activities associated with biomass gasification. Incorporating agricultural production, industrial process and wastewater treatment which is always ignored, the energy inputs in life cycle are accounted for the first commercial biomass gasification power plant in China. Results show that the non-renewable energy cost and GHG emission intensity of the biomass gasification system are 0.163 MJ/MJ and 0.137 kg CO2-eq/MJ respectively, which reaffirm its advantages over coal-fired power plants in clean energy and environmental terms. Compared with other biomass energy processes, gasification performs well as its non-renewable energy cost and CO2 intensity are in the central ranges of those for all of these technologies. Construction of the plant is an important factor in the process’s non-renewable energy consumption, contributing about 44.48% of total energy use. Wastewater treatment is the main contributor to GHG emissions. The biomass gasification and associated wastewater treatment technologies have critical influence on the sustainability and renewability of biomass gasification. The results provide comprehensive analysis for biomass gasification performance and technology improvement potential in regulating biomass development policies for aiming to achieve sustainability globally.
Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.
Current Chinese policy promotes the development of both electricity-propelled vehicles and carbon-free sources of power. Concern has been expressed that electric vehicles on average may emit more CO2 and conventional pollutants in China. Here, we explore the environmental implications of investments in different types of electric vehicle (public buses, taxis and private light-duty vehicles) and different modes (fast or slow) for charging under a range of different wind penetration levels. To do this, we take Beijing in 2020 as a case study and employ hourly simulation of vehicle charging behaviour and power system operation. Assuming the slow-charging option, we find that investments in electric private light-duty vehicles can result in an effective reduction in the emission of CO2 at several levels of wind penetration. The fast-charging option, however, is counter-productive. Electrifying buses and taxis offers the most effective option to reduce emissions of NOx, a major precursor for air pollution.
Water is essential for bioenergy production. Characterized as low carbon technology, crop-based bioenergy technology witnesses rapid development, inevitably putting pressure on global water resources. Therefore, it is crucial to carefully assess bioenergy technology’s overall impact on scarce water source for a sustainable bioenergy future. In this regard, this study aims to evaluate the life cycle water use of bioenergy from agricultural residues via the first pilot moving-bed pyrolysis polygeneration system in China. By using a tiered hybrid life cycle assessment, both direct and indirect water use are calculated. Results show life cycle water use is 3.89 L H2O/MJ and agricultural process dominates the total water use. Scenarios analysis shows different feedstock allocation ratios during agricultural production have striking influence on water use intensity. In addition, the choice of feedstock is another important influential factor. Under the 2020 Scenario in China’s 13th Five Year Plan, if all the bioenergy target could be met by polygeneration the estimated annual water use will be 6.6 billion m3, in magnitude up to around ten times the total water consumption in Denmark in 2013. In global scenario of potential feedstock available in 2060, the estimated water use for bioenergy produced by polygeneration will be 179-369 billion m3. Although the water use intensity of bioenergy production from agricultural residues by polygeneration is lower than that for other biomass conversion pathways, it is still higher than water intensity of conventional fossil energy products. Large-scale bioenergy production will have macroscopic effects on water demand. Finally, suggestions such as selecting high water-efficient biomass feedstock and reinforcing water-saving irrigation management to minimize water use in agriculture stage are proposed.
The paper considers opportunities to reduce emissions of CO2 through increases in commitments to wind in a representative US power market. A model is applied to simulate market operations for different wind levels focusing on implications of the reduction in clearing prices arising due to increasing inputs of zero marginal cost power from wind, a dilemma referred to as the missing money problem. The resulting decrease in income poses problems for existing thermal and nuclear generating systems, at the same time making investments in wind uneconomic in the absence offsetting policy interventions. Two options are considered to subsidize cost: an investment credit (IC) or a subsidy on production (PC). The dilemma could be addressed also with a carbon tax targeted to increase income. It is assumed that the cost associated with the IC and PC options should be borne by the consumer, offsetting benefits from lower wholesale prices. It is assumed further that income from the carbon tax should be rebated to the consumer offsetting related increases in clearing prices. IC and PC options offer opportunities to reduce emissions at low or even negative net costs to the consumer. Higher costs are associated with the option of a carbon tax.
Deploying high penetration of variable renewables represents a critical pathway for deep decarbonizing the power sector. The conflict between their temporal variability and limited system flexibility has been largely ignored currently at planning stage. Here we present a novel capacity expansion model optimizing investment decisions and full-year, hourly power balances simultaneously, with considerations of storage technologies and policy constraints, such as carbon tax and renewable portfolio standards (RPS). Based on a computational efficient modeling formulation, all flexibility constrains (ramping, reserve, minimum output, minimal online/offline time) for the 8760-hour duration are incorporated. The proposed model is applied to the northwestern grid of China to examine the optimal composition and distribution of power investments with a wide range of renewable targets. Results indicate that the cost can increase moderately towards 45% of RPS, when properly designing the generation portfolio: prioritizing wind investments, distributing renewable investments more evenly and deploying more flexible mid-size coal and gas units. Reaching higher penetrations of renewables is expensive and the reductions of storage costs are critically important for an affordable low-carbon future. RPS or carbon taxes to reach a same target of emission reduction in China will result in similar overall costs but different generation mixes.
