Small-scale CDM projects in a competitive electricity industry: How good is a simplified baseline methodology?

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Energy Policy 35 (2007) 3717–3728 www.elsevier.com/locate/enpol

Small-scale CDM projects in a competitive electricity industry: How good is a simplified baseline methodology?$ Ram M. Shrestha, A.M.A.K. Abeygunawardana Asian Institute of Technology, School of Environment, Resources and Development, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand Received 21 December 2005; accepted 2 January 2007 Available online 7 March 2007

Abstract Setting baseline emissions is one of the principal tasks involved in awarding credits for greenhouse gas emission (GHG) mitigation projects under the Clean Development Mechanism (CDM). An emission baseline has to be project-specific in order to be accurate. However, project-specific baseline calculations are subject to high transaction costs, which disadvantage small-scale projects. For this reason, the CDM-Executive Board (CDM-EB) has approved simplified baseline methodologies for selected small-scale CDM project categories. While the simplified methods help reduce the transaction cost, they may also result in inaccuracies in the estimation of emission reductions from CDM projects. The purpose of this paper is to present a rigorous economic scheduling method for calculating the GHG emission reduction in a hypothetical competitive electricity industry due to the operation of a renewable energy-based power plant under CDM and compare the GHG emission reduction derived from the rigorous method with that obtained from the use of a simplified (i.e., standardized) method approved by the CDM-EB. A key finding of the paper is that depending upon the level of power demand, prices of electricity and input fuels, the simplified method can lead to either significant overestimation or substantial underestimation of emission reduction due to the operation of renewable energy-based power projects in a competitive electricity industry. r 2007 Elsevier Ltd. All rights reserved. Keywords: Clean Development Mechanism; Baseline emissions; Competitive electricity markets

With growing international concern about global warming, renewable power generation technologies are considered an increasingly important option for greenhouse gas emission (GHG) reduction. The Kyoto Protocol of the United Nations Framework Convention on Climate Change (UNFCCC) has adopted three flexible mechanisms, two of which are credit-based schemes, i.e., Joint Implementation (JI)) and the Clean Development Mechanism (CDM) (UNFCCC, 1998). However, one of the principal challenges in awarding credits for GHG mitigation projects under CDM is determination of emission baselines.

A number of studies discuss emission baseline methodologies related to power generation projects (e.g., Anagnostopoulos et al., 2004; Flamos et al., 2004; Helioui et al., 2006; Kartha et al., 2004; Nag and Parikh, 2005; OECD/IEA, 2002; Rosen et al., 2004; Sharma and Shrestha, 2006; Shrestha and Shrestha, 2004; Shrestha et al., 2005; Shukla et al., 2004; Zhang et al., 2005, 2006). However, to the knowledge of the authors, most of the existing studies focus on determination of baselines in an electricity supply industry mainly dominated by a public utility. There have been few studies that discuss calculation of baselines in a competitive electricity market (PCF, 2001, 2003). In PCF (2001), the baseline for a run-of-river hydro project in the power sector of Chile1 is established based on

$ An earlier version of this paper with a different title was presented in the 24th USAEE/IAEE North American Conference, July 8–10, 2004, Washington, DC, USA. Corresponding author. Tel.: +662 524 5406; fax: +662 524 5439. E-mail address: [email protected] (R.M. Shrestha).

1 The Chilean power sector features an unregulated competitive bulk supply market, open access to transmission and distribution networks, private participation at all levels, and well-established regulatory and oversight agencies. Generators can enter into long- and medium-term

1. Introduction

0301-4215/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2007.01.002

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projected power generation capacity additions and plant operation. Subsequently, in PCF (2003), standardized emission baselines for small-scale CDM projects in the power sector of El Salvador2 are established following one of the simplified methodologies approved by the CDMExecutive Board (CDM-EB). The standardized (i.e., simplified) methods for baseline emissions are recommended in the literature over the project-specific detailed methods on the grounds of their low transaction costs, low data and modeling requirements, and ease of use. The CDM-EB has approved simplified baseline methodologies for selected small-scale CDM project categories3 (UNFCCC, 2005). The use of simplified methods is allowed by CDM-EB for small-scale GHG mitigation projects for which development of rigorous methods for baseline estimation would be prohibitively costly. But, the simplified methods may result in inaccuracies in the estimation of emission reductions due to a CDM project. This paper develops a rigorous methodology based on economic scheduling of power plants to calculate the emission reduction due to a CDM project in a competitive electricity market. It also illustrates the use of the methodology in a hypothetical case and compares the results with those obtained from a simplified method approved by CDM-EB. The organization of the paper is as follows: Section 2 reviews briefly existing literature on methods for calculating emission baselines. This is followed by a description of the electricity market considered in the study. An approach to determining emission baselines for dispatchable and non-dispatchable renewable energy (RE)based power projects under a competitive electricity market is discussed in Section 4. The approach is applied to estimate GHG emission reduction due to biomass and solar photovoltaic (PV) power projects in the case of a hypothetical market and the results are compared with that based on a simplified method approved by the CDM-EB along with sensitivity analyses in Sections 5 and 6, respectively. Key findings and final remarks are presented in Section 7. 2. Electricity project baselines An electricity project baseline represents the most likely or plausible scenario for the future development of the power sector in the absence of an electricity project under CDM. Establishment of emission additionality (i.e., reduction in emissions) is a prerequisite for a CDM (footnote continued) contracts with distribution companies and large consumers and can sell energy in the spot market. 2 The power sector of EI Salvador also features an unregulated bulk supply market. The wholesale electricity market in EI Salvador is composed of all generators, distributors and major users that are directly connected to the 115 kV transmission system. The wholesale market is composed of a contract market and a spot market as in Chile. 3 A small-scale CDM power project refers to a project with installed power generation capacity of less than or equal to 15 MW.

