Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certificate schemes

Journal of Cleaner Production 161 (2017) 299e316 Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsev...

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Journal of Cleaner Production 161 (2017) 299e316

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certiﬁcate schemes C. Suo a, Y.P. Li b, *, S.W. Jin a, J. Liu a, Y.F. Li b, R.F. Feng c a b c

Sino-Canada Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China Environment and Energy Systems Engineering Research Center, School of Environment, Beijing Normal University, Beijing 100875, China Canadian Light Source, Inc., Saskatoon, SK, S7N 2V3, Canada

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 January 2017 Received in revised form 27 April 2017 Accepted 22 May 2017 Available online 22 May 2017

In this study, a two-stage type-2 fuzzy stochastic programming (TTSP) method is developed for supporting clean production of energy systems with carbon and pollutant mitigation under uncertainty. TTSP can handle multiple uncertainties expressed as type-2 fuzzy sets, random variables and interval values; it can also provide an effective linkage between the pre-regulated energy and environmental policies as well as the associated economic implication. The TTSP method is then applied to planning energy system of Shanghai through introducing carbon emission trading (CET) and green certiﬁcate (GC) schemes. The solutions obtained can help generate energy-supply and electricity-generation schemes under different carbon trading ratios and various development plans of renewable energy. Results reveal that (i) the city’s future energy structure would transit to the clean-production pattern on the basis of CET and GC policies; (ii) replacing fossil fuels with renewable energy sources (i.e. wind and photovoltaic power) can effectively facilitate reducing the emissions of pollutants (e.g., SO2, NOx and PM) and greenhouse gas (e.g., CO2). The results can help decision makers adjust energy and electricity supply, make appropriate mitigation plan, as well as gain insight into the relationship between mitigation schemes and optimal clean-production pattern. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Carbon emission trading Clean production Green certiﬁcate Type-2 fuzzy Two-stage

1. Introduction With the development of economy and fast growth of population, energy consumption in China has a dramatic increase, where energy generated by fossil fuels accounts for more than 70%. The large consumption of fossil fuel leads to billions of tonnes (t) greenhouse gases (GHGs) being emitted into the atmosphere, possibly accelerating the rate of global warming (Jiang et al., 2013; Monkelbaan, 2014). As the primary GHGs, carbon dioxide (CO2) emissions amounted to approximately 32.3 billion t in 2014, which might lead to around 1 C increment in surface temperature (IEA, 2015). Meanwhile, emissions of air pollutants [e.g., sulfur dioxide (SO2), nitric oxide (NOx) and particulate matter (PM)] causing by fossil fuels combustion are undergoing continuous augment and brings serious environmental problems such as haze and acid rain (Zhou et al., 2014; Geng et al., 2016). The increasing seriousness of

* Corresponding author. E-mail address: [email protected] (Y.P. Li). http://dx.doi.org/10.1016/j.jclepro.2017.05.123 0959-6526/© 2017 Elsevier Ltd. All rights reserved.

global warming and frequent occurrence of environmental pollution have awaked people’s appeal for improving environmental quality through GHGs mitigation and air pollutants reduction (Chang et al., 2017). In response to such appeal, clean-production pattern, as an efﬁcient method to mitigate CO2 and pollutant emissions for energy systems, is drawing more and more attention (Pang et al., 2017). Therefore, effective measures that can help achieve clean-production pattern of energy systems are desired. Various measures, such as carbon tax, carbon capture techniques, as well as carbon emission trading (CET) have been adopted to cope with CO2 mitigation (Murray and Rivers, 2015; Petrescu and Cormos, 2015; Yu et al., 2016). Among them, CET has been widely considered as a cost-effective instrument to meet mitigation targets, which offers ﬂexibility via a market mechanism rather than compulsory regulations (Park et al., 2014). Green certiﬁcate (GC) scheme is not only capable of mitigating CO2, but also competent to reduce pollutants by promoting electricity generation from renewable energy, such as wind, solar, and biomass (Aune et al., 2012; Currier, 2013). Previously, a number of research efforts about CET and GC schemes were conducted to investigate their

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effectiveness on CO2 and pollutant mitigation, risk of market, as well as impact on energy systems. However, few studies were reported on the combination of CET and GC in energy systems planning. Besides, in an energy system with CET, trading ratio (TR) (deﬁned as how many carbon emission permits a CO2 emitter can purchase from the carbon trading market) plays a critical role of maintaining the stability of carbon trading market, which has signiﬁcant effect on CO2 trading and energy systems. In the practical energy systems planning problems, uncertainties exist among various energy-generation activities and economic, technical, environmental and political indicators (Zhu et al., 2013; Choi et al., 2016; Moret et al., 2016). For example, for CO2 mitigation through CET, penalties are needed to enforce managers mitigating CO2 emission from various energy-generation activities. Then, managers face problem of how many carbon permits are needed to be bought by measuring beneﬁts from energy activities and penalties from random excess carbon exceeding to given emission permits. One attractive method to solve such recourse (i.e. taking corrective actions after a random event takes place) problems is two-stage stochastic programming (TSP) (Li et al., 2014; Tajeddini et al., 2014; Simic, 2016; Yang et al., 2017). One problem of TSP is its inefﬁciency in treating ambiguity and vagueness of human judgements in decision-making processes. Fuzzy programming is an effective tool for tackling such uncertainty. However, some parameters (e.g., costs of purchasing energy resources) are often highly uncertain which is difﬁcult to obtain the membership grades as exact numbers in [0, 1] (Ibarra et al., 2015; Wang et al., 2016). Introduced by Zadeh in 1975, type-2 fuzzy sets can handle higher orders of fuzzy uncertainties due to having more design degrees of freedoms than conventional fuzzy sets (Moharrer

8 x a1 x a1 x a1 x a1 x a1 > > ; if q ; ; þ q r l > > a2 a1 a2 a1 a2 a1 a2 a1 a2 a1 > > > > > > x a1 a2 x x a1 x a1 a2 x > > ; if q ; ; þ q > r l < a2 a1 a2 a1 a2 a1 a2 a1 a2 a1 m~ A~ ðxÞ ¼ > a3 x x a2 a3 x a3 x x a2 > > > ; if ql ; ; þ qr > > a3 a2 a a a a a a a > 3 2 3 2 3 2 3 a2 > > > > a x > a x a3 x a3 x a x > 3 : ; if ql 3 ; ; þ qr 3 a3 a2 a3 a2 a3 a2 a3 a2 a3 a2

et al., 2015; Doctor et al., 2016). Table 1 summarizes the research works relevant to CET, GC and energy system management under uncertainty. Therefore, this study aims at developing a two-stage type-2 fuzzy stochastic programming (TTSP) method for supporting clean production of energy systems with carbon and pollutant mitigation under uncertainty. Through integrating two-stage stochastic programming (TSP), type-2 fuzzy programming (TFP) and interval

programing (IP) into a general framework, TTSP method can deal with uncertainties presented in terms of random variables, type-2 fuzzy sets and interval values. Then, the developed TTSP method will be applied to a real case of Shanghai for planning energy system where both CET and GC are taken into account to identify optimal clean-production pattern of local energy system. Different carbon trading ratios and a number of development plans associated with GC will be conducted to investigate the impact of CET and GC on energy system of Shanghai. Results will be conductive to adjust energy and electricity supply, make appropriate CO2 and air pollutant mitigation, as well as gain insight into the relationship between mitigation schemes and optimal clean-production pattern. 2. Methodology Type-2 fuzzy sets (T2FSs), as the extension of conventional fuzzy sets, are sets with fuzzy membership functions, i.e., membership grades of T2FSs are fuzzy sets in [0, 1] rather than crisp values. A ~ deﬁned on the universal set X is repretype-2 fuzzy set (T2FS) A sented as (Kundu et al., 2014):

~¼ A

n

o ðx; uÞ; mA~ ðx; uÞ u2Jx 4½0; 1; x2X

(1)

where x is the primary variable in universe X; u is the primary membership grade, i.e., the secondary variable in universe Jx ; Jx 4½0; 1 is the primary membership of x2X which is the domain of ~ A ðxÞ 1. Consider secondary membership function 0 mA~ ðx; uÞ ¼ m ~ which is represented by a type-2 triangular fuzzy set A ~ A~ ðxÞ can be ða1 ; a2 ; a3 ; ql ; qr Þ, the secondary membership function m written as

a þ a2 x2 a1 ; 1 2 a1 þ a2 ; a2 x2 2 a þ a3 x2 a2 ; 2 2 a2 þ a3 ; a3 x2 2

(2)

where a1 , a2 , a3 are real numbers, ql and qr are used to represent the spreads of primary possibilities. T2FSs can handle higher orders of possible real world uncertainties due to having more design degrees of freedoms which are useful in uncertain circumstances where it is difﬁcult to determine the exact membership functions (Moharrer et al., 2015; Doctor et al., 2016). The type-2 fuzzy programming (TFP) model can be deﬁned as follows:

Table 1 Literature review relevant to CET, GC and energy system management under uncertainty. Scopes

Sub-scopes

References

Carbon emission trading (CET)

Effectiveness of CET Allocation of carbon permit Impact of CET on energy systems Risk of GC market Impact of GC on energy systems Type-2 fuzzy programming Two-stage stochastic programming

