Optimal policy of energy innovation in developing countries: Development of solar PV in Iran

ARTICLE IN PRESS Energy Policy 37 (2009) 1116–1127

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Optimal policy of energy innovation in developing countries: Development of solar PV in Iran Ehsan Shafiei a, Yadollah Saboohi b,, Mohammad B. Ghofrani b a b

School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran Sharif Energy Research Institute (SERI), Sharif University of Technology, Tehran 11155-9567, Iran

a r t i c l e in f o

a b s t r a c t

Article history: Received 27 July 2008 Accepted 3 October 2008 Available online 31 December 2008

The purpose of this study is to apply managerial economics and methods of decision analysis to study the optimal pattern of innovation activities for development of new energy technologies in developing countries. For this purpose, a model of energy research and development (R&D) planning is developed and it is then linked to a bottom-up energy-systems model. The set of interlinked models provide a comprehensive analytical tool for assessment of energy technologies and innovation planning taking into account the specific conditions of developing countries. An energy-system model is used as a tool for the assessment and prioritization of new energy technologies. Based on the results of the technology assessment model, the optimal R&D resources allocation for new energy technologies is estimated with the help of the R&D planning model. The R&D planning model is based on maximization of the total net present value of resulting R&D benefits taking into account the dynamics of technological progress, knowledge and experience spillovers from advanced economies, technology adoption and R&D constraints. Application of the set of interlinked models is explained through the analysis of the development of solar PV in Iranian electricity supply system and then some important policy insights are concluded. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Energy R&D Knowledge spillover Developing countries

1. Introduction New energy technologies may have considerable impact on the performance, efficiency and environmental compatibility of the energy systems. The changes might be evolutionary providing considerable economical and social benefits and large value added. On the other hand, new technologies are developed through integrated process of research, development, demonstration and deployment activities (PCAST, 1999). Huge resources (i.e. economical, human resources, knowledge and social systems) ought to be allocated at various stages of these development processes. The extent of these resources might be very large and beyond the capability of a national system and it may be involved with high risks. Therefore, to identify the optimal allocation of resources for improvement of energy system, development of proper analytical tools is required. To provide a proper analytical tool for assessing energy technologies and research and development (R&D) resource allocation with an explicit perspective of a developing country, it would be necessary to consider the knowledge spillover from

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E-mail addresses: shafi[email protected] (E. Shafiei), [email protected] (Y. Saboohi), [email protected] (M.B. Ghofrani). 0301-4215/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2008.10.034

advanced to the developing regions. In general, agents are unable to fully appropriate all benefits from their own innovation activities and thus large externalities may arise in the production of knowledge which are referred to as knowledge or R&D spillovers. Empirical studies show that spillovers from R&D are prevalent, their magnitude may be quite large and social rates of return remain significantly above private rates (Griliches, 1992). Because of the externalities inherent in the production of knowledge, it is clearly important to understand the role of spillover and the process from which knowledge diffuses. The diffusion of knowledge occurs both in space and time domains. Nevertheless, traditional diffusion theories do not consider space variables and focus on knowledge diffusion over time. These studies were deepened by technology gap literature at macro-level (Canie¨ls, 1998). Models of technology gap investigate the knowledge spillover with geographical and technological distance (Canie¨ls and Verspagen, 2001; Keller, 1996; Verspagen, 1991). However, the dynamics of knowledge spillover in these studies has been considered as a black box and, hence, there is a need to specify the factors determining the direction and intensity of spillovers. At the firm level, Cohen and Levinthal (1989, 1990) introduced the concept of absorptive capacity for modeling the flow of technological and scientific knowledge. According to their study, firms invest in knowledge development to both generate new

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knowledge and assimilate and exploit the existing information. What a firm can learn from spillovers is a function of both the absorptive capacity of the firm and the amount of spillover available to be learned. Following Cohen and Levinthal, many empirical and theoretical works have been devoted to studying the absorptive capacity (see e.g. Zahra and George, 2002 for an overview). Although, the flow of knowledge and thus absorptive capacity have been more extensively studied at the firm level, the use of the concept of absorptive capacity has not been limited to the firm level and it ranges from the level of the individual to that of entire nation. At the macro-level, some studies have been done to examine the determinants of a country’s absorptive capacity, its relationship with national R&D activities and the general characteristics of the international technological environment (Criscuolo and Narula, 2001; Narula, 2004; Wamae, 2006). Criscuolo and Narula (2001) highlighted the importance of national absorptive capacity and knowledge accumulation. By aggregating upwards from firm level, they specified the relationship between the ability of a country to absorb foreign knowledge and its stages of technological development. In fact, this concept can be extended to the interactions among different world regions. By considering technological learning spillovers, different world regions may benefit from the learning efforts of other regions on a given technology. In the field of energy technologies, capturing this interaction could be a crucial issue in analyzing technological development in the context of bottom-up energy-systems models. Despite significant progress made in endogenizing technological change in bottom-up energysystems models over the past decade, the present state of these studies is still characterized by some weaknesses and limitations in representing the knowledge and experience spillovers. In fact, energy-systems models need to differentiate between different regions, while incorporating the process of cross-country technology diffusion or spillovers. In some bottom-up energy-systems studies, a common simplification for incorporating spillovers is to assume that learning is dependent on R&D, investment or production cumulated over all regions (Barreto and Kypreos, 2000, 2002, 2004a; Barreto and Klaassen, 2004). In addition, the degree of experience spillover among regions has been expressed in terms of spillover coefficients that represent the fraction of the technology installations made in a region added to the cumulative experience in another region (Barreto, 2001). However, the dynamics of knowledge and experience diffusion and accumulation has been considered as a black box and the spatial patterns of technology diffusion have not been thoroughly explored in the aforementioned approaches. In the case of developing countries or countries undergoing transition, where the role of national innovation capacity and their ability to absorb new knowledge are critical, sound models for incorporating the R&D and other factors enabling successful technology diffusion are rarely available. The present study is an effort responding to the above requirements and provides an analytical tool to identify the role of R&D in the process of development of innovative energy system in developing countries. For such purpose, an optimal control model for R&D resource allocation has been developed which takes into account the role of knowledge and experience spillover from advanced regions. Furthermore, the optimal R&D resource allocation is linked to the energy-systems model. This procedure provides a foundation for further development of a comprehensive model of energy technology assessment and R&D planning. In order to present the whole process of model operation, the paper is organized as follows. Modeling framework and scope of the study is presented in Section 2. In Section 3, the mathematical formulation of the model on resource allocation for R&D is briefly

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described. Subsequently in Section 4 the linkage of technology assessment model with the R&D resource allocation model is presented and a proper algorithm for solving the set of models is proposed. In Section 5, application of the set of models in a case study is described and the structures, main assumptions and data sources are further explained. Section 6 is devoted to the results and discussion. Finally, conclusions, policy implications and some prospects for future research are provided in Section 7.

