Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments.
Rural components are integral parts of China's economy, and hundreds of millions of China's residents still live in rural areas. Rural residents heavily depend on non-commercial energy due to the inaccessibility and unaffordability of commercial energy. Conventional use of solid biomass fuels threatens public health as well as environmental and ecological sustainability. Thus, rural energy transition must be promoted. By using a new dataset, we show China's rural energy transition to gain insights on where, how, and why this transition occurs in rural households. Unlike previous views, we find that after considering non-commercial energy, the per capita consumption of rural residential energy is considerably larger than that of urban counterparts. Moreover, migrations from rural to urban areas decrease rather than increase residential energy consumption. Furthermore, rural energy transition from low to high quality depresses energy consumption. Our results demonstrate how accessibility and affordability affect the fuel preferences of rural residents, thereby enabling us to identify the mechanisms of rural energy transition. We provide some insights and policy implications on the routes of China's rural energy transition, which may be further extended to other emerging and developing countries due to their similar rural energy use.
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.
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.
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.
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.
With the largest installed capacity in the world, wind power in China is experiencing a ∼20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet un-clear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and non-convex energy generation constraints. The model is applied to simulate a regional energy system (Jing-Jin-Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks-even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly.
The United States has committed to reduce its greenhouse gas emissions by 26%–28% by 2025 and by 83% by 2050 relative to 2005. Meeting these objectives will require major investments in renewable energy options, particularly wind and solar. These investments are promoted at the federal level by a variety of tax credits, and at the state level by requirements for utilities to include specific fractions of renewable energy in their portfolios (Renewable Portfolio Standards) and by opportunities for rooftop PV systems to transfer excess power to utilities through net metering, allowing meters to operate in reverse. The paper discusses the current status of these incentives.
China hosts the world’s largest market for wind-generated electricity. The financial return and carbon reduction benefits from wind power are sensitive to changing wind resources. Wind data derived from an assimilated meteorological database are used here to estimate what the wind generated electricity in China would have been on an hourly basis over the period 1979 to 2015 at a geographical resolution of approximately 50 km × 50 km. The analysis indicates a secular decrease in generating potential over this interval, with the largest declines observed for western Inner Mongolia (15 ± 7%) and the northern part of Gansu (17 ± 8%), two leading wind investment areas. The decrease is associated with long-term warming in the vicinity of the Siberian High (SH), correlated also with the observed secular increase in global average surface temperatures. The long-term trend is modulated by variability relating to the Pacific Decadal Oscillation (PDO) and the Arctic Oscillation (AO). A linear regression model incorporating indices for the PDO and AO, as well as the declining trend, can account for the interannual variability of wind power, suggesting that advances in long-term forecasting could be exploited to markedly improve management of future energy systems.
In the 21st Conference of the Parties to the UNFCCC held in Paris in December 2015, China pledged to peak its carbon emissions and increase non-fossil energy to 20% by 2030 or earlier. Expanding renewable capacity, especially wind power, is a central strategy to achieve these climate goals. Despite greater capacity for wind installation in China compared to the US (145.1 versus 75.0 GW), less wind electricity is generated in China (186.3 versus 190.9 TWh). Here, we quantify the relative importance of the key factors accounting for the unsatisfactory performance of Chinese wind farms. Different from the results in earlier qualitative studies, we find that the difference in wind resources explains only a small fraction of the present China-US difference in wind power output (17.9% in 2012); the curtailment of wind power, differences in turbine quality, and delayed connection to the grid are identified as the three primary factors (respectively 49.3%, 50.2%, and 50.3% in 2012). Improvements in both technology choices and the policy environment are critical in addressing these challenges.