Emission Assessment of Distributed Generation in Urban Areas Gianfranco Chicco, Pierluigi Mancarella and Roberto Napoli Abstract – The increasing diffusion of distributed generation within urban areas calls for deeper analyses aimed at evaluating the sustainability of energy generation and its environmental impact. In particular, cogeneration technologies enable enhanced energy efficiency and thus CO2 emission saving with respect to the conventional separate production of heat and electricity. However, the distributed cogeneration production could dramatically worsen the air quality on a local level, due to emissions of various hazardous pollutants such as NOx, CO, and so on. In addition, in urban areas the air quality regulation could be quite stringent, thus calling for a thorough environmental impact assessment at the planning stage. In this paper, the emission characteristics of distributed cogeneration are evaluated with respect to the conventional separate production of heat and electricity on the global and local levels through the emission balance approach. The analysis is carried out with reference to small-scale (below 1 MWe) distributed generation technologies available on the market, such as microturbines and internal combustion engines. In particular, after evaluating the emission break-even conditions for equivalence between distributed and centralized generation, it is shown how the specific results may depend upon the plant operation characteristics (at partial load) and upon the reference values for emission assessment. These aspects could be particularly relevant for energy policy formulations. Index Terms – air pollutants, cogeneration, distributed generation, environmental impact, local and global emissions. Nomenclature Subscripts represent energy sources or end use (y=cogeneration, e=electricity, t=thermal, d=demand, SP=separate production) and specify the measuring units or the pollutant (p). Superscripts refer to equipment or energy vectors. For the energy vectors, the same symbols are used for energy [kWh] or average power [kW]: W for electricity, Q for heat, F for fuel thermal content; X is a general energy vector (any of the previous ones). m is mass [kg]. The Greek letters η and µ respectively denote efficiencies and emission factors [mg/kWh]. Δ is used to denote differences.
I. INTRODUCTION The development of Distributed Generation (DG) [1] technologies in the last decade has been often related to the possibility of adopting cogeneration also on a small-scale. Indeed, the profitability of installing a Microturbine (MT) or an Internal Combustion Engine (ICE) could significantly increase by exploiting the heat from the thermodynamic cycle This work has been supported by the Regione Piemonte, Torino, Italy, under the research grant C65/2004. The authors are with the Politecnico di Torino, Dipartimento di Ingegneria Elettrica, corso Duca degli Abruzzi 24, 10129 Torino, Italy (email
[email protected],
[email protected],
[email protected]).
to supply local thermal users [1-4]. In addition, there are several potential cogeneration or CHP (Combined Heat and Power) applications on a small-scale basis (below 1 MWe), such as hospitals, shopping malls, residential blocks, sport centres, and so forth. Thus, the number of generation units installed over the urban territory is continuously growing, calling for new analyses related to the environmental impact assessment of DG, in particular due to air pollution increase. Generally speaking, adopting cogeneration can decrease, in absolute terms, the primary energy consumption with respect to the Separate Production (SP) of heat and electricity [2,3]. On a small-scale level, this occurs also thanks to the fact that the energy generation systems can be managed more effectively when privately owned. The lower fuel consumption brings about a decrease in the overall emissions of CO2, and could also decrease the absolute emissions of hazardous air pollutants regulated in most countries. However, whereas it is correct to refer to a global comparison when dealing with CO2 because of its global Greenhouse Gas (GHG) impact, a different analysis is needed for local air pollutants such as NOx, CO, SOx, and so on [4-7]. In fact, the DG CHP units more and more often are installed within urban areas, where air quality standards are often stringent because of the high population density. In addition, also due to road traffic pollution, margins before reaching the permitted overall pollution levels (set by local regulations, most of times) can be quite shallow. Therefore, the expected increasing penetration of DG into urban areas brings new important issues for both policy makers and energy system planners. In particular, from the policy point of view the major issue consists of establishing emission limits for DG installations, whereas energy planners are called to evaluate the additional (in case) local environmental impact of the DG systems. In this matter, the main concerns for DG designers and Energy Services Companies (ESCOs) refer to the additional cost of abatement systems that could decrease the plant overall profitability, or to the additional cost brought about by potential future policy regulations requiring to “internalize” the external costs [6] from energy generation and relevant emissions in urban areas. Suitable simplified models are needed to cope with the above issues from a point of view as general as possible. As such, the emission balance approach [4,5] is adopted in this work to assess the environmental impact due to the diffusion of DG in urban areas. In particular, the approach is based on the fact that the classical reference for electricity production, i.e., the large power plants, are often far from urban areas. Therefore, the most natural comparison for assessing the local environmental characteristics of DG installations are the