2. Modeling framework We intend to study the process of knowledge accumulation in developing countries more deeply based on the concept that has been developed by Cohen and Levinthal (1989, 1990) and Criscuolo and Narula (2001). The knowledge stock for a particular technology —e.g. solar photovoltaic—at aggregated national (or regional) level is focused here. In order to study the flow of technological and scientific knowledge for a particular type of technology at the macro-level, we consider two regions. It may be assumed that there is one technologically advanced region and the other is technically backward (called hereafter Frontier and Follower, respectively). In general, the Follower’s knowledge stock may be expanded through three simultaneous options: 1. The Follower invests on R&D on the technology under consideration. 2. The Follower implements a process of absorbing knowledge generated abroad by the Frontier for the technology under consideration. 3. Absorption of knowledge accumulated for the other similar technologies, which may be located inside or outside the Follower’s country. According to de Feber et al. (2002), ‘‘similar technologies’’ may be defined as a group of technologies sharing a common essential component. However, for limiting the scope of the paper, the spillovers across different technologies are not considered here. In the proposed model, R&D activities and accumulation of knowledge provide a basis for technological push which results in improvement of the level of investment cost for new technologies. Technological development is also stimulated by market pull, as the technology is often developed to meet a market need or demand. The comprehensive technological learning encompasses R&D and demonstration and deployment (hereafter referred to as D&D) programs (PCAST, 1999). Thus, we consider R&D and D&D (RD3 efforts) as the main innovation activities for developing energy systems and then present the mechanisms by which they contribute to energy-technology development taking into account the specific conditions of developing countries.

3. R&D planning model for developing energy technologies in developing countries One crucial issue in energy-systems models has been how to deal with the role of R&D in technological changes. R&D is one of the basic driving forces of technological progress, contributing to cost and performance improvements of emerging technologies. Therefore, it is important to gain insights on the allocation of scarce R&D resources for development of new energy technologies. In practical R&D planning, decisions are mainly founded on experts’ knowledge and experience. They are mainly based on the weighted sum of scores from the viewpoints of multi-criteria analysis (see e.g. Lootsma et al., 1988, 1990). However, in some

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cases, given the complex set of factors influencing decisions, expert-based estimates of R&D planning may lead to biased research assessment and hence probably to distorted results. Therefore, by introducing a quantitative approach, the overall outcome of priority setting and planning for R&D could be improved. To this end, we have developed an optimization model for planning R&D that would help to identify the optimal profile of R&D for developing the appropriate energy technologies in developing countries. The R&D model avails itself to studying the optimal allocation of R&D resources for new technologies based on the evaluation of their economic and environmental impact. The model of energy R&D resource allocation has been developed on the basis of optimal control theory. The main control variables are the flow of R&D activities and the main state variables are the level of knowledge stock for each technology. This model maximizes the total net present value of resulting R&D benefits taking into account the dynamics of technological progress, knowledge and experience spillover, technology adoption and R&D constraints. The base line mathematical formulation of the model is as (1)–(20). Over bar embellishment for characters determines the constants and exogenous parameters in the model. The nomenclatures have been presented following the model: max

 T X k  X REV t;t ð1 þ rÞt

t¼1 t¼1



 T X k  X COST t;t ð1 þ rÞt

t¼1 t¼1

þ

K t;T ð1 þ rÞT

(1)

k X

RDt;t pAt

(17)

RDt;t pBt

(18)

t¼1 T X t¼1

min

(19)

RDt;t  ut;t RDt;t1 pvt;t

(20)

Endogenous variables: REVt,t COSTt, Ckt,t RDt,t DDt,t ESt,t Kt,t Et,t

gt,t

Subject to

bt,t

REV t;t ¼ ðCkt;t =Ckt;0 ÞðY¯ t;t Ckt;0 þ Zt;t X t;t Cvt;0 þ Ht;t Cf t;0 Þ

(2)

COST t;t ¼ RDt;t þ ESt;t EC t;t

(3)

Gt,t qt,t

(4)

min

(5)

Ckt;T XCkt









K t;t ¼ ð1  dt;t Þ K t;t1 þ RDt;tm

(6) 

K t;t ¼ ð1  dt;t Þ K t;t1 þ RDt;tm þ gt;t yt;t ðK t;tD  K t;t1 Þ

gt;t ¼ gðRDt;t ; bt;t Þ;

0pgt;t p1

bt;t ¼ bðGt;t ; ot;t Þ;

0pbt;t p1;

0pot;t p1

r Ckt;0 Cvt;0 Cf t;0 Y t;t

(7)

Ht;t

(8)

X t;t

(9)

Zt;t X t;t Zt;t EC t;0



Gt;t ¼ lnðK t;t =K t;t Þ 

Et;t ¼ Et;0 þ

t X

(10)

U t;t ¼ U t;0 þ



Y t;t

t X

(11)



K t;0 Y t;t

(12)

t¼1

Et;t ¼ U t;t þ ESt;t

(13)



ESt;t ¼ qt;t ðEt;t1  Et;t1 Þ

(14)

qt;t pgt;t yt;t

(15)

8 < Y t;t  ðCkt;t  C cmp Þ t :0



Ckt;0  Et;0

t¼1

DDt;t ¼

revenues of innovation activities for technology type t at time point t costs of innovation activities for technology type t at time point t investment cost of technology type t at time point t R&D activities for technology type t at time point t D&D activities for technology type t at time point t experience spillover from the Frontier for technology type t at time point t Follower’s knowledge stock for technology type t at time point t Follower’s experience stock for technology type t at time point t absorption capacity for technology type t at time point t knowledge complexity for technology type t at time point t technological gap for technology type t at time point t experience spillover coefficient

Constants and exogenous parameters:



Ckt;t ¼ Ckt;0 ðEt;t =Et;0 Þlbdt ðK t;t =K t;0 Þlbst



max

Rt;t pRDt;t pRt;t

lbdt lbst  K t;t 

RDt;t  dt;t

dt;t min

if if

Ckt

cmp

Ckt;t 4C t

cmp

Ckt;t pC t

(16)

ot;t yt;t

discount rate initial investment cost of technology type t initial variable cost of technology type t initial fixed cost of technology type t new installed capacity of technology type t at time point t total installed capacity of technology type t at time point t input activity level of technology type t at time point t output energy flow of technology type t at time point t conversion efficiency of technology type t at time point t experience spillover costs for technology type t at time point t Frontier’s initial investment cost for technology type t Frontier’s initial cumulative experience for technology type t Frontier’s initial cumulative knowledge for technology type t learning-by-doing elasticity for technology type t learning-by-searching elasticity for technology type t Frontier’s knowledge stock for technology type t at time point t Frontier’s R&D for technology type t at time point t Frontier’s knowledge depreciation rate for technology type t at time point t Follower’s knowledge depreciation rate for technology type t at time point t floor cost for technology type t Follower’s innovation capacity for technology type t at time point t degree of spillover for technology type t at time point t

