International Journal of Sustainable Energy Planning and Management Vol. 29 2020 91 *Corresponding author - e-mail: maleki@sharif.edu International Journal of Sustainable Energy Planning and Management Vol. 29 2020 91–108 ABSTRACT The Iranian government has set a target of a 20% share of non-fossil fuel electricity generation by 2030, whose main result is reducing Green House Gas (GHG) emissions (about 182 million tonnes in 2017) to achieve the targets pledged under the Paris Climate Accord. So, this paper presents a comprehensive model on the expansion of non-fossil technology to evaluate the impact of increasing their share in Iran’s electricity supply system. This analytical approach is based on system dynamics (SD) that was developed based on dynamic behavior of electricity market, with an emphasis on the expansion of non-fossil fuels (solar photovoltaics, wind turbines, expansion turbines, and hydro power) in the supply side of this model by electricity price reformation. For this purpose, we developed four scenarios with different share percent of non-fossil technologies in Iran’s electricity system. The findings demonstrate that electricity price must be determined based on the costs of non-fossil technologies, as well as based on fossil fuel prices which are low in the current energy supply system and its value was predicted that increased to maximum of 2.03 cent USD/kWh. In conclusion, in the best scenario, the Paris Climate Accord criteria is achieved with a 20% growth of non-fossil fuels and increasing electricity price to 2.54 cent USD/ kWh in 2030 with 0.19 price elasticity of emission. 1. Introduction The energy sector plays a major role in global GHG emissions with about a 75-percent share, and there are critical actions in this sector that can make or break efforts to achieve global climate goals aimed at tackling the increasing global average temperatures started since the mid-20th century. Therefore, one of the most import- ant, globally adopted agreements was met in December 2015 called the historic Paris Agreement, which includes GHG mitigation actions covering the period 2020-2030, and its long-term goals include limiting the mentioned temperature rise to well below 2°C and pursuing efforts to limit the rise to 1.5°C [1]. Iran intends to participate by reducing its GHG emissions in 2030 by 4% compared to 2020 based on its Intended Nationally Determined Contributions (INDC). One of the most important solutions in GHG emissions mitigation is increasing the expansion of non-fossil power plants, such as renewable resources, hydropower, and expansion turbines, in the energy supply system. In 2018, about 2,807 PJ distributed on 86% NG, 8% gas oil and 5% fuel oil was consumed by power plants in the electricity supply system, and because of shortage of natural gas in cold months, this sector had to use gas oil for gas turbines and fuel oil for steam technologies [2]. As a result, about 1,280 Mt of CO2 equivalent of GHGs were emitted to Iran’s atmo- sphere, which is equal to more than 29% of the total Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 Ali Abbasi Godarzi, Abbas Maleki* Department of Energy Engineering, Sharif University of Technology, Azadi Street, P.O. Box 11155-8639, Tehran, Iran Keywords: System Dynamics; Green House Emission; Electricity Price; Modeling; URL: https://doi.org/10.5278/ijsepm.5692 mailto:maleki@sharif.edu https://doi.org/10.5278/ijsepm.5692 92 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 emissions in the country, demonstrating the importance of the energy supply system for GHG reduction (Fig. 1) [3]. Thus, new energy resources and technologies, such as non-fossil fuels, are required to ensure sufficient energy supply for the growing demand. The process of implementing Iran’s unconditional mitigation of GHG emissions will be facilitated and speeded up with increasing the share of non-fossil fuels in the electricity supply system, and Iran’s government intends to achieve a 20% share [4]. This share, as shown in Fig. 2, was 5% in 2018 [2]. This paper describes an analysis performed to assess reaching a 20% share for non-fossil fuels in the electric- ity supply system for Iran to meet these emission targets pledged in COP21. In particular, it attempts to determine the electricity price such that it enables non-fossil fuel power plants to compete with conventional power plants in gaining electricity market share and to compute the resulting overall costs. So, the main output of this paper is electricity price which determine share percent of non-fossil fuel power plant in Iran’s electricity produc- tion system. However, energy price reformation has not been effectively pursued in Iran, and therefore, there has not been a successful sensible reduction in utilizing fossil fuels in recent years. Iran’s parliament passed an energy reformation in 2010 and according to it, fossil fuel prices should increase to international prices within five years [5]. Hence, based on changes in fossil prices, share of these fuels should be decreased in Iran’s electricity production system, but this share changed only from 95.55% to 91.64% on years between 2010 to 2018 [2]. Indeed, the main concern of this paper is the possibility of electricity prices for the development of non-fossil power plants which, on one hand, can satisfy the growing electricity demand and, on the other hand, can help achieve a 20% share of non-fossil fuels in primary energy by 2030, which can mitigate Iran’s GHG emissions according to the Paris Accord targets. Electricity price has high impact of energy consump- tion in Iran and is an important input to all demand sec- tors that were shown in Fig. 1. So, this policy tool could Household, Commercial and Public (25%) Industry (17%) Transport (24%) Agriculture (2%) Refinery (3%) Power plants (29%) Figure 1: Shares of energy sections in GHG emissions in Iran, 2017 [3] Nomenclature Pt Electricity price in cent USD/kWh Pt I Electricity price index in cent USD/kWh AT Adjustment time in hour Sr Reference supply in kWh Dr Reference demand in kWh ES Effect of price on supply ED Effect of price on demand es Price elasticity of supply ed Price elasticity of demand EBp Effect of demand per supply balance on price F Import coefficient s Price sensivity of demand per supply balance λi CO2 equivalent emission factors in grCO2/ kWh λi . CO2 CO2 emission factor in grCO2/kWh λi . C Carbon emission factor in grC/kWh λi . N2O N2O emission factor in grN2O /kWh λi . CH4 CH4 emission factor in grCH4/kWh Pceillingt Price ceiling in cent USD/kWh α Variation of price ceiling CFi Capacity factor in % Oci.t Operation costs in cent USD/kWh Fci.t Fuel costs in cent USD/kWh soi.t Subsidy of power plants in cent USD/kWh efi.t Efficiency of power plants Ti.t Applied tax on power plants in cent USD/kWh PJ Petajoules Mt Million tonnes Subscripts i Power plants technology number (1 to 13) t Time International Journal of Sustainable Energy Planning and Management Vol. 29 2020 93 Ali Abbasi Godarzi, Abbas Maleki impose to these sectors in changing consumption pattern from fossil resources to their non-fossil types. Because of low electricity price of fossil power plants, supply side do not have