Renewable energy technology diffusion model for techno-economics feasibility

Renewable and Sustainable Energy Reviews 54 (2016) 1515–1524

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Renewable energy technology diffusion model for techno-economics feasibility Rajesh Kumar a, Arun Agarwala b a b

Scientist, DSIR, Ministry of Science & Technology and Research Scholar on Renewable Energy Modeling at IIT Delhi, India IDDC, IIT Delhi, India

art ic l e i nf o

a b s t r a c t

Article history: Received 29 December 2014 Received in revised form 21 May 2015 Accepted 21 October 2015

Diffusion of Renewable Energy Technologies (RETs) governs by the status of technology in terms of efficiency and techno-economical feasibility. The states plan for the deployment of resources for the development with special reference to sustainable environment. The demand and supply energy model help to provide more focus on the long term goals. The theory of diffusion modeling allows analysis of diffusion processes and study of growth rates of different technologies and underlying diffusion factors of the particular states, particularly for Indian 8 National Missions implication factors concerning to National Action Plan for Climate Change (NAPCC). The Energy Management Systems (EMS) and Distribution Management Systems (DMS) provide control on hybrid sources of energy generation and distribution over on-grid and off-grid applications. Energy Distributive system model for adequate accurate predictive analysis plays important role for sustainable country energy resources, in consideration of all influential factors in energy generation and distribution. For prediction aspects the important parameters are geographical location, seasonal influence, effect of climate change and state or area concession. With the abundant data and relative economic indicator, energy prediction is performed with close loop predictive system based of timing algorithm. The energy economics in free trade market like India, where the peak load varies abruptly due to season and community demand, its hourly prediction model is more useful. The energy model provides the estimation and prediction of hybrid power generation in consideration of the parameters on resource potential, technology, efficiency and consumption pattern. The monitor, statistical and prediction model with inbuilt mechanism for renewable energy certificate (REC) and perform, achieve and trade (PAT) incentives are explored in this paper. The paper presents energy models to enhance knowledge and skills in the efficient transfer and management of technology for optimally allocating different types of renewable resources, technology and economic feasibility. & 2015 Elsevier Ltd. All rights reserved.

Keywords: India Technology Diffusion PAT ESCert Renewable Energy Certificates (REC) Energy Efficiency

Contents 1. 2. 3.

4. 5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1516 Review of technology diffusion model in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1516 Policy instrument for renewable energy and energy efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1517 3.1. Renewable Energy Certificate (REC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1517 3.2. Perform, Achieve and Trade( PAT) on energy efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1518 3.3. Clean Development Mechanism (CDM) on Carbon Footprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519 Renewable energy technology diffusion model for techno-economics feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519 4.1. MATLAB simulation for RE technology diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1520 Renewable energy system operation model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1522 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1523

E-mail addresses: [email protected] (R. Kumar), [email protected] (A. Agarwala). http://dx.doi.org/10.1016/j.rser.2015.10.109 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1524 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1524

1. Introduction After the Kyoto protocol, the United Nations Framework Convention on Climate Change (UNFCCC) had impacted the energy scenario in developing countries, like India. The Ministry of New & Renewable Energy (MNRE) had created many schemes for the promotion of generation and use of renewable energy. In 2010 MNRE launched the National Solar Mission to target 20 GW offgrid and grid interactive solar power generation by 2022. The Energy star rating programme by Bureau of Energy Efficiency (BEE) is one of the successful initiatives to target energy efficiency and saving. Market Transformation for Energy Efficiency (MTEE) scheme targeted to save energy by accelerating the shift to energyefficient appliances in designated sectors through innovative measures that make the products more affordable. UNFCCC has designed Clean Development Mechanism (CDM) for the implementation at international level for promotion of energy efficiency via technology upgradation in developing countries. India had also implemented CDM and got benefits of the scheme [1,2]. Renewable Energy Certificate (REC) is a National level policy instrument to promote renewable power generation in India. Technologies such as wind, solar PV, solar thermal, biomass and hydro are eligible to earn RECs. Such schemes exist successfully in several parts of the world. Renewable Energy Certificate mechanism in India is a market based instrument to promote renewable energy generation through renewable purchase obligations (RPO) on the energy distributor [3,4]. The environmental attributes can be exchanged in the form of Renewable Energy Certificates, which are tradable. Perform, Achieve & Trade (PAT) is a market based mechanism to enhance cost effectiveness of improvements in efficiency in energy intensive industries through certification of energy saving which can be traded. The proposed implementation structure will be institutionalized through an existing institution to provide an extended hand for support to Designated Consumers (DCs) based on measurable performance indicators. The PAT mechanism is designed for review and implementation by Bureau of Energy Efficiency (BEE) under National Mission on Enhanced Energy Efficiency (NMEEE) [4,5]. To meet the desired target and support renewable energy, Central Electricity Regulatory Commission (CERC), Ministry of Power, has launched the REC mechanism in consultation with State Electricity Regulatory Commissions (SERC). Bureau of energy efficiency is working on PAT mechanism and ESCert trading under NMEEE for its 1st cycle 2012–15. In global scenario, solar energy technology diffusion rate is higher due to technological improvements resulting in cost reductions and government policies supportive of renewable energy development and utilization [6]. In India during last decade, wind energy achieved the maximum growth rate in view of regional resource potential. This paper analyses the potential implementation of hybrid power plant (PV/ Wind/Geothermal/fossil fuel) in view of an enhanced energy efficiency scenario [7,8].

