Thermoeconomic and environmental guidelines for

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cogeração para os geradores de termeletricidade na Amazônia. In: CIER 2007: Proceedings of the III Congreso CIER de la Energía: Retos y Perspectivas; 2007 ...
Proceedings of ECOS 2011 Novi Sad, Serbia July 4–7, 2011

Thermoeconomic and environmental guidelines for trigeneration projects in the Brazilian Amazon Ricardo Wilson Cruza, Luis M. Serrab and Miguel Angel Lozanoc a

Thermomechanical Engineering Center (NET), Escola Superior de Tecnologia/DEM, Universidade do Estado do Amazonas, Manaus, Amazonas, Brazil, [email protected] b Group of Thermal Engineering and Energy Systems (GITSE), Aragon Institute of Engineering Research (I3A), Department of Mechanical Engineering, University of Zaragoza, Spain, [email protected] c Group of Thermal Engineering and Energy Systems (GITSE), Aragon Institute of Engineering Research (I3A), Department of Mechanical Engineering, University of Zaragoza, Spain, [email protected]

Abstract: This work discusses guidelines on how well-established methodologies for environmental and thermoeconomic analysis should be taken into account to access the feasibility of CHCP plants, in the particular Brazilian Amazonia. In hot regions, such as Brazilian Amazonia, heat may be used in air conditioning and sanitary water heating for secondary issues helping to lower the electricity high peak demand during afternoon. By another standpoint, in such environment heat products shall be refrigeration for preservation of perishable food products, and air conditioning for comfort. It is widely proved that CHCP plants allow obtaining magnified technical and economic efficiency before other conventional thermal plants. However, an analytical methodology intended to evaluate the attractiveness of any thermal plant must access its sustainability level too, in face of environmental issues quantified on monetary basis, if possible. For such reasons, the guidelines this work discusses points towards LCA and ECT.

Keywords: Trigeneration, thermoeconomics, environmental cost, Pareto optimal.

1. Introduction Thermoeconomics reaches for planning and optimization by the classical economic capitalist standpoint, in which only the economic profitability matters. Even the Marxist acquaintance does not dare to go beyond this point; and what about the untransformed natural resources? Nowadays, it seems reasonable the environment must be paid a price for its lost natural resources. The classical economic struggle supply vs demand would guide the analysis of the CHCP project, if justified the price of the internal and the external irreversibilities. The Achilles’ hill of this method lies in that prices itself are insensitive to the relations between bulk external irreversibilities and environmental impacts per se, e. g. to the lost life-time cost of humans or life in general. Still, it is recursive for short time period analysis at least as a first approximation to how internal and external irreversibilities influence the internal costs of the plant. Another method is the ‘internalization of externalities’, but it suffers of lack of a definitive method as detached by [1]. In general, an externality occurs when in competitive equilibrium market the social marginal cost (e. g. health insurance) exceeds the private marginal cost associated to generation of externality [2, 3]. One method in world’s agenda is ‘free emissions market’ what needs a well-established legal framework to regulate it [2], but political uncertainties makes the cost of carbon abatement vary from 10 US$/ton to 25 US$/ton. Nevertheless, it is recursive too as firstversion analysis, if it takes into account the average carbon cost of projects in the years. A different approach ultimately appropriate to the Amazonian economy and environmental characteristics is to focus the analysis on the interrelated problems: the minimization of the internal costs of the CHCP plant [4]; and the double-objective minimization of the total monetary cost and

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the environmental costs of the plant [4, 5]. Both problems may be solved by linear programming optimization techniques.

2. Current scenario of the electricity market in Amazonia The Brazilian Amazonia covers 58% of the Brazilian territory with low human density. The biggest regional state is the Amazonas State, which capital is the city of Manaus. The majority of the north and the west part of the region are supplied by a distributed and isolated electric grid, in which the principal one supplies separately each capital, and the secondary one supplies each regional county [6]. The east and south sides of Amazonia are linked to the BIG. Table 1 shows the current supply configuration of the six most important states of the Brazilian Amazonia electric system. The independent systems attending the capitals are mostly hydro-thermal systems, while in the country Amazon the systems are fully thermal and are powered by Diesel engines ranging from hundreds to thousands of kWe, in which the Amazonas State holds the majority of units. The operational deficiencies are lack of steady fuel supplies during the period the regional waterway reaches its lower height of flood; long distances to deliver fuel supply; and bad logistic on maintenance. These inefficiencies make electricity in Amazonia unsustainable to the economy [6]. Currently, the distributed systems based on Diesel engines in Brazil are supported by the federal subsidy CCC which reduces a set of generation costs, e. g. fuel consumed by the power plants. Table 1. Number of power plants in Brazilian Amazon Hydroelectric Thermal State No. of Power No. of Power Units (MWe) Units (MWe) Acre 0 0 18 142