incentive to decrease GHG emissions. Since renewable energy resources have intermittent availability and fossil fuel costs contain uncertainty in their future pricing policy, we analyze the impact of both expansion capacity of non-fossil fuel power plants and fuel costs of fossil power plants on the trend of the expansion of these zero emission resources. So, determi- nation of electricity price that make the most impact on GHG emissions with calculation of emission elasticity (novel parameter) is considered in current study as research gap and this point directly was not investigated in previous papers. In this paper, we tried to set an energy policy path for an electricity pricing mechanism in Iran’s energy supply system for the realization of the Paris Accord targets, for which purpose research and development was done on decreasing GHG emissions based on the proposed method shown in Fig. 3. This paper is structured as follows. After reviewing previous studies, the methodology and the applications of system dynamics (SD) in an energy supply system were presented. In the continuation of this section, we describe the fuel cost and non-fossil fuel pricing mecha- nism to derive key electricity pricing components. Therefore, the SD model is constructed, described, and validated in Section 3. Results are discussed in Section 4. The paper finalizes with conclusion and policy implications. 2. Literature Review In order to understand Iran’s future energy consumption and emissions and to investigate the potential utilization of renewable energies, many studies have recently been conducted to simulate various future development path- ways. However, they lack an explicit description of how increasing the expansion of non-fossil resources aid in achieving the Paris Accord targets in their analytical model. Some of these articles have proposed analytical models to estimate the overall cost of utilizing renew- able resources for emission reduction or have provided general strategies for devising long-term energy poli- cies, but they have not provided a practical and eco- nomic method for increasing the expansion of non-fossil fuel technologies in the energy supply system. Kachoee et al. investigated the current Iranian elec- tricity supply and demand to forecast future generation trends in the power plant sector. Based on their results, this sector will emit about 668.2 Mt of CO2 equivalent of GHGs in the Business As Usual (BAU) scenario by 2040, which could be reduced to 294.6 Mt by adopting renewable development policies [6]. Setiartiti et al. developed four scenarios for transportation sector of Yogyakarta Province in Indonesia and showed that miti- gation scenario could reduce GHG intensity [7]. In 2017, Manzoor and Aryanpur presented a retro- spective optimization model for Iran’s power sector and showed that demand-side strategies and shifting to renewable supplies are two of the most important key drivers in achieving a low-carbon generation mix [8]. 31% 3% 27% 31% 0% 0% %0%% 0% 3% 2.48 PJ 1.83 PJ 57.52 PJ 3.16 PJ 1.08 PJ [PROCENTDEL] (Non-fossil fuels) Steam power plant Reciprocating engine (DG) Gas turbine Combined cycle plant Diesel generator Conventional coal plant Advanced supercritical coal Light water reactor Solar photovoltaic Small hydropower Large hydropower Wind turbine (on-grid) Expansion turbine Figure 2: Shares of power plants types in Iran’s electricity production system, 2018 [2] 94 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 Although fossil fuels still heavily dominate Iran’s elec- tricity supply system, especially natural gas, there are great and diverse renewable opportunities that should be considered as distributed generation in different province [9]. This practical solution can lead to the possibility of foreign and domestic investment opportunities. For instance, Shasavari and Akbari focused on potential bar- riers for promoting solar energy resources and increas- ing their expansion in the power grid, and claimed that this renewable energy has benefits that can absorb Foreign Direct Investment (FDI) [10]. Studies similar to the mentioned papers have been conducted in other countries, arguing that the cooperative planning frame- work in the development of non-fossil fuel power plants is capable and possible. Finding the most efficient method based on different incentives in the form of gov- ernmental executive scenarios is necessary for increas- ing the share of renewable resources in meeting the growing future electricity demand [11]. In 2019, Wahba et al. analyzed the effect of green strategy models on building design in areas with hot and dry climatic zones. They announced that building sector has a big responsibility in 62% of total electricity con- sumption and around 70% of resultant CO2 emissions and application of green wall is very powerful way that enhances the ecosystem health [12]. Burciaga et al. implemented Construction and Demolition Waste (CDW) strategies in reducing CO2 emissions of housing building and found that they can reduce 53% of CO2 [13]. Khan et al. presented integrated association model of green building rating tool (MyCREST) with Life Cycle Costing (LCC) and its final result was that criteria environmental management plan has lowest costing role in green building projects [14]. In 2018, Darabpour et al. focused on practical approaches toward sustainable con- struction industry by considering the experts’ opinion in Iran [15]. Candia et al. evaluate the flexibility of the Bolivian power generation system in terms of renewable energy and found that 30% participation of solar and wind tech- nology are required for grid reinforcements [16]. At 2020, a comprehensive investigation has been done by European researchers who developed a strategy under a Modern Portfolio Theory (MPT) for replacing conven- tional electricity generation technologies with renewables Supply reference Economic features Modeling of electricity price based on System Dynamics method Technical features Environmental features External inputs Scenario features (non-fossils share) Electricity price Total emissions Emission elasticity Results Deviation from Iran’s Paris Accord target Price module Demand module Production module Mathematical calculation by running of Stock and flow section Price reference Demand reference Figure 3: Research methodology flowchart and overall investigation structure International Journal of Sustainable Energy Planning and Management Vol. 29 2020 95 Ali Abbasi Godarzi, Abbas Maleki energies when defining efficient portfolios with less risk [17]. Yuan et al. presented a multi-region and multi-period model to explore the carbon and spillover impacts of investments in non-fossil fuel electricity generation and tried to explain how these investments affect CO2 emis- sions in China [18]. Atanasoae et al. assessed the employment impact of low capital cost of on-grid power generation on the expansion of renewable energy resources on Romania’s electricity supply system [19]. According to their investigation, these production tech- nologies can be profitable at less than 2300 Euro/kWh, depending on the self-consumption share of electricity produced by renewable resources. In