2. Review of technology diffusion model in India The Literature on energy Modeling concept is available from 1950 and scientist focus on renewable energy in the late decade of 1970 [9]. The earliest model Habbane et al. (1986) had developed a

modified solar radiation model to determine solar irradiance from sunshine hours for a number of stations located in hot dry arid climates [10]. Suganthi (2015) use soft computing techniques such as fuzzy logic, neural networks, and genetic algorithms are being adopted in energy modeling to precisely map the energy systems. The author made an attempt to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications [11]. Authors reviewed that the Fuzzy logic controller (FLC) is being widely used in solar PV applications for maximum power point tracking (MPPT). Also FLC are predominantly used for controlling the intermittent energy flow from the renewable energy sources. Neuro-fuzzy logic controllers are being used in wind energy systems. In addition it is found that other fuzzy based hybrid models such as fuzzy analytic hierarchy process (AHP), fuzzy data envelopment analysis (DEA), fuzzy genetic algorithm (GA), fuzzy Particle swarm optimization (PSO), fuzzy honeybee optimization are being explored in the modeling of solar, wind, bio- energy applications. The study found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/ wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates. In an Italian study by Gullia (2015) cautioned that the simulation-based models or the full empirical analyses may be misleading [12]. Dasgupta (2015) focus on two major drivers of energy demand: technological progress and energy price. The autor discuss the rebound effect to retard the effectiveness of the current policy regime under PAT aiming at reinforcing energy efficiency. In fact during the past decades many of the industries behaved as superconservationist with no incidence of backfire. Productivity growth accounting and estimation of parametric cost function using annual survey of industry data bring out important implications regarding the role of these two drivers [13]. Umamaheswaran (2015) reviews the emerging trends and barriers in the financing landscape and analyses the impact of policy performance . the design of a national renewable finance framework complete with targeted finance push instruments that can complement existing demand pull policies in facilitating investment [14]. The authors conclude that larger reforms in the electricity markets such as rationalizing power tariffs, removal of cross subsidies, improving the financial health of distribution utilities and pushing for open access across states can have a significant impact on RE market development. Kar (2015) discusses the major achievements, programs, policy, and incentives for the installation of wind power in India. Opportunities and challenges have been discussed along with the ways to remove barriers to achieve higher growth [15]. Hastik (2105) discuss interdisciplinary approaches such as the concept of Ecosystem Services can help to cover the wide range of aspects associated with these particular human–environment interrelations [16]. Behera (2015) gives a precise idea about different optimization techniques, their advantage and disadvantage with respect to a wind farm [17]. Aliyu (2015) discussed the existing government policies and legislations, and proposed others that can help speed up the adoption of RE in Nigeria [18]. Kailash (2011) explain a framework capturing the relationship between the licensors’ technological capabilities and licensees’ existing position in the industry value chain [19]. Kailash used industry value chain analysis (IVCA) for analyzing the