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Et;t 

Y t;t U t;t U t;0 cmp

Ckt A¯ t Bt

min

Rt;t

max

Rt;t ut;t vt;t

Frontier’s cumulative experience for technology type t at time point t Frontier’s new capacity installation for technology type t at time point t Follower’s cumulative capacity for technology type t at time point t Follower’s initial cumulative capacity for technology type t competitive cost for technology type t total R&D resources which can be allocated among different energy technologies at time point t total R&D resources which can be allocated to technology type t during the planning horizon minimum level of R&D which can be allocated for technology type t at time point t maximum level of R&D which can be allocated for technology type t at time point t R&D growth rate coefficient for technology type t at time point t R&D increment coefficient for technology type t at time point t

Subscripts: t

subscript for time point, t ¼ 1, 2, y, T subscript for technology type, t ¼ 1, 2, y, k time lag between the Follower’s R&D activity and its effects time lag between the Frontier’s R&D activity and its effects time lag for accessibility of the Frontier’s innovative knowledge

t m m*

D

The objective function of the model is to maximize the total discounted present value of the revenues of innovation activities minus the present value of the costs of innovation activities. The innovation benefits are the net value added for utilizing the new technologies in the energy system. The revenues of innovation activities are estimated according to the total reduction of energy-supply system costs. Eq. (2) reflects the revenues of innovation activities. In this equation, it is assumed that the reductions in variable and fixed costs are proportional to the reduction in investment cost. The weighting factors in the objective function (i.e. Y t;t ; Zt;t  X t;t and Ht;t ) reflect the demand for each technology. To evaluate the change in demand for each technology, it is assumed that adoption decisions are based upon rational behavior of consumers. The optimal trend of new and total installed capacity and output flow of each technology are determined with the help of the energysupply model through considering the technology characteristics, competition between technologies and economic and environmental constraints. Eq. (3) shows the costs of innovation activities that include the Follower’s R&D activities1 and the cost of experience spillover from the Frontier. In order to consider the effects of the periods after the planning horizon, the level of knowledge stock at the end of the last period may be included in the objective function to be maximized. The main constraints of the model are expressed by (4)–(20). Improvement of investment cost for each technology is represented according to Eq. (4). This relationship is a two-factor learning curve for each technology. The initial values in the learning curve (i.e. initial investment cost, initial knowledge and

1

R&D expenditures may be used as the typical measures of R&D activities.

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initial experience) have been set to the Frontier’s conditions and then the change in investment cost for the Follower is calculated with respect to the Frontier’s initial conditions. It is introduced a barrier in the maximum reduction of investment costs by defining the floor costs using constraint (5). This is the minimum level of investment cost that can be reached for a certain technology and sets a limit on how much the cost for a specific technology may decrease due to accumulation of experience and knowledge. Eq. (6) reflects the knowledge accumulation for the Frontier. The level of the Frontier’s knowledge stock may be controlled by the input flow of the Frontier’s R&D, which is an exogenous parameter at each time point, and the output flow of knowledge depreciation which reduces the level of knowledge in time at a constant rate. Eq. (7) represents the accumulation of Follower’s knowledge stock. This equation states that the Follower’s knowledge stock for each technology is augmented not only by carrying out R&D for this technology but also by acquiring some of the external knowledge through technological spillovers. It is assumed that the Frontier’s total knowledge can be accessible with some delays. In other words, there is a lag-time between the time at which the innovative knowledge is generated by the Frontier and the time at which it can be accessible by the Follower. Hence, if D is the lagn time, then K¯ t;tD  K t;t1 will be the amount of knowledge potentially accessible to the Follower at time point t and the interaction between absorption capacity and spillover degree determines the Follower’s ability to absorb it at time t. Eqs. (8) and (9) represent absorption capacity and knowledge complexity. Absorptive capacity is defined as the fraction of external knowledge that the Follower country is able to assimilate and exploit and it is a function of the Follower’s R&D efforts and knowledge complexity. Knowledge complexity is considered as a function of technological gap and national innovation capacity.2 According to Eq. (10), technological gap is defined as the logarithm of the ratio of the knowledge stocks of two regions. Eq. (11) is used to estimate the total cumulative experience for the Frontier. At the Frontier, the cumulative capacity of a given technology in the time period t corresponds to the summation of the past investments (in physical units) up to time t, plus the initial cumulative capacity that defines the starting point. This equation is used here to estimate the total cumulative experience for the Frontier. In fact, for simplicity, it is assumed that the Frontier’s cumulative experience is enhanced merely by its new capacity installations for the technology in each time point. The experience spillover and Follower’s cumulative experience are determined simultaneously with the help of relations (12)–(15). The level of experience stock for the Follower is augmented by its cumulative installed capacity for the technology and acquiring some of the external experiences through spillover from the Frontier country. qt,t is experience spillover coefficient for n technology t at time point t and the term Et;t1  Et;t1 is the amount of experience potentially accessible to the Follower at time point t. As mentioned before, the interaction between absorption capacity and spillover degree determines the Follower’s ability to absorb the external knowledge. This interaction may be interpreted as knowledge spillover coefficient. According to (15) it is assumed that the experience spillover coefficient cannot exceed the knowledge spillover coefficient. The underlying assumption and primary idea behind this constraint is that the Follower country cannot exploit the Frontier’s experience without (or before) internalizing the corresponding external knowledge.

2 The full details of the theoretical background and the functional specifications for these concepts are available in Shafiei (2008).

