Pricing strategy for renewable energy source electricity in the competitive hybrid electricity market
| Published date | 11 June 2018 |
| DOI | https://doi.org/10.1108/IMDS-08-2017-0341 |
| Date | 11 June 2018 |
| Pages | 1071-1093 |
| Author | Jiaping Xie,Yu Xia,Ling Liang,Weisi Zhang,Minghong Shi |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Pricing strategy for renewable
energy source electricity in
the competitive hybrid
electricity market
Jiaping Xie
Department of Operations Management,
School of International Business Administration,
Shanghai University of Finance and Economics, Shanghai, China
Yu Xia
School of International Business Administration,
Shanghai University of Finance and Economics, Shanghai, China
Ling Liang
Tourism and Event Management School,
Shanghai University of International Business and Economics,
Shanghai, China, and
Weisi Zhang and Minghong Shi
School of International Business Administration,
Shanghai University of Finance and Economics, Shanghai, China
Abstract
Purpose –To promote the development of renewable energy, the Chinese Government adopts the policy of
Feed-in Tariff and subsidy. However, the high purchase price and the intermittence limit the development of
renewable energy source electricity (RES-E). The purpose of this paper is to discuss the pricing strategy for
system operators to stimulate the development of the RES-E industry under the scenario of uncertain supply
and demand.
Design/methodology/approach –The authors establish a two-echelon supply chain investment model led
by a power grid operator considering the uncertainties in both demand and supply, and study the impact of
the power purchase price designed by a system operator using Stackelberg’s model.
Findings –There is an optimal capacity for RES-E generators, that is, independent of the market demand.
Besides, the optimal order of grid operators is independent of the uncertain RES-E supply and the purchase
price of fossil fuel. By properly setting the purchase prices, the system operator can stimulate the capacity
investment in renewable energy. Finally, increasing the punishment in power shortage can stimulate the
capacity investment in RES-E under certain conditions.
Practical implications –The result of this paper can mitigate the phenomenon of power abandonment in
the RES-E industry and promote the grid integration of RES-E.
Originality/value –Both uncertain demand and supply are considered in this paper. A heuristic algorithm
is provided to compute the optimal purchase price combination.
Keywords Renewable energy, Pricing strategy, Capacity investment, Demand and supply uncertainty
Paper type Research paper
1. Introduction
Since the twenty-first century, China has been trying to replace conventional fossil energy
with clean renewable energy, such as wind power and solar power, to ease the growing
pressure of air pollution and carbon emission. Renewable energy also shows the potential to Industrial Management & Data
Systems
Vol. 118 No. 5, 2018
pp. 1071-1093
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-08-2017-0341
Received 2 August 2017
Revised 7 November 2017
Accepted 28 November 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The work was supported by National Social Science Found of China (Grant No. 15ZDB161) and
National Natural Science Foundation of China (Grant No. 71273091).
1071
Pricing
strategy for
RES-E
adjust the irrational structure of power and weather the energy depletion crisis. However,
since renewable energy source electricity (RES-E) highly depends on the primary natural
energy output (such as wind force and sunlight), it is characterized by unstableness and
unpredictability, which is againstthe requirement for power providersto provide any amount
of power at any time for the power market. Although the Chinese wind power industry has
rapidly been developing since the implementation of the Law of Renewable Energy in 2016
(the number of installed wind turbines increases from less than6 gigawatts in 2007 to nearly
150 gigawattsin 2015, which is 24 times largerand surpasses the number of installedturbines
in Europe of 141 gigawatts, accounting for 33.55 percent worldwide), data in 2015 still show
that the proportion of RES-E to all generated electricity is only 3.3 percent.
With the market revolution in the power industry, according to the market rule of supply
and demand, the purchase price of electricity is directly impacted by the cost structure.
Compared with fossil source electricity, although the variable cost of RES-E is very low, the
fixed cost is high (Henriot, 2015). Therefore, in practice, the RES-E purchase price is higher
than that of fossil fuel. Unfortunately, since the intermittence of the RES-E, there is a lack of
motivation for grid operators to use renewable energy, which limits the development of
renewable energy. To deal with this problem, policies such as Feed-in Tariff and subsidy are
adopted by governments (Valentine, 2010). The aim of these polices is to reduce the actual
purchase price of RES-E to below that of fossil fuel. In October 2016, the National
Development and Reform Commission, which is the country’s economic planning agency,
unveiled a notification to adjust the purchase price of RES-E. Since then, the purchase price
of the RES-E of China has come down considerably. For example, the purchase price of
photovoltaic power dropped by 23.5–31.2 percent. In Germany, the purchase price of wind
power and solar power also started to decrease (Cludius et al., 2014). Besides, with the
development of the RES-E industry, the cost of RES-E will be further reduced, thus lowering
the purchase price of RES-E. It is expected that the purchase price of RES-E will
continuously decrease in the future. Under such background, the aim of this paper is to
study how the system operators design the purchase prices of RES-E and fossil fuel to
stimulate the RES-E generators to invest in capacity while guarantee the profits of each
member in the supply chain under market rules.
In the applied studies on the power industry, the power capacity investment, investment
period and operating planning are the foci and difficulties. Tishler et al. (2008) studied the
capacity investment problem with pricefluctuations in perfect competitivepower market. Ma
et al. (2015)studied the impact of uncertainprice on supply chain. However,since the property
of intermittence,their models were not applicableto RES-E. Hiroux and Saguan (2010)pointed
out that the uncertain output that comes from the property of intermittence of RES-E causes
extra cost to balance the demand and the supply of the power system. Ambec and Claude
(2012) regarded the uncertain supply coming from the intermittence of RES-E as one of the
main factors affecting the investigation on RES-E and fossil fuels. Chong and Zhou (2014)
claimed that a collaborative supply chain is one potential solution to deal with the problem of
uncertainty. In thispaper, we consider the uncertain output and demandof RES-E. However,
differentfrom the above studies, our studyfocuses on the design of a systemoperator’spolicy.
Several studies have also been done focusing on the hybrid electricity market (i.e. RES-E
and fossil fuels). Miah et al. (2012) discussed the relationship between the investment cost of
RES-E and penetration rate. Kong et al. (2017) investigated the optimal investment decision
of RES-E’s capacity with the consideration of the correlation of market power price and
output RES-E. Xie et al. (2017) reviewed the problem of RES-E’s capacity decision from the
perspective of supply chains. Liang et al. (2017) considered the problem of the daily
operation of enterprises in the RES-E industry. They suggested using revenue-sharing
contracts to deal with the possible moral hazard in aftermarket service. Fischer and Newell
(2008) studied the impact of the penetration rate on global warming in a power market with
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