R. Kumar, A. Agarwala / Renewable and Sustainable Energy Reviews 54 (2016) 1515–1524

competitiveness of Indian solar firms whilst market concentration index measured via herfindahl index is used to find out the segment concentration of the Indian solar industry. Patent proximity analysis is used to find the technological competitiveness of foreign solar firms. IVCA used by author to determine the market leader, market challenger and market followers. Authors also used a new framework on the vector analysis and proposed that incorporates the dynamism of technological growth with the traditional value analysis helps us to determine the competitiveness of the industry and explained through a sample set of solar firms from India. The model by K. Usha Rao (2010) provide the diffusion of RET, “A review of technology diffusion models with special reference to renewable energy technologies” [20]. Rao proposed diffusion models have been mainly limited to commercial products with little or no linkages to government policies. The authors see the challenge to build up experience in applying diffusion models to analyse diffusion of renewable energy technologies (RETs). The demand for RETs is being created by the government through a set of policies incentives and regulations. RET applications mainly originated in the market with government subsidy and continue to attract several incentives for varying periods ranging from 5 to 15 years or more. Since RETs face many market barriers, research so far has mainly dealt with the subject of challenges and analysis of barriers that constrain the diffusion of RETs. Rao Identified need for systematic study of RET diffusion using diffusion theory and models. While learning curve approaches have been effective for economic considerations, it is important to correlate the time series data to policy changes and also several factors, particularly, social and technological that influence diffusion processes. Kari Kristinssona (2008) explain sectoral systems of innovation framework to examine the relationship between technology policy and industrial development by comparing the emergence of the wind energy industry in Denmark and India [21]. Rakesh Basant (2002) analyse the technology strategies of six Indian firms in different product groups which are trying to build competitive manufacturing and technology capabilities. The linkages between corporate, technology, and manufacturing strategies are explored and the role of complementary assets is studied in order to identify patterns through which these firms are building capabilities of various kinds [22]. Rasmus Lema (2012) argue that the emphasis should shift from transfer of mitigation technology to international collaboration and local innovation [23]. Vidhisha Vyas (2013) examine the role of mergers and acquisitions (M&A) and technological efforts in determining the export competitiveness of firms belonging to the pharmaceutical sector in India [24]. Anil K. Choudhary (2013) highlight that an effective technology transfer model can play a key role in adoption of integrated nutrient management (INM) technology for sustainable production systems in the developing world, especially for resource- and knowledge-poor farmers of collateral socio agro-economic environments of developing nations [25]. S. Gopalakrishnan (1997) review the extant innovation research from three fields—economics, organizational sociology and technology management—in order to find points at which the fields' approaches and assumptions overlap [26]. V Kumar (2003) explain technology commercialization — policy initiatives in India, need for the study, design and methodology and their field study findings [27]. The MARKAL model used integrated energy system optimization framework that enables policymakers and researchers to examine the best technological options for each stage of energy processing, conversion, and use. This modeling framework was used to represent a detailed technological database for the Indian energy sector with regard to energy resources, indigenous extraction, imports, and conversion, as well as energy use across the five major end-use sectors agricultural, commercial,

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residential, transport, and industrial [28]. Kumar (2013) explored the energy model that provides the estimation and prediction of hybrid power generation in consideration of the parameters on resource potential, technology, efficiency and consumption pattern. The monitor, statistical and prediction model with inbuilt mechanism for REC and PAT incentives is explored [4]. The empirical study on the causal relationship between economic growth and renewable energy for 27 European countries in a multivariate panel framework over the period 1997–2007 using a random effect model is summarized in “Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis”, by Angeliki N. Menegaki (2011) [29]. National Instruments Labview and the Labview Control Design and Simulation Module can be used to simulate a full wind turbine system, including the turbine, mechanical drive train, generator, power grid and controller. AROMA model method has been employed in predictive model. This method has a higher accuracy of time sequence short period predictive one [30]. HOMER is a computer model developed by the U.S. National Renewable Energy Laboratory (NREL) to assist in the design of micro-power systems. HOMER finds the feasibility of the system by assessing whether it can adequately serve the electric and thermal loads through an hourly time series simulation over one year. It also estimates the life-cycle cost of the system, which is the total net present cost of installing and operating the system over its lifetime [28]. The RET Screen Plus Performance Analysis Module can be used worldwide to monitor, analyse, and report key energy performance data to facility operators, managers and senior decisionmakers. LINGO is a comprehensive tool designed to make building designs. It can solve Linear, Nonlinear (convex and non-convex/ Global), Quadratic, Quadratically Constrained, Second Order Cone, Stochastic, and Integer optimization models efficiently [31]. Leading vendors of power system planning tools are: Multi Area Production Simulation Software program (MAPS) from General Electric (GE), Plexos for Power Systems from Energy Exemplar, GridView from ABB, and PROMOD from Ventyx [26]. These models have constraints include hourly available sources, the voltage limits, the feeders’ capacity, the maximum penetration limit, and the discrete size of the available DG units, with in legal law available [32].

