Increasing the utilization of renewable energy is crucial to coping with climate change. As the world's largest C[O.sub.2] emitter, China has realized the importance of developing renewable energy, and in the U.S.-China Joint Announcement on Climate Change set a target of 20% non-fossil fuels in primary energy consumption (renewables and nuclear energy) by 2030 (XNA, 2014). Renewable energy investment is highly capital-intensive, and once built, it creates a "lock-in" effect on the power generation mix for decades. Thus, whether the future low-carbon goals can be achieved depends on the current power system layout. The decision of where to invest in renewable energy, and to what extent, needs to be considered from a forward-looking perspective.
However, there are significant differences in resource endowments and electricity demand from region to region in China. Geographically, the potential for renewable energy is greatest in the north of China, while the power demand in the east and south is higher due to advanced economic development in those regions. The imbalance of resource endowments and electricity loads makes the spatial deployment of renewable energy more difficult.
In the north of China, a coal-heavy power supply structure limits the possibilities for integrating further renewable energy into the power grid. The large-scale application of coal-fired combined heat and power (CHP) has led to serious wind curtailment problems during winter, with a curtailment rate of 20.6% in 2016 (NEA, 2017). Compared with traditional coal-fired units, CHP unit operation is more inflexible because it must generate electricity when providing heating supply. During the winter, high heating demand in the north of China forces the CHP plants in district heating grids into must-run mode. The regional conflicts between the existing power generation mix and renewable energy are significant. Yet, with the increase of ultra-high voltage technology, inter-regional power transmission may be conducive to the consumption of renewable energy. Consequently, both the coal-heavy generation mix and inter-regional transmission planning affect the optimal spatial deployment of renewable energy.
The deployment of renewable energy at the spatial level in a manner that achieves stringent low-carbon target cost efficiency is of particular concern to China. Dong et al. (2016) conducted an empirical analysis of the distribution and cluster patterns of the country's renewable energy industry. Zhang et al. (2017) examined the regional development status of renewable energy, and made projections for the future expansion of renewables in China. Pei et al. (2015) analyzed wind curtailment problems from a temporal-spatial perspective. Their results showed that the present coal-wind-nuclear power structure in the north of China is one of the main reasons for curtailment. Based on the current literature, we find there is a need to implement comprehensive energy planning to analyze the spatial deployment of renewable energy and its related influencing factors.
However, describing fluctuating renewable energy in the energy system model is complicated, because it requires a relatively high-frequency temporal resolution, generally based on hours. Traditional power generation expansion planning (GEP) models are widely applied for energy planning from a bottom-up perspective (Bird et al., 2011; Sullivan et al., 2014; Wright and Kanudia, 2014; Chang and Li, 2015; Li and Chang, 2015). However, these models often simplify the fluctuating and seasonal features of renewable energy, and ignore the dispatch characteristics of generation units, such as ramping limits and start-up and shut-down decisions. These are typically addressed by unit commitment (UC) models (Kaleta and Toczylowski, 2008; Keane et al., 2011; Weigt el al., 2013; Egerer et al., 2015; Moarefdoost el al., 2016). Few studies integrate short-team power dispatch decisions into a long-term GEP framework (Koltsaklis and Georgiadis, 2015; Abrell and Rausch, 2016; Egerer et al., 2016; Bertsch et al., 2017). Pfluger (2014) expanded the electricity system model PowerACE to assess least-cost pathways for decarbonizing Europe's power supply. Scholz et al. (2017) used high temporal and spatial resolution information in an integrated assessment model (IAM) to understand the potential contribution of concentrating solar power to the balancing of renewable energy in Europe. Perez et al. (2016) used a co-optimization planning model to quantify the economic effects of trading renewable energy certificates among the U.S. states.
In the Chinese context, Li et al. (2016) simulated the Chinese power sector in 2030 based on an UC model, which models the dispatch of six regional power grids at hourly intervals. Guo et al. (2017) integrated seasonal and diurnal temporal variations into a GEP framework by dividing one year into twelve time-blocks. Chen et al. (2018) expanded to ninety-six time-blocks based on Guo et al. (2017) to capture the impact of different charging modes of electric vehicles on power system investment. He et al. (2016) presented a high-resolution integrated model that optimizes both the long-term investment and the short-term dispatch, but did not consider the characteristics of CHP.