Small Hydro No. of Power Units (MWe)  

Amapá

1

77

5

197





Amazonas Pará Rondônia Roraima

2 2 1 0

275 8 400 217 0

114 53 40 72

1 629 252 616 113

 1 14 1

 0.630 0.063 0.005

Source: [7]

2.1. The electric market of Manaus From 275 MWe assigned to two hydro plants in Amazonas State shown in Table 1, 25 MWe relate to a private plant to meet a mining facility located on the north of the state, and remaining 250 MWe are allowed by the BHS plant, owned by the local public facility AESA to supply Manaus. BHS is not capable to attend to base load for more than 60 days from June to July during its maximum height of flood [6], and out that period BHS works as a low water height station. From the whole thermal power of Amazonas State, 1509 MWe supply Manaus where close to 60% is bought from independent producers which plants are thermoelectric Diesel engines. The remaining 40% of the system belongs to AESA. The whole public generation facility would be capable to provide 1759 MWe to the capital, but the reliability of the system does not ensure to more than continuous 1200 MWe, in a scenario where the power peak load rounds 1000 MWe during the dry period of the year [8]. Since 2010, the thermoelectric system runs fuelled by natural gas from the Urucu Basin through gas pipelines. In year 2012 it will be concluded the connection of the system to the BIG, but its generation framework will be kept running [9]. The industrial district of Manaus is responsible for about 44% of the electricity consumed by the system [10], in which refrigeration is the mandatory charge, responsible for 52% of the peak load in the afternoon, mainly during the hot and dry second semester of the year. These figures suggest opportunities for heat-recovery technologies such as CHCP plants intended to produce electricity, 3172

air conditioning and heat water for secondary uses (sanitary water, industrial cooking, etc.), and to lower the peak load profile of the system.

2.2. Brazilian legal rules on CHP Brazilian framework does not rule cogeneration when related to public facilities servicing public or private clients. Instead, the National legal concept of cogeneration applies to private CHP systems whose benefit is intended to private clients. For such a purpose, the Brazilian regulatory agency ANEEL defines by Resolution No. 21/2000 [11] as CHP plants those plants which attend the following restrictions relative to twelve months preceding the application for authorization: Minimum thermal energy efficiency

Et  0.15 E f Minimum combined energy efficiency Eem  Et    Ef

(1)

(2)

Values for  and  for fossil fuels are [11]:  = 2.0,  = 0.32 (Eem ≤ 5 MWe);  = 2.0,  = 0.32 (5 MWe < Eem ≤ 20 MWe); and  = 2.0,  = 0.32 (Eem > 20 MWe). Ef accounts on LHV basis. Methodologically, Resolution No. 21/2000 [11] focuses the CHP plant efficiency by the energy standpoint at a low level of disaggregation of equipment (Figure 1).

2.3. Environmental issues of the Amazonian capitals and country states Environmental issues are different in Manaus relative to the rest of the regional capitals, because of their different level and kind of industrialization. Manaus plays the role of a city-state, as it represents 37% of the GRP and 2% of the Brazilian GNP [9]. The Amazonas State is road-isolated from the rest of Brazil, but it translates into low pressure on the state natural resources as it helps to not intensify anthropic-gathering actions. In the case of the deep region the scenarios are all similar which issues result on different environmental burdens. The environmental issues of the capitals and country Brazilian Amazon can be methodologically viewed by the LCA perspective. Table 2 shows EI-99 damage and impact categories and their relative importance to the capitals and country Brazilian Amazon. As one can see in Table 2, Manaus is reached by all EI-99 impact categories, for what it is worth emphasizing the qualitative criteria assumed to classify the allocations therein as Y for ‘yes’ and N for ‘no’:  fossil smoke – relates to combustion of fossil fuel (industries, vehicles, etc.);  not measurable – in case of negligible significance. The majority of country towns are not reached by emissions (gases) and effluents (water), or it occurs on immeasurable scale basis, e. g. mineral and fossil fuel extractions upstream regional rivers;  global effect – relates to effects of global reach and measurable scale;  local effect – relates to effects of local reach and measurable scale.