3. Policy instrument for renewable energy and energy efficiency 3.1. Renewable Energy Certificate (REC) The REC mechanism has the prime objectives of RPO regulation, increased flexibility for participants to carry out RE transactions, overcoming geographical constraints, reduce transaction costs, development of all encompassing incentive mechanism and reduce risks for local distribution licensee [2,3]. In this mechanism, one REC is issued to the RE generator for one MWh electrical energy fed into the grid. The REC issued by SERC has a Unique Certificate Number with information on name of the issuing body, generator identity, type of generation technology, installed capacity of the generator, location of the generator and signature of the authorized person. The REC generator must apply within a period three months of the generation for issuance of RE certificate on grid connected RE projects of 250 kW and above [3]. Fig. 1 shows the active components of REC mechanism. The REC is available for trade up to 365 days after the date of issuance [24].

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Fig. 1. REC Mechanism – as on October, 2014.

2000000 1800000 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0

REC- 2013-14 REC Issued

4

14

01 ,2

ar M

14 Fe

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20 n,

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3 Ju

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3 ne

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01 Ju

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Ap

ay

Month

r-1

3

No. of REC

REC Redeemed

M

The RE generator identifies the RE potential and coordinates with state Government for the power sale agreement. The approved RE generator installs the RE plant and contacts state load distribution centre (SLDC) for the supply of energy to the grid and energy metering. SLDC monitors the energy distribution and certifies for the energy fed to the distributor for the issuance of REC by CERC. The REC issued by registry are tradable at two power exchanges within 365 days. The trading of REC is on two exchanges with centralised information and control mechanism at Central Electricity Regulatory Commission (CERC). The REC Trading started in April , 2011 with a slow response but has gained momentum within a few months operated through two exchanges PXIL and IEX. These exchanges offer easy access, transparent and fully electronic market place, and a robust and user friendly platform to trade on RECs. The trading from April, 2013 to March, 2014 is shown in Fig. 2, reflects that REC certificate issuance growth rate is sharp rise in March, 2014, due to closing of financial year. The REC issued are rising continuously, while the REC redeemed are stagnated below three lakhs, which create large closing balance. The rise in closing balance indicates the low demand for REC and the cost islikely to fall in future [33]. The REC closing balance on 31st March, 2014 is 56,55,977 and floor trade price for non-solar REC is INR 1500 ($28) and for solar REC is INR 12620 ($ 230). Total No. of RE generator registered with REC registry in August, 2014 is 2573 with total closing balance of REC at 9375785 which increase in November, 2014 to 619762 with closing balance 11426646.

Fig. 2. REC status from April, 2013 to March, 2014.

Table 1 Designated Consumer and Annual energy consumption. Industry Sector Annual Energy Consumption Norm to be DC (mtoe)

No. of Identified DCs

Aluminum Cement Chlor-Alkali Fertilizer Pulp & Paper Power Iron & Steel Textiles

11 92 23 22 70 154 110 197

7500 30000 12000 30000 30000 30000 30000 3000

3.2. Perform, Achieve and Trade( PAT) on energy efficiency The PAT mechanism designed to promote enhanced energy efficient technology to be adopted by industry to improve target on specified specific energy consumption (SEC) in a cost-effective manner. Perform Achieve and Trade (PAT) needs improvement to give its operational mechanism scale, complexities, timelines for successful delivery [2,3,5]. An Emission Trading Scheme, “Perform, Achieve and Trade” (PAT) is explored in relation to the existing carbon market in India, particularly the Clean Development Mechanism, Renewable Energy Certification and possible Nationally Appropriate Mitigation Actions (NAMAs). The Perform Achieve and Trade scheme incentivise energy-intensive large industries and facilities for

enhance energy efficiency, through technology upgrade and improvement in process. The incentive in the form of certification of enhance energy savings (ESCert) that could be traded. Perform Achieve and Trade scheme is monitored by Bureau of Energy Efficiency with proposed networks participation from designated consumers, state Governments, commodity exchanges, and other stakeholders. The PAT scheme currently identified 478 designated consumers from eight energy intensive industrial sectors namely, thermal power plants, iron and steel, cement, textiles, chlor-alkali, aluminum, fertilisers and pulp & paper as given in Table 1. The flow chart for the implementation of PAT Mechanism in India is shown in Fig. 3.

R. Kumar, A. Agarwala / Renewable and Sustainable Energy Reviews 54 (2016) 1515–1524

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Fig. 3. Flow Chart for the Implementation of PAT in India.

