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Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market
Applied Energy xxx (2015) xxx–xxx
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market q Federico de Bosio, Vittorio Verda ⇑ Energy Department, Politecnico di Torino, Italy
h i g h l i g h t s Thermoeconomic analysis of the off-design operation of a HPP-CAES is performed. An optimal strategy for air compressor operation is selected. Island mode and grid connected operations are compared. Economic analysis of the systems are performed considering the IPEX Market.
a r t i c l e
i n f o
Article history: Received 9 May 2014 Received in revised form 13 January 2015 Accepted 13 January 2015 Available online xxxx Keywords: Energy storage CAES Compressed air Thermoeconomics
a b s t r a c t Energy storage is regarded as a key factor to allow significant increase in the percentage of electricity generation from renewables. One of the most critical aspects related with energy storage is its economic feasibility, which intrinsically involves the analysis of the off-design conditions and the evaluation of the operating strategies using proper methodologies. This paper considers a promising system for mechanical energy storage constituted by a Compressed Air Energy Storage (CAES) integrated with a Hybrid Power Plant (HPP) and coupled with a wind farm. This system is modeled considering the South of Italy as the possible location. The HPP-CAES is simulated to operate on the Italian Power Exchange market, for one year, implementing suitable selling strategies. Cost analysis is performed using a thermoeconomic approach. Results show that reduced operating hours and large variations in the electricity production of the wind farm make the HPP-CAES cost-effective only when it is operated with the goal of solving local imbalances of the grid. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The share of renewable energy has significantly increased in the last few years, reaching a percentage of about 20% in the global electricity production. Similar trend is expected in the next years and a percentage of about 25% is foreseen in 2018 [1]. Hydro is the main renewable power source, with about 1138 GW installed, which is about 67% of the installed renewable power. Nevertheless the fastest-growing sources are wind and solar PV. Wind is expected to increase its installed capacity from the current 321 GW to about 559 GW, while solar PV from the current
q This paper is included in the Special Issue of Energy Storage edited by Prof. Anthony Roskilly, Prof. Phil Taylor and Prof. Yan. ⇑ Corresponding author. Tel.: +39 011 564 4449; fax: +39 011 564 4499. E-mail addresses: [email protected] (F. de Bosio), vittorio.verda@polito. it (V. Verda).
128 GW to about 308 GW. This evolution in the power mix brings some management issues. Electricity generation depends in fact on the availability of the wind or solar energy resources. Production may be intrinsically shifted with respect to the demand, therefore a lag of several hours may emerge between supply and demand. In addition production may be intermittent. The electric system is balanced by Gas Turbines (GT) or Pumped Hydropower Storage (PHS) plants, capable of reacting promptly to load variations and variable production of wind or solar plants [2]. Emerging technologies like hydrogen storage or new large-scale batteries are expected to contribute to balance Supply and Demand, however, at present they are not yet economically viable and often impaired by a low number of charge/discharge cycles. Other systems, such as flywheels or super capacitors, have limited capacity. The gap between discontinuous production and demand can be bridged with the help of other energy storage technologies, such as Compressed Air Energy Storage (CAES) plants, which are smaller
http://dx.doi.org/10.1016/j.apenergy.2015.01.052 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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Nomenclature
Abbreviations A aftercooler AC air compressor AEP Annual Energy Production (MW h/y) APH air pre-heater B exergy flow (MW) c unit cost of exergy (€/MW h) C cost rate (€/s) CAR compressed air reservoir CC combustion chamber E energy (MW h) ENG primary energy consumption (MW h/y) EM Electric Motors FC annual cost of fuel (€/y) GT Gas Turbine h annual operating hours (h/y) HPP-CAES Compressed Air Energy Storage integrated with a Hybrid Power Plant I intercooler IPEX Italian Power Exchange
than PHS systems in terms of power size. CAES plants can contribute to the successful integration of large amounts of wind electricity production into the energy system [3,4]. The advantage of introducing CAES plants into an existing energy system is mainly dependent on their economic convenience. As a consequence, most analyses available in the literature are focused on the economic feasibility and the optimal operation. Cavallo [5] has analyzed the cost of electricity produced by hybrid wind/compressed air energy storage, showing that it is affordable in various economic contexts. CAES systems make wind energy competitive on the long term, as the problems related with uncertainty and transmission costs are significantly reduced. In [6], three different strategies for optimizing the operation of a CAES plant on the spot market are presented. The maximum net earnings can be achieved using an ideal strategy, that is not applicable in real operation. In contrast, the simplified strategies allow one to achieve slightly smaller net earnings but can be easily implemented. Zafirakis and Kaldellis [7] have analyzed a dual mode wind park-CAES operating in autonomous island grids. The analysis shows that, with respect to conventional peak demand power plants, this kind