Chang et al., 2016; Ortas and Alvarez, 2016; Villoria-S aez et al., 2016 Park et al., 2012; Liao et al., 2015; Xu et al., 2016 Gambhir et al., 2014; Ermoliev et al., 2015; Zhu et al., 2015; Yu et al., 2016 Lind and Rosenberg, 2014; We˛ dzik et al., 2017 Pavaloaia et al., 2015; Ju et al., 2016; Pineda and Bock, 2016 Jin et al., 2014; Hassan et al., 2016; Nie et al., 2016 Zhou et al., 2015a; Majewski et al., 2017; Piazza et al., 2017

Green certiﬁcate (GC) Energy system management under uncertainty

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

~ Min f ¼ CX

(3a)

subject to:

n1 X

301

0 0 a± x ± b± r ; r ¼ m1 þ 1; m1 þ 2; :::; m1 rj j

(5c)

j¼1

AX B

(3b)

X0

(3c)

~ ~ 1n is vector of T2FS, X2fRgn1 , A2fRgmn , Rg where C2f B2fRgm1 , R represents a set of numbers. However, in real world problems, recourse actions against infeasibility are usually needed to be taken in decision-making processes to minimize economic penalties; moreover, some parameters may be expressed as probabilistic distributions or interval values. Type-2 fuzzy programming method fails to address such problems. Two-stage stochastic programming (TSP) is a powerful tool for problems with uncertainties described as probabilistic distributions, and the decisions can be made periodically (Li and Guo, 2015). Interval programming (IP) method is effective in dealing with uncertainties expressed as interval values. Therefore, through incorporating TPP into the framework of TSP, a hybrid TSP model can be formulated as follows:

n2 X

~ij x± ~0ij y± a w± þa ; i ¼ 1; 2; :::; m02 ; h ¼ 1; 2; :::; s j j h

(5d)

j¼1 n1 X j¼1

a± x± þ ij j

n2 X

a0± y± w± ; i ¼ m02 þ 1; m02 þ 2; :::; m2 ; h ij j h

j¼1

¼ 1; 2; :::; s

(5e)

0; x± 2X ± ; j ¼ 1; 2; :::; n1 x± j j

(5f)

y± 0; y± 2Y ± ; j ¼ 1; 2; :::; n2 ; h ¼ 1; 2; :::; s jh jh

(5g)

~ 2fRg ~ m1 n1 , a± 2fR± gðm1 m1 Þn1 , ~ 1n1 , d ~ 1n2 , a ~rj 2fRg where ~cj 2fRg j rj 0

0

~ m02 n2 , a± 2fR± gðm2 m02 Þn2 , x± 2fR± gn1 1 , y± 2fR± gn2 1 , ~ij 2fRg a ij j j

± ± A± r X Br ; r ¼ 1; 2; :::; m1

(4b)

A± X ± þ A0± Y ± w± ; i ¼ 1; 2; :::; m2 ; h ¼ 1; 2; :::; s i i h

(4c)

± m1 1 . R ~ denotes a set of I2FSs, and R± denotes a set of b± r 2fR g interval values. cj (j ¼ 1, 2, …, k1) and dj (j ¼ 1, 2, …, k2) imply positive parameters; cj (j ¼ k1þ1, k1þ2, …, n1) and dj (j ¼ k2þ1, k2þ2, …, n2) are negative parameters. Type 2 fuzzy parameters can be converted into deterministic values through utilizing type reduction techniques. Then, model (5) can be transformed into two submodels (i.e. the lower bound submodel corresponding to f and the upper bound submodel corresponding to f þ ) based on an interactive algorithm (Wang and Huang, 2015). The detailed solution method for solving the TTSP model is depicted in Appendix A.

x± 0; x± 2X ± ; j ¼ 1; 2; :::; n1 j j

(4d)

3. Case study

0; y± 2Y ± ; j ¼ 1; 2; :::; n2 ; h ¼ 1; 2; :::; s y± jh jh

(4e)

Min f ± ¼ C ± T1 X þ

s X

± ph D± T2 Y

(4a)

h¼1

subject to:

where x± and y± are the ﬁrst- and second-stage decision variables j jh

respectively; w± indicates random variable with probability ph h P ð sh¼1 ph ¼ 1Þ. The hybrid TSP method can handle uncertainties expressed as probabilistic distributions in right-hand side of constraints and interval values in left-hand side and objective. However, fuzzy uncertainty that comes from subjective judgement of decision makers cannot be solved by the hybrid TSP method. One potential approach to deal with uncertainties in terms of interval values, probabilistic distributions and fuzzy sets is incorporating TFP and the hybrid TSP within a general framework. When incorporating TFP and the hybrid TSP within a general framework, a two-stage type-2 fuzzy stochastic programming (TTSP) model is formulated. The TTSP model can be written as follows:

Min f ± ¼

k1 X

~cj x± þ j

j¼1

s X

n1 X j¼k1 þ1

~cj x± þ j

~ y± pjh d j j

k2 X s X j¼1 h¼1

~ y± þ pjh d j j

n2 X j¼k2 þ1

(5a)

h¼1

subject to: n1 X j¼1

0 ~rj x± a b± r ; r ¼ 1; 2; :::; m1 j

(5b)

3.1. Study area The city of Shanghai (31200 N, 121210 E) is located on China’s central eastern coast, bordering on Jiangsu Province and Zhejiang Province. As one of the biggest metropolitan districts in the world, Shanghai occupies an administrative area of around 6340 km2 with 16 city-governed districts and 24.2 million permanent population. The total energy consumption of Shanghai amounted to 110.8 million t standard coal equivalent in 2014, which was 2.1 times that in 2000. According to the statistic data, energy generated by fossil fuels account for 79.7% in the total consumption, including 37.2% coal, 5.7% coke, 28.0% oil products and 8.8% natural gas. Meanwhile, the electricity consumption also increased rapidly from 55.9 103 GWh in 2000 to 136.9 103 GWh in 2014, which was mainly generated from coal-ﬁred power plants. The continuing increase of energy demand and fossil fuel-dominated energy structure has brought about heavy emission of CO2 which may contribute to global warming and climate change. Besides, environmental pollution (such as haze) caused by the massive consumption of fossil fuels is becoming increasingly serious and brings much inconvenience to people’s life. The city has promised to control the CO2 emission increment within 6450 103 t in 2016, and the emission increment will continuously decrease in the future. Clean-air Action Plan of Shanghai was proposed in 2013 to reduce emissions of SO2, NOx and PM. However, the city is currently faced with a number of challenges to fulﬁll the mitigation goals. Firstly, the fossil fuel-dominated energy structure is needed to further adjust and optimize. Secondly, the conﬂict between energy demands and mitigation goals will be more prominent. Thirdly, the superiority of renewable energy has

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C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

not been fully played by now, although the city has a good condition to develop and make use of renewable energy due to the characteristics of subtropical climate, abundant sunlight and long coastline. In response to such challenges, trying to achieving clean production of energy system through introducing CET and GC schemes will vigorously promote CO2 and air pollutant mitigation, as well as achieve clean production of local energy system.

3.2. Modeling formulation The city’s main carbon emitters include oil reﬁning, coking and power plants (as shown in Fig. 1). According to the city’s development plan and CO2 mitigation goal, local government set total emission permit for the whole energy system. Based on grandfathering principle (for oil reﬁning and coking) and benchmark

4 X 5 X

Min f ± ¼

± ~ CER ik AERik

þ

i¼1

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Cost of purchasing energy resources

þ

2 X 5 h X

6 X 5 X

principle (for power plants), a target quantity of CO2 emission quota is allocated to each carbon emitter. If the actual emission exceeds initial quota, carbon emitter can buy extra permit from CET market; otherwise, it will pay high penalty. Conversely, if the initial quota is not exceeded, carbon emitter can bring beneﬁt by selling the surplus to CET market. Moreover, coal- and natural gas-ﬁred power plants must buy a number of green certiﬁcates from wind and solar power plants to stimulate the development of renewable energy, thus resulting in CO2 and pollutant mitigation. Besides, complexities and uncertainties exist in many components of the city’s energy system, such as the random characteristics of carbon emission, type-2 fuzzy uncertainty in economic data, as well as interval uncertainty in energy demand. The developed TTSP method can be used to deal with these uncertainties. Then, based on TTSP method, the city’s energy problem can be formulated as follows:

± ~ CEP jk AEPjk

þ

2 X 4 X 5 X

± ± ± ~ CPC k bk IPPmk þ IPCnk

m¼1 n¼1

j¼1 k¼1

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

Cost of purchasing CO2 initial permit

Cost of purchasing energy products

# # 4 X 5 h X ± ± ± ± ± ~ ~ ~ ~ þ FCPmk CPmk þ VCPmk GTPmk þ ymk DGTPmk FCCnk CCnk þ VCCnk GTCnk þ znk DGTCnk 1 ank

m¼1

n¼1

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ ﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

Cost of energy processing

Cost of energy conversion

# # 2 X 5 h 4 X 5 h X X ± ± ± ± ~ ~ ~ ~ þ FEPmk þ VEPmk EOPmk BVPmk þ FECnk þ VECnk EOCnk BVCnk m¼1

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Cost of capacity expansion for energy processing

þ

2 X 5 X

n¼1 k¼1

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Cost of capacity expansion for energy conversion