recent years, many papers were published suggest- ing that an energy policy domain based on System Dynamics (SD) has many advantages in providing a better comprehension of complex interactions between different variables. Furthermore, SD itself can also be combined with other scenario planning methods, which helps obtain solid results from the dynamic behaviors of energy systems such as the electricity supply system [20]. Liu et al. investigated the mobility management policy of Beijing’s transport sector and its effects on energy savings and emission reduction using SD approach [21]. Their results show that the effects of energy conservation and emission reduction are two key solutions in comprehensive dynamic policies, and their efficacy is assessed in their study. The cost-benefit analysis based on SD model has been done on the simulation of energy saving from com- bining renewable energy and energy efficiency improve- ments in reference [22]. The results showed that renewable energy has more social benefits than energy efficiency improvements, and every country should introduce appropriate renewable development policies for its emission reduction targets. Shafiei et al. presented an integrated SD model for Iceland’s energy system to explore the transition process towards a hydrogen- and biofuel-based market considering both supply and demand sides [23]. They again focused on the applica- tion of renewable-based energy system for making this transition pathway. From the above mentioned papers, it can be under- stood that the SD method is a suitable way of structuring the causal and indirect relationships with randomness and uncertainty aspects such as electricity price [24]. So, to develop insights into the economic impacts of electricity pricing, we present a dynamic model that provides useful policy implications for Iran’s future emission reduction, as there was a substantial increase in the installed capacity of non-fossil fuel technologies in the period under study. Indeed, reducing GHG emissions of power plants by increasing the share of non-fossil energy to 20% is key for Iran to meet its targets in Paris Accord. 3. Model This section provides a general model representing Iran’s electricity pricing that can be applied to the pro- posed pricing method of power plants owners, and its integrated system dynamics model has three subsys- tems: production, demand, and price. In order to understand the effect of technological and economical motivators on the whole electricity sector, it is important for the new non-fossil fuel and conventional capacities to be able to adequately serve the increasing electricity demand of the country. Apart from what is affected by the market, these motivators affect the pro- duced energy and certificate prices. We investigate the effective management of non-fossil fuel power plants expansion in Iran’s electricity supply system on its (elec- tricity) pricing mechanism. So, our model is designed by following a principle similar to the one in Klaus-Ole Vogstad’s PhD thesis [25] where a complex system is divided for clarifying the sectorial interactions. Through a review of the existing literature, the causal relationships of electricity pricing considering the share increase of non-fossil resources are presented, and after selecting other causal variables, Causal Loop Diagrams (CLDs) of modules will be constructed. After a qualita- tive examination of the causal relationships, three mod- ules are derived, and the required data are applied in causal relationships followed by the formulation of these relationships in the next stage. Finally, the integrated stock-flow diagram will be developed. Nevertheless, this study’s objective is to investigate the non-fossil fuel cer- tificate policy with the time horizon of 2020-2030 (the Paris Accord timeline). 3.1 Production module The production module is constructed to model the supply side of electricity energy system associated with fossil and non-fossil resources as shown in Fig. 4. As can be seen, the new power plant investments are based on investors’ expected profitability of the new capacity, which is influenced by price variation, capital 96 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 costs, O&M costs, fuel costs, and capacity factor. Increasing the expected price and capacity factor soar the expected profit, and conversely, increasing costs decreases it. Variations of profit versus total costs will affect the rate of return and investments of power plants. So, capacity expansion has two delays: 1) Requesting a construction permission, verification, and confirmation receiving, 2) Time required for investing in new power plant capacity. These two mentioned delays have been considered in the proposed CLD of the production module. Since capacity will grow with investment, the utiliza- tion of current and new power plants is a function of capacity and capacity factor, and is in a direct relation- ship with electricity price and total costs (O&M, capital, and fuel costs at Fig. 3). In this module, we considered 13 various competing generation technologies (see Table 1) which were divided into two categories: fossil and non-fossil fuel power plants. Using the capacity of these technologies depends on their profitability and new investments in capacity. 3.2 Demand module The demand module aims at clarifying the causal path from the electricity consumers’ behavior to factors affecting electricity price that comprise the affordability aspect of the energy market, as shown in Fig. 5. According to Fig. 5, demand variation in the electric- ity market is a function of price factors (price ceiling and price elasticity of demand) and real factors in economics (growth rate), with price having a negative effect and real factors having a positive effect on demand. A rise in demand increases the demand to generation ratio (D/G), leading to electricity price soaring which in turn results in decreasing the demand in the next feedback. Furthermore, demand also relies on external factors such as weather (temperature), which affects the level of genera- tion. On the other hand, price is the main feedback between the demand side and the supply side, which is described through the price elasticity of demand measured on a yearly basis. For modeling future development in our model, a fixed growth rate is considered exogenously for the demand module, which is a representation of Iran’s economy DemandDemand to generation ratio Price variation Equilibrium price Electricity generation Investment Expected trend of price Efficiency of power plant Expected profitability of new capacity Capacity factor TaxOperational costs Fuel costs + + + + Capacity - + + + - ++ - Rate of return Capital costs O and M costs + - - + Feed In Tariff - Figure 4: CLD of the electricity production module International Journal of Sustainable Energy Planning and Management Vol. 29 2020 97 Ali Abbasi Godarzi, Abbas Maleki T ab le 1 : T ec h n ol og ic al f ea tu re s of p ow er p la n ts i n e le ct ri ci ty s u p p ly s ys te m [ 26 , 27 , 28 , 29 , 30 , 31 ] N O . T ec h n ol og y C ap it al co st ($ /k W ) F ix ed O & M ($ /k W ) V ar ia b le O & M ($ /M W h ) E ff ic ie n cy (% ) P la n t li fe ti m e (y ea r) P la n t fa ct or (% ) S el f- co n su m p ti on (% ) D ec re as in g ra te o f in ve st m en t co st ( % /y ea r) U p p er l im it o n n ew ca p ac it y ad d it io n sa (M W /y r) T yp e b 1 S te am p ow er pl an t 11 00 9. 