electricity project. However, in order to establish the emission additionality of projects, it becomes necessary to define a baseline against which the additionality can be assessed and carbon credits for the project determined. The Marrakech Accord defines the baseline for a CDM project activity as the scenario that reasonably represents the anthropogenic emissions by sources of GHGs that would occur in the absence of the proposed project activity (UNFCCC, 2001). The most accurate way of developing a baseline for a CDM power project would be to develop a project-specific baseline, which involves simulation of the power system to find total GHG emissions from the power system with and without the CDM power project. However, a projectspecific baseline calculation involves high transaction cost, which disadvantages small-scale projects. For this reason, the idea of a standardized baseline was developed. The standardized baseline approaches for a project category in the power sector provide simplified procedures to estimate the levels of electricity generation and GHG emission avoided by the power system due to a CDM project in that category. In the literature on CDM baselines for power projects, three different approaches are suggested to determine standardized baselines, i.e., the operating margin approach, build margin approach, and combined margin approach (Kartha et al., 2004; OECD/IEA, 2002; Martens et al., 2001). The ‘‘operating margin approach’’ estimates GHG emission avoided by the power system due to a CDM plant on the basis of electricity generation that would be avoided by existing and future power plants in the system if the CDM power plant is operated. On the other hand, the ‘‘build margin approach’’ estimates GHG emission avoided by the power system through replacement of new power plant capacities (that would have been built otherwise) by the CDM power plant. The ‘‘combined margin approach’’ is the combination of the operating- and build-margin approaches and has been recommended as the most preferable among the three standardized baseline approaches on the grounds that it captures both the operating and build margin effects (e.g., OECD/IEA, 2002; Kartha et al., 2004). In the case of small-scale CDM project activities, CDMEB has approved simplified baseline methodologies for 15 different project categories (UNFCCC, 2005). Renewable electricity generation for a grid (under ‘‘Category I.D’’ of UNFCCC (2005)) is one of them. Two simplified approaches have been approved for calculation of the baseline emission coefficient for projects in Category I.D in a transparent and conservative manner, i.e., (i) the average of the ‘‘approximate operating margin’’ and the ‘‘build margin’’, and (ii) the weighted average emission of the current generation mix. In the first approach, the ‘‘approximate operating margin’’ is calculated as the weighted average emission of all generating sources serving the system, excluding hydro, geothermal, wind, low-cost biomass, nuclear, and solar generation, while the ‘‘build margin’’ is calculated as the weighted average emissions of

ARTICLE IN PRESS R.M. Shrestha, A.M.A.K. Abeygunawardana / Energy Policy 35 (2007) 3717–3728

recent capacity additions to the system.4 The second approach establishes the baseline emission coefficient (in kg CO2 equivalent/kWh) by calculating the weighted average emission of the current generation mix. The second approach is simpler and requires much less data than the first approach. In this study, we compare the GHG emission reduction derived from the rigorous method with that obtained from the second approach approved by the CDM-EB (hereafter called ‘‘simplified method’’). 3. Electricity market considered in the study We consider a competitive electricity market, where the generators of electricity (i.e., firms) submit their bids at the end of each day for each hour of the following day to an Independent System Operator (ISO), who manages the system operation. This is similar to the England and Wales power pool as described in Gross and Finley (2000). Individual generators bid with the objective of maximizing their own profits based on their forecasts of hourly market prices of electricity. The hourly bidding curves of individual generators consist of blocks of energy and their corresponding prices. Using the bids submitted by the generators, the ISO determines the most economic dispatch (or economic schedule of plants) for the next 24-h period that satisfies the hourly demands without violating physical and operating constraints of individual power plants. The hourly power demand at the industry level is forecasted by ISO. It is assumed that there is no demand side bidding and that the demand curves are vertical. 4. Methodology This study basically involves determination of GHG emission levels from a power system in a competitive market with and without the candidate CDM project. The approach used in this study to calculate the CO2 emissions from the electricity market consists of the following steps: (i) Determination of optimal self-scheduling for each generator: Before developing hourly bidding curves, individual generators need to determine the optimum hourly operation level of power plants for the 24-h period (hereafter ‘‘the self-scheduling problem (SSP)). The optimum hourly operation level of individual power plants depends on market price of electricity, operation cost and other operational constraints of the power plant). (ii) Formulation of the bidding curve for individual generators: Hourly bidding curves are developed based on an optimal bidding strategy using the information from self-scheduling. (iii) Scheduling of generators by the ISO model: The ISO decides hourly generation levels of participants to 4 Recent capacity additions are defined as the most recent 20% of the plants built or the five most recent plants (whichever is lower).

