Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects

Applied Energy 87 (2010) 3642–3651

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects Hongbo Ren a,*, Weisheng Zhou b, Ken’ichi Nakagami b, Weijun Gao c, Qiong Wu c a

Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, 603-8577 Kyoto, Japan College of Policy Sciences, Ritsumeikan University, 603-8577 Kyoto, Japan c Faculty of Environmental Engineering, The University of Kitakyushu, 808-0135 Kitakyushu, Japan b

a r t i c l e

i n f o

Article history: Received 23 February 2010 Received in revised form 10 June 2010 Accepted 13 June 2010 Available online 13 July 2010 Keywords: Multi-objective optimization Distributed energy system Operation Economic objective Environmental objective Sensitivity analysis

a b s t r a c t Along with the continuing global warming, the environmental constraints are expected to play more and more important role in the operation of distributed energy resource (DER) systems, besides the economic objective. In this study, a multi-objective optimization model is developed to analyze the optimal operating strategy of a DER system while combining the minimization of energy cost with the minimization of environmental impact which is assessed in terms of CO2 emissions. The trade-off curve is obtained by using the compromise programming method. As an illustrative example, the DER system installed in an eco-campus in Japan has been selected for case study. The distributed technologies under consideration include photovoltaics (PV), fuel cell and gas engine for providing electrical and thermal demands. The obtained results demonstrate that increasing the satisfaction degree of economic objective leads to increased CO2 emissions. The operation of the DER system is more sensitive when environmental objective is paid more attention. Moreover, according to the sensitivity analysis, the consideration of electricity buy-back, carbon tax, as well as fuel switching to biogas, has more or less effect on the operation of DER systems. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction In recent years, the distributed energy resource (DER) system has been recognized as an efficient, reliable and environmentally friendly alternative to the traditional energy system [1,2]. Usually, DER means small-scale power generators located within the electric distribution system at or near the end-users. A DER system can employ a wide range of technologies including: combined heat and power plants (CHP), photovoltaic systems (PV), small wind turbines and other systems using renewable energy sources (e.g. biogas digesters) [3]. They are expected to be widely spread to increase the efficiency of energy supply and to address global environmental problems. However, as the penetration increases, many problems become obvious. In addition to the technical, commercial, and safety issues due to the connection of generations within the distribution system, the use of renewable sources and CHP units usually adds more specific issues related to the actual methods used. One of which is the unbalance of energy demands between supply and demand sides. Generally, electrical and thermal loads usually change with the time, while the supply of electricity and heat is stable in some * Corresponding author. Tel.: +81 075 466 3348; fax: +81 075 465 8245. E-mail address: [email protected] (H. Ren). 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.06.013

situation for certain DER technologies. Therefore, for a specific DER system comprising various technologies, the operation should be optimized taking into consideration varying electrical and thermal demands [4,5]. Much research has been reported on this topic, and most of which focused on the optimal operation of a specific DER technology (CHP system mainly) from the economic point of view [6–14]. However, nowadays, environmental issues are becoming increasingly important and the operation problem becomes more challenging when the environmental burdens should be minimized at the same time when costs, too, are to be minimized. This is because the minimization of costs and environmental burdens are usually contradictory objectives, as it is often expensive to utilize environmentally friendly technologies. In order to deal with such a difficult problem, an innovative method, multi-objective optimization, is usually employed [15– 21]. Moreover, Kavvadias and Maroulis [22] developed a multiobjective optimization method for the design of trigeneration plants based on economic, energetic and environmental criteria. Mavrotas et al. [23] presented an integrated modeling and optimization framework for the CHP system planning in large consumers of the services’ sector based on mathematical programming considering the minimization of cost and the maximization of demand satisfaction. Becerra-López and Golding [24] promoted a multiobjective optimization method for the capacity expansion of