3.3. Clean Development Mechanism (CDM) on Carbon Footprint The Seventh Conference of Parties (COP-7) to the UNFCCC decided that Parties participating in CDM should designate a National Authority for the CDM and as per the CDM project cycle, a project proposal should include a written approval of voluntary participation from the Designated National Authority of each country and confirmation that the project activity assists the host country in achieving sustainable development. Accordingly the Central Government constituted the National Clean Development Mechanism (CDM) Authority for the purpose of protecting and improving the quality of environment in terms of the Kyoto Protocol. With the end of the first Kyoto Protocol (KP) commitment period on 31 December 2012, many questions have been raised about the future of the Clean Development Mechanism [34]. The progress of CDM projects are summarize in Figs. 4 and 5.

4. Renewable energy technology diffusion model for technoeconomics feasibility In order to achieve the most accurate predictive analysis, influential factors in energy application have been taken into full consideration during design of energy model in this article. Taking the collected abundant data and related economic indicator models through further strict calculations used to predict relative economic indicators [35,36]. In view of research and innovation on renewable energy technologies, the Bass model is used in this study, which is very useful tool for forecasting the initial adoption of an innovation for which no closely competing alternatives exist in the marketplace. A key feature of the model is that it embeds a "contagion process" to characterize the spread of word-of –mouth between those who have adopted the innovation and those who have not yet adopted the innovation. The model can forecast the long-term adoption and sales pattern of new technologies and new durable products under two types of conditions: 1. Recently introduced the product or technology;

Afforestation and Metal Production, Reforestation 1% Transport, Chemical , Other 0%

Energy Demand, Distribution 7%

Waste handling and disposal 2%

Manufacturing Industries 5%

Energy industries, 85%

CDM Approved Project Fig. 4. No of Projects approved in India up to 2012- sector wise. Waste handling Energy Demand, Distribution and disposal 4% 2%

Afforestation and Reforestation 1% Metal Production, Transport, Chemical , Other 3%

Manufacturing Industries 3%

Energy industries, 87%

CER Issued (Annual) Fig. 5. CER issued in India up to 2012 sector wise.

2. Pre-Launch of the product or technology, to exploit its market behavior is likely to be similar to some existing products or technologies whose adoption pattern is known. Suppose that the (cumulative) probability that someone in the target segment will adopt the innovation by time t is given by a non decreasing continuous function F(t), where F(t) approaches 1 (certain adoption) as t gets large. The derivative of F(t) is the probability density function, f(t) (Fig. 6), which indicates the rate at which the probability of adoption is changing at time t. To

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diffusion model is given by:   q f ðtÞ ¼ p þ N ðt  1Þ ½N N ðt  1Þ m

ð3Þ

The rate of diffusion in case m is the total potential for RET and N(t) is the potential at time t, the equation 3 further simplified as;  dN  q ¼ p þ N ðt Þ ½ m  N ðt Þ ð4Þ dt m

estimate the unknown function F(t) we specify the conditional likelihood L(t) that a customer will adopt the innovation at exactly time t since introduction, given that the customer has not adopted before that time. Using the foregoing definition of F(t) and f(t), we can write L(t) as by using Bayes's rule [36]; 

 q f ðtÞ ¼ p þ N ðt Þ ½1 F ðt ÞX ðt Þ N

ð1Þ

N(t)¼the number of customers who have already adopted the innovation by time t; N ¼Total Potential, a parameter representing the total number of customers in the adopting target segment, all of whom will eventually adopt the product; p ¼coefficient of innovation (or coefficient of external influence); and q ¼coefficient of imitation (or coefficient of internal influence). where x(t) ¼is a function of the marketing-mix variables in time period t There are several methods to estimate the parameters of the Bass model. These methods can be classified based on whether they rely on historical sales data or judgment for calibrating the model. Linear and nonlinear regression can be used if we have historical sales data for the new product for a few periods (years). Judgmental methods include using analogs or conducting surveys to determine customer purchase intentions. Perhaps the simplest way to estimate the model is via nonlinear regression. To get the potential at any time t the model in Eq. (1) and multiplying both sides by (N) we get:   q nðtÞ ¼ p þ N ðt  1Þ ½N  N ðt  1Þ N

4.1. MATLAB simulation for RE technology diffusion India has been bestowed with huge Renewable Energy (RE) potential; however it is not distributed uniformly across the country. Solar, wind, biomass and small hydro are the major RE sources in India. The potential of various RE sources, excluding solar, is shown in Fig. 8 below [4] India receives solar energy of approximately 5000 trillion kWh/ year equivalent. Hence both technology routes for conversion of solar radiation into heat and electricity, namely, solar thermal and solar photo-voltaic have the potential to produce significant amounts of clean energy [5]. The solar power available across India Adoption Cost

Early Majority

Annual adoption/ cost

Fig. 6. Adoption of Technology in time t.