4 X 5 X ± ± ± ± ± ± ~ ~ CPM CPM pk MRPpk GTPmk þ ymk DGTPmk APPmpk þ pk MRPpk GTCnk þ znk DGTCnk APCnpk

m¼1

k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

n¼1 k¼1

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

Cost of pollutant mitigation for energy processing

þ

2 X 5 X 5 X

Cost of pollutant mitigation for energy conversion

± ± ± ~ CCP mk MRMmk pmhk GTPmk þ ymk DGTPmk ACPmhk

m¼1

h¼1 k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ ﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Cost of CO2 mitigation for energy processing

þ

4 X 5 X 5 X

± 0 ± ± ~ CCC nk MRCnk pnhk GTCnk þ znk DGTCnk ACCnhk

n¼1 h¼1 k¼1

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Cost of CO2 mitigation for energy conversion

2 X 5 X 5 X

± ± ± ~ FSC k MRMmk pmhk GTPmk þ ymk DGTPmk ACPmhk

m¼1

h¼1 k¼1 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ ﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Financial subsidy of CO2 mitigation for energy processing

4 X 5 X 5 X

± 0 ± ± ~ FSC k MRCnk pnhk GTCnk þ znk DGTCnk ACCnhk

n¼1 h¼1 k¼1

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Financial subsidy of CO2 mitigation for energy conversion

þ

2 X 4 X 5 X 5 X

± ± 0 ± ± ~ ~ pmhk PEP mk AMmhk BVmhk þ pnhk PECnk ANnhk BInhk

m¼1 n¼1 h¼1 k¼1

|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} Penalty of excess CO2 emission

(6a)

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

Complexities of system

303

Uncertainty analysis

Imprecise economic/technical data

Intervals

Recourse actions

Type-2 fuzzy sets

Dynamic trading ratios

Interval programming

Two-stage programming

Type-2 fuzzy programming

Different recourse actions Type-2 fuzzy two-stage stochastic programming (TTSP)

Uncertain energy supply/demand Limited carbon emission quota

TTSP upper bound submodel

TTSP lower bound submodel

Various emission trading policies Random in carbon emission

Optimal solutions

Energy system of Shanghai

Local government Bring benifit

Pay extra cost Total emission permit Carbon emission trading market

Buy extra permit

Sell surplus permit

Assign initial permit Carbon emitters

Oil refining

Coking

Coal-fired power

Natural gas-fired power

Buy

Green certificate

Trading ratio Yes

Photovoltaic power

Sell

Actual emission exceed initial permit

Yes

Penalty

Wind power

No

Reallocate permit

Actual emission exceed reallocated permit

No

Stimulate the development of renewable energy, achieve clean production of energy system

Fig. 1. Framework of clean-production pattern for Shanghai’s energy system.

The objective function aims at minimizing the system cost covering cost for purchasing energy resources and products, cost for purchasing CO2 initial permit, cost for energy processing and conversion, cost for capacity expansion, cost for CO2 and pollutant mitigation, ﬁnancial subsidy for CO2 mitigation, as well as penalty for excess CO2 emission. A number of constraints are set to restrain the objective function, which can be deﬁned as follows: (1) Constraints for energy availabilities

AER± MAXAER± ik ik

ci; k

(6b)

± ± MAXAEPjk AEPjk

cj; k

(6c)

As a typical energy input city, all of coal, natural gas, crude oil, 90% of oil products, as well as 30% of electricity for Shanghai need to be imported from other regions. Therefore, the availabilities of energy from other regions are of great importance for the security of Shanghai’s energy system. Equations (6b) and (6c) are set to

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C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

make sure, in each planning period, the amount of energy purchasing must be not more than the available amount.

(6) Constraints for green certiﬁcate

(2) Constraints for mass balance

± AER± ECPmk GTPmk þ y± DGTPmk i ¼ 1; m ¼ 1; i ¼ 2; m ik mk ¼ 2; ck (6d) ± ECCnk GTCnk þ z± DGTCnk i ¼ 3; n ¼ 1; i ¼ 4; n AER± ik nk

¼ 2; ck (6e) The mass balance constraints accounts for the balance of energy ﬂows related to crude oil, washed coal, coal and natural gas. They are established to ensure that the input energy is greater than the output one. (3) Constraints for supply and demand balance

± ± m±j GTPmk þ y± DGTPmk þ AEPjk DEEjk m ¼ 1; j mk

¼ 1; 2; 3; 4; ck

(6f)

± ± þ y± DGTPmk þ AEPjk DEEjk m ¼ 2; j ¼ 5; ck GTPmk mk

(6g)

± ± GTCnk þ AEPjk þ z± DGTCnk 1 a± DEEjk j nk nk

n¼1

¼ 6; ck (6h) A city’s economic development and residents’ living improvement are closely related to energy resources. Therefore, constraints for supply and demand balance are generated to guarantee energy demand be met.

± APPmpk

GTPmk

2 X GTCnk GTCnk þ z± D GTC þ z± DGTCnk nk nk nk

n¼3

n¼1

a± nk

þ

y± mk

cn; k (6l)

GC scheme requires electricity producers to derive a ﬁxed quota of ﬁnal energy consumption from renewable energy sources. Constraints for green certiﬁcate are established for ensuring that electricity generated from coal- and natural gas-ﬁred power plants contains a certain ratio of electricity from wind power and photovoltaic power. (7) Constraints for CO2 emission

± ± AMmhk ACPmhk GTPmk þ y± DGTPmk 1 mk ± MRMmk cm; h; k

(6n)

± ± 1 ANnhk ACCnhk GTCnk þ z± D GTC nk nk ± MRCnk cn; h; k

(6o)

In CET scheme, CO2 emission permits can be reallocated to the most efﬁcient emitters via a market mechanism. However, excess CO2 may be emitted due to the randomness of CO2 generation. CO2 emission constraint represents that the excess amount of CO2 must be less than the actual emission amount. (8) Constraints for CO2-reallocation permit

± ± ± ACPmhk GTPmk þ y± DGTPmk 1 MRMmk AMmhk mk

(4) Constraints for pollutant emission 2 X

4 X

5 X

capacities (i.e., the sum of existing capacity and expanded capacity) for satisfying the production of energy processing/conversion.

DGTPmk 1

± MRPpk

± RPPmk

cm; h; k (6p)

m¼1

þ

2 X

± APCnpk GTCnk þ z± DGTCnk 1 a± nk nk

± ± ± 1 MRCnk GTCnk þ z± D GTC ANnhk ACCnhk nk nk

n¼1

± ± APpk 1 MRPpk

cp; k

± RPCnk

cn; h; k

(6i) For forcing emitters to mitigate pollutant emission and improving environmental quality, the government sets up a limitation for each pollutant. Constraint (6i) is formulated to secure that pollutants generated from energy processing and conversion technologies must be controlled within the limitations. (5) Constraints for capacity ± ± ± GTPmk þ y± DGTPmk CPmk þ EOPmk BVPmk mk

cm; k

± ± ± ± GTCnk þ z± DGTCnk CCnk þ EOCnk BVCnk OHnk nk

(6j) cn; k (6k)

Constraints (6j) and (6k) are formulated to insure sufﬁcient

(6q)

2 X m¼1

± RPPmk þ

5 X n¼1

± RPCnk þ

6 X

± ± ACEjk AEPjk ACk±

ck

(6r)

j¼1

Each CO2 emitter has a pre-regulated emission quota in the CET system. If this quota is not exceeded, the emitter can sell surplus to bring net beneﬁt. If the actual emission exceeds the pre-regulated quota, emitter must buy extra permit to reduce high penalty. The CO2 permits can thus be reallocated effectively instead of proportionally allocated to each emitter. Constraint for CO2-reallocation permit is used to guarantee the implement of CET. (9) Constraints for CO2 trading

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

2

5

X X ± ± ± ± ± ± IPPmk þ IPCnk RPPmk BVmhk RPCnk BInhk m¼1

n¼1

gk ATk±

ch; k (6s)

ATk± ¼

2 X

"

± ± ACPmhk GTPmk þ y± DGTPmk 1 IPPmk mk

m¼1 ± MRMmk

#

"

5

X ± ± ± ACCnhk 1 BVmhk þ IPCnk n¼1

GTCnk

þ ACk±

þ

z± nk

2 X

DGTCnk

± IPPmk

m¼1

5 X

#

± ± 1 MRCnk 1 BInhk

± IPCnk ch; k

n¼1

(6t) In an energy system with consideration of CET, the trading ratio (TR) (deﬁned as how many carbon emission permits a CO2 emitter can purchase from the carbon trading market) plays a critical role. This constraint indicates that the trading amount of emitters cannot exceed the practical trading amount from CET market managers. (10) Constraints for binary variable ± BVPmk

± BVCnk

¼1 ¼0

if capacity expansion is undertaken otherwise

(6u)

¼1 ¼0

if capacity expansion is undertaken otherwise

(6v)

± BVmhk ¼

if CO2 emission exceeds reallocated permit otherwise

1 0

(6x) ± ¼ BInhk

if CO2 emission exceeds reallocated permit otherwise

1 0

(6y) From a long-term planning point of view, the city’s energy demand keeps increasing due to economic development and

305

population growth. The existing equipment capacity may be not sufﬁcient to meet the increasing demand. Therefore, binary variables are needed to identify whether or not a capacity expansion needs to be undertaken. Moreover, binary variables are also needed to identify the relationship between CO2 reallocated quota and actual emission which is inﬂuenced by stochastic uncertainty. (11) Constraints for non-negative ± ± ± AER± ; AEPjk ; AMmhk ; ANnhk 0 ik

(6z)