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satisfy the hourly demands over 24 h at the minimum cost, and (iv) Calculation of CO2 emission from individual generators (and the power system as a whole) based on the economic scheduling by the ISO. In order to determine the level of emission reduction due to a small-scale renewable energy-based power plant, CO2 emissions are determined for the electricity industry following the above-mentioned steps for two cases: (i) CO2 emissions from the power system without a renewable power plant that operates under CDM (hereafter ‘‘base case’’) and (ii) CO2 emissions from the power system with a renewable power plant that operates under CDM (hereafter ‘‘CDM case’’). Here, we assume that small-scale renewable energy-based power projects would not be a choice in the electricity market in the absence of certified emission reduction (CER) benefits and that there are no renewable energy-based power plants in the base case. 4.1. SSP of a generator Participants in a competitive electricity market need to develop their bidding strategies not only for the sake of achieving a feasible dispatch of their units but also for maximizing their profits. The SSP is formulated in order to maximize the profit of the individual generator. Under electricity price uncertainty, the objective of a generator is to determine the generation levels that would maximize its expected profit subject to the plant operational constraints: (i) Constraints on maximum and minimum power output of the plant: The power output of the power plant should be within the maximum and minimum power output limits of the plant. (ii) Ramp rate constraints: The rate of change of power output of the plant should not exceed the allowable maximum limit. The rate of change of power output of a power plant is also called ‘‘ramp rate’’ in power system economics. The ramp rate is established to prevent undesirable effects (such as thermal stresses and fatigue) due to rapid changes in power outputs. (iii) Minimum up time constraints: Once a thermal plant is committed, it should remain operated for a minimum number of hours. This is called minimum up time of a power plant. (iv) Minimum down time constraints: Once a thermal plant is shut down, it should remain shut for a minimum number of hours. This is called minimum down time of a power plant. The minimum up and down time limits are imposed to provide time for temperature equalization within the turbine so as to maintain thermal stresses due to temperature differentials within limits of safety. These limits are a function of unit size and type. The detailed mathematical model of the SSP described here is given in Conejo et al. (2002). The objective function

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of the SSP can be expressed as ( ) T T X X Maximize E p1 ;...pT pt Q t  Ct, t¼1

n j   ko. est 2 est est 1 sest s  exp þ P  g s bt ¼ Pest a t t t t t 2 (1)

t¼1

where pt is the market-clearing price at hour t, E p1 ;...;pT fg the expected value operator with respect to p1, y, pT, Qt the power produced at hour t, Ct the total operation cost of the generator at hour t, and T the time span in hours. The market-clearing price (pt) in the objective function of SSP is a random variable and therefore, it makes the objective function probabilistic in nature. Here, forecasts of pt’s are obtained from a time-series forecasting procedure using historical hourly data on actual prices. Following Conejo et al. (2002), the expected value of random variable pt (i.e., E Pt fpt g) can be represented by the predicted (estimated) value of price at hour t, pest t , i.e., avg E Pt fpt g ¼ pest t ¼ pt ,

(2)

pavg t

is the average value of pt where Thus, the objective function in (1) can be rewritten as Maximize

T X fpest t Qt  C t ðQt Þg.

(3)

t¼1

The solution of the SSP provides the profit maximizing generation level (Qt*) of a generator at the estimated market-clearing price Pest t . 4.2. Formulation of bidding curve of a generator The approach used in this paper for formulation of the bidding curve for a generator is similar to that of Conejo et al. (2002). To describe the approach, first define the following notations: Put is the upper bound of estimated electricity price at hour t, PLt the lower bound of estimated electricity price at hour t, Qb,t the power bid by the generator at hour t, Pb,t the price bid by the generator at hour t, and Qmax the maximum power generation capacity of the generator. Following Conejo et al. (2002), the upper and lower bounds of estimated prices at a desired confidence level can be expressed as est Put ¼ pest t þ at st , est PLt ¼ pest t  bt st ,

where sest t the standard deviation of pt. It should be noted that parameters at and bt are obtained directly from the forecasting procedure and depend on the level of confidence considered, e.g., 99% or 95%. They are computed so as to cover 99% or 95% of the total area under the Log-normal distribution. The parameters at and bt are computed using the following expressions: n j   k o. 2 est þ Pest at ¼ exp 12 sest  Pest sest t t þ ga s t t t ,

and

ga is the a-percentage point of the N(0,1) distribution and it depends on the desired level of confidence. The bidding curve of a generator for hour t is formulated up to two blocks of power and their corresponding prices as a function of the optimal self-scheduled production (Qt*) in hour t, are described as follows: Case 1: if the desirable level of generation at hour t based on the generator’s optimal self-schedule is zero, Qt ¼ 0, the bidding curve consists of a single block, i.e., Qb;t ¼ Qmax at Pb;t ¼ Put (see Fig. 1(a)); Case 2: if 0oQt*oQmax, the bidding curve consists of the following two blocks: Qb;t ¼ Qt at Pb;t ¼ PLt and Qb;t ¼ Qmax 2Qt at Pb;t ¼ Put (see Fig. 1(b)); and Case 3: if Qt ¼ Qmax , the bidding curve consists of only one block: Qb;t ¼ Qmax at Pb;t ¼ PLt (see Fig. 1(c)). Note that bidding curve of the generator in each case guarantees at a given level of confidence that the power bid accepted is Qt*, i.e., the optimal self-scheduled level of power at hour t. 4.3. Economic scheduling of generators by ISO Economic scheduling of plants is carried out by the ISO to determine the levels of power to be supplied by each generator at different hours of a day so as to minimize the total daily cost of meeting power demand subject to the following constraints: (i) hourly demand constraints, (ii) constraints related to power bid of each plant at each price (i.e., power supply by a power plant cannot exceed the level of power bid offered by the generator at the bid price), (iii) minimum up and down time constraints of each plant, and (iv) ramp rate constraints of each plant. The model used in this study is similar to that of Gross and Finley (2000) except that reserve requirements (the additional capacity needed to cover contingencies including forced outages and abnormal loads) are not considered here. 4.4. Calculation of CO2 emissions avoided by a CDM plant This step requires calculation of total CO2 emissions from the electricity industry with and without the CDM power plant. Total CO2 emission from the power industry without the CDM plant is also called the baseline emission. Determination of levels of power to be supplied by different generators in the base case involves self-scheduling and formulation of a bidding curve by the generators and economic scheduling by the ISO as described in Sections 4.1–4.3. Baseline emission is then calculated based on fuel consumption that corresponds to power outputs, of the generating plants (fuel consumption is calculated using input–output curve of the generators) and carbon content of fuels used. We now turn to the approaches used to calculate CO2 emission from the system in two cases: those are (a) a power