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regional power generation systems. Haesen et al. [25] introduced a methodology for long-term planning of DER placement and sizing while accounting for multiple objectives. According to above studies, currently, the multi-objective optimization method has been employed mainly for three possible applications: first, the plan and operation optimization for single DER equipment, especially the CHP plant; second, the design of local power system from the viewpoint of utility gird; and third, the optimization of distribution gird while including DER technologies. However, for a specific DER system integrating CHP and renewable technologies, the research on its optimal operation from the viewpoint of end-users while considering conflict objectives has never been done. In addition, the influence of some promotion policies on the trade-off relationship between economic and environmental performances is uncertain. In this study, a multi-objective linear programming (MOLP) methodology is applied to determine the optimal operating strategy for a DER system where various DER technologies are installed and could be exploited to satisfy part of the energy needs. These resources are examined from all aspects using specific mathematical model. Considering the existing constraints, a series of solutions are derived providing planners the flexibility to choose the appropriate solution with respect to the given situation. As an illustrative example, the DER system in Kitakyushu Science and Research Park (KSRP), Japan has been discussed and the trade-off relationship between economic and environmental performances is analyzed. In addition, in order to understand various aspects affecting the operation of DER systems, the effects of introducing electricity buy-back and carbon tax, as well as fuel switch to the biomass energy, are discussed. 2. Modeling and optimization 2.1. Concept of the multi-objective optimization problem As a complex decision-making problem, the operational plan of DER systems inherently involves multiple and conflicting objectives. Therefore, mathematical models become more realistic if dis-

Input Data Customer information Technical information Market information

Economic objective Min=Cost

Main constraints Energy balance Equipment availability

Pure economic optimization

Environmental objective Min=Emissions

Pure environmental optimization

Trade-off Analysis

Formulation of MOLP problem

tinct evaluation aspects, such as cost and environmental concerns, are explicitly considered by giving them an explicit role as objective functions rather than aggregating them in a single economic indicator objective function. This task can be formulated as a multi-objective problem whose solution vector is not determined by a unique combination but rather by a set of optimal arrays. The points of solution form which is commonly called the non-dominated or Pareto optimal set, whose graphics for three dimensions is a surface, and when only two objectives are involved, it degenerates to a trade-off curve that constitutes the range of optimal choices available for planners. For each one of the Pareto arrangements, it is impossible to improve one objective without worsening another. The determination of the optimal arrays to plan the integrated energy system is basically a linear programming problem that can be approached with several numerical techniques [26–28]. Fig. 1 illustrates the general flow chart of the multi-objective optimization model for the operation of DER systems. By integrating the necessary input information, the model can provide decision support to planners by rationalizing the comparison among different alternative solutions, thus enabling the planners to grasp the inherent conflicts and trade-offs among the distinct objectives for selecting a satisfactory compromise solution from the set of non-dominated solutions. 2.2. Mathematical formulation of the MOLP model The MOLP model aims at providing decision support to planners for selecting the operating levels of various generation units throughout the planning period. 2.2.1. Objective functions The MOLP model considers two objective functions, which quantify the total energy cost and the environmental impact, both to be minimized. The environmental impact objective function attempts to capture the increasing awareness of environmental externalities resulting from energy generation. By keeping the objectives separate, trade-offs between different objectives are clearly illustrated, and more informed design decisions can be made. In this case, it allows to find and to rank the best integrated generation technology solutions from the super structure, which are both cost effective and less polluting. The solutions returned by the model are an approximation to the optimal – such a solution cannot be made less polluting without being more costly; or cheaper without emitting more. 2.2.1.1. Economic objective function (annual energy cost minimization). As shown in Eq. (1), total energy cost fC involves the following terms: cost for the consumed fuels C F ; operation and maintenance (O&M) cost of the generating units C O ; cost for purchasing electricity from the electric utilities C G , as well as the revenue from selling electricity from the on-site generation back into the grid C S :

Min f C ¼ C F þ C O þ C G  C S

The fuel cost is related to the fuel consumption in various generating units and is composed of monthly basic fee and volumetric fee, as shown in follows:

CF ¼

X

Fbasem

m

Non-dominate Solutions (Pareto Set)

þ

X X X X m

Final Solution

þ Fig. 1. Flow chart of the multi-objective optimization model.

ð1Þ

X u

d

h

Fpurm;d;h;u

P

u Dgeni;m;d;h;u

þ Esali;m;d;h



ai

i

!

 Fprim;d;h

ð2Þ

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651

where Fbase is the basic charge for fuel consumption, Dgen is the amount of power generation for on-site use, Esal is the amount of electricity sales, Fpri is the unit charge, and Fpur is the consumption of fuel for direct end-use. a denotes the efficiency of power generation. i, m, d, h, and u are respectively, technology indicator, month, day, hour and end-uses (including electricity, cooling, heating and hot water). The O&M charge is illustrated in Eq. (3). It is composed of fixed part which is proportional to the rated capacity of the equipment, as well as the variable part which is a function of the amount of power generation.