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D&D activities are represented with the help of relationship (16). We define D&D efforts as the monetary value of new capacities that are implemented while the investment cost is still above the competitive cost. Thus, we use a conditional function to estimate these innovation activities. Competitive cost for each technology is the cost at which the technology becomes economically competitive with the existing conventional technologies in the energy system. The procedure of estimating the competitive cost will be presented in Section 4. It is assumed according to (16) that D&D for each technology encompasses the total investment for construction of capacities with the prices higher than competitive cost. Obviously, these capacity constructions are not economically attractive in commercial energy systems. However, capacity expansion in this case is mainly performed with the aim of stimulating demand pull and increasing market experiences. If the investment cost is lower than the competitive cost, the process of technology commercialization and diffusion will be facilitated. Although the capacity expansion can still contribute to the technology development, the technology is also commercially competitive to provide energy services in the energy systems. We have, therefore, considered D&D expenditures to be zero. Constraints on R&D resources, bounds on maximum and minimum R&D level and dynamic constraints for R&D resource allocation are formulated with the help of relationships (17)–(20). R&D force or technology push may be formulated by proper definition of these bounds and the parameters may be specified by the scenarios for each technology. Similar constraints for D&D activities can also be defined in the model. The most important variables of the model of R&D resource allocation are the level of investment cost (Ckt,t) and flow of R&D activities (RDt,t) for each technology. The model returns the optimal profile of R&D for improvement of investment cost in the planning horizon. The proposed specification of the relationships in the model provides smooth functions and finite lower and upper bounds on the variables and mathematical expressions. For instance variables yt,t, bt,t, gt,t, lt,t and qt,t are limited between 0 and 1 and variable Gt,t is often a one-digit number. The physics of the problem and the scale and units of measurement used for the variables, lead to suitable finite lower and upper bounds for other variables. Furthermore, most of the constraints in the model have been defined as equality. Hence, the solution algorithm will not have to spend time determining which constraints are binding and which are not. These characteristics for the model can facilitate using the local search algorithms for optimization. It is therefore possible to identify a globally optimal solution for this non-linear problem through the application of global optimization algorithms.

supply system costs, environmental impact and social acceptability. The results of this model reveal the impact of diffusion of new technologies on the long-term development of the energysupply system. In other words, energy-technology assessment and prioritization of new energy technologies are provided by a model of the energy-supply system that contains a detailed representation of the current and emerging technologies characterized in terms of their technical and economical indices such as costs, conversion efficiency and emission factors. The main variables of the energy-supply model are Xt,t (input activity level of each technology at time point t), Yt,t (new installed capacity of each technology at time point t) and Ht,t (total installed and available capacity) and the model returns the optimal values of these variables in the planning horizon. The benefits of introducing new technologies in the energy system are concluded in energy-supply model and they are then transferred into the R&D planning model. For this purpose, integration of the energy-supply model into the R&D planning model is done with the help of optimal level of new installed capacity (Y¯t,t), technology output activity (Z¯ t;t X¯ t;t ) and total ¯ t,t). In the opposite direction, profiles of existing capacity (H improvement in values of technology costs are provided with the help of the R&D resource allocation model. The energy-supply model is further run with new revised inputs that are extracted out of the R&D model results. The main endogenous variables of R&D model (i.e. investment costs) are used as exogenous parameters in the energy-supply model and the main endogenous variables of energy-supply model (i.e. new and total capacity and output activity) are the exogenous parameters in the R&D model. The process of solving the interlinked energy-supply and R&D resource allocation models is an iterative algorithm, beginning with an energy-supply solution. A schematic overview of the algorithm is presented in Fig. 1. According to this flowchart, the algorithm provides a step-bystep solution described as the following:

 Step (1): Initial values are set for technology costs, which  



4. Linking R&D planning with the energy-technology assessment model R&D activities and accumulation of knowledge provide a basis for technological push which results in improvement of the level of technical indices of new technologies. Technological development is also stimulated by market pull. Technology is often developed to meet a market need or demand. Energy system is considered as demand side of technologies. Market penetration and demand potential for any energy technology are considered in a rational decision-making process and with the help of the energy-supply model. This model is used as an analytical tool to review the long-term technological and structural development of the energy-supply system. The optimal structure is usually identified according to a set of criteria that may include total



are required for energy-supply model to drive its system optimization. Step (2): The second step of the algorithm is an energy-supply solution. Energy-technology assessment is followed by this step and total energy-supply system costs are minimized. Step (3): Analysis of the results of energy-supply model for assessment of energy technologies, extraction of optimal capacity and activity for each technology are followed at the third step. Step (4): In this step there are two options for emerging technologies that have not been selected during the technology assessment process. The former one is stimulation of demand potential or market pull for each technology which may be specified by a set of capacity, activity, economic and environmental constraints in the energy-supply model. These constraints control the market penetration of technologies directly or indirectly. By doing so, the critical market penetration rate and also the competitive cost for each technology can be estimated. The latter option is R&D force or technology push which may be formulated by defining bounds on maximum and minimum R&D levels, dynamic constraints for R&D resource allocation and total R&D resources which can be allocated among different energy technologies. Parameters of constraints for both options are exogenous and specified through a scenario analysis. Step (5): With the inputs from steps (3) and (4), R&D resource allocation model is run.

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step (1): Initial values for Costs of Energy Technologies

step (2): Energy Supply Model (Technology Assessment)

step (3): Analysis of the Results of Technology Assessment

NO Technology Selection ?

YES

Market Pull (Scenario Generator for Market Penetration) Step (4) Technology Push (Scenario Generator for R&D Force)

step (5): Technology Development Model (Optimal Control of Innovation Activities)

step (6): Improvement of Costs of Energy Technologies Fig. 1. Step-by-step algorithm for solution of the set of models.

 Step (6): The results of R&D resource allocation model are analyzed and optimal level of costs for each technology is then determined. To start the next iteration, initial values for technology costs at step (1) are replaced with the new values at step (6) and energysupply model inputs are updated. The above process is iterated until the solution is converged. The convergence criterion may be determined by either the energy-supply or the R&D resource allocation model results. If the results of R&D resource allocation model are utilized for testing the convergence of the consequent iterations, the convergence criterion shall be the comparison of the values of investment costs in two subsequent iterations. Alternatively the procedure for testing the convergence of the iterations would be to compare the values of data resulted from technology assessment model in the consequent iterations.

5. Application of the set of models To test the applicability of the set of interlinked models, they are applied to the electricity supply system in Iran. In fact, Iran is considered as a Follower country which can try to exploit the external knowledge and experiences generated by industrialized countries. Thus, the global level of knowledge and experience may be considered as the Frontier. Solar photovoltaic (PV) is chosen as an emerging technology in Iranian electricity supply system. Photovoltaic is a solar power technology that uses solar cells to convert light from the sun directly into electricity and it has been considered as one of the most promising energy technologies of the 21st century. We investigate the resource requirement for development of this technology and also its impact on Iranian electricity supply system using the set of models that has been developed in the present paper.