This constraint requires that decision variables must be nonnegative. The speciﬁc nomenclatures of variables and parameters are given in Appendix B. 3.3. Data acquisition Modeling inputs are estimated based on local government ofﬁcial reports, statistical yearbooks and development plans, or derived from published papers. For example, the information of residual capacity and capacity expansion for energy processing and conversion technologies was derived from the 12th Five-Year-Plan on Shanghai energy development (2009e2015) and the 12th Five-Year-Plan on Shanghai electric power development (2009e2015); data of energy demand came from China energy statistical yearbook (2010e2014) and Shanghai statistical yearbook (2015). Tables 2 and 3 show costs of purchasing energy, purchasing CO2 initial permit, as well as ﬁxed and variable operation and maintenance for energy processing and conversion technologies that are presented in terms of type-2 fuzzy sets. These parameters are mainly collected from published papers by Piao et al. (2014) and Zhou et al. (2015b). 4. Results and discussion In this study, 15 scenarios were examined considering ﬁve carbon trading ratios (i.e. 0.80, 0.85, 0.90, 0.95, and 1.00) and three GC development plans. Plan 1 (P1) is the baseline plan in which the ﬁxed quotas of ﬁnal energy consumption from renewable energy sources for electricity producers would be 2.50%, 3.55%, 4.60%, 5.30% and 6.00% in period 1 to period 5, respectively (i.e., for each 100 GWh electricity, there must be 2.50 GWh that is generated from renewable energy sources in period 1). Plan 2 (P2) assumes that the quotas would decrease to 1.00% in period 1, 1.30% in period 2, 1.60% in period 3, 1.90% in period 4 and 2.00% in period 5. In plan 3 (P3), these quotas are all have an improvement, and would reach to 4.00%, 5.50%, 7.00%, 8.50% and 10.00%, correspondingly.

Table 2 Cost of purchasing energy and CO2 initial permit. Planning period k¼1 3

Crude oil ($10 /TJ) Washed coal ($103/TJ) Coal ($103/TJ) Natural gas ($103/TJ) Gasoline ($103/TJ) Kerosene ($103/TJ) Diesel oil ($103/TJ) Fuel oil ($103/TJ)

~ ~ ~ ð30:04; 32:15; 36:69Þ ~ ~ ~ ð4:37; 4:41; 4:44Þ ~ ~ ~ ð3:06; 3:21; 3:37Þ ~ ~ ~ ð10:08; 10:91; 11:58Þ

k¼2

k¼3

k¼4

k¼5

~ ~ ~ ð27:71; 32:10; 36:48Þ

~ ~ ~ ð26:12; 27:77; 29:53Þ

~ ~ ~ ð22:71; 26:20; 30:04Þ

~ ~ ~ ð22:27; 24:75; 27:20Þ

~ ~ ~ ð3:95; 3:99; 4:01Þ ~ ~ ~ ð2:77; 2:90; 3:05Þ

~ ~ ~ ð3:71; 3:73; 3:77Þ ~ ~ ~ ð2:60; 2:72; 2:86Þ

~ ~ ~ ð3:12; 3:29; 3:46Þ ~ ~ ~ ð2:64; 2:81; 2:98Þ

~ ~ ~ ð3:20; 3:35; 3:51Þ ~ ~ ~ ð2:68; 2:88; 3:08Þ

~ ~ ~ ð10:29; 11:81; 12:73Þ

~ ~ ~ ð10:72; 11:90; 12:86Þ

~ ~ ~ ð13:08; 14:63; 16:25Þ

~ ~ ~ ð13:51; 14:70; 16:38Þ

~ ~ ~ ð138:68; 141:50; 145:61Þ ~ ~ ~ ð113:81; 115:92; 119:50Þ

~ ~ ~ ð146:61; 149:93; 153:94Þ ~ ~ ~ ð121:74; 123:81; 127:83Þ

~ ~ ~ ð153:00; 154:44; 160:65Þ ~ ~ ~ ð129:06; 132:18; 135:52Þ

~ ~ ~ ð159:24; 162:38; 167:20Þ ~ ~ ~ ð135:88; 137:16; 142:67Þ

~ ~ ~ ð168:32; 172:55; 176:74Þ ~ ~ ~ ð140:40; 143:20; 147:42Þ

~ ~ ~ ð121:57; 124:20; 127:64Þ ~ ~ ~ ð71:92; 73:00; 75:52Þ

~ ~ ~ ð130:04; 133:24; 136:54Þ ~ ~ ~ ð76:25; 78:09; 80:06Þ

~ ~ ~ ð138:50; 141:32; 145:42Þ ~ ~ ~ ð80:68; 82:29; 84:72Þ

~ ~ ~ ð145:00; 143:50; 152:25Þ ~ ~ ~ ð86:28; 88:41; 90:59Þ

~ ~ ~ ð153:72; 156:11; 161:41Þ ~ ~ ~ ð92:04; 94:51; 96:64Þ

Coke ($103/TJ)

~ ~ ~ ð26:31; 25:60; 27:63Þ

~ ~ ~ ð32:94; 33:16; 34:58Þ

~ ~ ~ ð38:93; 39:08; 40:88Þ

~ ~ ~ ð40:36; 41:40; 42:38Þ

~ ~ ~ ð44:84; 45:18; 47:08Þ

Electricity ($103/GWh)

~ ~ ~ ð415:26; 425:69; 456:93Þ ~ ~ ~ ð0:046; 0:055; 0:062Þ

~ ~ ~ ð436:14; 451:36; 470:79Þ ~ ~ ~ ð0:054; 0:061; 0:069Þ

~ ~ ~ ð456:93; 466:17; 476:28Þ ~ ~ ~ ð0:062; 0:070; 0:077Þ

~ ~ ~ ð463:86; 477:26; 491:58Þ ~ ~ ~ ð0:069; 0:077; 0:085Þ

~ ~ ~ ð484:65; 495:10; 512:28Þ ~ ~ ~ ð0:077; 0:083; 0:092Þ

CO2 initial permit ($103/t)

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C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

Table 3 Cost of energy processing and conversion. Planning period

Fixed operation and maintenance cost Oil reﬁning ($103/TJ) Coking ($103/TJ) Coal-ﬁred power plant ($106/GW)

k¼1

k¼2

k¼3

k¼4

k¼5

~ ~ ~ ð3:85; 3:92; 4:00Þ ~ ~ ~ ð4:50; 4:61; 4:72Þ ~ ~ ~ ð146:00; 148:00; 150:00Þ

~ ~ ~ ð3:80; 3:89; 3:98Þ ~ ~ ~ ð4:35; 4:48; 4:60Þ ~ ~ ~ ð143:50; 146:00; 148:40Þ

~ ~ ~ ð3:76; 3:80; 3:85Þ ~ ~ ~ ð4:00; 4:15; 4:30Þ ~ ~ ~ ð140:00; 143:00; 146:00Þ

~ ~ ~ ð3:71; 3:75; 3:80Þ ~ ~ ~ ð3:80; 3:92; 4:05Þ ~ ~ ~ ð137:60; 142:10; 142:50Þ

~ ~ ~ ð3:70; 3:75; 3:80Þ ~ ~ ~ ð3:65; 3:78; 3:90Þ ~ ~ ~ ð135:00; 137:50; 140:00Þ

~ ~ ~ Natural gas-ﬁred power plant ($106/GW) ð125:00; 127:50; 130:00Þ ~ ~ ~ Wind power plant ($106/GW) ð690:00; 695:00; 700:00Þ ~ ~ ~ Photovoltaic power plant ($106/GW) ð732:00; 738:50; 745:00Þ

~ ~ ~ ~ ~ ~ ð120:00; 123:00; 126:00Þ ð116:00; 118:00; 120:00Þ ~ ~ ~ ~ ~ ~ ð680:00; 685:00; 690:00Þ ð670:00; 675:00; 680:00Þ ~ ~ ~ ~ ~ ~ ð725:00; 730:00; 735:00Þ ð725:00; 730:00; 735:00Þ

~ ~ ~ ~ ~ ~ ð113:00; 114:50; 116:00Þ ð110:00; 112:00; 114:00Þ ~ ~ ~ ~ ~ ~ ð660:00; 665:00; 670:00Þ ð650:00; 665:00; 660:00Þ ~ ~ ~ ~ ~ ð714:00; 719:00; 725:00Þ ð710:00; 715:00; 7 20:00Þ e

Variable operation and maintenance cost ~ ~ ~ Oil reﬁning ($103/TJ) ð2:15; 2:33; 2:50Þ ~ ~ ~ Coking ($103/TJ) ð3:80; 4:00; 4:20Þ ~ ~ ~ Coal-ﬁred power plant ($103/GWh) ð8:40; 8:63; 8:86Þ

~ ~ ~ ð2:00; 2:20; 2:40Þ ~ ~ ~ ð3:65; 3:83; 4:00Þ ~ ~ ~ ð8:30; 8:47; 8:64Þ

~ ~ ~ ð1:90; 2:05; 2:20Þ ~ ~ ~ ð3:50 3:68; 3:86Þ ~ ~ ~ ð8:20; 8:35; 8:50Þ

~ ~ ~ ð1:85; 1:99; 2:10Þ ~ ~ ~ ð3:40; 3:55; 3:69Þ

~ ~ ~ ð1:80; 1:95; 2:10Þ ~ ~ ~ ð3:20; 3:40; 3:60Þ

~ ~ ~ Natural gas-ﬁred power plant ($103/GWh) ð8:00; 8:17; 8:35Þ ~ ~ ~ Wind power plant ($103/GWh) ð49:00; 52:00; 55:00Þ