CO ¼

X

Dcapi  Domfi þ

i

XXXXX i

m

d

h

Dgeni;m;d;h;u  Domv i

u

ð3Þ where Dcap denotes the rated capacity of a specific equipment, Domf and Domv are the fixed and variable O&M costs, respectively. The cost for electricity purchase is shown in Eq. (4) and is composed of the demand charge and energy charge. The demand charge is proportional to the maximum electricity purchase in a month. The energy charge is calculated by multiplying the amount of energy consumed with the regulated electricity tariff.

CG ¼

X

X

Edem  max

m

þ

XXXX m

d

! Epur m;d;h;u

u2felectricityg

Epur m;d;h;u  Eprim;d;h

ð4Þ

u

h

where Edem is the demand charge, Epur is the amount of electricity purchased from the utility grid, and Epri is the regulated tariff rate for electricity purchase. The income from electricity sales is proportional to the amount of sold electricity, as shown

CS ¼

XXXX m

i

d

Esali;m;d;h  Sprii;m;d;h

ð5Þ

h

where Esal is the amount of electricity sales, and Spri is the price for selling electricity back to the grid. 2.2.1.2. Environmental objective function (annual CO2 emissions minimization). The environmental impact associates with the total CO2 emissions from the electric grid CEG , as well as that from consumed fuels CEF .

Min f E ¼ CEG þ CEF

ð6Þ

The CO2 emissions from electric grid are calculated by multiplying the total amount of purchased power with the carbon intensity of grid electricity, as shown

CEG ¼

XXXX m

d

h

Epurm;d;h;u  Ecin

ð7Þ

u

where Ecin is the carbon intensity of the grid electricity. It means the carbon emissions per unit of electricity generated within a specific utility grid in a given year. It can be measured in kg/kW h and obtained by investigation on the local utility grid. As shown in Eq. (8), the CO2 emissions from consumed fuels for both DER and non-DER uses are calculated by multiplying the total amount of fuel consumption with the carbon intensity of the specific fuel type. ! P X X X X  u Dgeni;m;d;h;u þ Esali;m;d;h  X CEF ¼ Fpurm;d;h;u þ m

 Fcin

d

h

i

ai

u

ð8Þ

where Fcin is the carbon intensity of the fuel consumed. It means the carbon emissions per unit of fuel consumption and is usually different for various fuel types. Generally, it can be measured in

kg/kJ and obtained by investigation on the local utility supplying specific kind of fuel. 2.2.2. Main constraints There are two categories of constraints related to: the energy balance between demand and supply, as well as the availability of generating units. 2.2.2.1. Energy balance. Reliability requirements are coped for by observing the principle according to which generators in operation must satisfy the instantaneous energy demand as well as provide a reserve margin. This leads to the following constraint:

Loadm;d;h;u 6 du 

X

!

Dgeni;m;d;h;u þ Epurm;d;h;u

i

þ cu  Fpurm;d;h;u þ ku 

X

Hreci;m;d;h;u

8 m; d; h; u

ð9Þ

i

where Load is the energy demand, Hrec is the amount of heat recovered from DER technologies. d, c and k denote the utilization efficiencies of electricity, purchased fuel and recovered heat for specific end-use, respectively. 2.2.2.2. Availability of generating units. As far as the operation of generating units is concerned, the power that can be generated by any type of unit cannot exceed its rated capacity affected by an availability factor. Details are shown as follows:

X

8 i; m; d; h

Dgeni;m;d;h;u þ Esali;m;d;h 6 Dcapi  Rnewi;m;d;h

ð10Þ

u

where Rnew is the availability factor of the renewable technologies (PV, wind, etc.). For the non-renewable technologies, it is fixed to be one. Additional constraint should be set to limit how much heat can be recovered from each type of technology, as shown in

X

Hreci;m;d;h;u 6

u

X

! Dgeni;m;d;h;u þ Esali;m;d;h

u



bi

ai

8 i; m; d; h ð11Þ

where b is the heat recovery efficiency of the DER technology considering combined heat and power. 2.3. Solution method There are a lot of methods for solving multi-objective optimization problems, such as compromise programming, global criterion method, and goal programming [29]. In this study, the developed MOLP model is programmed in the LINGO software and the compromise programming method has been employed to solve it [30]. To apply compromise programming, the decision model is modified so as to include only one objective. The aim in this method is to minimize the distance of the criterion values from their optimum values. Considering this, the decision problem is formulated as follows:

Min z ¼ /

ð12Þ

Besides the constraints illustrated above, the following constraints should be taken into consideration.