The following sub-sections describe the structure, assumptions and data sources that have been used in the models for technology assessment and R&D planning. 5.1. Technology assessment Electricity supply model for Iran, which covers from secondary energy supply (or primary energy in the case of renewable energy carriers) to final electricity consumption, is here considered for assessment of electricity generation technologies. Electricity supply system of Iran is modeled by using MESSAGE-V.3 MESSAGE is a dynamic linear programming model based on reference energy system (RES) which minimizes the total discounted energy-system cost, including the investment cost, the variable and fixed operational and maintenance costs and environmental damage costs on the supply side. The model can optimizes over existing as well as new advanced technologies which may be deployed in the future. The technologies represented in the system are: existing steam power, new steam power, existing gas turbine, new gas turbine, existing combined cycle, new combined cycle, existing diesel, new diesel, conventional coal, advanced coal, light water nuclear, small hydro, large hydro, geothermal, wind (off-Grid), wind (on-Grid), solar PV (off-Grid), solar PV (on-Grid), electricity storage system and transmission and distribution. In this case study, competitiveness of the above electricity generation alternatives is examined. It is assumed that solar PV allows for learning in the costs, while it is assumed constant costs along the horizon for the conventional power plant technologies. The time horizon of the model is 40 years, beginning in 2010 and continuing until 2050. The base year is 2005, eight 5-year periods are considered and a discount rate of 10% is applied to the calculations. 3 The latest version of MESSAGE model enhanced and used by International Atomic Energy Agency (IAEA).

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5.2. R&D planning R&D planning is studied in the horizon 2010–50. Eight 5-year periods are considered. Similar to the energy-supply model, the base year is 2005 and a discount rate of 10% is applied to the calculations. The main underlying assumptions for implementation of the R&D model in Iran are described in the following. 5.2.1. Frontier’s knowledge accumulation According to Barreto and Kypreos (2004b), the knowledge stock for solar PV technology in 1997 was almost 14894 millions US$98. This value is adjusted to the base year 2005 as 24775 millions US$2005. Table 1 shows the assumptions on Frontier’s R&D during the planning horizon. The time series data are chosen based on MERGE-ETL database (Bahn and Kypreos, 2003). Furthermore, the annual knowledge depreciation rate for knowledge is assumed to be zero. 5.2.2. Frontier’s experience accumulation Based on the global level of cumulative installed capacity for solar PV, the initial Frontier’s cumulative experience is 3000 MW in the base year. It is assumed that the global cumulative installed capacity grows at an average of almost 15% per year between 2010 and 2050. 5.2.3. Follower’s knowledge and experience accumulation One important issue is estimation of initial levels of knowledge and experience for solar PV in Iran in the base year of 2005. First we assume that a low level of cumulative installed capacity (2 MW) to be available before 2010. For the purpose of estimation of the initial values for experience in Iran, the above value, which reflects the real practical market experience, was adjusted by a scaling factor. The scaling factor is derived by dividing the magnitude of the global electricity generation capacity by the corresponding value for Iran. By using this procedure the initial experience of 168 MW is obtained (i.e. 5.6% of the global value). In order to estimate the initial value for knowledge stock in Iran, we assume that the ratio of the Follower’s knowledge stock to the Frontier’s one equals the corresponding ratio for the experience stock. Hence, the initial knowledge stock of 1389 millions US$2005 is resulted for Iran. 5.2.4. Degree of spillover Degree of spillover for solar PV is the degree to which the generated knowledge by the Frontier for this technology may spillover to a pool of knowledge potentially available to the Follower. Degree of spillover is a parameter that is limited between 0 and 1. This parameter determines the level of technology appropriablity which is shaped by external factors such as patent policy (Cohen and Levinthal, 1990). For implementation of the R&D model, we consider here a level of 80% for this parameter.

However, it can be taken as a policy parameter that can control the accessibility of external knowledge. 5.2.5. Learning rates In order to calibrate the two-factor learning curve, the progress ratios for learning-by-doing and learning-by-searching are considered as 0.81 and 0.90, respectively. These values have been adapted from Barreto and Kypreos (2004b). 5.2.6. R&D constraints A maximum growth constraint is considered in the model that would prevent R&D investments for solar PV technology in a given period to exceed 20% of the R&D expenditures of the previous period.

6. Results and discussion Process of solution iteration between the models of energy supply and R&D resource allocation has been flown according to the algorithm presented in Fig. 1. The first step of the iteration is an energy-supply solution. This section summarizes the results of the iterative implementation of the set of models for energytechnology assessment and R&D planning. The main results relevant to policy issues are presented in the followings. 6.1. Optimal path of innovation activities Fig. 2 represents the optimal R&D resource allocation for development of solar PV technology in Iran. The figure also shows the optimal new capacity installation for this technology. R&D (technology push) and new capacity installation (market pull) are two driving forces that act as complementary channels for technology development. R&D responds to market needs, but market experience is essential to achieve competitiveness. In general, there are positive feedbacks between R&D and new capacity installation. In this case, R&D may increase the knowledge spillover and the possibilities of solar PV technology to diffuse. New capacity installation, on the other hand, may Annual R&D Expenditures Periodical New Installed Capacity 40

35000

35

30000

30

25000

25

20000

20 15000

15

10000

10

500

5 0

0 2010 2015 2020 2025 2030 2035 2040 2045 Time Points

Periodical New Installed Capacity (MW)

Finally, it is assumed that the electricity demand grows at an annual average rate of almost 6% per year between 2010 and 2050. In order to consider demand fluctuations within a year rather than considering only annual demand, each year is subdivided into 36 load regions (12 seasonal regions and 3 daily regions).

Annual R&D Expenditure (Million US$2005)

1122

Fig. 2. Optimal R&D expenditures and new installed capacity for solar PV in Iran.

Table 1 Frontier’s R&D expenditures for solar PV technology.

Annual R&D expenditures

Unit

2010–15

2015–20

2020–25

2025–30

2030–35

2035–40

2040–45

2045–50

M$2005/year

810

985

1160

1375

1590

1875

2160

2440

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contribute to enhance the market experience and also the effectiveness and profitability of R&D efforts. In the time span 2010–30, the optimal R&D expenditures are increasing. Afterwards, total R&D expenditures show a downward trend. New capacity addition is ever increasing during the planning horizon. It characterizes demand pull for this technology and weighting factor in the objective function of R&D planning model and thus affects the amount of revenues of R&D activities. The incentive to undertake more R&D investments depends partly on the returns that these investments are expected to earn. While the coefficient of R&D revenues in the objective function is increasing over time, the expected R&D revenues will be higher and, hence, the incentive to undertake more R&D investments is strengthened in the time span 2010–30. In this time span annual R&D is rising from 2.5 to 36 million US$ (in 2005 constant prices) and annual new capacity installation increases from 3.16 to 38.04 MW. Besides the R&D revenues, the level of absorptive capacity can also affect the pattern of R&D resource allocation. Fig. 3 shows the optimal trend of knowledge complexity and absorptive capacity over time. According to Fig. 3, the knowledge complexity for solar PV is very high (0.92) and the absorptive capacity is very low (6%) in year 2010. So, there is a huge potential to enhance the country’s capacity to absorb and exploit the external knowledge. Therefore, enhancing the level of absorptive capacity to exploit the benefits of knowledge spillover is the main incentive to undertake more R&D investments than in any other situation.