4. Assessment models Monetary and environmental issues influence CHCP projects both endogenously and exogenously. The former relates to internal costs and the later to surrounding costs. Aside from the internal costs standpoint, externalities are case apart, as the consequences reach human being and biota.

4.1. Internal costs of the CHCP plant based on exergy Internal costs focus the internal efficiency of the plant. Assessment of the internal costs of CHCP plants is a thermoeconomic problem which may be treated as an ECT problem [1, 12]. In general, once defined a highest level of possible disaggregation for the CHCP plant (Figure 1), the internal costs of any plant can be assessed by a cost balance equation written as

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A | αT B*   Y |  T

(3) In the exergetic case, Yj is the physical depreciation of asset j along its life span (usually negligible), and to j is assigned the external exergy flow feeding the plant. In the monetary case, Bi* is usually denoted as Ci, Yj is usually denoted as Zj (section 4.2.1), and to j is assigned a price or a tariff of respectively fuel and electricity feeding the plant (section 4.2.1). Full details about these terms and the propositions for cost allocation can be found elsewhere [1, 12].

Fig. 1. Levels of disaggregation of a productive system (the flows are exergetic) [6]. Table 2. Relation of EI-99 damage categories to the capitals and country Amazon Damage categories

Manaus

Other capitals

Country Amazon

Human health impact category Carcinogenic effects on humans Respiratory effects caused by organic substances Respiratory effects caused by inorganic substances Damage caused by climate change Effects caused by ionizing radiation Effects caused by ozone layer depletion

Y

Y

N

(fossil smoke)

(fossil smoke)

(not measurable)

Y

Y

Y

(fossil smoke)

(fossil smoke)

(fossil smoke)

Y

Y

Y

(fossil smoke)

(fossil smoke)

(fossil smoke)

Y

Y

Y

(global effect)

(global effect)

(global effect)

Y

Y

N

(local effects)

(local effects)

(not measurable)

Y

Y

Y

(global effect)

(global effect)

(global effect)

Y

Y

N

(local effect)

(local effect)

(not measurable)

Y

Y

Y

(local effect)

(local effect)

(local effect)

Ecosystem quality impact category Quality damage caused by ecotoxic effects Damage caused by the combined effect of acidification and eutrophication Damage caused by land occupation and land conservation Resources impact category Damage caused by extraction of minerals Damage caused by extraction of fossil fuels

Y

Y

Y

(local effect)

(local effect)

(local effect)

Y

N

N

(upstream)

(not measurable)

(not measurable)

Y

N

N

(upstream)

(not measurable)

(not measurable)

Source: adapted from [4]. Note: Y – yes; N – no.

The concept of cost in economics is based on the assumption of linear independence of actions and reactions of market players, allowing them to be additive – i. e. costs are extensive properties. So 3174

are the exergetic, the monetary costs and the environmental damage costs as well. It can be proved [12] the linear independence of the exergetic costs implies (3). To practical, the mathematical model governing the dependence of costs to production factors is the first degree function. It is well known however, the actual dependence for large ranges of variation of the production factors really draws not a straight line, but the relationship must retain linear independence whatever may the function be. The first degree function which strongly simplifies models is just a matter of sizing properly the range of variation of the production factors. Hence the exergoenvironmental costs balance equation [1], focusing environmental damage costs, may be written as

A | αT  F    A |  T

(4)