/ P ðfC  fC min Þ  ðwC =fC min Þ

ð13Þ

/ P ðfE  fE min Þ  ðwE =fE min Þ

ð14Þ

wC þ wE ¼ 1

ð15Þ

where / corresponds to the Tchebyshev distance, fC min and fE min are the optimum values of the two objectives when optimized

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651

Fig. 2. Image of the ecological activities introduced in KSRP.

independently, and wC and wE are corresponding weight coefficients reflecting the relative importance of the two criteria. The use of weight coefficients allows the planners to express their preferences regarding the criteria, and must satisfy the constraint as shown in Eq. (15).

3

Hourly load (MW)

electricity

cooling

heating

3. Illustrative example In this study, as an illustrative example, the DER system installed in KSRP, which is the first science and research park in Japan, has been selected for analysis [31]. Based on the concept to create a technology innovation cluster to create new environmental industries that will lead the world of the 21st century, various kinds of measures in which ecological consideration are incorporated have been introduced (see Fig. 2).

hot water

3.1. Energy demand profiles 2

1

0

1 4 7 10 13 16 19 22 1 4 7 10 13 16 19 22 1 4 7 10 13 16 19 22 08-Jan 30-Apr 27-Aug

Hours Fig. 3. Hourly load profiles in various seasons.

Primary energy

Grid Utilities

Solar energy

PV system (153 kW) Fuel cell (200 kW)

City gas

Gas engine (160 kW) Primary energy

In this study, the hourly load demands of 8760 h for electricity and thermal (including heating, cooling and hot water) are employed according to data measured in 2006. The electrical and thermal demands show considerable hour by hour fluctuation. The nature of the variations depends on a large number of factors. To illustrate this, for simplification, three days have been selected from the annual dataset to represent an extreme day in the heating season, a day in the mid-season, and a summer day (see Fig. 3). The winter day, 8th January, has the highest thermal requirement of the year, and the daily heat-to-power ratio is 3.9. The thermal demand occurred from morning until night and the coincidence

Electricity distribution center

Electricity

Absorption chiller and heater

Cooling

Heat exchanger

Heating

Hot-water Gas boiler Electricity

Fig. 4. Schematic illustration of the DER system in KSRP.

Thermal

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651

Table 1 Characteristics of DER technologies installed in KSRP. Item

CHP

Capacity (kW) Power efficiency (%) Heat efficiency (%) Current operation schedule

PV

Fuel cell

Gas engine

Single-crystal

Multi-crystal

200.0 40.0 20.0 24 h

160.7 28.7 47.7 8:00–22:00

129.6 13.3 – 24 h

23.4 7.2

between the thermal and power demand is high – all of the electrical demand occurring coincidentally with the thermal demand. The spring day, 30th April, has a low thermal demand (hot water only) and on this day the daily heat-to-power ratio is only 0.2. The summer day, 27th August, has an intermediate thermal requirement (cooling mainly) and the daily heat-to-power ratio is 2.1. The thermal demand occurs mainly during 9:00 in the morning through 20:00 in the evening.

3.2. DER system in KSRP As mentioned above, as one of the most important ecological activities, a distributed energy system incorporating fuel cell and gas engine CHP plants, as well as PV cells has been installed. As shown in Fig. 4, the insufficient electricity is provided by the utility electricity. The heat discarded from the fuel cell and gas engine is utilized to supply the heat load for heating, cooling and hot water. The insufficient thermal demands are served by a gas boiler. Detailed characteristics of various DER technologies are illustrated in Table 1.

3.3. Electricity and gas tariffs Another important input to the model is the market data, such as electricity and gas tariff rates. In this study, a commercial time of use (TOU) tariff structure of Kyushu electric power co., Inc. is adopted [32]. As shown in Fig. 5a, structure of electricity tariff is composed of basic charge, daytime unit rate, night unit rate and peak charge.

