Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities

Highlights

• We use the cost crossover method to group 344 prefectural-level Chinese cities into four cost-risk levels.

• We use the K-means clustering algorithm to group 344 prefectural-level Chinese cities into four investment-profit levels.

• 85.17% of current coal-fired power plants from the investigated cities are generally under cost-risk.

• 36.63% of them can be replaced by distributed solar PV projects.

• Investing in 65.99% of the cities will achieve an IRR of higher than 8%, and a DPBP of less than 15 years.

Abstract

This paper analyzes if solar photovoltaic technology is economically feasible enough to compete with coal-fired power in Chinese cities in the subsidy-free context. Considering this, this paper further investigates how profitable investing in solar PV projects is. This paper firstly analyzes to what degree local coal-fired power plants can be replaced by distributed solar power in 344 prefectural-level cities in China. Levelized Cost of Electricity of solar PV power and the local desulfurized coal benchmark price are used for simplified cost crossover math to identify the replacement risk of local coal-fired power plants. Four risk-levels and their corresponding cities are identified, i.e. deemed no cost-risk, potentially at cost-risk, at cost-risk, and substantially at cost-risk. As a whole, 85.17% of current coal-fired power plants from the investigated cities are under cost-risk. Levelized Profit of Electricity, Net Present Value, Internal Rate of Return, and Discounted Payback Period are calculated for each city, and grouped using K-means algorithm. The cities are clustered into four groups, i.e. high return, medium return, moderate return, and low return. The results show that 65.99% of all the cities could achieve a moderate or higher financial return. The cost-risk and investment profit results are mapped for a better understanding of the regional variation in China.

Introduction

Currently, 75% of electric power in China, as a pillar of the economy, still heavily depends on coal-fired power plants [1]. The rapid development of the Chinese economy will demand massive electricity consumption in the future. As indicated by statistics in 2015, 38% of SO₂ and 42% of NO_x (among total emissions) were produced from coal-fired power generation [1], and 40% of total carbon emissions were contributed from coal combustion [2]. Apart from the significant air pollution and greenhouse gas emissions, the following are also becoming major constraints on sustainable economic development: water pollution, noise pollution, and the pressure on water resources [3]. Solar photovoltaic (PV), as a budding new energy technology, has received intensely growing interest, both from academia and from industry. Because of the maturity of the technology and its declining costs, solar PV power production has been acknowledged as a promising technology with the potential to replace coal-fired power generation [4]. China has formulated a target of 15% non-fossil primary energy by 2020, and recently announced another ambitious target of 20% non-fossil primary energy by 2030 [5]. Solar energy is certainly a promising contributor and a strategically important technology for realizing these targets.

Distributed solar PV projects have been expanding since 2013, mostly because of incentives created by the policy “Notice to play the role of the leverage of electricity tariff to promote the healthy development of solar PV industry” on August 30th, by National Development and Reform Commission (NDRC) [6]. This policy allowed distributed solar PV projects to receive a 0.42 CNY/kWh Feed-in Tariff (FIT). Since then, Distributed Generation (DG) projects have enjoyed policy support for fifteen years from the central government in the form of FIT, demonstration projects [7], and free grid-connection services [8]. Furthermore, local governments are encouraged to provide matching financial subsidies based on national subsidies [9]. Financial aid from local governments—in the form of cheap loans, tax breaks, low-cost land-use rights, and subsidized electricity—makes investment in solar PV more attractive, at times even sending the market into a ‘feverish frenzy’ [10]. More and more DG projects are being built in cities, e.g. residential buildings, commercial buildings, industrial buildings, public utilities (e.g. roads, schools, hospitals, stations, airports, and parks). In response to these incentives, China's domestic DG PV market has seen steady growth, with its cumulative installed capacity rising from 3.10 GW in 2013 to 4.67 GW in 2014, to 29.66 GW in 2017, then surging to 50.61 GW by the end of 2018 [11]. The newly installed DG capacity across different provinces by the end of 2018 is shown in Fig. 1. Noticeably, solar resources vary regionally across China. The solar resources in the northwest are much more abundant than in the southeast. Nevertheless, actual installations of distributed solar PV are concentrated in the southeast, because of the vastly greater availability of rooftop spaces.

Compared with large-scale solar PV projects, Distributed Generation is a key solution for energy provision in urban areas, given its smaller scale relative to utility-scale projects, higher efficiency, and higher penetration rates. Recently, DG investment costs are being significantly driven down through technological innovations and economies of scale (Fig. 2).

In Fig. 2, the blue and green lines with markers represent the current state-of-the-art solar technologies’ efficiencies, as reported from recognized test centers. Among these, the blue lines represent solar cell efficiencies which were best reported for “one-sun” (non-concentrator) single-junction cells and submodules. The green lines represent solar module efficiencies. They were both measured under the global AM1.5 spectrum (1000 W/m²) at 25 °C [12]. The markers represent different technologies, including: crystalline silicon, multi-crystalline silicon, CIGS (CuInGaSe2), CIGS thin-film, CdTe, CdTe thin-film, perovskite, organic, and organic thin-film/polymer. The lines without markers (orange) represent Chinese solar C–Si module prices and system prices [13]. Note: 1 CNY = 7.08 USD (on Aug. 23, 2019).

China's energy consumption, unlike that of developed countries (for instance, Germany and Spain), is growing rapidly. There are still pressures on energy provision, fossil fuel replacement, and pollution mitigation. The Chinese government has been making substantial efforts on the development of renewable energy, such as solar PV [13]. Phase-out of coal-fired power is by no means a unanimous call. Significant amount of literature studied the technological advances and social equity on replacing coal by solar electricity. But little effort has been made on economic competitiveness and affordability in city-level, especially in large-scale Chinese cities.

This paper studies two main problems: (1) Are China's photovoltaic projects generally economically competitive enough with traditional coal-fired power generation? (2) How investment profits would differ from city to city in the context of subsidy-free. This study combines a Levelized Cost of Electricity (LCOE) dataset and a local desulfurized coal benchmark price dataset in order to present simplified cost crossover math. Four cost-risk levels and their corresponding cities are identified, i.e. “deemed no risk”, “potentially at risk”, “at risk”, and “substantially at risk”. This risk analysis considers both technical aspects and market aspects. In addition, this paper uses the K-means clustering algorithm to group 344 prefectural-level Chinese cities into four investment-profit dimensions. This study considers local resource endowments (represented by PV power generation) and local economic conditions (represented by electricity market price and DCB price) to calculate the economic attributes for the assessment. Four city groups were identified according to different profit levels, i.e. cities of “high return”, “medium return”, “moderate return”, and “low return”.

This paper is organized as follows. Section 1 is the introduction and background. Section 2 provides the literature review. Section 3 describes the methodology of the crossover algorithm and the K-means clustering algorithm. Section 4 presents the results. Section 5 presents the discussions. Section 6 draws the conclusions.