residential boilers spread over the urban fabric [4,5]. The paper is structured as follows. Section II introduces the performance and characterization models for energy and emission analysis of cogeneration systems. Section III presents and discusses the local and global emission balances. In addition, as a general tool for assessing the environmental impact of distributed cogeneration with respect to the conventional generation, emission break-even analyses are carried out, representing the results with relevant maps. Section IV contains some numerical applications referred to real cases, that highlight some practical aspects for the machine typologies most available in the market, namely MTs and ICEs. Section V draws the concluding remarks. II. ENERGY PERFORMANCE AND EMISSION MODELS FOR DG ENVIRONMENTAL ASSESSMENT STUDIES A. Cogeneration system energy performance models The energy performance characteristics of a CHP system can be described through the electrical efficiency ηW and the thermal efficiency ηQ , respectively electrical and thermal output to fuel thermal input ratio:
ηW =
Wy Fy
, ηQ =
Qy Fy
, EUF = ηW + ηQ
(1)
In addition, it is possible to describe the actual amount of primary energy contained in the fuel used to produce useful energy output through the Energy Utilization Factor (EUF) indicator [2,4,8]. The EUF is defined as the sum of the electrical and thermal efficiencies and in practice represents the overall cogeneration efficiency. The efficiencies in (1), in particular the thermal one, depend in general upon the technology, the heat recovery and generation system (e.g., for hot water or steam generation), the load level and the outdoor conditions [1,4,8].
B. The emission factor model As far as the emissions of a given pollutant (as well as CO2) are concerned, it is possible to evaluate them for any combustion equipment on the basis of a model such as m =μ ⋅X X p
In (2), μ
X p
X p
(2)
is the so-called emission factor (i.e., the specific
emissions in [mg/kWh]) [4,8,9] for the generic pollutant p; m pX is the mass of pollutant p emitted while producing the
useful energy output X, for instance useful electricity W [kWhe] or useful heat Q [kWht]. The term μ pX is in general a function of the specific combustion equipment and of its operating conditions, and can vary under out-of-design conditions, aging, maintenance conditions, and so forth. In particular, focusing on the carbon dioxide (not a “conventional” pollutant, but however of great interest because of its GHG effect), the CO2 emission characterization can be readily derived by the CHP efficiency models. In fact, in this case the most convenient approach is first to evaluate
F the emission factor μ CO referred to the thermal input F 2
[kWht] generated by burning the fuel, and then to estimate the “conventional” emission factor, referred to the equipment output (heat and/or electricity) through the relevant efficiency figures as in (1). This approach is effective any time complete combustion can be assumed, which represents a very good approximation in most cases [4]. Therefore, once given the fuel typology and thus the chemical reaction occurring in the case of complete combustion, the CO2 emissions can be evaluated as a function of the relevant efficiency only. As far as the other pollutants are concerned, instead, it is not possible to draw general analytical models for the emission factors [4,5,8,9], and case by case it is necessary to resort to the emission characterization of the specific equipment through data available from the manufacturers or even better from field measurements. C. Emission characterization of cogeneration equipment With reference to the evaluation of the emissions of air pollutants other than CO2, the most severe policy concerns refer to the “hazardous” combustion products that need to be limited in order to reduce the threatening to human health and environment. In most cases, the focus is on NOx (in the form of both NO and NO2), CO, Unburned Hydrocarbons (UHC), Particulate Matter (PM), and SOx [8,9]. In particular, for natural gas-fuelled units the most significant emissions are from NOx and CO, whereas SOx and PM are basically negligible [8,9]. The pollutant emission characterization of the different technologies is normally based on emission inventories performed by the various environmental protection agencies and research groups worldwide (see for instance [8,9]). In general, the emission characteristics of different units also from a same typology (for instance within the MT or ICE family) can be quite different from each other, due to the specific combustion device design, abatement system and so on. Thus, it is tough to draw general emission models and it is rather preferred, for general studies, to refer to average values from the inventory emission figures, if no more precise data are available for the specific units considered in the analysis.