Peak time rate Day time rate 0.11 $/kWh Night time rate 0.08 $/kWh 0.05 $/kWh

Base charge: 17.08 $/kW 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

(a) Electricity tariff structure (Commercial TOU agreement) Volume rate: 0.45 $/m 3 Base charge=fixed charge (725.75 $/month) + flow-rate tariff(7.51 $/m 3) + maximum demand season charge (0.01 $/m 3) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

(b) City gas tariff structure (Total energy system agreement) Fig. 5. Electricity and gas tariff structures in the local area.

Fig. 5b shows the structure of gas tariff [33]. The total energy system agreement which is suitable for distributed energy system has been employed here. It is composed of basic charge and volumetric charge. Basic charge is made up of monthly fixed charge, flow-rate tariff, as well as maximum demand season basic charge. 4. Results and discussion In this section, the approach described above is used to provide a satisfactory compromise solutions based on the MOLP model with a data realization. It should be indicated that in the following optimal calculation, all DER technologies are assumed to be able to operate at any time throughout the whole day. 4.1. Single objective optimization As mentioned above, the two objectives that have been considered are competitive, since a decrease of the one leads to an increase of the other. Table 2 displays the payoff matrix when each objective is optimized independently from the other, as well as the results of current situation. As to the current situation, fuel cell serves about 38.98% of the total electricity requirements, which is just second to the utility grid. When the economic objective is optimized, low cost operation is executed that may, however, leads to increased CO2 emissions since the conventional energy suppliers have more emissions and vice versa. Compared with the current situation, annual energy cost is reduced by 0.02 million dollars (2.82%), and annual CO2 emissions are also decreased by 0.05 millions of metric ton of CO2 equivalent (1.81%). In addition, it can be found that the operation hours of both fuel cell and gas engine are reduced, which leads to reduced electricity supply share correspondingly. When the environmental objective is optimized, compared to the cost optimization scenario, annual energy cost is increased by 0.03 million dollars (4.35%), and total CO2 emissions are reduced by 0.02 millions of metric ton of CO2 equivalent (0.74%). On the other hand, comparing with the current situation, total CO2 emissions can be reduced by about 2.53% but at the price of higher energy cost. Furthermore, in this scenario, it can be found that gas engine is operated during the whole year and supplied 27.93% of the total electrical requirements. The operation time of fuel cell is also increased but supplies less share of electricity load than the gas engine. This is because that gas engine has a higher total efficiency (76.4%) than fuel cell (60.0%), which leads to less carbon emission rate. Looking into the results, it can be also found when the operation mode is changed from economic optimization to environmental optimization, instead of the utility grid, the DER technologies become the dominant supplier of electrical requirements. Therefore, it can be deduced that compared with the economic merits, the DER technologies contribute more environmental merits. Fig. 6 illustrates how the various technologies are operated to satisfy the electrical requirement while cost minimization is considered. It can be found that fuel cell and gas engine are operated only during 8:00–22:00 through the whole year. This is because of the relatively high electricity tariff during this period. On the contrary, in the night time, from the economic point of view, the customer prefers to purchase from the grid rather than generate on-site. Furthermore, the figure shows that on winter and summer days, both fuel cell and gas engine operate at the rated capacity. However, on a spring day (30th April), the fuel cell operates near the rated capacity and gas engine only supplies marginal electricity at some peak hours. On the one hand, this is because of the relatively low electrical requirement on a spring day (see Fig. 3); on the other hand, because of the small thermal load, the fuel cell with a high power efficiency and low heat efficiency (low heat-to-power

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651 Table 2 Results with each type of operation mode. Total CO2 emissions (million tons)

Share of electricity supply (%) Utility

PV

Fuel cell

Gas engine

Fuel cell

Gas engine

Current situation Min fC Min fE

0.71 0.69 0.72

2.77 2.72 2.70

39.38 59.09 41.83

3.45 3.45 3.45

38.98 21.85 26.79

18.19 15.61 27.93

8760 5125 6866

5110 4864 8760

1000

PV

Grid

Fuel cell

Gas engine

750 500 250

0

1 4 7 10 13 16 19 22

08-Jan

4 7 10 13 16 19 22

4 7 10 13 16 19 22

30-Apr

27-Aug

Fig. 6. Electric supply and demand matching for the DER system (energy cost minimization).