Knowledge Complexity

Absorptive Complexity

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 2010

2015

2020

2025 2030 Time Periods

2035

2040

2045

Fig. 3. Optimal trend of knowledge complexity and absorptive capacity.

1.0

Technological Gap

0.9 2.5

0.8 0.7

2.0

0.6 1.5

0.5 0.4

1.0

0.3 0.2

0.5

In summary, during the first stage of the development and in the time span 2010–30, increase in the country’s background knowledge reduces the technological gap (see Fig. 4). The level of knowledge complexity is diminished by reduction of technological gap and enhancement of national innovation capacity in these periods. The level of absorptive capacity is inversely related to the state of knowledge complexity; however, it is also enhanced by the increasing amount of R&D. In the time span 2030–50, new installed capacity has a significant growth and is rising to 6132 MW in year 2050. At first glance it is expected that because of the increased market experience, R&D expenditures should still be promoted. However, in Fig. 2 it can be seen that optimal R&D expenditures are decreasing after 2030. This trend is initiated from the values of factors that (directly or indirectly) determine the state of the system. Knowledge stock, and thus technological gap, directly determines the state of solar PV development process. On the other hand, knowledge complexity, and thus absorptive capacity, is correlated with the level of knowledge gap. According to Fig. 4, national innovation capacity for solar PV in year 2030 is being promoted to 78% of the state-of-the-art and the technological gap reduced to the level of 0.26 (82% of the Frontier’s knowledge). It leads to reduction in the knowledge complexity up to 0.27 (73% of the minimum value) and consequently the absorptive capacity of external knowledge approaches to 95% in year 2030 (see Fig. 3). Based on the above findings, the country would enjoy a high level of absorptive capacity in 2030. It would facilitate assimilation of external knowledge and thus the R&D needs is reduced. In addition, increase in the effectiveness of R&D efforts (by increasing market experience) and decrease in the marginal impact of knowledge on the improvement of the cost of technology (due to the shape of the learning curve) also are negatively influencing the pattern of R&D in the time span 2030–50. In summary, during the final stages of the development and in the time span 2030–50, the country gradually approaches the Frontier and the knowledge gap remains relatively fixed over time. However, the level of knowledge complexity is still decreasing due to the enhancement of national innovation capacity in these periods. The absorptive capacity is remaining close to the maximum value, because of very low level of knowledge complexity and very high level of market experience and thus effectiveness of R&D efforts. Finally, the R&D undertaken is really insignificant compared to that of Frontier and cannot have a direct effect on the level of knowledge considerably. However, the main role of R&D in this case is enhancement of the knowledge and experience spillovers through increasing the absorptive capacity. 6.2. Critical rate of market penetration

National Innovation Capacity

0.1

National Innovation Capacity

Technological Gap

3.0

1123

Stimulation of potential demand or market pull for solar PV technology is implemented by definition of market penetration on new capacity constraints in the energy-supply model. Following the proposed algorithm in Fig. 1 and implementation of iteration between models of technology assessment and R&D planning, we obtain a critical market penetration rate of 9.6% for solar PV. This is the minimum annual growth rate of new capacity installation to guarantee the effectiveness of R&D activities and development of solar PV in Iran to be competitive in the long run.

0

0.0 2005 2010 2015 2020 2025 2030 2035 2040 2045 Time Periods

Fig. 4. Optimal trend of factors influencing knowledge complexity: technological gap and national innovation capacity.

6.3. Competitive cost According to the proposed algorithm in Fig. 1, the competitive investment cost for solar PV in Iranian electricity supply system is

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estimated to be 1475 US$/kW. If the solar PV investment cost is lower than the above value, this technology becomes economically competitive with the existing conventional technologies in the electricity supply system and thus, the process of technology commercialization and diffusion will be facilitated. 6.4. Combination of innovation activities In order to achieve a proper level of investment cost for solar PV technology and to ensure competitiveness and diffusion, sustained activities in research, development, demonstration and deployment (RD3) are required. R&D and D&D are two driving forces that act as complementary channels for cost reduction. Fig. 5 displays the optimal combination of these innovation activities in each period for solar PV development in Iran. Until period 2035–40, D&D activities have a considerable share in total RD3 efforts. By achieving the competitive cost (1475 US$/kW) in year 2040, D&D expenditures become zero thereafter. 6.5. Knowledge accumulation Fig. 6 displays the optimal trend of the knowledge spillover and accumulation for solar PV development in Iran. The knowledge is cumulative in nature. It can be seen that the knowledge stock in its cumulative form takes on an S-shape. However, knowledge spillover is non-cumulative variable and takes on a tilted bell-shape. The Frontier’s knowledge stock is increasing corresponding to the amount of the Frontier’s R&D reported in Table 1. The area between the Follower’s knowledge and the Frontier’s knowledge D&D

R&D 450 Million US$2005

400 350

curves shows the absolute knowledge gap which is initially very high and is reduced by the Follower’s knowledge development. For the Follower’s knowledge stock, an S-shaped growth pattern has been observed where its carrying capacity is increased corresponding to the level of Frontier’s knowledge stock. Because of huge technological gap and insignificant R&D (see Fig. 2) in the initial stage of development, knowledge has a high degree of complexity and hence the absorptive capacity will be very low (see Fig. 3). As a result, the Follower’s knowledge has a slow growth in the early stages. Gradually, increased R&D efforts and reduced knowledge gap provide foundation for overcoming the initial slow growth stage that is then followed by a rapid growth stage. The process of rapid growth continues until the incremental pattern of knowledge spillover is stopped. As the country’s knowledge stock is increased and the knowledge gap is reduced, the balancing effects of negative feedback of knowledge spillover slow down the Follower’s growth. Hereafter the Follower’s knowledge stock begins rising by a smaller amount each period. However it will not reach a fixed stationary equilibrium and dynamically track the trend of Frontier’s knowledge development with a relatively fixed gap. It can be seen that the knowledge spillover has taken a tilted bell-shaped curve where its maximum value corresponds to the time of maximum growth rate of the Follower’s knowledge. At the high level of the technological gap the available knowledge for absorption has a high degree of complexity. Development of the Follower’s background knowledge reduces the knowledge complexity and thus enhances the absorptive capacity. Enhancement of the absorptive capacity augments the knowledge spillover to reach a maximum level. Then as the Follower approaches the Frontier, the R&D is reduced and the quantity of the knowledge that can be acquired is diminished. Therefore, the assimilation of the external knowledge becomes more difficult and hence the amount of knowledge spillover is reduced. Finally, the gradual increase in the knowledge spillover during the final stages of the development corresponds to the Frontier’s knowledge growth and the absorption capacity which has been enhanced sufficiently.