III. EMISSION BALANCE APPROACHES AND EMISSION BREAK-EVEN ANALYSES A. Local and global emission balances Quantitatively, local and global emissions can be modelled by means of the so-called emission balance approach [4,5] according to the following formulation: Δm p = ( m p ) y − ( mWp + m Qp ) SP
(global)
(3)
Δm p = ( m p ) y − ( m Qp ) SP
(local)
(4)
In particular, in (3) and (4) the subscript y refers to the emissions from the cogeneration process, and SP to the emissions from separate production of the same amount of the cogenerated electricity Wy and/or heat Qy. The rationale of this approach lies behind the fact that the CHP system is compared to the separate production of the same quantities of heat and electricity produced in
cogeneration from the standpoint of each pollutant emissions. However, the global emission balance (over a certain time frame) accounts for both mass emissions of the pollutant p from the separate generation of heat and electricity. Instead, the reference electrical energy is produced, in an ideal model, by an equivalent power plant located “far” from the user. Thus, when evaluating the local emission balance, the separate production reference shrinks to the heat generation, as occurring in boilers spread over the urban area where also the DG system is sited. Apparently, the global emission balance (3) addresses fully the evaluation of CO2 emissions, but it fails in assessing correctly the local contribution to air pollution due to the separate production of electricity, somehow really generated “far”, and thus most of times impacting relatively slightly over the urban areas. The relation (4), instead, is definitely more suitable to evaluate the local impact from NOx, CO and the other non-GHG pollutants, although weighing “too much” the environmental impact from DG, not accounting at all for the separate electricity production. The reality will be somewhere in the middle, as for instance addressed by adopting computational models for the air pollution dispersion in the atmosphere [10]. However, this kind of detailed calculation depends upon the territorial characteristics, meteorological conditions, emission source physical characteristics (stack height, exhaust flow velocity), and so forth, thus being related to the very specific case. Therefore, for general analyses, such as for general policy decisions or assessment of the environmental impact from large DG penetration into urban areas, the emission balance approach can be considered as a good reference standpoint.
resulting emission break-even map would be expressed in terms of emission factors (2) referred to the electricity output. Then, by comparing the break-even values to the actual emissions from every DG specific unit (in every efficiency configuration, for instance at both full and partial loads), it would be straightforward to estimate the DG environmental impact on both local and global bases. Of course, in theory several emission break-even maps can be drawn, depending on the reference emission characteristics assigned to the separate generation. In this sense, the typical approaches can refer to evaluating average emissions from residential boilers and electrical power plants, or in alternative from state-ofthe-art technologies, respectively low-emission highefficiency boilers and low-emission high-efficiency combined cycles. Clearly, the reference numerical values considered for separate generation must be carefully chosen, since they can change significantly the outcomes of the analysis. IV. APPLICATIONS OF EMISSION ASSESSMENT AND BREAK-EVEN MAPS A. 3-D emission break-even maps As a first numerical application of the emission break-even analysis described in Section III, let us consider the case of NOx, which represents the air pollutant about which there are most regulatory concerns due to its hazardous effects on human health. In particular, let us consider the NOx emissions from average reference technologies. For instance, it is W possible to assume emission factors values of about ( μ NO ) SP X