4 0. 48 41 .2 30 75 6. 8 0 0 F 2 R ec ip ro ca ti ng en gi ne ( D G ) 80 0 8 5 40 –4 5 10 80 0. 7 0 11 9– 81 1 F 3 G as t ur bi ne 55 0 4. 4 0. 64 34 .3 –3 8. 9 12 70 0. 8 0 0 F 4 C om bi ne d cy cl e pl an t 76 0 4. 3 0. 41 50 –5 5 30 80 1. 9 0 0 F 5 D ie se l ge ne ra to r 55 0 3. 8 0. 74 33 10 70 6. 5 0 0 F 6 C on ve nt io na l co al pl an tc 16 00 64 0 35 .3 30 85 5. 5 0 0 F 7 A dv an ce d su pe rc ri ti ca l co al 37 00 88 0 46 –5 0 40 85 5. 6 0. 7 0 F 8 L ig ht w at er re ac to rd 48 00 92 0. 5 31 40 80 10 0 0 F 9 S ol ar p ho to vo lt ai c 40 00 50 0 0 25 25 0 3 48 –6 70 N F 10 S m al l hy dr op ow er 20 00 14 0 0 40 50 0. 5 0 19 2 N F 11 L ar ge h yd ro po w er 15 00 10 .8 0 0 50 15 0. 5 0 10 80 N F 12 W in d tu rb in e (o n- gr id )e 15 00 48 0 0 20 30 1. 4 1. 5 30 55 N F 13 E xp an si on t ur bi ne 78 0 30 0. 45 0 15 70 0 0 11 5 N F a A n up pe r li m it o f a te ch no lo gy f or m ax im um c ap ac it y of i ts p ow er p la nt t ha t is i m po se d on t he m od el . b T yp e of t ec hn ol og y is p re se nt ed t ha t F i s re la te d to t he f os si l fu el s an d N F i s no n- fo ss il p ow er p la nt s. c T he c ou nt ry w il l co ns tr uc t ca pa ci ty o f th is t ec hn ol og y ab ou t 65 0 M W u nt il 2 03 0. d A cc or di ng t o nu cl ea r sa nc ti on t ha t w er e im po se d on c ou nt ry , it c ou ld b e on ly i ns ta ll 1 00 0 M W c ap ac it y of t hi s te ch no lo gy s im il ar t o B us he hr ’s n uc le ar p ow er p la nt u nt il 2 03 0. e B as ed o n ca pa ci ty f lu ct ua ti on s of w in d tu rb in e in t he c ou nt ry , ca pa ci ty o f th is t ec hn ol og y w il l be f iv e ti m e un ti l 20 30 . 98 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 3.3 Price module The price module focuses on management of electricity price formation in the energy market, which includes total demand and total supply with consideration of import and export, as shown in Fig. 6. Power plants should come up with an accurate esti- mate of the required power to supply the total electric- ity demand. On the other hand, offering electricity to the market with a lower price than the real one is a major reason for a rise in the energy consumption rate, with the difference between two prices being paid by the government as subsidy, which is a subject of controversy in Iran. Nevertheless, price variations are not considered as a driving force, and its value is assumed about 6cents/kWh in different scenarios [32]. To determine electricity price, generation scheduling of each unit can be performed as separate optimization tasks, allowing optimization across utilities’ production systems, with import being considered as external provision. 3.4 Integrated module We integrate the three modules into the CLD, and develop the stock and flow diagram as shown in Fig. 7. Price ceiling Effect of price on demand Price of electricity Price elasticity of DemandEquilibrium price Capacity factor Demand Demand to generation ratio Demand growth rate Electricity generation Capacity + - + + + + + + + - Figure 5: CLD of the electricity demand module Export Demand of electricity Domestic demand Price of electricity Supply of electricity GenerationImport Price of electricity market Price ceiling Average annual price Forecasting of next years + + + + + - + Figure 6: CLD of the electricity price module International Journal of Sustainable Energy Planning and Management Vol. 29 2020 99 Ali Abbasi Godarzi, Abbas Maleki In order to check the structural consistency and validity of the model, verification tests and some new important causal paths are utilized to explain the real electricity pricing mechanism. After the addition of the new causal paths, the final structure of the model is presented according to these modifications. Commonly, reviews show that price adjustment time, price index, and demand to supply ratio should be inserted into a balanced loop between supply and demand for electricity pricing. Electricity demand has a direct impact on the demand of non-fossil fuel energies, as well as to some extent on the demand of oil, coal, natural gas, and nuclear energy. Furthermore, we consid- ered a certain share of non-fossil fuel power plants in electricity generation for modeling the exogenous effect of these energies on the final electricity price. Attracting private investors is a very crucial issue in electricity market. The government should pro- vide sufficient support through allocation of incen- tives to attract them to constructing power plants, especially non-fossil fuel ones, to cope with the growing electricity demand in the future. These investment motivators are considered in the “Feed-in Tariff”, “fuel costs”, and “tax” parameters whose values will affect both operational costs and expected profitability of new capacities. 3.5 Greenhouse gas emissions In this paper, GHG emissions are evaluated based on CO2 equivalent concept estimated by the Eq. 1: where λi.CO2, λi.C, λi.N2O, and λi.CH4 are emission factors of CO2, Carbon, N2O, and CH4, respectively. Eq. 1 is a measure of how much energy the emission of one tonne of a certain gas will absorb over 100 years relative to the emissions of one tonne of CO2. Moreover, in Eq. 1, relation factors α, β, γ, and δ are 1, 3.7, 265, and 28, respectively [33]. In this paper, the mentioned emission factors in Eq. 1 have been valued based on real data collected from var- ious installed power plants in Iran (as shown in Table 2). Our model has a nonlinear and complex structure that will cause some difficulties for investigators in describ- ing demand, production, and pricing principles of the above modules. Therefore, we implement our model in Vensim software [34]. The details of the models and principle equations are presented in Appendix A. 2 2 4. . . .i i CO i C i N O i CH λ αλ βλ γλ δλ= + + + (1) P ric e o f e le c tric ity P rice va ria tion P rice A djustme nt T ime P rice inde x E ffe ct of de ma nd pe r supply ba la nce on price + P rice se nsivity of de ma nd pe r supply ba la nce D e ma nd P rice e la sticity of de ma nd D e ma nd pe r supply ba la nce - + + R e fe re nce price S upply G e ne ra tion P rice e la sticity of supply - - - + Import coe fficie nt D e ma nd to ge ne ra tion ra tio Price variation 0 E quilibrium price E le ctricity ge ne ra tion Inve stme nt E xpe cte d tre nd of price E fficie ncy of pow e r pla nt E xpe cte d profita bility of ne w ca pa city C a pa city fa ctor T a x O pe ra tiona l costs F ue l costs + + + C a pa city - + + + - ++ - R a te of re turn C a pita l costs O a nd M costs + - -+ F e e d In T a riff - P rice ce iling E ffe ct of price on de ma ndP rice of e le ctricity 0 P rice e la sticity of D e ma nd 0 E quilibrium price 0C a pa city fa ctor 0 D e ma nd 1 D e ma nd to ge ne ra tion ra tio 0 D e ma nd grow th ra te E le ctricity ge ne ra tion 0 - + + + + + + - E xport Import + + + + + + S ha re of non- fossil pow e r pa lnts + E mission fa ctors T ota l e missions D e via tion from Ira