~ ~ ~ ð7:80; 7:95; 8:10Þ ~ ~ ~ ð46:50; 48:80; 51:00Þ

~ ~ ~ ð7:70; 7:82; 7:95Þ ~ ~ ~ ð44:00; 46:00; 48:00Þ

~ ~ ~ ð8:10; 8:26; 8:43Þ ~ ~ ~ ð7:50; 7:65; 7:80Þ

~ ~ ~ ð8:00; 8:15; 8:30Þ ~ ~ ~ ð7:30; 7:40; 7:50Þ

3

Photovoltaic power plant ($10 /GWh)

1200

4500

a) Oil refining

1100

3300

1000

2100

900 11402

3

Trading amount (10 t)

~ ~ ~ ~ ~ ð42:00; 4 4:40; 46:80Þ ð40:00; 42:50; 45:00Þ e ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ð164:10; 166:60; 169:00Þ ð160:00; 163:00; 166:00Þ ð157:50; 159:50; 161:40Þ ð154:00; 156:00; 158:10Þ ð150:01; 152:50; 155:00Þ

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

c) Coal-fired power

900 6000

8868

5000

6334

4000

3800 2200

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

e) Wind power

3000 690

1800

510

1400

330

1000

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4 =0.85

P1

P2 P3 k=5 =0.90

150

b) Coking

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

P1

P2 P3 k=3 =1.00

P1

P2 P3 k=4

P1

P2 P3 k=5

d) Natural gas-fired power

P1

P2 P3 k=1

P1

P2 P3 k=2

f) Photovoltaic power

P1

P2 P3 k=1 =0.95

P1

P2 P3 k=2 =0.80

Fig. 2. Lower bound of carbon trading under all scenarios.

4.1. Carbon trading In this study, CET and GC schemes are considered for helping achieve the CO2 mitigation target of the study area. Figs. 2 and 3 depict the solutions for CO2 trading under different scenarios which are helpful for investigating the impacts of CET and GC on CO2 mitigation. Coal- and natural gas-ﬁred power plants would be CO2 purchasers, while oil reﬁning, coking, wind power and photovoltaic power would be the roles of CO2 sellers. Results show that trading amounts for both CO2 buyers and sellers would increase with time. For example, under P1, the trading amounts for coal- and natural gas-ﬁred power would rise from [4373.16, 4810.81] 103 t and [3593.11, 3806.75] 103 t in period 1 to

[10303.38, 10372.17] 103 t and [5358.02, 5483.04] 103 t in period 5, respectively. This is due to the increasing of energy demand and total CO2 allowance quota. In addition, trading amounts for CO2 purchasers would increase with the increasing of g, while that for CO2 sellers would decrease. For example, CO2 trading amount for coal-ﬁred power would increase form [4373.16, 4810.81] 103 t under g ¼ 0.80 to [4550.73, 5006.15] 103 t under g ¼ 1.00; conversely, that for coking would decline slightly from [1016.78, 1327.52] 103 t to [977.11, 1275.12] 103 t. In fact, increasing of g indicates the increasing of tradable CO2 and the relaxing in mitigation requirement which would lead to the energy structure altering to fossil fuel-dominated one. Different plans are linked with different GC shares (i.e. different

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

2100

4500

a) Oil refining

1800

3300

1500

2100

11402

3

Trading amount (10 t)

1200

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

c) Coal-fired power

900 6000

8868

5000

6334

4000

3800 2250

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

e) Wind power

3000 690

1850

510

1450

330

1050

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4 =0.85

P1

P2 P3 k=5 =0.90

150

307

b) Coking

P1

P2 P3 k=1

P1

P2 P3 k=2

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

P1

P2 P3 k=3

P1

P2 P3 k=4

P1

P2 P3 k=5

P1

P2 P3 k=3 =1.00

P1

P2 P3 k=4

P1

P2 P3 k=5

d) Natural gas-fired power

P1

P2 P3 k=1

P1

P2 P3 k=2

f) Photovoltaic power

P1

P2 P3 k=1 =0.95

P1

P2 P3 k=2 =0.80

Fig. 3. Upper bound of carbon trading under all scenarios.

requirements of developing renewable energy) which would affect the city’s CO2 emission. Trading amounts for coal- and natural gasﬁred power plants would decrease with the increasing of GC shares; comparatively, that for wind and photovoltaic power plants would rise. For example, in period 1, trading amounts for natural gas-ﬁred power plant would be [3593.11, 3806.75] 103 t in P1, [3647.01, 3863.85] 103 t in P2 and [3533.05, 3743.12] 103 t in P3. Wind power plant would sell [1286.50, 1350.85] 103 t, [1272.50, 1336.15] 103 t, and [1303.22, 1368.41] 103 t in three plans, respectively. Results indicate that developing renewable energy would be helpful for addressing the issue of CO2 mitigation. Therefore, various measures associated with policy support schemes (such as GC scheme and ﬁnancial subsidies) need to be adopted to stimulate the development of renewable energy and curb CO2 emission of the city. 4.2. Pollutant emission Fossil fuel-dominated energy structure has caused serious air pollution for Shanghai, which brings great pressure on pollutant emission control and inconvenience to people’s life. Fig. 4 unfolds the results of pollutants (i.e. SO2, NOx and PM) emission under three GC plans and ﬁve trading ratios, implying that GC plan and trading ratio would play parts in pollutant emission. For example, in period 1, the amount of SO2 emission would increase from [211.72, 248.76] 103 t (g ¼ 0.80) to [215.33, 253.20] 103 t (g ¼ 1.00). In fact, a lower g indicates a conservative CO2 emission management strategy, resulting in the transition of fossil-fuel dominated energy structure into renewable energy-dominated one and reduction of pollutant emission; on the contrary, a higher g corresponds to a higher tradable amount of CO2 permit and a higher level of pollutant emission, implying a reduced assistance of energy structure optimization. Furthermore, GC scheme, as a mandatorily policy support scheme of developing

renewable energy, would also affect pollutant emission. Results show that amounts of pollutant emission would be reduced when GC shares are increased. For instance, in period 1, amount of NOx emission would decrease from [527.49, 553.16] 103 t when share of GC is 1.00% (P2) to [512.75, 537.25] 103 t when share of GC is 2.50% (P3). It is indicated that policies relative to optimizing energy structure and stimulating the development of renewable energy should be adopted to improve environmental quality. 4.3. Energy supply Energy resources used in Shanghai can be divided into nine types, including crude oil, washed coal, gasoline, kerosene, diesel oil, fuel oil, coke, coal and natural gas. Energy supply schemes (over the whole planning horizon) under all scenarios are presented in Fig. 5, indicating that energy supply varies with 15 scenarios in terms of amount. For example, under P1, amount of natural gas would rise from [3402.05, 3742.26] 103 TJ (g ¼ 0.80) to [3480.96, 3829.07] 103 TJ (g ¼ 1.00). Fig. 6 unfolds the solutions of energy supply under 15 scenarios in detail (period 1). From the results, amounts of crude oil, washed coal, coal and natural gas would augment with g; conversely, amounts of imported gasoline, kerosene, diesel oil, fuel oil and coke would be depleted. This is mainly because that the rising of g implies the increasing of tradable CO2 amount and the relaxing in CO2 mitigation, which would result in the growth of local energy generation activities and the reduction of energy importing. Moreover, in a GC scheme-a quantity-based policy support scheme, electricity producers are mandatorily required to derive a ﬁxed quota of ﬁnal energy consumption from renewable energy sources. Thus, energy resources connected with electricity generation would be affected by GC share. Amounts of coal and natural gas would have a decrease in response to the increase of GC share. For instance, amount of coal would reduce by [70.42, 77.85] 103 TJ when GC share ascend to 4.00% from 1.00%.

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

k=4

k=5

=0.80

=0.80

=0.90

=0.85 =0.90 =1.00

=1.00

=0.90

=0.85

=0.85 =0.90 =1.00

=0.80

=0.85

=0.85 =0.90

=1.00

=0.85 =0.90 =1.00

=0.95

=0.80

0

=0.95

=0.95

=0.80

=0.80

=0.80

=0.90

=0.90 =1.00

=0.85 =0.90

=0.90

=1.00

=0.85 =0.90 =1.00

=1.00

=0.95 =0.80

=0.85

=0.95 =0.80

=0.85

=0.95 =0.80

=0.90

=0.90 =1.00

=0.85

=0.85 =0.90 =1.00

=0.90 =1.00

=0.85 =0.90 =1.00

=0.95 =0.80

=0.85

=0.95 =0.80

=0.85

=0.95

=0.80

=0.85

=0.95

=0.95

=0.95

=0.95

=0.80

=0.80

=0.80

=0.80

=0.95

P1

=0.85

=0.90 =1.00

=0.85

=0.85

=0.90

=0.90 =1.00

=0.95

P3

=0.90

=0.85 =0.90

=0.90 =1.00

=1.00

=0.90 =0.95

=0.95 =0.80

=0.95 =0.80

=0.85

=0.95 =0.80

=0.85

=0.85 =0.90 =1.00

=1.00

=0.95

=1.00

=0.95 =0.80

=1.00

=0.85 =0.90

=0.90 =1.00 =0.95 =0.80

=0.85

=0.95

=0.80

=0.85

=0.95

=1.00

PM

Upper

=0.95

=0.95

=1.00 Lower

=0.95 =0.80

=0.80

=1.00

NOx

Upper

=0.90

=0.90 =1.00

=0.90 =1.00

=1.00 Lower

=0.95 =0.80

=0.80

=1.00

SO2

=0.95 Upper

=0.85

k=3 =0.80

=0.85

k=2 =0.80

=0.85

=0.80

=0.90 =1.00

k=1 Lower

=0.85

308

=0.95

P2

Fig. 4. Pollutant emission under three GC plans and ﬁve trading ratios.