= 500 mg/kWhe for the average electricity generation from Q the power grid in Italy [12], and ( μ NO ) SP = 200 mg/kWht for X
B. Emission break-even analyses and maps On the above premises, a first step towards DG emission assessment in urban areas could be to compare the characteristics of the specific DG equipment to the available systems for separate production of heat and electricity. According to [11], a possible approach consists of running an emission break-even analysis indicating for what thermal and/or electrical efficiency of a DG unit the produced emissions of a given air pollutant (as well as CO2) would be the same as the emissions produced by the reference technologies for separate production. For instance, referring to the unitary production of electricity, it is possible to perform such an analysis with the DG electrical and thermal efficiencies as variables, i.e., to evaluate what are the equivalent emissions from the separate generation for every possible efficiency configuration of the DG units. In particular, if based on the unitary electricity production, the (m p ) y
local Qy
CHP plant (ηW ,η Q )
Wy
the average heat generation from residential boilers in urban areas, with an equivalent fuel mix as input [4]. The relevant emission break-even maps in a 3-D form for both global and local comparisons, expressed in terms of emission factor with respect to the electricity output (i.e., in [mg/kWhe]), are shown respectively in Fig. 2 and Fig. 3. The emission break-even map for the CO2, based upon a global balance approach because of its GHG characteristics, is reported in Fig. 4. In particular, the map refers to average CO2 emission values for the Italian power system, with W ( μ CO ) = 530 g/kWhe estimated on the basis of the data 2 SP reported in [13]. Similarly, an average CO2 emission value is Q estimated for the boiler production equal to ( μ CO ) = 270 2 SP
g/kWht, considering a fuel mix as input and on the basis of the CO2 emission factors for the fuels reported in [4].
(m p ) y (m Qp ) SP
Qy boiler
(m Qp ) SP
global Qy
boiler
Qy CHP plant (ηW ,η Q )
(mWp ) SP Wy
Wy equivalent power plant
Fig. 1. Emission balance models for assessing the environmental impact due to distributed cogeneration systems.
conventional separate generation of the same amount of electricity and/or heat.
NOx emission factor [mg/kWhe] 2000
1600
1200 800 0
400 0.2 0 0.1 electrical efficiency η W [pu]
0.2
0.3
0.4
0.4 thermal efficiency η Q [pu]
0.6 0.5
Fig. 2. 3-D global emission break-even map for NOx (average technologies for separate production). NOx emission factor [mg/kWhe] 1200
B. 2-D emission break-even maps Alternatively, it is possible to get the same type of information as for 3-D emission break-even maps through 2D emission break-even maps. For instance, Fig. 5 shows the data for a local comparison as in Fig. 3, only for discrete values of thermal efficiency and with reference to CO emissions. In this case, it is possible to assume an emission Q factor equal to ( μ CO ) SP = 40 mg/kWht for the average heat generation from residential boilers in urban areas, with an equivalent mix of fuels as input [4]. Similarly, Fig. 6 shows a 2-D CO emission break-even map on a global basis, assuming the average CO emission from the equivalent power W plant in the Italian case1 equal to ( μ CO ) SP = 300 mg/kWhe. From such maps, entering the relevant value of thermal and electrical efficiencies it is possible to evaluate, for the specific DG equipment, the relevant emissions from the reference separate generation, comparing them to its actual emissions.
800 600 400 200 0 0.1
0.2
electrical efficiency η W [pu]
0.4 0.3
0.4
0.5
0.6
0 0.2 thermal efficiency η Q [pu]
CO emission factor [mg/kWhe ]
700
1000
600 500 400 0.7
300
ηQ
200 100
0
0 0
Fig. 3. 3-D local emission break-even map for NOx (average technologies for separate production).
0.2
0.3
0.4
0.5
electrical efficiency
Fig. 5. 2-D local emission break-even map for CO (average technologies for separate production).