Ratio of CO2 emissions (fE-fEmin)/fEmin (%)

Total cost (million dollars)

Share of electricity supply (kW)

Type of solution

1 0.8 K

Operation time (h)

Annual energy cost minimization wE=0

J I H G F E

0.6 0.4

Annual CO2 emissions minimization wc=0

D C

0.2

B A

0

0

1

2

3

4

5

6

Ratio of annual energy cost (fC-fCmin)/fCmin (%)

Share of electricity supply (kW)

Fig. 8. Trade-off relationship between economic and environmental characteristics.

1000

PV

Grid

Fuel cell

Gas engine

750 500 250

0

1 4 7 10 13 16 19 22

4 7 10 13 16 19 22

4 7 10 13 16 19 22

08-Jan

30-Apr

27-Aug

Fig. 7. Electric supply and demand matching for the DER system (CO2 emissions minimization).

ratio) is preferred. Therefore, for this scenario, fuel cell is the dominant DER technology and gas engine is employed as a supplement. As to the scenario of environmental optimization, Fig. 7 shows the energy balance on three typical days. The operation schedule is quite different from the economic optimization scenario. Gas engine becomes the dominant DER technology and operates around the whole year. Fuel cell is employed on winter and summer days but not on a spring day. Looking into the figure, it shows that during the night hours on a winter day, all the electrical requirements are served by the DER technologies. It means that although the electricity charge is relatively low in the night, because of the reasonable environmental merits, DER technologies become popular instead of the utility grid. 4.2. Multi-objective approach results The MOLP solution for the operational optimization of the DER system produces a Pareto optimal front, as shown in Fig. 8, generated by 11 possible non-inferior solution points (or optimal operational strategies) for the competing objectives including energy cost and CO2 emissions. The solution points A to K correspond to the weight coefficient of the economic objective at the range of

0–1. As observed, a high reduction in the energy cost is gained if the selection of solutions passes from the right extreme (CO2 emissions minimization) to point B than when it changes from point B to the left extreme (energy cost minimization). This means that passing from solution A to B provides a small increment in the CO2 emissions but produces a high decrease in the energy cost. In order to understand the operational strategy that affects the economic and environmental characteristics, Fig. 9 illustrates the optimal operating results at various trade-off points. In general, as the increase of economic objective satisfaction degree (from A to K), the share of electricity requirement from utility grid has an increase from 41.8% to 59.1%. Correspondingly, the share of thermal load from recovered heat is decreased from 42.8% to 26.5%. In addition, looking into the figure, it can be found that as the degree of economic objective satisfaction increases from 0 to 0.5 (from A to F), the share of thermal load from recovered heat has a decrease of 11.8%. However, when the satisfaction degree rises from 0.5 to 1 (from F to K), the recovered heat share is reduced by only 3.2%. This means that as more attention is paid to the environmental performance (near point A), the operation becomes more sensitive to the change of satisfaction degree of the objective. Furthermore, according to the results obtained and presented in Fig. 9, the operating hours of gas engine reduce from 8760 to lower than 5000 as the objective is changed from emissions minimization to cost minimization. As to the fuel cell, when the satisfaction degree of economic objective increases from 0 to 0.1 (from A to B), the running hours have a sharp decrease from 6866 to 5125. However, as the further increase of the satisfaction degree, the running hours keep at a steady level. It means that compared with fuel cell, gas engine is more sensitive to the change of optimization objectives. This is because on the one hand, as the environmental objective is paid the main attention, gas engine has longer running time than fuel cell due to its higher total efficiency which is the determining factor of the environmental performance; on the other hand, as the economic objective is focused, the running hours of gas engine are reduced below that of fuel cell due to its lower power efficiency which is recognized as the main factor affecting the economic performance.

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651

Less energy cost

Coefficient wc

Less CO2 emissions 1.0 0.5 0.0

Annual operating hours

Fuel cell

Gas engine

8000 7000 6000 5000 4000

Recovered heat

Direct gas combustion

Share of thermal load

100% 80% 60% 40% 20% 0%

PV

Grid

Fuel cell

Gas engine

Share of electricity load

100% 80% 60% 40% 20% 0%

A

B

C

D

E

F

G

H

I

J

K

Fig. 9. Optimal operating results at various trade-off points.