Results imply that CET and GC schemes are effective for optimizing energy structure and promoting clean production of the local energy system.

4.4. Electricity supply Fig. 7 reveals the impact of CET and GC on electricity supply.

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

2750

2580

2930

2600

2700

2280

2370

525

540

=0.85

=0.90

=0.95

=1.00

1050

3550

3600

3450

525

=0.80

=0.85

=0.90

=0.95

3500

=1.00

3650

15100

16300

3550

3850

14600

15800

3450

3750

14100

=0.80

=0.85

Lower bound

=0.90

=0.95

15300

=1.00

3950

3

i) Natural gas

3

h) Coal

Upper (10 TJ)

3

3700

3350

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

3

=0.80

3800

f) Fuel oil

3650

=1.00

3

1100

=0.95

Lower (10 TJ)

1080

=0.90

3

1150

=0.85

2650

=1.00

16800

15600

3

1130

1030

3

=0.80

1200

g) Coke

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

510

2320

=1.00

Upper (10 TJ)

=0.95

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

=0.90

Upper (10 TJ)

1180

=0.85

3

3

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

3

=0.80

=0.95

3

555

=0.90

Upper (10 TJ)

540

3750

570

e) Diesel oil

=0.85

Upper (10 TJ)

2420

Lower (10 TJ)

2330

Upper (10 TJ)

555

2230

2550

=0.80

2470

d) Kerosenel

3

2830

=1.00

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

=0.95

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 =0.90

2800

c) Gasoline

3

=1.00

=0.85

Lower (10 TJ)

=0.95

=0.80

Upper (10 TJ)

=0.90

3

3

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 =0.85

2500

Lower (10 TJ)

12300

3

10300

Lower (10 TJ)

12400

Upper (10 TJ)

10400

Lower (10 TJ)

2650

b) Washed coal

Upper (10 TJ)

3030

12500

3

Lower (10 TJ)

2660

10500

2380

Lower (10 TJ)

2700

2740

a) Crude oil

=0.80

Lower (10 TJ)

3130

12600

10600

309

=0.80

=0.85

=0.90

=0.95

3650

=1.00

Upper bound

Fig. 5. Energy supply under 15 scenarios.

Results show that along with the decreasing of g, electricity from coal- and natural gas-ﬁred power would decrease from [333.01, 336.18] 103 GWh and [104.07, 105.06] 103 GWh to [326.27, 329.21] 103 GWh and [99.47, 100.36] 103 GWh, respectively. Such a reduction is mainly due to the diminution of tradable CO2 permit limits the development of coal- and natural gas-ﬁred power. To ensure the city’s electricity demand being satisﬁed, electricity from other conversion technologies and other regions would raise correspondingly. In addition, the amount of electricity from each conversion technology would vary with three plans. In detail, electricity from wind power and photovoltaic power would increase with the raising of GC shares, while that from coal- and natural gas-ﬁred power would decrease. Although CET and GC schemes are helpful for developing renewable energy, electricity from renewable energy (i.e. wind and solar) still contributes to a small level in the city’s electricity-supply mix by contrast with coaland natural gas-ﬁred power. For instance, when g ¼ 0.80, coal-ﬁred power would account for [63.12, 64.02] %, natural gas-ﬁred power for [19.24, 19.52] %, while renewable energy contribute [6.57, 6.61] % (i.e., wind power for 5.20e5.23%, photovoltaic power for 1.36e1.38%) which is far lag behind developed countries. Therefore, incentive measures or laws related to stimulate utilization of renewable energy should be adopted to fully play the advantages of the city’s subtropical climate, abundant sunlight and wind, thus further promoting the cleaner production of local electricity. 4.5. System cost For Shanghai’s energy system, system cost includes cost of purchasing energy resources and products, cost of purchasing CO2

initial permit, cost of energy processing and conversion, cost of capacity expansion, cost of pollutant mitigation, cost of CO2 mitigation, ﬁnancial subsidy of CO2 mitigation and penalty of excess CO2 emission. Fig. 8 presents the system cost under different trading ratios and plans, indicating that the system cost would decrease with the increase of trading ratio g and increase with the rising of GC shares. For example, the system cost would be $[4744.77, 5537.57] 109 when g ¼ 0.80; when g ¼ 1.00, it would be $[4631.62, 5414.51] 109. A higher g implies that more CO2 emission permit would be available for emitters, which would lead to a lower amount of excess CO2 emission and a lower penalty of excess emission. Decisions at a higher g would result in a lower system cost, but the capacity of dealing with emergency for carbon trading market and the requirement of CO2 mitigation would decrease; on contrary, decisions at a lower g would be helpful for mitigating CO2 emission and maintaining the stability of carbon trading market, but with a higher system cost. Additionally, the system cost would increase from $[4691.76, 5476.33] 109 under P2 (corresponding to the lowest GC share), $[4744.77, 5537.57] 109 under P1, to $[4797.78, 5598.44] 109 under P3 (corresponding to the highest GC share). This is due to the high operating cost of wind power and photovoltaic power which are beneﬁcial for cleaner-production pattern of local energy system. In practice, when the plan aims to a lower system cost, the mitigation requirements may not be adequately met; however, planning with a higher system cost may guarantee that these requirements be met and energy system turns into cleaner-production pattern. Therefore, there is a tradeoff among system cost, trading ratio and GC share.

P1

P2

P3

P1

P2

P3

P1

P2

P3

310

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

=1.00 =0.95 =0.90 =0.85 =0.80 =1.00 =0.95 =0.90 =0.85 =0.80 =1.00 =0.95 =0.90 =0.85 =0.80 =1.00 =0.95 =0.90 =0.85 =0.80 =1.00 =0.95 =0.90 =0.85 =0.80 =1.00 =0.95 =0.90 =0.85 =0.80

2131.37

2467.71

726.06

838.60

363.73

395.03

2129.26

2465.27

722.45

834.43

365.55

397.00

2125.04

2460.38

718.85

830.28

367.38

398.99

2120.82

2455.49

715.28

826.15

369.22

400.98

2110.27

2443.38

708.20

2131.37

2467.71

817.97

726.06

401.30

369.51

838.60

363.73

395.03

2129.26

2465.27

722.45

834.43

365.55

397.00

2125.04

2460.38

718.85

830.28

367.38

398.99

2120.82

2455.49

715.28

826.15

369.22

400.98

2110.27

2443.38

708.20

2131.37

2467.71

817.97

726.06

401.30

369.51

838.60

363.73

395.03

2129.26

2465.27

722.45

834.43

365.55

397.00

2125.04

2460.38

718.85

830.28

367.38

398.99

2120.82

2455.49

715.28

826.15

369.22

400.98

2110.27

2443.38

708.20

817.97

401.30

369.51

455.73

486.05

108.96

114.41

837.74

879.19

458.01

488.48

109.50

114.98

841.93

883.58

460.30

490.93

110.05

115.55

846.16

888.00

462.60

493.38

110.60

116.13

850.37

892.44

462.97

493.78

110.69

116.22

851.05

893.15

455.73

486.05

108.96

114.41

837.74

879.19

458.01

488.48

109.50

114.98

841.93

883.58

460.30

490.93

110.05

115.55

846.16

888.00

462.60

493.38

110.60

116.13

850.37

892.44

462.97

493.78

110.69

116.22

851.05

893.15

455.73

486.05

108.96

114.41

837.74

879.19

458.01

488.48

109.50

114.98

841.93

883.58

460.30

490.93

110.05

115.55

846.16

888.00

462.60

493.38

110.60

116.13

850.37

892.44

462.97

493.78

110.69

116.22

851.05

893.15

147.65 =1.00 148.39 =0.95 149.13 =0.90 149.88 =0.85 150.00 =0.80 147.65 =1.00 148.39 =0.95 149.13 =0.90 149.88 =0.85 150.00 =0.80 147.65 =1.00 148.39 =0.95 149.13 =0.90 149.88 =0.85 150.00 =0.80 Crude oil Washed coal

155.04

2746.07

3025.07

575.98

633.01

155.81

2732.41

3010.02

573.12

629.86

156.59

2718.82

2995.05

570.27

626.73

157.37

2705.29

2980.15

567.43

623.61

157.50

2691.83

2965.32

562.92

618.66

155.04

2817.91

3104.49

155.81

2803.89

3089.05

156.59

2789.94

3073.68

580.53

638.58

157.37

2776.06

3058.39

577.64

635.41

157.50

573.06

630.36

568.35

644.98

583.43

641.78

2762.25

3043.17

155.04

2790.01

3069.01

580.54

638.60

155.81

2776.13

3053.74

577.66

635.42

156.59

2762.32

3038.55

574.78

632.26

157.37

2748.57

3023.43

571.92

629.11

3008.39

567.38

157.50

Gasoline

2734.90

Kerosene

Diesel oil

Fuel oil

Coke

624.12

Coal

Natural gas

Fig. 6. Impact of CET and GC on energy supply (period 1).