CO2 emission factor [g/kWhe]
2000 1600 1200 800 0
400 0.2
0.2
0.3
0.4
0.6 0.5
0.4 thermal efficiency η Q [pu]
CO emission factor [mg/kWhe ]
700
2400
0 0.1 electrical efficiency η W [pu]
0.1
600
0.7
500
ηQ
400 0
300 200 100 0 0
0.1
0.2
0.3
0.4
0.5
electrical efficiency
Fig. 6. 2-D global emission break-even map for CO (average technologies for separate production).
Fig. 4. 3-D global emission break-even map for CO2 (average technologies for separate production).
Given the efficiencies and the relevant emissions for every operating point of the DG unit, from the maps considered it is possible to evaluate the emission balances (3) and (4), and thus the environmental performance with respect to the
1 The emissions of the equivalent power plant have been averagely estimated by considering data obtained from various sources. It has to be noted that after the energy market restructuring the data related to the production systems are scarcely available, being typically considered as private information.
emission factor [mg/kWh e]
400 350 300 250 200
global local
2000
50% load 75% load
1500
100% load
1000
500
average load
0,23
0,24
0,25
0,26
0,27
0,28
electrical efficiency
ηQ
100
Fig. 9. Assessment of a MT emissions and comparison with local and global emissions from separate production (average technologies).
50 0
0 0
0.1
0.2
0.3
0.4
0.5
electrical efficiency
Fig. 7. 2-D local emission break-even map for CO (state-of-the-art technologies for separate production). 400 emission factor [mg/kWhe ]
MT
2500
0 0,22
0.7
150
An example of this kind of evaluation is shown in Fig. 9, where the actual NOx emission characteristics for a 30-kWe MT analysed in [5,14] are shown for different sampled operating points as opposed to the relevant local and global emissions from separate production. In practice, the differences between the local emissions and the MT actual emissions, and between the global emissions and the MT actual emissions, yield the global balance (3) and the local balance (4), respectively. NOx emission factor [mg/kWhe ]
The same approach can be adopted by using state-of-theart values for the separate production emission factor. For instance, Fig. 7 and Fig. 8 reports respectively the 2-D local and global emission break-even maps for the CO by assuming Q W ( μ CO ) SP = 20 mg/kWht and ( μ CO ) SP = 200 mg/kWhe, typical values for the Italian case. Apparently, in this case for the same values of thermal and electrical efficiencies of the DG system, the emission break-even points are much lower than for average technologies. This type of behaviour usually applies to all types of pollutants, for which considering stateof-the-art technologies as opposed to average ones reduces consistently the emission break-even points and thus the environmental “competitiveness” of DG systems with respect to the separate production.
350
0.7
300
ηQ
250 200
0
150 100 50 0 0
0.1
0.2 0.3 electrical efficiency
0.4
0.5
Fig. 8. 2-D global emission break-even map for CO (state-of-the-art technologies for separate production).
C. Partial-load emission maps and emission assessment of MT units The major upside of an approach based on emission breakeven maps is its generality. In fact, the input to the system evaluation are the electrical and thermal efficiencies of the specific DG equipment, and these can be evaluated under every operating point, including, then, partial load and more in general out-of-design operation. This aspect is crucial when dealing with MTs, for which the emission profile for some pollutant can be utterly depending upon the loading level [5,14], while for ICEs the emission factors do not change significantly at partial load [5,8,9].