Diffusion is also seen as a five stage process – awareness, interest, evaluation, trial, and adoption [6]. They correspond to different stages of consumers’ adoption during market development classified as innovators/early adopters, early and late majority and laggards according to the time of adoption, since the technology is introduced in the market as shown in Fig. 7. However, there is an uncertainty with regard to the extent and time for diffusion of technology.

Late Majority

R&D & Innovation Cost

Stagnation Early Adopters Maturity cost Laggards

Innovator

Time Fig. 7. RET Diffision Curve - Adoption and cost v/s time.

ð2Þ

Given at least four observations of N(t) we can use nonlinear regression to estimate parameter values (N,p,q) to minimize the sum of squared errors. An important advantage of this approach is that users need not know when the product was introduced into the market. They only need to know the cumulative sales of the product for the estimation periods. The parameters of the Generalized Bass model could be estimated via a modified version of the nonlinear regression estimation: we recommend estimating p and q via nonlinear regression, and obtaining the estimates for the impact of marketing effort via managerial judgment. A mixed influence model designed by Bass, to represent the first purchase growth of a new product, durable in marketing [8]. The Bass model is a mixed influence model with three parameters p, q and m; p represents the coefficient of innovation and q is the coefficient of imitation and m is the total potential. The Bass

Fig. 8. Renewable Energy (Excluding Solar) Potential in India.

R. Kumar, A. Agarwala / Renewable and Sustainable Energy Reviews 54 (2016) 1515–1524

in different regional location is calculated with the help of Table 2 average data on solar energy over the complete year. The wind speed available across India in different regional location for the calculation of total wind power potential with the help of Table 3. The input argument options is a structure, which contains several renewable energy parameters that are used with a given MATLAB optimization routine. Then MATLAB displays the fields of the structure options. Accordingly, before calling linprog.m the preferred parameters for rate of diffusion for RE technology is set in the options for linprog.m using the optimset command as clear all; clear all; clc; m ¼ 40; for i ¼1:9 N ¼[1 1.5 2 2.5 4 6 8 11 14]; for j¼1:10 p ¼[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0]; q ¼[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0]; rate(i,j)¼p(j) þ((q(j)/m)*N(i)*(m-N(i))); end end rate

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Table 3 Mean Average Wind Speed (MAWS) and Mean Average Wind Power Density at Major City WIND MONITORING STATIONS in India as on 30.11.2014. Sr. no. Station

Latitude N (in Deg.)

Longitude E (in Deg.)

MAWS MAWPD (m/sec) (W/sq m)

Mast Height (m)

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

08 08 21 20 19 17 14 15 14 14 12 13 16 09 11 33 21 22 17 18

77 77 70 86 73 73 77 78 77 77 78 75 74 77 76 74 69 71 73 75

7.32 7.08 6.78 5.72 6.58 7.42 5.85 5.58 5.41 6.96 7.31 7.52 6.44 6.30 6.36 6.89 6.85 5.79 5.69 5.68

30 20 20 80 25 120 25 25 50 80 80 20 25 20 20 50 120 50 25 25

KANYAKUMARI MUPPANDAL DHANK 1 PARADEEP AUNDHEWADI JAGMIN ALANGARAPETA BANDERLAPALLI VAJRAKARUR VYSAPURAM KODIGANIPALLI B.B. HILLS CHIKKODI KAILASAMMEDU NALLASINGAM BIDDA LAMBA VIREWADI KAS SAUTADA

352 406 312 204 294 410 244 240 202 285 306 498 264 251 324 336 261 168 194 167

The MATLAB analysis the technology adoption at any point of time versus the rate of diffusion keeping total target potential for any RE Technology at the value of m 15, 20 and 40 with innovation parameter p at 0.5 and policy inferences q at 0.5 in Fig. 9. The MATLAB analysis the technology adoption at any point of time versus the rate of diffusion keeping total target potential for any RE Technology at the value of m at 40 with innovation parameter p at 0.5 and with different value for policy inferences q, as in Fig. 10. The Policy and innovation impact analysis the technology innovation at any point of time versus the rate of diffusion keeping total target potential for any RE Technology at the value of m 40 as in Fig. 11. Table 2 Mean monthly global solar radiant exposure (M J m  2 day  1) over major cities in India. Station/Month