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a

b

Ptu

Pt

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c

u

L

L

Pt

Pt Qmax

Qt*

Qmax

Qmax

Fig. 1. Bid curves for the proposed bidding strategy.

industry with a dispatchable CDM power plant, and (b) a power industry with a non-dispatchable CDM power plant. 4.4.1. CO2 emission of the industry with a dispatchable renewable power plant The flow chart of the methodology for calculating CO2 emission reduction with a dispatchable power plant is illustrated in Fig. 2. As shown in the figure, the methodology involves two main steps, that is, calculation of total CO2 emissions from the electricity industry (a) without the biomass CDM power plant and (b) with the biomass plant. The difference in total CO2 emission from the electricity industry with and without the biomass plant is the CO2 emission reduction due to operation of the biomass plant. As described earlier, the level of power supplied by each generator (i.e., the ISO’s dispatch schedule) is needed to calculate CO2 emissions from the electricity industry. First, the self-scheduling and bidding curves for each generator (without CDM plant) are derived using plant-specific cost and technical data as well as electricity price data as described in Sections 4.1 and 4.2. The ISO’s dispatch schedule without the CDM plant is then obtained by using the bids submitted by individual generators and system load data (as described in Section 4.3). The levels of fuel input needed corresponding to power outputs of individual plants are calculated using the plant-specific input–output curve data. The level of CO2 emission from each generator is then calculated based on fuel input and carbon content of the fuel used. Total CO2 emissions from the electricity industry (i.e., baseline CO2 emissions) are obtained by summation of CO2 emissions of all individual generators. In the second step, we determine the ISO’s dispatch schedule and the corresponding total CO2 emission from the electricity industry with the CDM power plant. In order to determine the ISO dispatch schedule with the CDM plant, we need to develop the bidding curve of the CDM power plant besides that of other generators in the industry. Here, it is assumed that a dispatchable CDM power plant operating in a competitive market has to submit bids to ISO. It should be noted that a dispatchable CDM generator determines its profit maximizing selfschedule by considering not only electricity revenue but also the revenue from CER credits (It is assumed that the CER market is perfectly competitive). Thus, the SSP of a

dispatchable CDM plant can be expressed as Maximize QC t

T X C C C fPest t Qt þ PCER ERðQt Þ  C t ðQt Þg,

(4)

t¼1

where PCER is CER price, QtC is power generation by the CDM generator at hour t and ER(QtC) is expected total emission reduction from the power system, which is a function of QtC. It is assumed that information on baseline CO2 emission factor (E0) (defined as average CO2 emission per unit of electricity generation in the baseline) is available to each participant. Note that such information is published periodically in some countries. For example, in the US, monthly and annual data on electricity generation and fuel consumption of utility and non-utility power plants as well as CO2 emission coefficient of fuels are published in EIA (2005).5 If ECDM is the emission factor of the CDM plant, Eq. (4) can be rewritten as Maximize QC t

T X C C C fPest t Qt þ PCER ðE 0  E CDM Þ Qt  C t ðQt Þg. t¼1

(5) In this study, we consider the case of a CDM power plant that uses biomass produced on a sustainable basis (i.e., biomass harvesting rate is assumed to be equal to the growth rate of biomass resource). Thus ECDM is zero. After developing the self-schedule for the CDM plant, the same bidding strategy as described in Section 4.2 is used for the CDM plant. Scheduling of generators including the CDM plant by ISO is then determined using the ISO’s economic scheduling model described in Section 4.3. 4.4.2. CO2 emission of the electricity industry with a nondispatchable renewable power plant The flow chart of the methodology for calculating CO2 emission reduction with a non-dispatchable renewable plant is illustrated in Fig. 3. It is assumed that the ISO 5 In Japan, the Ministry of Economy, Trade and Industry (METI) publishes a report about monthly/annual electricity generation and fuel consumption of different electric utilities (METI, 2004) while CO2 emission coefficients can be obtained from the utilities’ Corporate Social Responsibility reports.

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Fig. 2. Flowchart of the methodology for calculating CO2 emission reduction with a dispatchable renewable plant.

will always purchase the expected level of power generation from the non-dispatchable power generator (i.e., one based on PV in this study). That is, no bidding is required for the non-dispatchable generator. Thus in the presence of a nondispatchable renewable power plant, total power demand to be met by the rest of the generators will be modified and will be equal to total market demand net of the expected level of power supply by the non-dispatchable generator. Like in the case of the dispatchable plant, the optimal levels of generation by the rest of the generators are determined for the modified power demand using the economic scheduling model of the ISO and the corresponding level of total CO2 emission from the system is then calculated.

4.5. Description of the hypothetical power system We consider a hypothetical power market, which consists of five generators (or ‘‘participants’’) three of which are coal based (hereafter called ‘‘Coal-1’’, ‘‘Coal-2’’ and ‘‘Coal-3’’) and two of which are oil based (hereafter called ‘Oil-1’’ and ‘‘Oil-2’’). We consider the same set of generators as in Wang and Shahidehpour (1994), the cost and technical characteristics of which are given in Table 1. We consider hypothetical predicted market-clearing hourly prices of electricity for a typical day and the corresponding values of their lower and upper bounds (at 99% confidence level) as given in Table 2. Shut down costs of power plants are assumed to be zero in this study.