From inspection of Fig. 9, it is apparent that when the MT operates at high loading (75% and 100% of the capacity) and at high electrical efficiency, its emissions are below both the local ones (from reference boilers producing the same amount of cogenerated heat) and the global ones (from reference boilers and the power plants producing respectively the same amount of cogenerated heat and electricity). Instead, when the loading level drops at 50% of the full load, the NOx emissions increase dramatically and become far higher than both the global and the local emissions. An integrated evaluation of the MT operation for different load levels could be run for instance by considering an average load opportunely weighting the different operation points. In particular, Fig. 9 reports the results for an average load obtained by weighting the operation of the MT with weight 0.3 for the full load, 0.5 for the 75% load, and 0.2 for half load. In this case, also corresponding to an average electrical efficiency, the equivalent emissions are slightly higher than the local ones, while they are still quite below the global ones. This means, with very good approximation, that the actual additional NOx emission pressure due to the presence of DG generation from the considered 30-kWe MT operating in the conditions indicated above would be almost negligible with respect to the traditional boilers adopted for thermal-only production. D. Partial-load emission maps and emission assessment of ICE units The variation in the emission level at variable loading is quite common for MTs, whose performance also in terms of emissions is typically optimized at high loads or anyway lies within a narrow range of operating points close to the full load. Instead, ICEs have been historically designed for a
larger range of operating points, from both the energy performance and the emission points of view. An example of emission assessment for a 180-kWhe ICE, from the results found in [5], in the same fashion of Fig. 9, is reported in Fig. 10. From visual inspection, the main aspect immediately apparent is that the load level does not affect significantly the emission profile, as expected. Hence, the average load level, calculated on the basis of the same weighting coefficient as for the MT above, exhibits emission characteristics close to the nominal values. However, in this case at high loading the NOx emissions are quite higher with respect to the ones for the MT in Fig. 9; at the same time, the electrical efficiency (and the EUF, not shown in the figure) are higher as well, so that the corresponding CO2 emission reduction is quite more consistent. This is again a typical behaviour for current ICEs, exhibiting average emissions higher than MTs, although quite constant at varying loads and with significantly higher electrical and overall efficiencies. NOx emission factor [mg/kWhe ]
2500
ICE global 75% load
2000
local
1500
1000
50% load
0 0,30
100% load
average load
500
0,32
0,34
0,36
0,38
0,40
numerical results cannot be generalized, although they are exemplificative of some typical behaviour for units currently available in the market. In particular, when setting up constraints about the emissions of specific types of units, it is important to reckon that values given only for full-load operation could be insufficient to assess the actual emission profile of the machines over their operating life span, and could lead to unbiased favour towards some technologies rather than other ones. The comparison between cogeneration and separate production may be strongly conditioned by the reference values adopted for the separate production entries. In general, adopting average values the modern DG units can be competitive, to a good extent, in terms of CO and NOx, while emissions from state-of-the-art values for the SP are often too low for the current DG units. Of course, it is up to the energy planners or policy makers to establish, case by case, which references are most suitable for the energy system evaluation, also considering that the emission data are effected by high uncertainty. In addition, in urban areas the “background emission noise” given by the initial polluting level could change consistently the evaluation of policy makers, by hurdling or favouring the spread of DG in certain sites as opposed to other ones. The approach and the assessment techniques presented in this paper can provide important hints to regulators, planners and decision-makers, for evaluating the trade-off between the local air pollution impact due to dispersed generation units and the benefits in terms of energy saving and CO2 emission reduction brought by adopting cogeneration on a wide basis.
electrical efficiency
Fig. 10. Assessment of an ICE emissions and comparison with local and global emissions from separate production (average technologies).
V. CONCLUDING REMARKS This paper has presented various techniques for evaluating the environmental impact, in terms of air pollutant and CO2 emissions, due to diffusion of cogeneration units in urban areas. In particular, the evaluation is carried out on a comparative basis with respect to the conventional generation of heat (boilers) and electricity (power plants). To this purpose, the local and global emission balances have been adopted in order to assess, respectively, the local air worsening due to hazardous pollutants such as NOx, CO, THC, and so forth, and the global change due to GHG emissions (mainly from CO2). From a general standpoint, the emission balances can be effectively evaluated after drawing break-even emission maps for the relevant pollutants, through which it is possible to assess the incremental environmental pressure due to distributed cogeneration equipment with respect to the classical solutions of separate production of heat and electricity. The numerical analyses run in the examples show how the emission impact from MTs (in terms of NOx, in the specific case) may depend consistently on the operation mode and then on the specific regulation strategy adopted, while these aspects have smaller influence on the emissions from ICEs (in general higher than for MTs). However, the specific
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