300 250

6.6. Reduction in investment cost of solar PV

200 150 100 50 0 2010

2015

2020

2025 2030 2035 Time Periods

2040

2045

Fig. 5. Optimal combination of innovation activities for development of solar PV in Iran.

Fig. 7 compares the process of development of cost of solar PV between Iran and the Frontier. The trend of investment cost at the Frontier has been exogenously calculated with the help of solar PV learning curve. However, the level of investment cost for Iran has been endogenously estimated with the help of R&D planning model. Due to the low levels of initial knowledge and experience for Iran, the initial cost is very high compared to that of the 18000 Iran

100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 2010

Frontier

16000 Frontier’s Knowledge Stock

14000 US$/KW

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Knowledge Spillover Knowledge Stock

12000 10000 8000 6000 4000 2000 0 2005

2015

2020

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2035

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2045

Fig. 6. Optimal trend of knowledge spillover and accumulation for solar PV.

2010

2015

2020 2025 2030 Time Periods

2035

2040

2045

Fig. 7. Comparison of the development of cost of solar PV between Iran and the Frontier.

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7000 Electricity Generation (MWyr)

45000 Total Installed Capacity (MW)

1125

40000 35000 30000 25000 20000 15000 10000

6000 5000 4000 3000 2000 1000

5000 0

2010 DG 9 Grid Connected 9

0 2015 21 21

2020 63 40

2025 214 64

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2035 2940 159

2040 2045 10915 40528 251 661

Without Storage With Storage

2010 2.1

2015 4.9

2020 14.6

2025 49.4

2030 0.0

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2040 0.0

2045 2639

0.0

0.0

0.0

0.0

182

677

2515

6700

Fig. 8. Optimal total installed capacity for distributed generation (DG) and grid connected solar PV in Iran.

Fig. 9. Electricity generation from DG solar PV in two options of with and without storage system.

Frontier. However, accumulation of knowledge and experience in Iran, by its own innovation activities and exploiting external sources of knowledge and experience, leads to a decrease in the knowledge and experience gap and hence the difference between the cost curves are gradually diminished over time.

neously generated and delivered to the end-use sector. Fig. 9 shows the optimal trend of electricity generation from DG solar PV in two options of with and without storage system. It is observed that the amount of output electricity is insignificant until period 2025, which is due to the very low level of total installed capacity (o300 MW). In these periods, according to Fig. 7, the solar PV investment cost is much higher than the competitive cost, and thus, the capacity expansion will not be economically attractive. However, the constraint of minimum annual growth rate of new capacity installation (i.e. critical rate of market penetration) leads to installation of the very low level of capacity until 2025. Owing to the limitation of electricity generation in these periods, capacity building for the storage system will not be economically attractive and thus, the generated electricity is directly delivered to the consumers. After the period 2030, the solar PV investment cost gradually approaches to its competitive cost, and thus, the process of the technology commercialization and diffusion will be facilitated. By increasing the level of total installed capacity for solar PV, capacity buildup for the storage system will be economical and due to the electricity demand fluctuations during a year, operation of solar PV with storage system will be more attractive. In this system, all of solar PV electricity generation is stored for using in on-peak load regions. Due to the difference between on- and offpeak electricity demand, using storage system may reduce the total investment cost of electricity supply system. It leads to increase in the penetration of distributed solar PV with storage system. Thus, incorporating the storage system into distributed solar PV can strengthen market pull and similar to the discussions on results presented in Fig. 8, it enhances D&D and R&D activities. By this mechanism, the cost of solar PV is reduced to the extent that it will be competitive even without storage system in the last period. Besides, it should be mentioned that the operation of solar PV generation without storage systems in the last period is mainly arising from the effects of final periods on the process of technology assessment. Model tries to minimize new capacity building in the last period and, thus, instead of increasing in the volume of storage system, it is economically preferred to deliver the surplus generation of solar PV to the end users without storage. Fig. 10 shows the optimal trend of electricity generation from grid connected solar PV in two options of with and without storage system. Interpretation of the results in this case is (more or less) the same as that of Fig. 9. However, in this case, total electricity generation is insignificant and solar PV without storage system will not be economically competitive at all. Because of very high level of investment cost, the growth rate of new capacity installation, and thus electricity generation, in the grid connected

6.7. Assessment of the impact of distributed generation (DG) on solar PV development Fig. 8 shows the optimal level of total installed capacity for DG and grid connected solar PV in Iran. The growth rate of new capacity installation in the grid connected case continues to remain at the minimum allowable value (9.6%) until period 2040. However, its implementation will be accelerated as soon as it becomes competitive in cost in the last period. The high growth rate of DG system is due to the local generation and utilization of electric power. It leads to reduction in the cost and loss of energy in power network. In fact, increase in DG capacity is an indication of market pull stimulation that can be performed more economically. This process can directly enhance D&D activities by increasing the level of market experiences. Furthermore, it can strengthen the incentive to undertake more R&D investment by increasing the expected R&D revenues (i.e. improvement of the coefficients of R&D revenues in the objective function of the R&D planning model). This mechanism accelerates the reduction in the investment cost of solar PV and, finally, leads it to the competitiveness in the connected grid in the last period. Although the implementation of solar PV in grid connected case is still negligible compared to that of DG case, but its total installed capacity will rise considerably by extending the planning horizon. 6.8. Impact of electricity storage on penetration of solar PV Although the solar electricity generation has a high potential for application, such energy-conversion system is intermittent in nature and thus cannot be scheduled. One of the mechanisms to allow a substantial contribution of this intermittent electricity sources is energy storage. Energy storage increases the usefulness of solar PV by absorbing excess solar PV generation and allowing solar PV energy to be used when it is not produced. Furthermore, large-scale energy-storage deployment can increase the flexibility in utility system operation (Denholm and Margolis, 2007). For this purpose, we have evaluated solar PV technology in two general forms: with storage and without storage system. The former provides electricity to the consumers directly or via storage. However, in the latter form the electricity is simulta-

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Electricity Generation (MWyr)

160 140 120 100 80 60 40 20 0

2010

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Without Storage

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0.0

0.0

2045 0.0

With Storage

0.0

0.0

0.0

0.0

23.2

36.6

57.9

152.4

Fig. 10. Electricity generation from grid connected solar PV in two options of with and without storage system.