n’s P a ris A ccord ta rge ts E mission e la sticity + ++- criteria COP21 Figure 7: Stock and flow diagram of the integrated model http://λi.CO http://λi.CH 100 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 3.6 Validation In order to test the model, we examine how the model output fits the historical data by performing the behavioral reproduction test. As shown in Fig. 8 and Fig. 9, for two modules (production and demand) from 2007 to 2018, our model-simulated behavior well matches the behavior of the real system. Also, by comparing the data in the time horizon mentioned above, statistical error values, such as Mean Average Error (MAE) and Root Mean Square Percentage Error (RMSPE), were evaluated in our model based on Eq. 2 and Eq. 3. where Ri and Si represent real value and the simulated value of i, respectively, and n represents the quantity of the data. The values of MSE and RMSPE for the production module are 3.38% and 3.59%, respectively, with their values for the demand module being 4.37% and 4.54%, respectively. Our model has good conformity to histori- cal trends. All efforts in R&D, competition of technologies, and government’s laws in the energy sector are reflected as changes in electricity demand and production. Indeed, if 1 1 n i i ii R S MAE n R = − = ∑ (2) 2 1 1 n i i ii R S RMSPE n R =  −  =   ∑ (3) 710 760 810 860 910 960 1010 1060 1110 1160 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 E le ct ri ci ty p ro du ct io n (P J) Real Simulated Figure 8: Simulated and real electricity production in Iran’s energy system Table 2: Pollutant and GHG emission factors in Iran power sector by power plant types for the year 2017 (gr/kWh) [3] Ownership Type of Plant CO2 C N2O CH4 Governmental Sector Steam 684.874 186.784 0.002 0.015 Combined Cycle 493.708 134.648 0.001 0.010 Gas 832.395 227.017 0.002 0.016 Diesel 811.159 221.225 0.007 0.033 Private Sector Steam 680.974 185.720 0.001 0.012 Combined Cycle 497.376 135.648 0.001 0.011 Gas 752.758 205.298 0.002 0.015 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 101 Ali Abbasi Godarzi, Abbas Maleki behaviors of these module outputs are reproduced by the final model, this model passes the behavior-reproduction test [35]. Error values obtained by this test confirm the validity of the results. 4. System simulation and results One of the main weaknesses of the existing system dynamics models in the literature is the unstructured process of policy scenario development. Through a structured process, we can apply a common view of the future of non-fossil fuel power plants to finding the plau- sible combination of modules, and then to developing scenarios [36]. Electricity producers are managing two types of elec- tricity production: traditional (fossil) and renewable (non-fossil) resources. Based on electricity market price, the capacity mix of non-fossil fuels and traditional resources will be defined. Since a simple relationship between electricity production, demand, and price cannot be obtained, we tried to derive such a relationship by considering two performance measures: First, pro- moting non-fossil fuels to reduce GHG emission from electricity production. Second, bringing the attention of electricity producers to the economic gains of renewable generation. According to the environmental and economic factors of developing Iran’s electricity supply system and to determine the conditions under which the electricity 545 595 645 695 745 795 845 895 945 995 1045 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 E le ct ri ci ty d em an d (P J) Real Simulated Figure 9: Simulated and real electricity demand in Iran’s energy system production system meet the Paris Accord target by 2030, four possible1 scenarios defined by varying share per- cent of non-fossil fuel power plants. Wide range of share percent is chosen in order to assess electricity price in increasingly GHG emissions models. The reference scenario has a 5% share of non-fossil resources in the power plant sector, describing the Iran’s energy system status quo (In 2018). The “Non- Fossil Fuels 1 (NFF1)” with a 10% share presents a low growth of non-fossil fuels in the electricity production system. Such an ineffective policy and unfavorable con- ditions would exacerbate energy efficiency and the state of infrastructure. “Non-Fossil Fuels 2 (NFF2)”, the medium scenario, corresponds to the average of share percent, NFF3 sce- nario corresponds to the upper limit of share percent, and NFF1 scenario corresponds to the lower limit of share percent. In NFF2 scenario, non-fossil fuels have a 15% share of the electricity supply. Moreover, “Non- Fossil Fuels 3 (NFF3)”, where non-fossil fuels have a 20% share, demonstrates a high growth in electricity production and it is an optimistic scenario that can be applied to Iran’s future energy supply system. Almost all foreseeable scenarios for the futures fall between the NFF1 and NFF3 scenarios. An overview of the four mentioned scenarios is presented in Table 3. 1 In this paper, the base year, time of scenario implementation, and time horizon are selected at 2017, 2020, and 2020-2030, respectively. 102 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 In fact, we have set up a method to simulate the energy system for achieving the long-term goals of Iran’s Paris Accord targets, which consists of economic, environmental, and social goals. In short, the investment policy will change energy prices which are considered constant (or compounded with inflation) in applying investment decisions. So, in this paper, we integrate long-term investment decisions and short-term opera- tional features. If we try to estimate the electricity price in the future with the reference scenario, where non- fossil fuels have a 5% share, we can see that between 2020 and 2030, the price is stable and has a routine pro- file in each year (Fig. 10). As shown in Fig. 10, the electricity price peaks in the 5th month (August) of each year due to the rise in demand in this month, with a growth rate of about 3% for each year. Conversely, the electricity price has reached its lowest in the 8th month (November) of each year that has the lowest electricity demand. However, after this month, the price witnesses a sharp increase, with its variation also substantially increasing, which happens because the increased demand must be sup- plied. This pattern is repeated through years between 2021 to 2030. Also, this estimation is done for the other scenarios, and the result are shown in Fig. 11. As shown in Fig. 11, the reference scenario has lower electricity prices than other scenarios, but does not mit- igate the increase in prices in the time horizon. This trend can also be viewed in other scenarios, with the maximum value of electricity price occurring in NFF3 scenario which is 2.54 cent USD/kWh at 2030. The growth in price indicates redundancy in supply capacity (increase in wind, hydro, solar and expansion turbine), therefore reducing the usage of fossil fuels. This hap- pens because the share of the mentioned non-fossil tech- nologies has been increasing in the period under study, and GHG emissions will probably have lower values in different scenarios compared to the reference