4.6. Impact of CET and GC on electricity supply Carbon emission trading (CET) is a cost-effective instrument to meet mitigation targets by effectively allocate carbon permit through a market mechanism. Green certiﬁcate (GC), as a mandatorily policy support scheme, is also capable of mitigating carbon

emission by promoting electricity generation from renewable energy. To investigate the impacts of CET and GC on Shanghai’s energy system, four schemes associated with carbon mitigation scheme are considered, including mitigation with both CET and GC (WTG), with GC only (WG), with CET only (WT) and without CET and GC (OTG). Results of electricity supply under these four cases are

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

388 352 372 345

311

a1) k=1

a2) k=2

a3) k=3

a4) k=4

a5) k=5

b1) k=1

b2) k=2

b3) k=3

b4) k=4

b5) k=5

c1) k=1

c2) k=2

c3) k=3

c4) k=4

c5) k=5

d1) k=1

d2) k=2

d3) k=3

d4) k=4

d5) k=5

e1) k=1

e2) k=2

e3) k=3

e4) k=4

e5) k=5

356 338 340 331

324 177 117 157 112

117 102

97 54 34 31 46 38 28 30 25

22 8.3 21.9 17.9 7.7

7.1 13.9 9.9 6.5

5.9 62 54 46

P3

P1

P2

P3

=0.80

=0.85

P1

P2

P3

=0.90

P1

P2

=0.95

P3

=1.00

Fig. 7. Electricity supply [(a) coal-ﬁred, (b) natural gas-ﬁred, (c) wind, (d) photovoltaic and (e) importing].

5700 5500 5300 5100 4900 4700 4500 Lower bound

Upper bound

P1

Lower bound

Upper bound

P2 Fig. 8. Solutions of system cost.

Lower bound

Upper bound

P3

P1

P2

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Upper

Lower

P2

System cost ($109)

P1

Lower

30

Upper

38

Lower

3

Electricity supply (10 GWh)

137 107

P3

WTG

312

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

Coal-fired power

Natural gas-fired power

Wind power

Photovoltaic power

Importing

k=1

k=1

k=1

k=1

k=1

k=5

k=2

k=4

k=5

k=2

k=4

k=3

WG

k=1

k=2

k=3

k=2

k=4

WT

k=2

k=3

OTG

k=2

k=3

k=2

k=4

Lower bound

k=4

k=5

k=2

k=4

k=3

k=2

k=4

k=3 k=1

k=5

k=3

k=3 k=1

k=2

k=4

k=2

k=4

k=2

k=1

k=5

k=3

k=5

k=3

k=5

k=3

k=3 k=1

k=1

k=1

k=5

k=2

k=4

k=2

k=4

k=2

k=4

k=3

k=5

k=3

k=5

k=3

k=5

k=1

k=1

k=1

k=5

k=2

k=4

k=2

k=4

k=2

k=4

k=3

k=5

k=3

k=5

k=1

k=5

k=1

k=1

k=5

k=4

k=4

k=3

k=5

k=1

k=4

k=2

k=1

k=5

k=4

k=5

k=3

k=5

k=2

k=4

k=3

Upper bound

Fig. 9. Comparison of electricity supply under different schemes (103 GWh).

presented in Fig. 9, indicating that generation activities of coal- and natural gas-ﬁred power would be conﬁned by the implement of CET and GC. For example, in period 1, electricity supply from coalﬁred power would be [326.27, 329.21] 103 GWh under WTG, [327.90, 330.85] 103 GWh under WG, [329.53, 332.50] 103 GWh under WT, and [331.17, 334.15] 103 GWh under OTG. By contrast, electricity from wind power and photovoltaic power would be encouraged by CET and GC. Moreover, results also show that the development of renewable energy would be promoted to the most extent under WTG. For instance, the lower bound of electricity from renewable energy would reach 33.46 103 GWh (including 26.51 103 GWh from wind power, and 6.95 103 GWh from photovoltaic power), which would have an increase of 1.10% compared with WG and 2.01% in comparison with WT. Therefore, from view of achieving the optimal clean-production pattern for local energy system, both CET and GC schemes should be adopted by managers.

CO2 which are emitted during the generation of the imported energy and electricity. To investigate the impact of importing energy and electricity on carbon emission, two cases are considered, including carbon emission trading with consideration of CO2 from imported energy and electricity (CCE) and without consideration of CO2 from imported energy and electricity (NCE). Fig. 10 shows the results of carbon emission trading under CCE and NCE. Results indicate that CO2 purchases for coal- and natural gas-ﬁred power would increase under NCE; however, CO2 sales for oil reﬁning, coking, wind power and photovoltaic power would decline slightly. For example, in period 1, trading amount for coal-ﬁred power would be [4421.35, 4862.85] 103 t under NCE, [48.19, 52.04] 103 t higher than that under CCE. Trading amount for wind power would be [1268.69, 1337.48] 103 t under NCE, which would be less than [1286.50, 1350.85] 103 t (under CCE). This is because that under NCE, more CO2 permits would be available, which would be helpful for the development of energy processing/conversion technologies with lower operating cost and higher emission rate.

4.7. Comparison of carbon emission trading from CCE and NCE 5. Conclusions With the rapid economic development and continuous population growth, energy and electricity demand is increasing year by year. As a highly urbanized city, but with no energy resources, Shanghai is facing energy and electricity shortages. Therefore, for ﬁlling the demand gap, energy and electricity must be imported from other regions. Although the imported energy and electricity are generated in other regions, Shanghai should be responsible for

In this study, a two-stage type-2 fuzzy stochastic programming (TTSP) method has been developed for supporting clean production of energy systems under uncertainty. TTSP method integrates the optimization techniques of two-stage stochastic programming (TSP), type-2 fuzzy programming (TFP) and interval programming (IP) into a general framework, which can not only deal with

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

2300

4600 a) Oil refining

3

Trading amount (10 t)

313

b) Coking

2000

3840

1700

3080

1400

2320

1100

1560

800 11000

800 5900 c) Coal-fired power

d) Natural gas-fired power

9600

5400

8200

4900

6800

4400

5400

3900

4000 2300

3400 700 e) wind power

f) Photovoltaic power

2060

600

1820

500

1580

400

1340

300

1100

200 CCE NCE CCE NCE CCE NCE CCE NCE CCE NCE k=1

k=2

Lower bound for CO2 seller

k=3

k=4

CCE NCE CCE NCE CCE NCE CCE NCE CCE NCE

k=5

k=1

Upper bound for CO2 seller

k=2

k=3

Lower bound for CO2 buyer

k=4

k=5

Upper bound for CO2 buyer

Fig. 10. Comparison of carbon trading under CCE and NCE.

uncertainties in the forms of interval values, type-2 fuzzy sets and random variables, but also have advantage in policy analysis. Then, TTSP model has been applied to supporting the optimal cleanproduction pattern of Shanghai’s energy system on the basis of carbon emission trading (CET) and green certiﬁcate (GC). Results obtained from the TTSP model show that: (a) coal-ﬁred power is the primary CO2 and pollutant emitter, and it tends to cleanproduction pattern through introducing CET and GC schemes (contributing to about 2.43% increment of renewable energy); (b) CO2 and pollutants (i.e. SO2, NOx and PM) emissions can be reduced through replacing fossil fuels by renewable energy sources (e.g., wind and photovoltaic power); (c) the combination of CET and GC will be the optimal clean-production pattern for Shanghai’s energy system compared with single CET or GC scheme (2.43% increment of renewable energy for integration of CET and GC, 1.40% for GC and 0.42% for CET). The results are helpful for adjusting energy and electricity supply, making appropriate mitigation plan, as well as realizing the optimal clean production of the local energy system.

suggestions.

Appendix A. Solution method According to an interactive algorithm developed by Wang and Huang (2015), the TTSP model can be converted into two deterministic submodels. However, since the ﬁrst-stage decision variables x± are considered as uncertain inputs, it is difﬁcult to j þ determine whether its lower bound ðx j Þ or upper bound ðxj Þ cor-

responds to the lower bound of system cost. Therefore, an optiis deﬁned by having zj ð0 zj 1Þ being decision mized set of x± j

variables (Huang and Loucks, 2000). Let x± ¼ x j þ zi Dxj where j

Dxj ¼ xþ x j . When the objective is minimizing system cost, the j lower bound of objective function (i.e. f ) is ﬁrstly formulated and solved; then, upper bound f þ can be formulated based on the ﬁrst solution of f . In detail, f can be formulated as follows:

Acknowledgements This research was supported by the Beijing Natural Science Foundation (L160011), National Key Research Development Program of China (2016YFC0502803 and 2016YFA0601502), the 111 Project (B14008), and Fundamental Research Funds for the Central Universities (JB2016191). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and

Min f ¼

k1 X j¼1

þ

n1 X

c j xj þ

j¼k1 þ1 n2 s X X

j¼k2 þ1 h¼1

subject to:

þ c j xj þ

þ pjh d j yj

k2 X s X j¼1 h¼1

pjh d j yj

(A.1a)