January

June

December

Annual Average

Srinagar New Delhi Jodhpur Jaipur Varanasi Patna Shillong Ahmedabad Bhopal Ranchi Kolkata Bhavnagar Nagpur Mumbai Pune Hyderabad Visakhapatnam Panjim Chennai Bangalore PortBlair Minicoy Thiruvananthapuram

4.77 13.22 15.53 15.30 12.91 13.01 14.11 16.34 15.80 15.63 13.53 17.92 16.15 16.57 17.29 19.64 17.42 19.88 17.62 20.42 18.44 17.77 19.93

22.26 22.54 23.58 23.94 20.87 20.27 16.42 21.67 19.92 16.75 17.17 22.31 18.85 17.49 19.32 20.13 17.49 16.67 20.59 17.72 13.94 16.01 17.38

6.99 11.93 14.84 13.47 12.15 11.87 14.43 15.23 16.48 14.68 12.68 16.55 15.38 15.46 16.45 17.96 16.32 18.61 14.96 17.35 17.09 16.59 18.07

15.40 18.25 19.97 19.42 17.68 17.25 16.27 19.30 18.65 16.39 16.17 20.99 18.34 18.25 19.51 20.34 18.51 20.00 19.34 19.70 17.27 18.34 19.45

Fig. 9. RE Technology Diffusion versus Potential at a time with variation in total Potential.

In context to India the Technology diffusion model to define the rate of diffusion; Policy innovation – Indian RE diffusion, similar to other developing counties, needs innovation on policy instrument. The three major policies are centrally focus, which are supported by regional states policy are; a. Renewable Energy Certification stated in 2011 and provide RE promotion support in two categories – Solar REC and NonSolar REC. The REC closing balance on 31st March, 2014 is 56,55,977 with steadily decrease in prize which need innovation. In the current scenario the REC policy innovation contribute to 0.5–0.7 out of 1 in the Fig. 11. b. Perform Achieve and Trade (PAT) started in 2012 with its 1st phase complete during 2012–15. The PAT programme start energy audit and yet to issue the energy saving certificate, ECert to the identified designated customer across 8 different

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Cornell University, INSEAD, and the World Intellectual Property Organization. The GII identifies seven parameters of an enable innovative activities, (i) Knowledge and technology outputs, (ii) Creative outputs, (iii) Institutions, (iv) Human capital and research, (v) Infrastructure, (vi) Market sophistication, and (vii) Business sophistication. National Innovation Foundation (NIF) scouted more than 2, 10,000 technological ideas, innovations and traditional knowledge practices from India. NIF collaborations with various R&D and academic institutions, Agricultural and Veterinary Universities and others has helped in getting thousands of grassroots technologies validated and/or value added for commercial use. India set up the National Innovation Council (NInC) to discuss, analyse and help implement strategies for inclusive innovation in India and prepare a Roadmap for Innovation 2010–2020. NInC supports for creation Cluster Innovation Centers (CICs), which will act as hubs for connecting various regional/national actors and stakeholders in symbiotic relationships. Fig. 10. RE Technology Diffusion versus Potential at a time with impact of Policy.

5. Renewable energy system operation model

Fig. 11. RE Technology Diffusion versus Technology Innovation and policy.

sector. In the current scenario the PAT policy innovation contribute to 0.3 to 0.4 out of 1 in the Fig. 11. c. The subsidy of Renewable Technology product and energy efficient technology products successful scheme of Government of India and mostly support off-grid application. These are combination of many small schemes. In the current scenario the PAT policy innovation contribute to 0.6 to 0.8 out of 1in the Fig. 11. Technology Innovation: The Innovation on use of renewable energy in an enhanced efficient way, for a developing country like India, is defined by the two parameters; 1. The total budget expenditure on the research and development in view Indian gross domestic production (GDP). Indian expenditure on scientific research is approximately 0.9% of the GDP during and its contribution to research on renewable and energy efficiency is nominal. 2. The Grassroots innovation at the country side and its traditional knowledge. India Innovation ranking stands at 78 out of total 143 countries according the Global Innovation Index 2014 (GII), co-published by