scenario. So, in order to encourage investments in renewable capacity and sustain the development of traditional capacity in the electricity generation sector, it is essen- tial to reform the current electricity price and apply the following price pattern (Fig. 11) which will develop a proper business model for electricity producers. However, choosing between NFF1, NFF2, and NFF3 patterns is also dependent on GHG emission reduction Table 3: Scenario features and their assumptions Scenario Growth grade Share percent variations as driving force Time horizon Reference – No change from 5% 2020–2030 NFF1 low 5% – 10% 2020–2030 NFF2 medium 5% – 15% 2020–2030 NFF3 high 5% – 20% 2020–2030 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 1 2 3 4 5 6 7 8 9 10 11 12 E le ct ri ci ty P ri ce (c en t U SD /k W h) Time (Month) 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Figure 10: Electricity price variations in Reference scenario on monthly basis International Journal of Sustainable Energy Planning and Management Vol. 29 2020 103 Ali Abbasi Godarzi, Abbas Maleki for attaining the Paris Accord targets. The variations of this reduction are presented in Fig. 12. In the beginning of the time horizon (2020), the amount of greenhouse gas emission is 193.75 Mt of CO2eq. In the reference scenario, it will reach 292.01 Mt of CO2eq with an average growth rate of 4.2% until 2030. Thanks to GHG emission reduction policies, by increas- ing the share of non-fossil fuels, it is expected that GHG emissions plummet to 213.92, 188.83 and 178.23 Mt of CO2eq in NFF1, NFF2, and NFF3 scenarios, respec- tively. As a result, if the government adopts NFF3 sce- nario, realizing the Paris Accord targets would be feasible. Based on Fig. 12, GHG emission in the reference scenario is increasing substantially over time due to the large share of fossil fuels in electricity production. Moreover, in this scenario, the deviation from COP21 criteria is about 106.01 Mt of CO2eq, which signifies the 1.4 1.6 1.8 2 2.2 2.4 2.6 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 E le ct ri ci ty p ri ce (c en t U SD /k W h) Reference NFF1 NFF2 NFF3 Figure 11: Average electricity price variations in different scenarios on monthly basis 170 190 210 230 250 270 290 310 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 T ot al e m is si on (M t. C O 2) Reference NFF1 NFF2 NFF3 COP21 criteria Figure 12: The total GHGs emission in different scenarios 104 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 importance of focusing on decreasing the share of pol- lutant technologies, such as steam power plants, and increasing the share of combined cycle and non-fossil resources. In NFF1 scenario, GHG emission deviation has decreased to 27.92 Mt of CO2eq, but has not met COP21 criteria. Indeed, applying a 5% increase in the share of non-fossil technologies in electricity production in this scenario could decrease emissions of high GHG-emitting power plants but is not sufficient for satisfying the Paris Accord targets. However, GHG emissions have followed an upward trend after 2026 and the gradual growth the share of non-fossil fuels falls short of controlling this increase. In NFF2 scenario, the deviation optimistically falls to 2.83 Mt of CO2eq above COP21 criteria, and it is proven that increasing the share of non-fossil resources in electricity production is necessary for GHG emission mitigation, thus contributing to achieving the Paris Accord targets. However, total emissions start rising after 2028, therefore stopping the government from real- izing COP21 criteria in the Accord deadline using this scenario. The government is aware of the challenges and is seek- ing a number of reforms in electricity price to improve the performance of non-fossil power plants, including private sector in the generation of green electricity and imple- mentation of a power pool in a competitive market. Based on 2.36 cent USD/kWh of electricity price in NFF2 sce- nario in 2030, formation of this market can effect on decrease of GHG emissions. Although, attain 15% share of non-fossil power plants that will cause to emit only 2.83 Mt of CO2eq above COP21 criteria, is acceptable in this environmental accord. As a result, in the final sce- nario, NFF3, the deviation from COP21 criteria drops to 7.77 Mt of CO2eq under Iran’s Paris Accord targets, fol- lowing a declining trend until 2030. So, by adopting the policies for reaching a 20% share of non-fossil technologies, Iran can meet its Paris Accord targets, and this achievement will be sustainable even after 2030 (the Paris Accord deadline) and tackle rising GHG emissions with 2.54 cent USD/kWh of elec- tricity price. Furthermore, price elasticity of emission is one of the main indicators of the amount of GHG emission relative variation versus electricity price relative variation, pre- senting the amount of GHG that would be emitted for increasing the electricity price to improve the share of non-fossil fuels. It is evaluated according to the follow- ing equations. where εE, ∆E, ∆P, E, and P are price elasticity of emis- sion, emission variation, price variation, emission aver- age, and price average in the calculation period, respectively. This equation is applied to different scenarios, and the results are presented in Table 4. In the reference scenario, the share of non-fossil fuels did not change, so the calculation of εE is undefined. Based on Table 4, the average values of εE for NFF1, NFF2, and NFF3 are 0.1, 0.17, and 0.19, respectively. In fact, NFF3 scenario has both the highest variation in the share of non-fossil resources in electricity production and the price elasticity of emission. Nevertheless, εE should increase in this scenario compared to the two previous ones, meaning that it is possible to achieve Iran’s reduction target (according to its INDC) as stated in the Paris Accord. It should be noted, however, that the difference between NFF1 and NFF2 scenarios is larger in terms of εE mainly due to the high expansion of non-fossil technologies. Thus, one unit change of elec- tricity price in NFF3 scenario leads to a 190,000 tonnes of CO2eq decrease in GHG emissions. So, by considering only the environmental efficacy of the energy supply improvement by non-fossil resources, it is fair to con- clude that Iranian price policies are effective for emis- sion reduction. Another notable finding is the higher emission elastic- ity of Iran’s electricity market in NFF3 scenario that can diffuse more share of non-fossil resources in the market. Indeed, after 2027 (Table 4), εE increases and the ten- dency of the electricity market to change price for emis- sion reduction soars. In 2027, electricity price in NFF3 scenario will reach to 2.34 cent USD/kWh that is near to / /E E E P P ε ∆ = ∆ (4) Table 4: Price elasticity of emission in different scenarios at various time periods Scenario 2020– 2021 2021– 2022 2022– 2023 2023– 2024 2024– 2025 2025– 2026 2026– 2027 2027– 2028 2028– 2029 2029– 2030 NFF1 0.01 0.06 0.10 0.03 0.09 0.10 0.27 0.03 0.24 0.14 NFF2 0.17 0.13 0.31 0.17 0.14 0.07 0.10 0.20 0.28 0.19 NFF3 0.30 0.20 0.22 0.11 0.19 0.10 0.22 0.06 0.20 0.28 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 105 Ali Abbasi Godarzi, Abbas Maleki this price in NFF2 scenario at 2030. According to higher emission elasticity in NFF3 rather than NFF2, increasing of electricity price in NFF3 cause to decrease GHG emissions between 2027 and 2030 and COP 21 criteria will available for Iran. But because of less value of εE, this action will not occur in NFF2 and decreasing of GHG emissions will stop at 2.83 Mt of CO2eq above COP21 criteria. This approach puts emphasis on proposing appropri- ate policies contributing to the competitiveness of non-fossil resources, considering εE, that leads to the environmentally sustainable development of the electric- ity supply system, which can be done through reforming the current electricity market price. 