We consider two types of CDM plants, i.e., one based on PV (which is non-dispatchable) and the other based on biomass (which is dispatchable). In the case of both types of plants, we limit GHGs to CO2 in our analysis. In order to examine the effects of variation in number of CDM plants, we consider various combinations of five different PV projects (each of 10 MW capacity) and similarly five different combinations of biomass power projects (each of 10 MW capacity). The SSP was solved using the non-linear optimization software SBB (GDC, 2002a), while the economic scheduling problem of the ISO was solved using the software CPLEX (GDC, 2002b). The hourly scheduling of generators and calculation of associated CO2 emissions have been carried out over a period of a day. We compare the values of CO2 emission derived from the rigorous economic scheduling method discussed in the Section 4 with those obtained from the simplified method approved by CDM-EB for the project category of renewable electricity generation for a grid (which is described under CDM project ‘‘Category I.D’’ of UNFCCC (2005)). Under the approved simplified method for Project Category I.D by CDM-EB, the baseline emission is calculated as the product of quantity of electricity produced by the renewable power generating unit and weighted average emission per unit of electricity generation under the current generation mix. In this study, we calculate the weighted average emission of the system using data on total fuel consumption, CO2 emission coefficient of the fuel used and

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Fig. 3. Flowchart of the methodology for calculating CO2 emission reduction with a non-dispatchable renewable plant.

Table 1 Characteristics of power plants Power plant type

Fixed O&M cost ($/kW-yr) Variable O&M cost ($/MWh) Fuel cost ($/MBtu) Unit start up cost ($) Unit capacity block (MW)

Block heat rate (MBtu/MWh)

Coal-1

Coal-2

Coal-3

Oil-1

Oil-2

22.4 12.4 163.2 46,930.0 90.0 192.0 261.0 277.0

24.9 12.4 162.7 38,416.0 112.0 184.0 294.0

21.6 13.7 143.9 6085.0 9.0 64.0 127.0 144.0

21.6 13.7 204.6 3841.6 30.0 265.0 361.0 381.0

15.3 10.0 204.6 3841.6 29.0 70.0 141.0 187.0 216.0 24.470 18.517 16.490 16.138 16.054 170.0 200.0 10 9 77.0

13.385 10.421 9.746 9.721

Ramp up rate (MW/h) Ramp down rate (MW/h) Minimum up time (h) Minimum down time (h) CO2 emission rate (kg/MBtu)

13.371 11.358 10.212

140.0 130.0 12 6 95.0

120.0 110.0 10 6 95.0

total generation by each generating unit in the system. The mathematical expression for calculation of weighted average emission factor (WAEF) of the generation mix is expressed as

WAEF ¼

I X i¼1

, FC i EC i

I X i¼1

EG i ,

(6)

36.856 11.440 9.770 9.745 80.0 70.0 9 4 95.0

18.126 11.014 10.946 10.931 130.0 160.0 2 2 77.0

where WAEF is the weighted average emission factor of the generation mix of the power system during the 24-h period, EGi the total electricity generation by plant i during 24-h period in MWh, FCi the fuel consumption by plant i to produce EGi MWh of electricity in kcal, ECi the emission coefficient of the fuel used by plant i in kg/kcal, I the total number of plants in the system.

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Table 2 Hourly projected price and system demand data for a hypothetical market

Table 3 Generation mix and CO2 emissions in base case

Hour

Generator

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Estimated price ($/ MWh)

Lower bound of the price ($/ MWh)

Upper bound of the price ($/ MWh)

System demand (MW)

33.30 26.52 22.15 23.10 22.59 23.15 24.64 24.75 25.50 27.58 31.59 35.59 41.05 41.60 38.98 39.73 42.02 42.09 40.74 38.80 39.63 46.14 39.03 33.67

27.22 21.63 18.04 18.81 18.40 18.85 20.07 20.15 20.76 22.46 25.73 28.99 33.43 33.88 31.74 32.36 34.22 34.28 33.18 31.60 32.27 37.58 31.79 27.42

40.75 32.51 27.20 28.36 27.74 28.43 30.26 30.39 31.31 33.86 38.79 43.70 50.40 51.08 47.86 48.79 51.59 51.68 50.02 47.64 48.66 56.66 47.93 41.35

578 457 456 447 478 666 828 961 996 1060 1100 1150 1199 1211 1199 1199 1062 806 945 883 818 745 685 669

Note that the total electricity generation of the individual power plants in the system in the baseline is obtained from the dispatch data of the system without the CDM plant.

5. Total CO2 emission reduction due to biomass power projects under CDM CO2 emissions by individual generators in the base case (i.e., without the CDM power plant) based on the economic scheduling as well as the total emission from the entire system are presented in Table 3 along with their corresponding levels of electricity generation in the hypothetical electricity market. A biomass power generator determines its self-schedule and participates in bidding like any other generator in the system. Therefore, the self-scheduling and bidding strategy of the biomass generator depend upon the predicted market-clearing electricity price and CER price. Thus the CER price can also affect economic scheduling of generators by the ISO. Table 4 presents the change in CO2 emission from different generators and also the change in total CO2 emission from the industry with the operation of the biomass plants under the CDM at CER price of $2/tonne of CO2. Total CO2 emission from the electricity industry is found to be reduced by 97.8 tonnes with power generation by a 10 MW biomass power plant at CER prices of $2/tonne of CO2 and higher.