case remain at the minimum allowable value (9.6%) until period 2025. Limitation of electricity generation in these periods, prevent capacity building for the storage system and thus, all of generated electricity is directly delivered to the consumers. The amount of solar PV electricity generation in period 2030 is increased to extent that the capacity installation for the grid connected storage system and also the operation of solar PV with storage system will be beneficial. Therefore, the mode of solar PV operation switches to the with storage system. 7. Conclusions and policy implications In the present research work, an analytical instrument was developed for assessment of energy technologies, R&D resource allocation and technology development. Development of new technologies has been considered according to the specific conditions of developing countries. The analytical instrument includes two models interlinked with each other: energytechnology assessment and optimal R&D resource allocation. Energy-technology assessment and prioritization of new energy technologies are provided by the model of the energysupply system that contains a detailed representation of current and emerging technologies characterized in terms of their technical indices. R&D resource allocation model is an optimization model that reflects the optimal profile of R&D for developing the appropriate energy technologies in developing countries. In this model, based on the economic and environmental impacts of new technologies, the optimal allocation of R&D resources for them is studied. The model of energy R&D resource allocation is based on the theory of optimal control. The control variables are the flow of R&D activities for each technology. The state variables are the level of knowledge stock by technology type. This model maximizes the total net present value of resulting R&D benefits taking into account the dynamics of technological progress, knowledge and experience spillovers, technology adoption and R&D constraints. R&D activities and accumulation of knowledge provide a basis for technological push which results in improvement of costs for new technologies. Market penetration and potential demand for any energy technology are analyzed in a rational decision-making process and with the help of energy-supply model. Linkage between the energy-supply model and the model of R&D resource allocation is established with the help of optimal level of new installed capacity, technology activity and total existing capacity of technologies. In the opposite direction, optimal values of technology costs, which are concluded from the results of R&D

resource allocation model, can be used as input data in the energy-supply model. These iterations are continuing to meet the convergence criteria. The process of linking the optimal R&D resources allocation with the energy-systems model provides a foundation for further development of a comprehensive model of energy-technology assessment and R&D planning. Furthermore, the interaction between two models provides a set of analytical tools for comprehensive analysis of the impact of technological innovation and R&D planning. The application of the set of models in electricity supply system of Iran shows that the effects of results of technology assessment (i.e. solar PV capacity installation), knowledge and experience level of the Follower country relative to the Frontier and absorptive capacity on the pattern and growth of R&D resource allocation are considerable. The results obtained using this modeling approach provide some important policy insights. Generally, they highlight the role of energy RD3 in technology development. R&D responds to market needs, but market experience is essential to achieve competitiveness. There are positive feedbacks between R&D (technology push) and new capacity installation (market pull). R&D may increase the knowledge spillover and the possibilities of solar PV technology to diffuse. New capacity installation, on the other hand, may contribute to the enhancement of the market experience and also the effectiveness and profitability of R&D efforts. One valuable policy insight concerns the process which should be followed in the market to reach the competitive cost level for solar PV. When the investment cost of solar PV is higher than its competitive cost, it will not be selected during the technology assessment process. This technology will become competitive only if experience is available with it. Stimulation of potential demand or market pull in this case can facilitate the process of technology commercialization and diffusion. To do so, capacity expansion should be performed with the aim of stimulating demand pull and increasing market experiences. It has been shown in the results that the minimum annual growth rate of new capacity installation to guarantee the effectiveness of R&D activities and long-run development of solar PV in Iran should be 9.65%. Impact of DG and storage systems on technology development may be additional important result that is obtained. The results show that DG may have large contribution in solar PV development in Iran. In fact, increase in DG capacity is a kind of market pull stimulation that can be performed more economically. This process can directly enhance D&D activities by increasing the level of market experiences. Furthermore, it can strengthen the incentive to undertake more R&D investments by increasing the expected R&D revenues. This mechanism accelerates the reduction in the investment cost of solar PV and, finally, it leads it to its comparative advantage in grid case too. An important factor that can accelerate the process of cost reduction for solar PV in Iran is using storage systems. Due to the difference between on- and off-peak electricity demand, using storage system may reduce the total investment cost of electricity supply system. It leads to increase in the penetration of solar PV (specially the distributed ones) with storage system. Thus, incorporating the storage system into solar PV can strengthen market pull and similar to DG effects, it leads to enhancement in D&D and R&D activities. By this mechanism, the cost of solar PV can be reduced to the extent that it will be competitive even without storage system. However, capacity buildup of the storage system would be economically competitive if the level of total electricity generation from solar PV has reached a level that can be considered as sufficiently grown up.

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One of the most important results obtained by the application of the set of models is that the R&D undertaken by the Follower country is really insignificant compared to that of Frontier. In fact, the main role of R&D undertaken by the Follower country is enhancement of the knowledge and experience spillovers through increasing the absorptive capacity. This would allow taking advantage of the positive externalities of innovation and spillover processes such that long-term environmental and economic benefits can be derived. In other word, co-operation between industrialized and developing countries may enable developing countries to profit from the experience of the industrialized countries and access to new and more environmentally compatible technologies at lower costs. Finally, in this paper, the primary ideas on the role of R&D in knowledge spillover and technology development in developing countries have been formulated in the context of energy-systems models and effort has been made to keep the specifications as simple as possible. The structural form of the model can be improved along various aspects. For the future developments of the model, we therefore suggest some important aspects such as: analysis of R&D and knowledge spillovers among different technology types, analysis of reverse spillovers of knowledge and experience from developing to advanced countries and evaluation of its impact on global development of technologies, exploring the process and mechanism of experience spillover among different regions and technologies and analysis of degree of complementary or substitutability between knowledge and experience in technology development process. References Bahn, O., Kypreos, S., 2003. Incorporating different endogenous learning formulations in MERGE. International Journal of Global Energy Issues 19 (4), 333–358. Barreto, L., 2001. Technological learning in energy optimisation models and deployment of emerging technologies. Ph.D. Dissertation, no 14151. Swiss Federal Institute of Technology, Zurich. Barreto, L., Klaassen, G., 2004. Emissions trading and the role of learning-by-doing spillovers in the ‘bottom-up’ energy-systems ERIS model. International Journal of Energy Technology and Policy 2 (1–2), 70–95. Barreto, L., Kypreos, S., 2000. A post-kyoto analysis with the ERIS model prototype. International Journal of Global Energy Issues 14 (1–4), 262–280. Barreto, L., Kypreos, S., 2002. The role of spillovers of technological learning in a ‘bottom-up’ MARKAL model of the global energy system. Paul Scherrer Institute, Villigen.

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