4.3. Effect of introducing electricity buy-back In order to promote the spread of distributed energy systems, electricity buy-back has been available in many countries. In Japan, the buy-back price for electricity from fossil fuel and biomass energy systems is between 0.03 $/kW h and 0.06 $/kW h. However, for the electricity from PV system, the buy-back price is relatively high, which is about 0.24 $/kW h [32]. As the introduction of Renewables Portfolio Standard (RPS) policy and Green Power Certification System, the electricity buy-back price is thought to have further increase. In this study, a uniform buy-back price is assumed for the electricity out of the DER system and the effect of price on the operation is discussed, while considering the trade-off between economic and environmental objectives. As shown in Fig. 10, when the excess electricity is bought back by the utility grid for free, as the increase of satisfaction degree of economic objective, the power generation from DER technologies is decreased and all for on-site use. On the contrary, while considering a buy-back price of 0.1 $/kW h, after the satisfaction degree of economic objective is over 0.1, as the increase of satisfaction degree, total power generation from DER also increases. However, the on-site electricity consumption from the DER system is reduced. This is because the introduction of electricity buy-back makes

the power generation out of DER more economic for sales than for on-site use. Furthermore, as the buy-back price increases to 0.2 $/kW h, the DER system begins to sell electricity as long as the satisfaction degree of economic objective is increased to 0.1. As the further increase of the satisfaction degree to 0.4 or more, the sold electricity even exceeds the electricity for on-site use. In addition, at the same satisfaction degree of economic objective, the rise of buy-back price does not always lead to the increase of power generation from DER technologies. For example, when the satisfaction degree is between 0.1 and 0.4, the total on-site power generation is decreased as the buy-back price increases from 0 to 0.1 $/kW h. This is because the relatively low price does not make the electricity sales popular when economic performance is not considered as the main objective. Therefore, as more attention is paid to the economic objective, the operation of DER system becomes more sensitive to the buy-back price, which affects the energy cost only. 4.4. Effect of introducing carbon tax In order to realize a low-carbon society, besides the promotion of renewable energy in a direct way (e.g. subsidies), some other policies, for example, the carbon tax, are also examined by the

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H. Ren et al. / Applied Energy 87 (2010) 3642–3651

6

Grid purchase

DER generation for on-site use

DER generation for sales

Electricity load (GWh)

4

2

0

-2

-4

P1: Buy-back price 0 $/kWh P2: Buy-back price 0.1 $/kWh P3: Buy-back price 0.2 $/kWh P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

0

0.1

0.2

0.3

0.4

P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3 P1 P2 P3

0.5

0.6

0.7

0.8

0.9

1

Degree of economic objective satisfaction Fig. 10. Results of electricity balance regarding degree of economic objective satisfaction at various electricity buy-back prices.

60 Carbon tax rate

0 $/kg-C

0.1 $/kg-C

0.2 $/kg-C

50

40

0

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mance. As discussed above, the operation of DER technologies has larger environmental effect than the economic one. Therefore, when a relatively low carbon tax is introduced, the economic benefits translated from the environmental merits of DER technologies cannot make up the economic losses due to the increased DER operation, which leads to reduced operation of DER technologies. However, as carbon tax rate is relatively high, the economic benefits from reduced CO2 emissions will exceed the economic losses, which results in increased operation hours of DER technologies. According to above discussion, unless the tax rate is relatively high, the introduction of carbon tax does not stimulate the operation of DER technologies. In addition, because of the low share of carbon tax cost among the total energy cost, the effect of carbon tax on the operation of DER system is marginal. 4.5. Effect of fuel switching to biogas As one of the ways to increase the share of renewable energy in total energy supply, biomass is considered to be the most promising renewable energy sources. The most effective method for biomass utilization is gasification and used as the fuel for on-site CHP plants. On the one hand, by installing the gasification equipments beside the biomass resources and the DER equipments near the energy demands, it will be easier for the transportation of biogas rather than the biomass resources. On the other hand, the small

Electricity share from DER (%)