Coal-1 Coal-2 Coal-3 Oil-1 Oil-2 Total

Electricity generation (MWh)

CO2 emission (tonnes)

3593 4310 2902 7614 2179

3782 2836 1874 3088 1443

20,598

13,023

Table 4 Changes in CO2 emission (tonnes) by generators with the operation of CDM biomass power plants of different capacity at CER price of $2/tonne of CO2a Generator

Coal-1 Coal-2 Coal-3 Oil-1 Oil-2 Total

Combined biomass power plant capacity 10 MW

20 MW

30 MW

40 MW

50 MW

110.4 64.0 42.1 138.0 44.5

118.0 50.9 73.7 68.2 63.5

125.5 130.8 62.3 124.7 1.9

133.0 140.4 51.2 156.3 46.9

190.5 149.8 77.5 187.7 95.5

97.8

226.9

316.8

425.4

546.0

a The positive figures represent reduction in CO2 emission while the negative figures represent an increase in the emission.

When the combined (i.e., system-wide) capacity of biomass power projects (each of 10 MW capacity) is increased to 20, 30, 40 and 50 MW, the total reductions in CO2 emission was found to be 226.9, 316.8, 425.4 and 546.0 tonnes, respectively.6 It should be noted that emission reduction due to power generation from biomass plants of combined capacity up to 50 MW is found to remain unchanged at the CER prices of above $2/tonne of CO2. This is because all combinations of the biomass power plants considered in the study (up to 50 MW of total capacity) are found to be economically attractive to operate at their full capacity even at the CER price of $2/tonne of CO2. With the operation of a biomass power plant, generation requirements based on fossil fuels would be reduced. However, as can be seen from Table 4, depending upon the economic schedules determined by the ISO in the presence of the CDM biomass plant, it is possible that at different levels of combined capacity of biomass power projects (each of 10 MW capacity here), some fossil fuel-based generators may supply more power than that in the base case, while others would generate less. As a result, with the operation of the biomass power plants, some thermal generators would emit more CO2 than in the base case. 6 By definition, a small-scale CDM project cannot have a capacity of more than 15 MW. In this study, each individual project is assumed to have a capacity of 10 MW. The combined capacities of 20, 30 MW, etc., are considered here to analyze the cases of having more than one smallscale CDM project of the same type.

ARTICLE IN PRESS R.M. Shrestha, A.M.A.K. Abeygunawardana / Energy Policy 35 (2007) 3717–3728 Table 5 CO2 emission reductions (tonnes) derived from economic scheduling and simplified methods at CER price of $2/tonne of CO2 Number of biomass projects

1 2 3 4 5

Combined capacity (MW)

10 20 30 40 50

Energy supplied by biomass plants (MWh)

230 450 670 890 1110

CO2 emission reduction based on

Economic scheduling

Simplified methoda

97.8 226.9 316.8 425.4 546.0

145.4 284.5 423.6 562.7 701.8

a Approach approved by CDM-EB for project category renewable electricity generation for a grid.

As can be seen from Table 5, the simplified method (based on weighted average emissions of the generation mix) is found to overestimate CO2 emission reductions due to biomass power generation as compared to the corresponding figures based on the rigorous economic scheduling method. Interestingly, the level of overestimation is observed to fall with the number of the biomass power plants of the same size (i.e., 10 MW): There would be an overestimation of 49% with only one biomass plant of 10 MW capacity included in the industry and about 29% with five biomass plants (i.e., with a combined capacity of 50 MW). It is of interest to ask how changes in fuel prices could change the level of overestimation of CO2 emission reductions. For this purpose, sensitivity analyses with five different levels of prices (i.e. base case price, 10% and 20% higher prices than in the base case as well as 10% and 20% lower prices) of coal, oil and biomass are carried out. Note that the prices of coal and oil used in the base case are presented in Table 1, while the biomass price used in the base case is $170.35 per MBtu. As can be seen from Table 6, the simplified method overestimates CO2 emission reduction due to biomass power generation as compared to the figure based on economic scheduling. This is observed not only at the coal price figure used in the base case but also at other values of coal price considered in the study. Interestingly, the level of overestimation of emission reduction does not change monotonically with coal price. Similarly, sensitivity analyses with variation in prices of oil (Table 7) and biomass (Table 8) show that the simplified method would overestimate CO2 emission reductions due to biomass power generation (as compared to the figures based on economic scheduling). As shown in Table 9, the simplified method is found to underestimate CO2 emission reduction due to biomass power generation as compared to the estimation based on economic scheduling method at some values of electricity price (i.e. at 10%, +10% and +20% of the base case

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Table 6 Sensitivity of CO2 emission reduction (tonnes) with a biomass plant capacity of 10 MW at CER price of $2/tonne of CO2 to variations in coal price Change in coal price (from the base case value)

20% 10% 0% (base case) +10% +20%

Energy supplied by biomass plants (MWh)

230 210 230 240 230

CO2 emission reduction based on

Economic scheduling

Simplified method

43.4 60.9 97.8 124.4 97.7

152.5 133.9 145.4 150.6 145.5

Table 7 Sensitivity of CO2 emission reduction (tonnes) with a biomass plant capacity of 10 MW at CER price of $2/tonne of CO2 to variations in oil price Change in oil price (from the base case value)

20% 10% 0% (base case) +10% +20%

Energy supplied by biomass plants (MWh)

240 210 230 240 240

CO2 emission reduction based on

Economic scheduling

Simplified method

148.2 83.3 97.8 95.9 129.6

153.2 133.4 145.4 152.3 152.0

Table 8 Sensitivity of CO2 emission reduction (tonnes) with a biomass plant capacity of 10 MW at CER price of $2/tonne of CO2 to variations in biomass price Change in biomass price (from the base case value)