Load restoration is an important issue for power system restoration after a blackout. A second order conic programming (SOCP) model is proposed based on the information gap decision theory (IGDT) to maximize load pickup considering the uncertainty of load increment. Because distribution functions of load increment are difficult to obtain, the optimization of load pickup is transformed to maximize the fluctuation range of load increment by the IGDT. The derived optimal fluctuation range can ensure that the reenergized system is secure, and the amount of load pickup is always better than the specified expectation. Moreover, because the optimization model of the fluctuation range is a mixed-integer nonlinear model which is challenging to solve accurately and efficiently, the nonlinear model is transformed into a SOCP model that can be efficiently solved using CPLEX. The efficiency of the IGDT-based SOCP model is validated using the New England (10-machine 39-bus) system. The simulation results show that the derived load pickup shows expected robustness with respect to the load increment uncertainty.
This paper focuses on economic dispatch (ED) in power systems with intermittent wind power, which is a very critical issue in future power systems. A stochastic ED problem is formed based on the recently proposed versatile probability dis-tribution (VPD) of wind power. The problem is then analyzed and proved to be strictly convex. Although such convex optimiza-tion is tractable in many cases, it may take a long time to solve due to its large scale. This paper proposes a dual decomposition method to decompose the large problem. Then two methods are employed to solve the decomposed problem, namely, the subgra-dient method and a faster method, limited-memory BFGS with box constraints (L-BFGS-B, a quasi-Newton method). Case stud-ies were conducted to verify the efficiency of the dual decomposi-tion and L-BFGS-B method for solving the stochastic ED problem.
This paper makes an in-depth analysis on demand-driven natural resource requirements in China via the methods of thermodynamic input-output analysis and structural path analysis, in order to reveal the connections between the country's rapid economic development and its intensive use of natural resources. The main natural resources investigated include crops, forestry, rangeland, aquatic products, coal, crude oil & natural gas, ferrous metal ores, nonferrous metal ores, nonmetallic minerals and other primary energy, and exergy is adopted as a common metric for the resource accounting. In 2012, the total domestic resource exergy input into Chinese economic system amounted to 130.1 EJ, of which 44.6% was induced by investment demands. The embodied resource use (ERU) in China's exports was equivalent to over one fifth of its domestic resource supply. The two integrative sectors of Manufacturing and Construction accounted for 44.1% and 28.7% of the national total ERU, respectively. We identified critical supply chain paths starting from resource extraction to final demand, as well as key industrial sectors in driving the extraction, transmission and final use of embodied resources. The top 50 paths were responsible for 30.4 EJ of the ERU. The identification of resource supply chains from a systemic perspective is of great importance when resource and environmental policies are to be applied to concrete industrial sectors and other economic agents. Integrated approaches that take account of consumption-based resource indicators should be developed for resource conservation and cleaner production, particularly for the economic system with a complex supply network.
Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change. Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles. The connected between environment and energy systems are also tightened under elevated penetration of renewables. This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration. An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.
In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modelled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model predictive control (MPC) based method is proposed for the aggregator to clear the transactive market. The real-time charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the real-time requests and preferences of the EV owners. As such, the aggregatorb's decisions in the real-time EV charging management and regulating power markets can be optimized. At the same time, the charging requirements and response preferences of the EV owners are respected. Case studies using real world driving data from the Danish National Travel Surveys were conducted to verify the proposed framework.
The re-emergence of 2-phase bipolar DC distribution network, which utilizes the neutral wire for efficient distribution, has spurred research interest in recent years. In practice, system efficiency (power loss) and voltage unbalance are major concerns for the planning and design of the 2-phase DC bipolar network. While most of the existing methodologies are power electronics solutions, there are very few works on resolving the problem from the power system perspective. This paper proposes a model based optimization method by firstly formulating the power flow model for 2-phase DC bipolar network using the single line modeling technique and nodal analysis. Secondly, a binary integer load distribution model is proposed to consider the redistribution of unipolar loads across the two unipolar distribution poles. Together with the power flow model, the system power loss and system voltage unbalance indices are formulated as a binary integer quadratic model. Thirdly, a multi-objective optimization model is formulated and solved using the weighted sum approach. The proposed method is applied to a DC LED lighting system design which considers both voltage unbalance and power loss. Using a 15 bus single source and a 33 bus multi-source network as case studies, the developed power flow model is validated with very high accuracy. Compared to existing iterative methods, the proposed model-based approach is able to significantly improve the voltage balancing across the distribution system.
The assessment of the economic potential of wind electricity is of critical importance for wind power development in China. Based on the wind resource data between 1995 and 2014 and geological assumptions, this paper calculates economic potential of China’s onshore wind electricity. Furthermore, it builds an econometric model to update the net-present-value model, based on a survey sample of various wind farms. Results show that the economic potential of China’s onshore wind electricity is 8.13 PWh per year with a feed-in-tariff price at 0.60 yuan (about 9.6 U.S. cents) per kilowatt-hour.