In which the allocation of costs proceeds as for the exergetic case and details about it can be found elsewhere [1, 12]. All the environmental costs in (4) derive from a precedent LCA of the CHCP plant covering equipment and operation, and are expressed by the particular damage index selected. As shown by the discussion about Table 1, the EI-99 dimensionless ‘single-score’ (SS) index is suitable for CHCP projects in the area of Manaus (section 4.2.2). It is useful to mention Bejan’s [1] approach to waste flows (exhaust gas, refrigerant exhaust heat, etc.), by which costs of waste should be allocated to those equipment which follow the one in which it is ‘generated’. In case of internal combustion engines and gas turbines, these are the origin of waste losses; and in case of steam cycles waste losses are generated in the steam generator. The waste loss cost is allocated as an inflow to the loss generator, allowing the irreversibilities to be distributed downstream, as an accounting gimmick to deal with internal irreversibilities of the plant, and allocate its internal costs (highest level of disaggregation). As it shall be detached on sections 4.2.1 and 4.2.2, it is unnecessary to assign value to energy losses of the plant when the focus is global economic and environmental relations to the surroundings (lowest level of disaggregation).

4.1.1. Optimization of internal costs The simplification of first degree function cost allowed (3) and (4). Even when for power machines the analysis deals with large performance interval ranges, e. g. internal combustion engines – which power is assumed a polynomial function of engine charge but thermal energy losses obey decreasing non such linear functions [6] –, it is possible sectioning large charge intervals into smaller discrete intervals in order to retain per-interval linearity. This approach has the additional advantage of not requiring a known characteristic function for each unit of the plant (usually nonlinear), which leads to the optimization to be solved by complicated nonlinear methods [13]. For the linear assumption the optimal exergetic, exergoeconomic or environmental unit costs1 which results on minimum total cost for the plant per discrete interval can be assessed by the generalized linear programming model [14] Min

TK   i X i  W j 

(5)

j

s.t.

Restrictions on the Xi Restrictions on the Wj Other feasible restrictions The restrictions may be of any kind (equality, inequality, per decision variable, etc.), whatever the physical problem is. Notice, as the level of disaggregation of the plant must be as high as possible and (5) is supposed written in exergy terms, (1) and (2) do not apply as restrictions.

4.2 Generalized global costs of the CHCP plant The discussion herein intends to methodize the optimization of monetary and environmental costs of the CHCP plant simultaneously on energy basis at its lowest level of disaggregation (Figure 1), Exergetic unit costs of physical flows are defined by Bi* = iBi. By extension, one can define exergoeconomic unit costs of the physical flows by Ci = ciBi; and equally, exergoenvironmental unit costs by F.i = iBi.

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assumed that is the way by which the plant relates to market and environment. The two considered costs are the total annual cost – the economic objective –, and the annual cost of environmental damage – the environmental objective. It is important to define which of the energetic products is mandatory between options electricity and refrigeration/heat. The mandatory product commands the thermal generation set towards its demand to be fully attempted along the annual curve demand of the system which must be previously collected.

4.2.1 Economic objective The total annual monetary cost has two parcels: the variable cost and the constant cost as in [6]

TACmon  VAC  CAC (6) In a CHCP project VAC relates purchase of electricity, fuel consumption, cost of rejected excess heat (generally to allow modulation of demand heat, performed by induced-draft cooling towers), and the surplus electricity sold to the grid (another figure to modulation). VAC is as follows [15]

VAC  cP P  c f F  cL L  cS S (7) CAC relates cost of operation and maintenance and cost of amortization of investment of assets as CAC  COM  AICA   f OM  crf ICA (8) Both costs are evaluated from the investment cost2, the former by factor fOM (usually 0.5) and the latter by factor crf = [t (1+t)p] / [(1+t)p – 1]. It is usual to adopt the TJLP from state BNDES bank for discount rate in Brazil, currently on yearly 12% and 30 years life cycle. Function TACmon is then written as TACmon  cP P  c f F  cL L  cS S 

  f OM  crf IC (9) Nevertheless, as discussed in last paragraph on next section, the environmental burden of losses (L) is negligible so it may be assumed cost-effective negligible too.