To better understand the spatial deployment of renewable energy in China, this article makes three significant improvements compared to previous literatures by: (1) integrating hourly features of power dispatch, especially ramping limits, into traditional GEP model, which enables analyzing the impact of short-term power dispatch on long-term capacity investment; (2) modeling China's unique CHP operation mode in the energy system model to characterize the conflict between central heating supply and low-carbon energy transformation; and (3) analyzing the renewable energy deployment from a systematic perspective, which enables us to understand the inherent mechanism between power generation mix, power grid planning, and the large-scale utilization of renewable energy. Based on this framework, this study addresses two research questions: (1) what is the optimal spatial deployment of renewable energy to achieve relevant low-carbon targets? And (2) what are the effects of a coal-heavy generation mix and electricity transmission infrastructure expansion on the spatial deployment of renewable energy in China? Based on the analysis, policy recommendations will be developed for the Chinese government to make long-term strategic decisions about renewable energy deployment.
This paper is organized as follows. Section 2 provides a description of the conceptual framework. The detailed modeling, data, and scenario designs are presented in Section 3. Section 4 discusses the effects of a coal-heavy generation mix and electricity transmission infrastructure expansion on the spatial deployment of renewable energy. Section 5 presents the conclusions and recommendations.
2.1 Coal-heavy Generation Mix
Coal is the most important resource for power generation in China. In 2016, the proportion of the installed capacity of coal-fired power units was 57.3%. Coal power plants have more restrictive operational requirements than gas-fired power plants; for example, their ramping is slower. Furthermore, China has a large number of coal-fired CHP units providing district heating that cannot be shut down or easily modulated during the winter heating periods, which limits the flexibility of the power supply. The capacity of CHP reached 283 GW in 2014, accounting for 34% of the total coal-fired capacity (CEPYEB, 2015). In the Northern China and Northeast China power grids, the proportion exceeds 50%.
Figure 1 explains why the above-mentioned inflexible generation mix conflicts with renewable energy through an hourly electricity supply and demand curve. The difference between the daily load curve and the must-run capacity is the potential for other generation units. In an extreme case, even if no other units are dispatched, the amount of available renewable energy alone may exceed the above residual load, causing a renewable energy curtailment problem, especially for wind power. The feed-in profile of wind is typically high in winter mornings, while at this time the load curve is at the lowest point of the day. Due to this hourly mismatch of supply and demand, it is necessary to introduce a power dispatch model integrated with a power expansion model to better characterize renewable energy.
2.2. Inter-regional Electricity Trade
First, a stylized two-region model for electricity trade is presented as a reference for our empirical analysis (see Figure 2). Regions A and B are characterized by marginal cost supply curves ([MC.sub.A] and [MC.sub.B]) and demand curves ([D.sub.A] and [D.sub.A]). The step-wise supply curve is assumed to include two technologies: renewable energy with zero marginal cost and thermal power with different marginal costs [P.sub.A] and [P.sub.B]. Region A approximates the electricity supply characteristics of Northwest China, where thermal power is relatively cheaper and renewable energy has greater potential. In contrast, Region B reflects the characteristics of East China. Through mapping the demand curves to the supply curves, we can find the equilibrium electricity price and trade amount at a given time.
In time [T.sub.1], without regional trade, regions A and B meet demand at the equilibrium prices of zero (region A) and [P.sub.B] (region B). If inter-regional trade is made possible, regardless of the power transmission costs, the equilibrium price becomes [P.sub.A] by mapping [MC.sub.A+B] and [D.sub.A+B]. Region A becomes an electricity exporter and more renewable energy ([Q.sub.2]-[Q.sub.1]) is consumed due to the trade. But, electricity trade can increase the utilization of renewable energy mainly in situations of regional over-supply. For example, there is still a large...
The Spatial Deployment of Renewable Energy Based on China's Coal-heavy Generation Mix and Inter-regional Transmission Grid.
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COPYRIGHT GALE, Cengage Learning. All rights reserved.