5. Conclusion and policy implications This study investigates the expansion policy of non-fos- sil fuels and its impact on GHG emission reduction and the electricity market to meet Paris Accord targets. For analyzing the practicality of this method and its implica- tions, four scenarios with various growth rates of non-fossil technologies were presented: reference, NFF1, NFF2, and NFF3. If the private sector could be encouraged to invest in these low-carbon power plants by reforming the electric- ity price, NFF2 scenario with a 5%-15% expansion of non-fossil technologies is suitable for the mid-term devel- opment of the power plants for GHG emission reduction and electricity price must be increased to 2.36 cent USD/ kWh by 2030. Although GHG emissions in this scenario is about 10.60 Mt of CO2eq over COP21 criteria, the aver- age value of emission elasticity in this scenario is 0.17 and the policy maker can decrease 170,000 tonnes of CO2eq with a single unit increase in electricity price in each year (between 2020-2030). In NFF3 scenario electricity price will increase to 920 IRR/kWh that is about 0.18 cent USD/kWh over NFF2 in 2030. On the other hand, NFF3 scenario with a 5%–20% expansion of non-fossil technologies decreases GHG emissions to 178.23 Mt of CO2eq (7.77 units lower than COP21 criteria) which will keep its downward trend in the long run even after 2030, and the government is assured that the Paris Accord targets would certainly be achieved. As a result, a 15% share of non-fossil fuels is considered as the driving force to decrease GHG emis- sions (in NFF2), but it individually fails at decreasing emissions for successful achievement of the Paris Accord targets. The share of non-fossil fuels must be increased to 20% (NFF3), especially while emission elasticity in this scenario is higher than NFF1, NFF2, and the reference scenarios. However, there are many barriers to the successful implementation of NFF3 scenario, the main ones being underpricing the input fuel for power plants and low FITs for renewable energies. Therefore, the government must modify the performance of the generation sector by reforming electricity price and developing a competitive market in order to attract the private sector to invest in the expansion of non-fossil technologies as low-emis- sion power plants. Without tackling these issues, the impact of reform attempts is temporary, and after a while, GHG emissions start following a rising trend. Based on the presented results, the policy makers must decide to apply energy price reform to Iran’s electricity market to develop a suitable plan for reducing the emis- sion of the power plant sector. On the other hand, in the current dynamics model, the uncertainty of fuel prices is not considered, which can be added to the relevant equations in future works. Furthermore, other pollutant sectors such as transport and industry (see Fig. 1) can be investigated by similar approaches. Acknowledgement This paper belongs to an IJSEPM special issue on Sustainable Development using Renewable Energy Systems[37].” References [1] B. Kata, S. Paltseva and M. Yuana, “Turkish energy sector development and the Paris Agreement goals: A CGE model assessment,” Energy Policy, pp. 84–96, 2018. https://doi. org/10.1016/j.enpol.2018.07.030 [2] “Electric power industry statics,” Ministry of Energy, Tavanir org., Iran, 2018. [3] “Energy balance 2017 (In Persian).,” Iran Ministry of Power, Tehran, 2019. [4] “Third National Communication to United Nations Framework Convention on Climate Change (UNFCCC),” National climate change office, Department of environment of Iran, Tehran, 2017. [5] “Iran: the chronicles of the subsidy reform.,” International Monetary Fund (IMF)., 2011. [Online]. Available: http://www.imf.org/external/pubs/ft/wp/2011/wp11167.pdf. [Accessed 2015]. https://doi.org/10.1016/j.enpol.2018.07.030 https://doi.org/10.1016/j.enpol.2018.07.030 http://www.imf.org/external/pubs/ft/wp/2011/wp11167.pdf 106 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 [6] M. Kachoee, M. Salimi and M. Amidpour, “The long-term scenario and greenhouse gas effects cost-benefit analysis of Iran’s electricity sector,” Energy, vol. 143, no., 2017 http://doi. org/10.101/j.energy.2017.11.049. [7] L. Setiartitia and R. A. Al Hasibi, “Low carbon-based energy strategy for transportation sector development,” International Journal of Sustainable Energy Planning and Management, vol. 19, pp. 29–44, 2019. http://dx.doi.org/10.5278/ijsepm.2019.19.4. [8] D. Manzoor and V. Aryanpour, “Power sector development in Iran: A retrospective optimization approach,” Energy, vol. Part 1, no. 140, pp. 330–339, 2017. https://doi.org/10.1016/j. energy.2017.08.096. [9] S. Hosseini and et al., “A review on green energy potentials in Iran,” Renewable and Sustainable Energy Reviews, no. 27, pp. 533–545, 2013. https://doi.org/10.1016/j.rser.2013.07.015. [10] A. Shahsavari and M. Akbari, “Potential of solar energy in developing countries for reducing energy-related emissions.,” Renewable and Sustainable Energy Reviews, vol. 90, pp. 275– 291, 2018. https://doi.org/10.1016/j.rser.2018.03.065. [11] P. Menanteau, D. Finon and M. L. Lamy, “Prices versus quantities: choosing policies for promoting the development of renewable energy,” Energy Policy, vol. 8, no. 31, pp. 799–812, 2003. https://doi.org/10.1016/S0301-4215(02)00133-7. [12] S. Wahba, B. Kamil, K. Nassar and A. Abdelsalam, “Green Envelop Impact on Reducing Air Temperature and Enhancing Outdoor Thermal Comfort in Arid Climates,” Civil Engineering Journal, vol. 5, no. 5, pp. 1124–1135, 2019. http://dx.doi. org/10.28991/cej-2019-03091317. [13] U. Burciaga, P. Sáez and F. Ayón, “Strategies to Reduce CO2 Emissions in Housing Building by Means of CDW,” Emerging Science Journal, vol. 3, no. 5, pp. 274–284, 2019. http://dx.doi. org/10.28991/esj-2019-01190. [14] J. Khan and e. al., “Embedded Life Cycle Costing Elements in Green Building Rating Tool,” Civil Engineering Journal, vol. 5, no. 4, pp. 750–758, 2019. http://dx.doi.org/10.28991/cej-2019- 03091284. [15] M. Darabpour et al., “Practical Approaches Toward Sustainable Development in Iranian Green Construction,” Civil Engineering Journal, vol. 4, no. 10, pp. 2450–2465, 2018. http://dx.doi. org/10.28991/cej-03091172. [16] R.A.R. Candia et al., “Techno-economic assessment of high variable renewable energy penetration in the Bolivian interconnected electric system,” International Journal of Sustainable Energy Planning and Management , vol. 