20% 10% 0% (base case) +10% +20%

Energy supplied by biomass plants (MWh)

230 230 230 170 150

CO2 emission reduction based on

Economic scheduling

Simplified method

97.8 97.8 97.8 24.3 13.2

145.4 145.4 145.4 107.5 94.8

price) and overestimate at other values of the electricity price (i.e., at base case price and 20% below that level). For the electricity prices considered, CO2 emission reduction was overestimated by as high as 49% (in the base case) while the level of underestimation was as high as 33% (in the 10% case). These results show that there could be substantial differences between the emission reduction estimates based on the simplified method and those based on economic scheduling. Further, they also show that the simplified method could either overestimate or

ARTICLE IN PRESS R.M. Shrestha, A.M.A.K. Abeygunawardana / Energy Policy 35 (2007) 3717–3728

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Table 9 Sensitivity of CO2 emission reduction (tonnes) with a biomass plant capacity of 10 MW at CER price of $2/tonne of CO2 to variations in electricity price

Table 11 Changes in CO2 emission (tonnes) by different fossil fuel-based power plants with the operation of PV power plants of different capacitya Fossil fuel-based plant

Change in electricity price (from the base case value)

20% 10% 0% (base case) +10% +20%

Energy supplied by biomass plants (MWh)

130 150 230 186 190

10 MW

Economic scheduling

Simplified method

67.1 140.8 97.8 131.9 152.0

81.3 94.1 145.4 119.0 123.7

Table 10 Sensitivity of CO2 emission reductions (tonnes) (at CER price of $2/tonne of CO2 and biomass plant capacity of 10 MW) to variations in power demand Change in power demand (from the base case)

10% 5% 0% (base case) +5% +10%

Energy supplied by biomass plants (MWh)

200 220 230 230 40

Combined PV plant capacity

CO2 emission reduction based on

CO2 emission reduction based on

Economic scheduling

Simplified method

173.5 151.0 97.8 98.9 29.6

126.4 139.1 145.4 144.0 25.3

underestimate the emission reduction depending upon the value of electricity price. A sensitivity analysis was also conducted with respect to changes in power demand. Table 10 shows that the differences between the emission reduction estimated by the economic scheduling and simplified methods could be substantial: The simplified method is found to underestimate the emission reduction by as high as 27% at power demand 20% below the base case value, while it could overestimate by as high as 49% in the base case. Thus, the simplified method need not consistently over or underestimate the CO2 emission reduction due to biomass plants with the variation in power demand. 6. CO2 emissions avoided due to PV power generation under CDM Changes in CO2 emissions of the industry due to PV power plants under the CDM were calculated for five different cases, i.e., with PV power projects of combined (i.e., system-wide) capacities of 10, 20, 30, 40 and 50 MW. The solar energy availability profiles for a typical day for PV plants are based on Travers and Kaye (2003).

20 MW

30 MW

40 MW

50 MW

Coal-1 Coal-2 Coal-3 Oil-1 Oil-2

4.5 6.1 6.6 2.2 9.7

12.7 9.8 13.1 4.5 19.4

21.0 13.5 19.6 6.7 29.0

2.9 99.9 26.0 38.1 7.0

4.9 103.6 52.9 3.3 26.8

Total

29.2

59.7

89.9

114.2

131.5

a

The positive figures represent reduction in CO2 emission while the negative figures represent an increase in the emission.

Table 11 presents the changes in CO2 emission from different fossil fuel-based plants as well as total CO2 emission reduction from the electricity industry with the operation of the PV plants under the CDM. Total CO2 emission avoided from the electricity industry is found to increase at a decreasing rate with number of identical PV power projects (alternatively, with the combined PV plant capacity). Unlike in the case with biomass plants, some fossil fuel-based generators would emit more CO2 than that in the base case in some cases with PV plants (e.g., when combined PV plant capacities were 40 and 50 MW). How does emission reduction due to PV power plants based on the economic scheduling compare with the corresponding value obtained from the simplified approach approved by CDM-EB? Table 12 presents the levels of electricity generation with different levels of combined capacity of PV power plants under the economic scheduling approach and compares the corresponding values of emission reductions estimated using the economic scheduling and simplified methods. It can be seen from the table that the simplified method would overestimate total CO2 emission reduction. Further, the level of overestimation would increase with the combined capacity of PV power plants: For example, the simplified method would overestimate CO2 emission reduction by about 17% with only one 10 MW PV power plant in operation while the figure would increase to about 30% when five PV power plants are in operation (i.e., with a combined capacity of 50 MW). Like in the biomass case, we analyse the sensitivity of CO2 emission reduction due to the operation of a 10 MW PV plant to variations in power demand and prices of coal, oil and electricity. The results are shown in Tables 13–16. Note that the simplified approach is found to overestimate CO2 emission reductions due to PV power generation in each case as compared to the estimates based on economic scheduling. 7. Conclusion and final remarks This paper has presented a rigorous method based on economic scheduling to calculate CO2 emission reduction

ARTICLE IN PRESS R.M. Shrestha, A.M.A.K. Abeygunawardana / Energy Policy 35 (2007) 3717–3728 Table 12 CO2 emission reductions (tonnes) (due to PV power plants) based on the economic scheduling and simplified method Number of PV projects

1 2 3 4 5

Combined capacity (MW)

Electrical energy supplied by PV plants (MWh)