4.2.2. Environmental objective based on LCA LCA subdivides into phases: GSD, LCI, LCIA and LCAI [14, 16, 17]. The GSD phase defines what is intended to obtain from the LCA study and to what extent the study should proceed. The LCI phase is assigned extreme importance, as from its accuracy depends all LCA. It is on LCI phase where resides LCA ‘cradle-to-grave’ philosophy, and makes the boundaries of the plant to be extended from the natural stock of resources consumed on construction of assets and fuels, to the environmental deposit where those stuffs are disposed. Among various available databases, the Ecoinvent database [18] is one of the most frequently updated databases and is especially suited to complex LCA analysis, which must be implemented on a long and detailed step-by-step sequence ending on a predefined level between midpoint results or endpoint results (i. e. focusing the final consequences, what is usually preferable for decision making). EI-99 is an endpoint approach which allows to select the analysis to be performed by three different points of view: egalitarian, say, the ‘holistic’ perception of environmental impacts; hierarquist, where the scientific societal and political perceptions are well-mandatorily defined; and individualist, say, the ‘selfish’ perception, usually restrict to systems headed by only one decision maker. In general, the hierarquist perspective is well suited for academic works. EI-99 distributes evaluated damages among three damage categories (the consequences to the environmental burdens) [4, 16]. EI-99 damage categories subdivide into eleven impact categories (Table 1). LCA studies recommend adopting dedicated software, e. g. SimaPro® [19].

2

The investment cost of secondary assets such as buildings, vehicles, etc., must be allocated to the main typical equipment of CHCP plants, namely thermal generator set, heat recovery steam generator and heat exchanger set allowed producing sanitary heat water or heating air, depending on the planned superstructure of the CHCP plant. The same shall be the case for the environmental costs of the secondary assets (section 4.2.2).

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The methodological sequence to determination of the environmental objective of one production system s (employed equipment, consumed fuels, energetic products, etc.), from LCI phase to LCIA phase based on EI-99, is briefly as follows:  The LCI phase collects and combines diverse k elements (natural or transformed) which compose system s, each one under a particular unit (m, kg, kWh, etc.). Element k is then related to the ‘functional unit’ of system s resulting on the inventory factor ILCI-k; i. e. on basis of the unit of product of system s which from now on governs all the LCA study for that system.  The next LCIA phase determines indexes for the inventoried data classified among EI-99 eleven impact categories listed on Table 2. The index of each element k per impact category w is assessed by the following relation

I LCIAw    f k ,w  I LCI -k  k

(10)

Notice fk,w is the transform to unit of ILCI-k to damage category unit of ILCIA-w, however loosing not relation to the functional unit of system s.  Then, the ILCIA-w sum to be grouped into one of the d damage categories of EI-99. Each one of those damage categories has appropriate units which can be seen elsewhere [16].

DCd   I LCIAw d

(11)  Finally, EI-99 index Single Score (SSs) for the system s is given d

SS s       DCd s s

(12)

Values for  and  can be found elsewhere [16], while it is automatic in SimaPro. Index SSs is a measure of the environmental cost of a particular system s3. Equation (12) returns values which unit is point/(functional unit). Each SSs point represents ‘one thousandth of the annual environmental load of one average European inhabitant’. Physical assets (equipment, vehicle, buildings and land) may be associated its environmental costs to ‘environmental investment cost’ (or ‘environmental liabilities’), while operations environmental costs are set in annual basis. As it may be nowadays assigned a monetary value as ‘compensation tax’ to any environmental damage, at least in principle, then the environmental cost of physical assets mirrors on a monetary value which may be levelized on annual basis (or ‘amortized annually’) by the same above capital recovery factor crf. Then total environmental annual cost – i. e. the annual environmental cost of inflow of natural resources, in case, purchase of electricity and fuel; outflow of products, in case, surplus electricity; and the amortized environmental cost of physical assets –, is written as the linear relation

TACenv  SS P P  SS L L  SS S S 

 SS Acrf  A (13) In general, experience has shown the environmental costs of losses and the environmental cost of physical assets have negligible contribution to total environmental cost, if coted to the environmental costs of production of the plant along its working life span [1, 4, 5, 16], then the parcels relative to L and A in (13) are negligible for practical means.

4.2.3 Dual-objective optimization on Pareto Optimal map The last phase of a LCA study is LCAI (interpretation) phase. In this section, it is focused the use of Pareto optimal map on optimization of the objectives of the project as a LCAI phase procedure. On given two independent objectives TACmon and TACenv, a Pareto optimal solution achieved from a set of dual-objective feasible solutions, moving to another feasible solution implies losing at least In case of the optimization of internal costs, assessed by (5), Xi = F.i = SSi for flows, and Wj = A.j = SSj for physical assets. However, care must be taken when accounting energetic terms in LCA, as in this case it must be done in exergy.