22, pp. 17–38, 2019. http://dx.doi.org/10.5278/ijsepm.2659. [17] P.M. Fernández, F. deLlano-Paz, A. Calvo-Silvosa and I. Soares “An evaluation of the energy and environmental policy efficiency of the EU member states in a 25-year period from a Modern Portfolio Theory perspective,” International Journal of Sustainable Energy Planning and Management, vol. 26, pp. 19–32, 2020. http://doi.org/10.5278/ijsepm.3482. [18] R. Yuana, J. Rodrigues, A. Tukker and P. Behren, “The impact of the expansion in non-fossil electricity infrastructure on China’s carbon emissions,” Applied Energy, no. 228, pp. 1994– 2008, 2018. https://doi.org/10.1016/j.apenergy.2018.07.069. [19] P. Atanasoae, R. Pentiuc, D. Milici, E. Olariu and M. Poienar, “The Cost-Benefit Analysis of the Electricity Production from Small Scale Renewable Energy Sources in the Conditions of Romania,” Procedia Manufacturing, no. 32, pp. 385–389, 2019. https://doi.org/10.1016/j.promfg.2019.02.230. [20] H. Qudrat-Ullah, “Modelling and simulation in service of energy policy,” Energy Procedia, vol. 75, pp. 2819–2825, 2015. https://doi.org/10.1016/j.egypro.2015.07.558. [21] X. Liu, S. Ma, J. Tian, N. Jia and G. Li, “A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO2 emissions: A case study of Beijing,” Energy Policy, vol. 85, pp. 253–270, 2015. https://doi. org/10.1016/j.enpol.2015.06.007. [22] Y.-H. Shih and C.-H. Tseng, “Cost-benefit analysis of sustainable energy development using life-cycle co-benefits assessment and the system dynamics approach,” Applied Energy, vol. 119, pp. 57–66, 2014. https://doi.org/10.1016/j. apenergy.2013.12.031. [23] E. Shafiei, B. Davidsdottir, J. Leaver, H. Stefansson and E. I. Asgeirsson, “Simulation of alternative fuel markets using integrated system dynamics model of energy system,” Procedia Computer Science, vol. 51, pp. 513–521, 2015. https://doi. org/10.1016/j.procs.2015.05.277. [24] O. Tang and J. Rehme, “An investigation of renewable certificates policy in Swedish electricity industry using an integrated system dynamics model,” International Journal of Production Economics, vol. 194, pp. 200–213, 2017. https:// doi.org/10.1016/j.ijpe.2017.03.012. [25] K.-O. Vogstad, A system dynamics analysis of the Nordic electricity market: The transition from fossil fuelled toward a renewable supply within a liberalised electricity market, Doctoral thesis: Norwegian University of Science and Technology, Department of Electrical Power Engineering, 2004. [26] N. Park, S. Yun and C. Eui, “An analysis of long-term scenarios for the transition to renewable energy in the Korean electricity sector,” Energy Policy, vol. 52, pp. 288–96, 2013. https://doi. org/10.1016/j.enpol.2012.09.021. [27] “Executive and technical deputy,” Renewable Energy Organization of Iran (SUNA), 2012. [28] “Technical and development projects deputy,” Iran Water and Power Resources Development Company, 2012. http://doi.org/10.101/j.energy.2017.11.049 http://doi.org/10.101/j.energy.2017.11.049 http://dx.doi.org/10.5278/ijsepm.2019.19.4 https://doi.org/10.1016/j.energy.2017.08.096 https://doi.org/10.1016/j.energy.2017.08.096 https://doi.org/10.1016/j.rser.2013.07.015 https://doi.org/10.1016/j.rser.2018.03.065 https://doi.org/10.1016/S0301-4215(02)00133-7 http://dx.doi.org/10.28991/cej-2019-03091317 http://dx.doi.org/10.28991/cej-2019-03091317 http://dx.doi.org/10.28991/esj-2019-01190 http://dx.doi.org/10.28991/esj-2019-01190 http://dx.doi.org/10.28991/cej-2019-03091284 http://dx.doi.org/10.28991/cej http://dx.doi.org/10.28991/cej http://dx.doi.org/10.5278/ijsepm.2659 http://doi.org/10.5278/ijsepm.3482 https://doi.org/10.1016/j.apenergy.2018.07.069 https://doi.org/10.1016/j.promfg.2019.02.230 https://doi.org/10.1016/j.egypro.2015.07.558 https://doi.org/10.1016/j.enpol.2015.06.007 https://doi.org/10.1016/j.enpol.2015.06.007 https://doi.org/10.1016/j.apenergy.2013.12.031 https://doi.org/10.1016/j.apenergy.2013.12.031 https://doi.org/10.1016/j.procs.2015.05.277 https://doi.org/10.1016/j.procs.2015.05.277 https://doi.org/10.1016/j.ijpe.2017.03.012 https://doi.org/10.1016/j.ijpe.2017.03.012 https://doi.org/10.1016/j.enpol.2012.09.021 https://doi.org/10.1016/j.enpol.2012.09.021 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 107 Ali Abbasi Godarzi, Abbas Maleki [29] “Projected cost of generating electricity.,” IEA and NEA, 2010. [Online]. Available: https://www.iea.org/publications/ freepublications/publication/. [Accessed 2015]. [30] “Deputy of planning affairs,” Iran power generation transmission & distribution management company, 2012. [31] “Renewable energy essentials: hydropower.,” IEA, 2010. [Online]. Available: http://www.iea.org/publications/ freepublications/publication/hydropower_essentials.pdf. [Accessed 2015]. [32] V. Aryanpur and E. Shafiei, “Optimal deployment of renewable electricity technologies in Iran and implications for emissions reductions,” Energy, no. 91, pp. 882–893, 2015. https://doi. org/10.1016/j.energy.2015.08.107. [33] IPCC, “”Fourth Assessment Report (AR4),”,” Intergovernmental Panel on Climate Change, 2007. [34] “Vensim 7.3,” September 2018. [Online]. Available: https:// vensim.com/vensim-software/. [35] J. Shin, W.-S. Shin and C. Lee, “An energy security management model using quality function deployment and system dynamics,” Energy Policy, no. 54, p. 72–86, 2013. https://doi.org/10.1016/j. enpol.2012.10.074. [36] H. S. Becker, “Developing and using scenarios-assisting business decisions.,” Journal of Business & Industrial Marketing, no. 4, pp. 61–70, 1989. https://doi.org/10.1108/ EUM0000000002725. [37] Østergaard, P.A.; Johannsen, R.M.; Duic, N. Sustainable Development using Renewable Energy Systems. Int. J. Sustain. Energy Plan. Manag. 2020, 29, http://doi.org/10.5278/ ijsepm.4302. https://www.iea.org/publications/freepublications/publication https://www.iea.org/publications/freepublications/publication http://www.iea.org/publications/freepublications/publication/hydropower_essentials.pdf http://www.iea.org/publications/freepublications/publication/hydropower_essentials.pdf https://doi.org/10.1016/j.energy.2015.08.107 https://doi.org/10.1016/j.energy.2015.08.107 https://vensim.com/vensim https://vensim.com/vensim https://doi.org/10.1016/j.enpol.2012.10.074 https://doi.org/10.1016/j.enpol.2012.10.074 https://doi.org/10.1108/EUM0000000002725 https://doi.org/10.1108/EUM0000000002725 http://doi.org/10.5278/ijsepm.4302 http://doi.org/10.5278/ijsepm.4302 108 International Journal of Sustainable Energy Planning and Management Vol. 29 2020 Policy Framework of Non-Fossil Power Plants in Iran’s Electricity Sector by 2030 Appendix A The main equations that describe system dynamics model used in this papers, are presented below: Eq. A 11 t t tP P Price change dt+ = + ∫ Eq. A 2 I t tP PPrice change AT − = Eq. A 3 * se r r r P S S ES S P   = =     Eq. A 4 * de r r r P D D ED D P   = =     Eq. A 5*It t pP P EB= Eq. A 6 s p D EB F S   =     Eq. A 7( )1 tt t t ceilingP P Price change dt P+ = + ∫ ≤ Eq. A 8( )1 1 0 1t tceiling ceilingP P whereα α+ = + < ≤ Eq. A 9 [ ]12*12 12*12 12*12 @ 2018 . . Input matrix S D P= Eq. A 10[ ]12*12 @ 2019 2030 Output matrix P −= Eq. A 11. . i t i t P CF Oc = Eq. A 12. .. . . i t i t i t i t i t Fc so Oc T ef − = + _Ref24840863