Distributed solar thermal energy in China : a regional analysis of building energy costs and CO2 emissions

Abstract

Energy consumed in buildings accounts for about 40% and 25% of total annual energy consumption in the United States (U.S.) and China, respectively. This paper describes a regional analysis of the potential for distributed energy resources (DER) to save energy and reduce energy costs and carbon emissions in Chinese residential buildings. The expected economic performance of DER is modeled for a multi-family residential building in different Chinese climate zones. The optimal building energy economic performance is calculated using the DER Customer Adoption Model (DER-CAM), which minimizes building energy costs for a typical reference year of operation. Several types of DER, including combined heat and power (CHP) units, solar thermal, photovoltaics (PV), and battery storage are considered in this analysis. Estimating the economic performance of DER technologies requires knowledge of a building’s end-use energy load profiles. EnergyPlus simulation software is used to estimate the annual energy performance of commercial and residential prototype buildings in the two countries. Figures ES-1 and ES-2 show energy usage intensity for residential and commercial buildings in representative and Chinese cities. Figure ES-1 - Annual energy usage intensity of office complexes in representative U.S. cities and shopping malls in representative Chinese cities Figure ES-2 – Annual energy usage intensity of residential buildings in representative Chinese cities This study investigates in depth the factors influencing the adoption of solar thermal technology in Chinese residential buildings. Each factor’s impact on solar thermal installation in residential buildings is evaluated through DER-CAM sensitivity analysis and the results are explained by using a sensitivity coefficient. The solar thermal variable cost ($/kW) sensitivity coefficient is affected by buildings’ heating load and the availability of solar radiation. As shown in Figure ES-3, the solar thermal variable cost sensitivity coefficient goes down with the buildings’ heating load. The Chinese city with the highest annual total heating demand, Harbin, is most sensitive to solar thermal technology cost. In contrast, Guangzhou, in southern China where heating demand is relatively low, is less sensitive to technology cost. Natural gas prices also play an important role in whether solar thermal technology is attractive. In general, solar thermal energy is attractive in places where natural gas prices are high. In the cities where natural gas prices are lower, customers are less likely to install solar thermal water heaters or other solar thermal technologies because these installations may not be cost effective. Figure ES-3 – Impact of heating load on solar thermal adoption’s sensitivity to variable cost and natural gas price Where solar radiation is ample, the price of solar technologies has less influence on whether this technology is adopted. Conversely, in places where solar radiation is limited, solar technologies will not be selected even when technology cost is low. As a result, solar thermal installation is not sensitive to technology cost. Figure ES-4 shows the rank of sensitivity coefficients of solar thermal variable cost. Figure ES-4 – Impact of heating load and solar radiation on solar thermal’s sensitivity to variable cost In summary, for solar thermal technology in Chinese residential buildings, the northern and eastern parts of China are more sensitive to changes in the cost of the technology. That is, if technology costs decrease in the future, residents living in these regions will be likely to adopt more solar thermal systems than those living in other regions. The southern part of China is less sensitive to technology cost. Cities like Lhasa on the Tibetan Plateau and Chengdu in the Sichuan Basin exhibit the least sensitivity to solar thermal technology costs. Factors that may positively or negatively affect the procurement of solar thermal systems are: • Large domestic water and space heating loads • Abundant solar resources • High cost of alternative energy • Availability of area for collectors Regression coefficients give us quantitative indicators of what will happen if technology costs decrease. In certain cities, reducing solar thermal variable cost yields promising increase of solar thermal adoption. However, the sensitivity of solar thermal adoption to its variable cost varies with building’s heating load and cities solar radiation. Solar thermal technologies compete with PV technologies in regions where prices of alternative fuels like natural gas are higher. In Guangdong, Yunnan, and Tibet provinces, it is seen more competition between these two types of solar systems if technology costs reduce or natural gas prices increase. Heat storage is the complementary technology because the combined use of solar thermal and heat storage technologies makes it possible to save the solar energy generated in the daytime for use during the evening when demand is high. Therefore, an increase in installations of one technology will boost customers’ investments in the other. Subsidies to encourage investment in solar thermal technologies should be attributed to regions sensitive to technology cost. Incentive policies, such as providing to investors a fixed amount of subsidy for each kW installed, is more effective in northern China. Prices of conventional fuels like natural gas will play an important role in customers’ investment decisions. Higher natural gas prices are indirect incentives to residents to switch to solar thermal. The relationships among different distributed technologies must be considered when making policies. For example, giving incentives to both solar thermal and PV might not be effective because these two solar technologies compete for the same space, and the availability of space will limit the maximum number of solar collectors that can be installed.