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one objective [4, 5]. Many optimal solutions may be achieved among a couple of objectives (TACmon , TACenv) ranging from TACmon.max to TACenv.max, such a way when plotted the set of optimums trace a geometric locus named ‘Pareto Frontier’ of trade-off optimal solutions, i. e. the Pareto optimal map (Fig. 2). The dual-objective optimization problem can be addressed as [5] (14) DO  TACmon v  , TACenv v   s.t. Restrictions on design variables v Other feasible restrictions Where both objective-functions are v-dependent, according to (9) and (13) – i. e. these variables relates to a vast set of restriction equations concerning mass and energy balances, number of units (related to integer variables), maximum (or minimum) power assignable per unit equipment (or couple, or triple, etc.), operational restrictions, legal restrictions from (1) and (2), and so on, such a way all restrictions shall keep first degree linearity (upon integer and continuous variables). It is important to mention that no restrictions must be assigned to properties in order to keep the first degree linearity for the model (i. e. the properties are assigned given values per linear interval). Min

Fig. 2. Pareto optimal map showing maximum trade-offs and regions. Given (14) is linear, it is solved by a MILP technique; otherwise it demands a much more difficult MINLP technique [13]. The optimization of the TACmon function gives answers to other questions such as synthesis, operations planning, etc. For such a case data for hourly and daily consumption of electricity, cooling and heat sanitary water for one precedent year is demanded. From such data representative working and non-working days are elected, as to simplify computational works [4, 15, 20]. In the context of the current problem, an appropriate solution to (14) is the   constraint method, suitable for problems which both linear equations and nonlinear equations, for what mathematic basement can be found elsewhere [4, 5]. Nonlinearity would be the aforementioned discussed case for the properties, but also if the above capital recovery factor crf did not assume constant values for interest rate and life span. The   constraint method consists of formulating a surrogate optimization problem which in case of dual-objective optimization one of the objective-functions turns into another upper restriction to the remaining objective-function. The problem is written as Min TACmon v  (15) s.t. TACenv v   

sup   sup Restrictions on design variables v Other feasible restrictions The methodology to be followed consists of the following steps [4, 5]:  Firstly find TACmon.max and TACenv.max by solving the two linear programming model

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Min

TACmon v 

(16)

s.t.

Min

Restrictions on design variables v Other feasible restrictions (17) TACenv v  s.t.

Restrictions on design variables v Other feasible restrictions The solutions from (16) and (17) show up the trade-off between the couple (TACmon.max , TACenv.min) and the couple (TACmon.min , TACenv.max), i. e. the maximum value of one of the functions locate where one have the minimum value of the other function (Fig. 2).  Secondly, partition the interval [TACenv.min , TACenv.max] (Fig. 2) into a number of sub-intervals (say, 20 to 50), in which every extreme point is a value of  .  Solve (9) and (13) for each  and plot the Pareto map to analyse the trade-off consequences.

4.3 Application case The herein methodological guidelines are to be applied to a 150 MWe thermoelectric power plant owned by the public facility AESA, in Manaus, Brazil, which is powered by ten prime Diesel engines. The analysis intends to assess the upgrading of the overall technical, economic and environmental efficiencies of the plant, as to supply cold water for air conditioning and hot water to industrial and sanitary uses of neighbour industrial power plants, both working fluids being pumped through a pipeline grid. At present time the study collects data from site and the plants.