314

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

k1 n1 þ X X arj Sign aþ x þ arj Sign a xþ b r j rj rj j

(A.1b)

j¼k1 þ1

j¼1

k1 þ n1 X X aij Sign aþ x þ aij Sign a xþ j ij ij j j¼k1 þ1

j¼1

k2 n2 X X 0 0 0 þ y þ þ arj Sign a0þ a rj Sign arj yjh jh rj

x j

w h

(A.1d) (A.1e)

y jh 0; j ¼ 1; 2; :::; k2 ; ch

(A.1f)

yþ 0; j ¼ k2 þ 1; k2 þ 2; :::; n2 ; ch jh

(A.1g) xþ ðj ¼ k1 þ 1; k1 þ 2; :::; n1 Þ, jopt

x jopt ðj ¼ 1; 2; :::; k1 Þ, ¼ 1; 2; :::; k2 Þ and

yþ ðj jhopt

¼ k2 þ 1; k2 þ 2; :::; n2 Þ can be ob-

tained through model (A 1). Based on the above solutions, the upper bound f þ can be formulated as follows: k1 X

þ cþ j xj þ

j¼1

s X

n1 X j¼k1 þ1

cþ j xj þ

k2 X s X

þ pjh dþ j yj þ

j¼1 h¼1

n2 X j¼k2 þ1

pjh dþ y j j

(A.3b)

i h ± þ fopt ¼ fopt ; fopt

(A.3c)

(A.2a) subject to: k1 n1 X X þ arj Sign a xþ þ arj Sign aþ x bþ r rj j j rj

(A.2b)

j¼k1 þ1

j¼1

k1 n1 X X þ aij Sign a xþ þ aij Sign aþ x ij j j ij k2 n2 X X 0 þ 0 þ þ arj Sign a0 arj Sign a0þ rj yjh þ rj yjh j¼k2 þ1

j¼1

wþ h

(A.2c)

x j 0; j ¼ k1 þ 1; k1 þ 2; :::; n1

(A.2d)

xþ j 0; j ¼ 1; 2; :::; k1

(A.2e)

y jh 0; j ¼ k2 þ 1; k2 þ 2; :::; n2 ; ch

(A.2f)

yþ 0; j ¼ 1; 2; :::; k2 ; ch jh

(A.2g)

Solutions of

x jopt ðj

ik

purchasing amount of energy resource i in period k (103 TJ) AER± ik ~ CEP purchasing cost of energy product j in period k ($103/TJ or

¼ k1 þ 1; k1 þ 2; :::; n1 Þ,

xþ ðj jopt

$103/GWh) ± amount of purchasing energy product j in period k (103 TJ AEPjk or 103 GWh) 3 ~ CPC k cost of purchasing CO2 initial permit in period k ($10 /t) bk ratio of purchased permit in initial permit in period k (%) ± CO2 permit for energy processing technology m in period k IPPmk (103 t) ± initial CO2 permit for energy conversion technology n in IPCnk period k (103 t) ~ FCP ﬁxed operation and maintenance cost of energy promk

j¼k1 þ1

j¼1

f ± system cost ($106) i type of purchasing energy resources, with i ¼ 1 for crude oil, i ¼ 2 for washed coal, i ¼ 3 for coal, i ¼ 4 for natural gas j type of purchasing energy products, with j ¼ 1 for gasoline, j ¼ 2 for kerosene, j ¼ 3 for diesel oil, j ¼ 4 for fuel oil m energy processing technology, with m ¼ 1 for oil reﬁning, m ¼ 2 for coking n energy conversion technology, with n ¼ 1 for coal-ﬁred power plant, n ¼ 2 for natural gas-ﬁred power plant, n ¼ 3 for wind power plant, n ¼ 4 for photovoltaic power plant k planning period h CO2 generation level, with h ¼ 1 for low, h ¼ 2 for lowmedium, h ¼ 3 for medium, h ¼ 4 for medium-high, h ¼ 5 for high p type of air pollutant, with p ¼ 1 for SO2, p ¼ 2 for NOx, p ¼ 3 for PM ~ purchasing cost of energy resource i in period k ($103/TJ) CER jk

h¼1

þ

i h þ y± ¼ y jhopt ; yjhopt ; cj; h jhopt

Appendix B. List of symbols

0; j ¼ k1 þ 1; k1 þ 2; :::; n1 xþ j

Min f þ ¼

(A.3a)

(A.1c)

0; j ¼ 1; 2; :::; k1

y ðj jhopt

cj

j¼k2 þ1

j¼1

i h þ x± ¼ x jopt ; xjopt ; jopt

¼ 1; 2; :::; k1 Þ,

y ðj ¼ k2 þ 1; k2 þ 2; :::; n2 Þ and yþ ðj ¼ 1; 2; :::; k2 Þ can be objhopt jhopt tained. Through solving models (A 1) and (A 2), optimal solutions will be obtained as follows:

cessing technology m in period k ($103/TJ) ± capacity of energy processing technology m in period k CPmk (103 TJ) ~ VCP mk variable operation and maintenance cost of energy processing technology m in period k ($103/TJ) ± generation target of energy processing technology m in GTPmk period k (103 TJ or 103 GWh) (ﬁrst-stage decision variable), ± þ y± DGTP , ¼ GTPmk where GTPmk mk mk þ , y 2½0; 1 DGTPmk ¼ GTPmk GTPmk mk

~ FCC nk ﬁxed operation and maintenance cost of energy conversion technology n in period k ($106/GW) ± capacity of energy conversion technology n in period k CCnk (GW) ~ VCC variable operation and maintenance cost of energy pronk

cessing technology m in period k ($103/GWh) generation target of energy conversion technology n in GTCnk period k (103 GWh) (ﬁrst-stage decision variable), where ± þ þ z± DGTC , , ¼ GTCnk DGTPmk ¼ GTPmk GTPmk GTCnk nk nk znk 2½0; 1

C. Suo et al. / Journal of Cleaner Production 161 (2017) 299e316

a±nk ratio of green certiﬁcate in electricity generated by nonrenewable energy for energy conversion technology n in period k (%) ~ FEP mk ﬁxed cost of capacity expansion for energy processing technology m in period k ($106) ~ variable cost of capacity expansion for energy processing VEP mk

technology m in period k ($106/TJ) ± capacity expansion option of energy processing techEOPmk nology m in period k (TJ) ± binary variable for identifying whether or not a capacity BVPmk expansion action of processing technology m needs to be undertaken in period k ~ ﬁxed cost of capacity expansion for energy conversion FEC nk

technology n in period k ($106) ~ variable cost of capacity expansion for energy conversion VEC nk

technology n in period k ($106/GW) ± capacity expansion option of energy conversion techEOCnk nology n in period k (GW) ± binary variable for identifying whether or not a capacity BVCnk expansion action of conversion technology n needs to be undertaken in period k ~ cost of unit pollutant p mitigation in period k ($103/t) CPM pk

± MRPpk mitigation ratio of pollutant p in period k (%)

± APPmpk amount of pollutant p emission for unit energy pro-

cessing m in period k (103 t/TJ) ± pollutant p emission for unit energy conversion n in APCnpk period k (103 t/GWh) ~ CCP mk cost of CO2 mitigation for processing technology m in period k ($103/t) ± CO2 mitigation efﬁciency of processing technology m in MRMmk period k (%) pmhk probability of CO2 generation level h occurrence for processing technology m in period k (%) ± amount of CO2 emission per unit generation amount for ACPmhk processing technology m in period k (t/TJ) ~ cost of CO2 mitigation for conversion technology n in CCC nk

period k ($103/t) ± CO2 mitigation efﬁciency of conversion technology n in MRCnk period k (%) p’nhk probability of CO2 generation level h occurrence for conversion technology n in period k (%) ± amount of CO2 emission per unit generation amount for ACCnhk conversion technology n in period k (t/GWh) ~ ﬁnancial subsidy of CO2 mitigation in period k ($103/t) FSC k

~ PEP mk penalty of excess CO2 released from processing technology m in period k ($103/t) ± amount of excess CO2 generated from processing techAMmhk nology m in period k under level h (103t, ± ± ± ± ± ¼ GTPmk ACPmhk ð1 MRPmk Þ RPPmhk ) AMmhk ± binary variable for identifying whether or not the actual BVmhk emission exceeds reallocation permit for processing technology m under level h in period k ~ PEC penalty of excess CO2 released from conversion technolnk

ogy n in period k ($103/t) ± amount of excess CO2 generated from conversion techANnhk nology n in period k under level h (103t, ± ± ± ± ± ANnhk ¼ GTCnk ACCnhk ð1 MRCnk Þ RPCnhk ) ± binary variable for identifying whether or not the actual BInhk emission exceeds reallocation permit for conversion technology n under level h in period k

315

MAXAER± maximum available amount of energy resource i in ik period k (103 TJ) ± maximum available amount of energy product j in MAXAEPjk period k (103 TJ or 103 GWh) ± the amount of energy resource i consumption per unit ECPmk generation amount of processing technology m in period k (TJ/ TJ) ± the amount of energy resource i consumption per unit ECCnk generation amount of conversion technology n in period k (TJ/ GWh) ± electricity generation hour of emitter j in period k (h) gjk

m±j generation ratio of energy product j (%)

± DEEjk total demand of energy j in period k (103 TJ or 103 GWh)

± APpk allowance of air pollutant p in period k for the entire system

± RPPmk amount of CO2 reallocation for processing technology m in period k (103t) ± amount of CO2 reallocation for processing technology m in RPCnk period k (103t) ACk± amount of CO2 emission allowance for entire energy system in period k (103t) gk trading ratio of CO2 in period k (%) (trading ratio ¼ practical amount of CO2 trading from market managers/available amount of CO2 trading

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Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certificate schemes

Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certificate schemes

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