The research work explored bottom-up, proposed under MARKAL framework, dynamic linear programming model, which depicts both the energy supply and demand sides of the energy system. It presents policymakers and planners in the public and private sectors with extensive details on energy-producing and energy-consuming technologies, and provides an understanding of the interplay between the various fuel and technology choices for given sectoral end-use demands. The model employs demand for energy from the industrial, commercial, residential, and transportation sectors over a given timeframe, and information on available energy technologies to determine from where the demanded energy would originate. It ties these different components of the economy together through a Reference Energy System (RES), which links together the resource supplies, process technologies, and end-use technologies affecting the energy system. Then, subject to constraints defined by the user (such as limits on technology, or caps on various emissions), the model determines the least-cost mix of energy suppliers and technologies to satisfy energy demand [37–39]. The MARKAL framework model in Fig. 12 defined a relationship to interconnect the conversion and consumption of energy. This user-defined network includes all energy carriers involved in primary, conversion and processing (power plants, refineries, etc.), and end-user demand for energy services (industries, automobiles, residential space conditioning, etc) that may be disaggregated, by sector (residential, manufacturing, transportation and commercial) and by specific functions within a sector. The optimization routine used in the model’s solution selects an option from each of the sources, energy carriers and transformation technologies to produce the least-cost solution, subject to a variety of constraints. The user defines technology costs, technical characteristics (conversion efficiencies) and energy service demands [40] Integrated model for its applicability of the three platform needs diversified renewable energy on different projects and the building integrated data analysis platform is classified into six main modules [30]. As indicating in the energy model, three layers platform are classified into six module on the basis of function. The present study classified these six modules as data monitoring, analysis for present and future prediction, statistical analysis in different resource, cost and technology scenario, grid interactive parameter to study grid parity, energy system for better management of generation and distribution, and certainly reporting and planning feature based on feedback. The different function

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component work assignment in the present study is reported in third layer in Fig. 13 and briefed as; a. System management. Its purpose is to realize the system initial disposition and management, to accomplish the basic disposition for the clients and projects with specific energy source, so as to be well prepared for later data analysis. b. Data monitoring and analysis. Its function is principally to realize the monitoring function to data, Ajax and Flash technologies are adopted to conduct monitoring over historical data and real timing data. c. Statistical analysis. Statistical analysis is carried out according to each economic indicator of renewable energy source separated as different project categories so as to find out which energy source is suitable to a particular climate building project. d. Predictive analysis – Based on the locally collected data, predictive analysis is done with time sequent algorithm so that to provide a reliable base for project popularization.

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e. Report management – This part is to achieve management, inquiry and leading out functions over reports. f. Grid Parameter; The smart grid concept with integrated power generation sources and for the present study energy from renewable sources like solar, wind, hydro and geo-energy. The grid technology to upload the energy from different source has variant load factor and certainly transmission and distribution losses, which invite policy intervention for grid parity.

6. Conclusion The paper discuss the various innovation on renewable energy technology, energy efficiency and support policy to increase the rate of RET diffusion in India or any developing country. The MARKEL model has been study and apply this model innovation on its application to diffusion of RETs considering generation, demand and supply scenario, in India. The demand for RETs can be improved by the Government policies under 8 National Missions

Fig. 12. MARKEL framework on Renewable Energy.

Fig. 13. RET and RE Function Model Diagram.

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through a set of policies incentives and regulations The model proposes to develop algorithmic formulas for diversified renewable energy sources and building integrated projects. The existing diffusion models can be a useful set of tools to study insights to the diffusion mechanisms and in assessing the effectiveness of the diffusion strategies of renewable energy technologies (RET) in developing countries like India. Furthermore, a statistical and analytical function is envisaged for this platform which can make comparative display of the same indicators of different projects or different indicators of the same project, hence providing a basis for popularization of renewable energy saving in different areas. In this paper an integrated approach for the RE Technology diffusion is considered with component of technology, conversion, availability of sources, cost and policy. Though the RETs have huge potential to fulfill the global demand of electric power, the initial cost incurred in setup of such technology and difficulty in getting financial support is a major barrier for the technology diffusion. The research work analyze RET policies and point out to the continuing barriers to the large-scale adoption of RETs in India. An analytical conceptual framework categorized various factors which influence the diffusion and adoption process as technology characteristics, micro-environment, government’s role, types of users and market structure. The lack of large-scale success does not imply the inappropriateness of technology; rather efforts would be required to create an environment to promote the adoption of such technology. As the adoption process begins with the interaction of user, social and government in a complex manner,

Acknowledgments The authors gratefully acknowledge the cooperation extended to us by BEE, CERC and NREL for providing valuable inputs to compile the paper. The author acknowledges Department of Science and Technology for providing excellent resources and library facility for the research work.

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