5. Conclusions This article is intended to set guidelines to CHCP projects, by the economic and the environmental standpoint, in Brazilian Amazon. Big consumers such as hospitals and hotels are natural candidates to installation of CHCP plants. The work methodizes how the economic and environmental issues may unite towards feasible projects upon the current social, economic and legal issues. Specifically in the case of the environmental assessment, the completeness of the LCA method allows to search minimum environmental cost. As it is shown on Table 1, the city of Manaus collects qualitative reasoning to the EI-99 LCIA method. Differently of other regional cities, Manaus sets the highest industrial electric demand for air conditioning, what justifies the installation of CHCP plants by that sector – what is already focused by Brazilian CHP legislation – or, what would be an evolution, the conversion of the local power plants to CHCP. Two assessment approaches are suggested by this paper: the one in which focuses the optimization of exergetic, economic and environmental internal costs of the CHCP plant, and another one which seeks to optimize the economic and environmental relations of the plant with the technosphere and the environmental surroundings by a Pareto optimal approach. The former is simpler to implement, as it may be done by conventional computational software, e. g. Excel or EES. However the latter is more computational intensive, what demands mathematical background and the help of more sophisticated computer software, e. g. LINGO. Both approaches are proposed as methodologies the modern planning engineering should adopt not only in Amazon but in whole Brazil also.

Acknowledgments The first author of this work is grateful for the support of the Brazilian Committee for Improvement of Higher Education Personnel (CAPES), during his research stage at Zaragoza University, Spain.

Nomenclature AESA ANEEL

Amazonas Energia S. A. Agência Nacional de Energia Elétrica

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A AICA [A] B* B BHS BIG BNDES C c CAC CCC CHP CHCP COM crf D E ECT EI-99 F f GNP GRP GSD IPCC k L LCA LCI LCIA LCAI MILP MINLP P p S T t TJLP UN VAC

Asset (equipment, real state or land), in (respective functional unit)/yr Amortization of investment cost of asset, $/yr Incidence matrix of flows (+1 for inflow, 1 for outflow and 0 for no flow) Vector of exergetic costs of flows, kW Vector of exergies of flows, kW Balbina Hydro Station Brazilian Interconnected Grid Brazilian Social and Economic National Development Bank Exergoeconomic cost of flow, $/s or $/h or $/yr Unit monetary cost (exergoeconomic, $/kW; electricity and heat, $/kWh; fuel, $/kg) Constant monetary annual cost, $/yr Fuel consumption fund Combined heat and power (cogeneration) Combined heat, cooling and power (trigeneration) Operation and maintenance cost, $/yr Capital recovery factor Vector of destroyed exergy, kW Cumulative cogenerated energy accounted for the last twelve months, kWh/yr Exergetic cost theory Eco-indicator 99 LCA method Fuel consumption, kg/yr Factor of operation and maintenance cost, impact factor of EI-99 (relating one impact element to one of the EI-99 impact categories) Gross National Product Gross Regional Product Goal and scope definition phase of LCA United Nations Intergovernmental Panel on Climate Change Unit exergetic cost of flow, kW/kW Excess produced heat, kWh Life cycle assessment (or analysis) Life cycle inventory phase of LCA Life cycle inventory analysis phase of LCA Life cycle assessment interpretation phase of LCA Mixed integer linear programming Mixed integer nonlinear programming Purchase electricity, kWh Life cycle of an asset, yr Surplus electricity, kWh Total cost (exergetic, kW; exergoeconomic, $/h or $/h or $/yr) Discount ratio, yearly % Long Term Interest Ratio of BNDES United Nations Variable monetary cost, $/yr

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X Exergetic, exergoeconomic or environmental cost of flow yr Year (as unit) Y Vector of amortization of assets (exergetic, kW; monetary, $/s or $/h or $/yr) W Exergetic, exergoeconomic or environmental cost of external resource Z Monetary amortization of an asset on its life span, $/s or $/h or $/yr Greek symbols [] Production matrix  Fuel weighting factor  Vector of environmental costs of external natural resources, (EI-99 point)/yr  Cogeneration factor  Unit environmental cost of flow, (EI-99 point/kW)  Generic coefficient of the proportional part of any first degree equation  Normalization factor  Vector of external resources (exergetic, kW; monetary, $/s or $/h or $/yr)  Vector of environmental costs (physical flows and assets), (EI-99 point)/yr  EI-99 weighting factor Subscripts and superscripts A Relative to assets i Flows j Assets (equipment, real state and land) em Relative to electromechanical F Relative to physical flows f Relative to fuel K Relative to exergetic (ex), or monetary (mon), or environmental (env) total cost Relative to element k which belongs to impact category w k,w L Relative to excess produced heat OM Relative to operation and maintenance P Relative to electricity bought from the grid S Relative to surplus electricity t Relative to thermal energy

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