Electricity share from DER (%)

government. In Japan, the carbon tax research committee has studied the possible tax rate between 0.06 and 0.15 $/kg C, and analyzed its effect on the reduction of carbon emissions [34]. The Ministry of the Environment also said in a statement that the tax should be 0.02 $/kg C [35]. However, in some European countries, relatively high carbon tax has been introduced. For example, in the Netherlands, the tax rate is 0.05 $/kg C for electricity. Furthermore, a higher tax rate of about 0.2 $/kg C is adopted in Sweden for natural gas [36]. In this study, considering a more and more severe global warming policy, the carbon tax rate is assumed to reach a value of 0.2 $/kg C and its effect is examined. As shown in Fig. 11, for various carbon tax rates, the electricity share from DER technologies decreases from 58% to 41% as the economic satisfaction degree increases from 0 to 1. At the two extreme points (cost minimization and emissions minimization), the DER equipments have the same operating strategies for different carbon tax rates. On the contrary, between the two extreme points, different operating strategies are adopted for various tax rates. For example, at the same economic satisfaction degree, when the tax rate increases from 0 to 0.1 $/kg C, the electricity share from DER decreases by about 1–2%. However, as the tax rate further increases to 0.2 $/kg C, the on-site electricity share shows a small trend of increase, compared to that at the tax rate of 0.1 $/kg C. This is because the carbon tax has direct effect on the economic performance but an indirect effect on the environmental perfor-

1

Degree of economic objective satisfaction wc Fig. 11. Results of electricity share from DER technologies regarding degree of economic objective satisfaction at various carbon tax rates.

80 City gas

Biogas with the same price

Biogas with a half price

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50

40 0

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Degree of economic objective satisfaction wc Fig. 12. Results of electricity share from DER technologies regarding degree of economic objective satisfaction for different fuel types.

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DER equipments make it feasible to generate power effectively and reduce the collection range of biomass resources which reduces the transportation cost. In this study, the hypothetic switching from city gas to biogas is discussed and its effect on the operation of DER system is evaluated. According to the results presented in Fig. 12, the fuel switching to biogas leads to increased electricity share from DER technologies. When the environmental objective is optimized, the DER technologies serve about 71% of the total electricity requirements while using biogas. Unless the economic satisfaction degree increases to 0.9, the share of DER electricity keeps at a relatively high level. However, when the single economic objective is optimized, the share of DER electricity has a sharp decrease to about 41%, which is the same as the case using city gas. This means that the fuel switching to biogas results in great environmental effect on the DER system. Unless the environmental objective is omitted absolutely, the introduction of biogas leads to obvious change of the operating strategy for the DER system. In addition, if the biogas is adopted with a lower price which is half of current city gas price, the electricity share from DER technologies will keep around 70% for all the economic satisfaction degrees ranging from 0 to 1.

5. Conclusions In this study, a MOLP model is developed for identifying the operational characteristics of a distributed energy system, in order to meet the energy demands of a local area while considering both economic and environmental objectives. In the present case study, the compromise programming method has been implemented in order to select the final operating strategy from the set of possibly optimal solutions. In addition, the effects of introducing electricity buy-back and carbon tax, as well as fuel switching to biogas, on the operation of DER system have been discussed. Regarding the specific case study, the following conclusions can be deduced: (1) Compared with pure economic optimization, the consideration of environmental objective results in increased share of electricity from DER equipments, and correspondingly less CO2 emissions but increased total energy cost. (2) The change of optimization objective leads to the shift of optimal operation schedules. Compared with current situation, the operation time of DER equipments is reduced with the economic objective, but is increased with the environmental objective. Furthermore, as energy cost is paid more attention, fuel cell is the dominant DER technology; contrarily, when emissions are taken as the main role, gas engine becomes dominant. (3) As more attention is paid to the environmental performance, the operation becomes more sensitive to the change of satisfaction degree of a single objective. In addition, compared with fuel cell, gas engine is more sensitive to the change of optimization objectives. (4) The introduction of electricity buy-back increases the electricity share from DER technologies. As more attention is paid to the economic objective, the operation of DER system becomes more sensitive to the buy-back price. (5) Unless a relatively high tax rate is introduced, the adoption of carbon tax has marginal influence on the operation of DER systems. (6) Fuel switching from city gas to biogas results in great effect on the operation of the DER system due to its reasonable environmental merits. In the following study, besides the operation strategy, the technology combination of the DER system, as well as the number and

capacities of each kind of equipment selected should be integrated into the optimization model.

Acknowledgements This research has been supported by Ritsumeikan Global Innovation Research Organization Fund and Global Environment Research Fund by the Ministry of the Environment Japan (E-0804). Acknowledgement is also due to two anonymous reviewers who provided us with helpful comments.

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