10 20 30 40 50

54 108 162 216 270

CO2 emission reduction based on

Economic scheduling

Simplified method

29.2 59.7 89.9 114.2 131.5

34.1 68.2 102.4 136.5 170.6

Table 13 Sensitivity of CO2 emission reduction (tonnes) (due to a 10 MW PV power plant) to variation in coal price Change in coal price (from the base case)

20% 10% 0% (base case) +10% +20%

CO2 emission reduction based on Economic scheduling

Simplified method

28.7 30.0 29.2 24.6 12.4

35.8 34.4 34.1 33.9 34.1

Table 14 Sensitivity of CO2 emission reduction (tonnes) (due to a 10 MW PV power plant) to variations in oil price Change in oil price (from the base case)

20% 10% 0% (base case) +10% +20%

CO2 emission reduction based on Economic scheduling

Simplified method

24.1 27.9 29.2 17.6 19.8

34.5 34.3 34.1 34.3 34.2

from a renewable energy-based CDM power project in a competitive electricity industry. The method is applied in the case of a hypothetical electricity industry and the results are compared with that based on a simplified approach approved by CDM-EB. It has been shown that emission reduction could be either overestimated or underestimated significantly by the simplified method in the case of dispatchable power plants under the CDM (i.e., biomass plants in the present study). Whether there would be an overestimation or underestimation in emission reduction would depend upon values of input fuel price, electricity price and power demand. Over the range of the electricity prices considered for the hypothetical electricity industry in the present study, the simplified method was

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Table 15 Sensitivity of CO2 emission reduction (tonnes) (due to a 10 MW PV power plant) to variations in electricity price Change in electricity price (from the base case)

20% 10% 0% (base case) +10% +20%

CO2 emission reduction based on

Economic scheduling

Simplified method

8.2 26.7 29.2 29.3 26.7

33.8 33.8 34.1 34.6 35.2

Table 16 Sensitivity of CO2 emission reduction (tonnes) (due to a 10 MW PV power plant) to variations in power demand Change in power demand (from the base case)

10% 5% 0% (base case) +5% +10%

CO2 emission reduction based on

Economic scheduling

Simplified method

28.3 30.3 29.2 27.8 29.6

34.9 34.6 34.1 33.8 33.6

found to overestimate the emission reduction by as high as 49% in some cases and underestimate by as high as 33% in other cases. For a given set of prices and power demand in the base case, the study also showed that the level of overestimation of emission reduction by the simplified method would decrease with the increase in the combined capacity of biomass power projects. In the case of PV power plants in the hypothetical electricity industry considered, the study has found that the simplified method would result in an overestimation of CO2 emission reduction. Sensitivity analyses showed that this result was unaffected by variations in prices of fuels for electricity generation and electricity price as well as by changes in power demand. Contrary to the results in the case of biomass power projects, the study shows that the level of overestimation by the simplified method would increase in the case of PV plants with the increase in the combined capacity of PV power projects. The study is primarily focused on the comparison of emission reduction estimates between the rigorous economic scheduling and a simplified approach and has not considered the economic viability of the renewable power projects as such from the investor’s perspective. In particular, it should be noted that we have not considered the capacity cost of the renewable energy-based power projects while formulating the bidding. Also note that this study has not considered the reserve requirements in the scheduling of generators by the ISO. However, in practice,

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the ISO needs to commit sufficient generation to meet the forecasted load and reserve requirements throughout the scheduling horizon. It should be noted here that although, we use a static 24h dispatch model to calculate CO2 emission reduction from a renewable energy-based CDM power project, the net CO2 emission reduction due to a renewable project in a competitive electricity industry over the project’s life cycle would depend on many factors such as demand variation and entry/exit decisions of non-CDM suppliers in industry. It would therefore be of interest to examine the effects on CO2 emission reduction from the electricity industry of the entry/exit decisions of non-CDM power producers over the life of the CDM project in a future research. The rigorous economic scheduling method would calculate the CO2 emission reduction due to operation of a CDM plant in a competitive electricity industry more accurately. However, it requires comprehensive data and more expensive analysis. Furthermore, the results based on the economic scheduling approach can be highly sensitive to assumptions of the model parameters (e.g., fuel prices and demand forecast). This may present a limitation to the use of the rigorous approach from the individual project developers’ perspective. Acknowledgement We would like to thank an anonymous reviewer of the journal for helpful comments on an earlier version of the paper. However, we only are responsible for any remaining error in the paper. References Anagnostopoulos, K., Flamos, A., Psarras, J., 2004. Application of the Multiple Benchmark System (MBS) to selected case study projects. Climate Policy 4 (1), 45–63. Conejo, A.J., Nogales, F.J., Arroyo, J.M., 2002. Price-taker bidding strategy under price uncertainty. IEEE Transactions on Power Systems 17 (4), 1081–1086. Energy Information Administration (EIA), 2005. Electric power monthly, August 5 /http://www.eia.doe.gov/eneaf/electricityS. Flamos, A., Anagnostopoulos, K., Askounis, D., Psarras, J., 2004. Eserem—a web-based manual for the estimation of emission reductions from JI and CDM projects. Mitigation and Adaptation Strategies for Global Change 9 (2), 103–120. GAMS Development Corporation (GDC), GAMS/SBB, 2002a. User Notes. GDC, Washington, DC. GAMS Development Corporation (GDC), GAMS/CPLEX, 2002b. User Notes. GDC, Washington, DC. Gross, G., Finley, D., 2000. Generation supply bidding in perfectly competitive electricity markets. Computational & Mathematical Organization Theory 6, 83–98.

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