of systems allows lower electricity production costs and fuel consumption. Mason and Archer [8] have compared the combination of wind with natural gas combined cycles and with CAES. The first system is currently characterized by the lowest cost, but the second option becomes more convenient for higher cost of the natural gas. In [9], the wind farm and CAES system are optimized together, using the levelized cost of electricity as the objective function, while in [10], various configurations for a CAES system are analyzed and the corresponding energy efficiency is obtained. Marano and co-workers [11] performed the optimal management of a system composed by a CAES coupled with a wind farm and a photovoltaic plant using dynamic programming. The total operating cost is considered as the objective function to minimize. In [12], the rated power and capacity of a CAES system are selected in order to maximize the economical profits. Mauch and co-workers [13] have modeled a wind farm coupled with a CAES system operating in the day-ahead electricity market. The analysis shows that revenues are not sufficient to pay the investment costs
LCOE MGP MI MSD _ m n NG O&M p Pd Ps TCI W WACC Wel WT
Levelized Cost of Energy (€/MW h) Day-Ahead Market Intra-Day Market Ancillary Service Market mass flow rate (kg/s) lifetime (y) natural gas Annual Operation and Maintenance Expenditures (€/y) pressure (bar) demand met parameter spillage parameter Total Cost of Investment (€) mechanical power (MW) Weighted Average Capital Cost electric power (MW) wind turbines
Greek symbols g efficiency q density (kg/m3)
back. In [14], energy and economic analysis of a distributed CAES (DCAES) system is considered. The compressors are located close to a district heating network, in order to recover the heat available from compressed air cooling. The DCAES is more efficient than conventional CAES, but compressed air must be supplied to the cavern through a pipeline, therefore the economic performance strongly depends on the distance. In [15], an economic analysis of a Compressed Air Energy Storage (CAES) is conducted, with the aim of maximizing the profits from peak power sales. A central CAES system and a distributed CAES system are considered. Manchester and Swan [16] have analyzed the possibility to operate for one year a CAES system of small power size according to the regulation in force in Canada, showing that an increase of 30% in the incomes is obtained. In [17], the optimal operation of a conventional gasfired power system combined with a wind farm and a CAES is investigated using a mixed integer non-linear programming approach. Maximum profit and minimum cost are considered as the objective functions in the analysis. Yucekaya [18] has proposed a mixed integer programming approach to investigate the optimal operation of a CAES. Uncertainties in the forward prices and profits are considered in the analysis. In [19], a Low-Temperature Adiabatic Compressed Air Energy Storage system is introduced. With respect to high temperature CAES, this system is characterized by lower efficiency, but it is able to operate in a larger range of part load. As discussed in the present paper, partial load operation has an important economic impact on CAES systems. The convenience of installing a Hybrid Power Plant capable to store electricity by means of air compression (HPP-CAES) is analyzed in this paper, considering the regulation currently in force in Italy. A HPP-CAES combined with a wind farm is simulated to operate in the South of Italy, thus both wind and market data refer to this geographical region. The energy system has been modeled according to the size (240 MW) considered by the Italian electricity transmission grid operator (Terna S.p.A.) as able to effectively contribute to solving congestion problems in the South of Italy [20]. Therefore this is the nominal power assumed for the discharge section of the HPP-CAES. Such an analysis requires considering offdesign operations due to variable production of the wind turbines
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx
and the level of storage as well as time shift between storage and generation. To incorporate these features, cos analysis is performed using thermoeconomics [21]. To evaluate the economic convenience of the HPP-CAES, the plant is simulated to operate on the Italian Power Exchange market, for one year, implementing suitable selling strategies. Firstly, a scenario in which the energy storage device operates in the Day-Ahead and Ancillary Service Markets to satisfy the demand regardless of the price of sale and with no possibility to integrate the energy provided by the wind farm with electricity bought from the grid, has been considered. Subsequently, the possibility of expanding the contribution to the reduction of local imbalances between production and demand has been investigated, considering the option to buy electricity from the grid on the MGP market. 2. HPP-CAES architecture and operation The architecture of the HPP-CAES system considered in the present analysis is shown in Fig. 1. It consists of three sections: (1) charge section, (2) air compressed reservoir and (3) discharge section. The charge section is constituted by four air compressors (AC1–AC4) with three intercoolers (I1–I3) and one aftercooler (A). Compressors are powered with electricity provided by a wind farm with a nominal power of 270 MW. The use of intercoolers is convenient in order to reduce the compression work required in each compressor stage. Water, flowing in an open loop circuit, is used as the air coolant. After the compressors stages, an aftercooler cools down the air before entering the storage reservoir. According to a previous analysis performed by RSE [22], the optimal size of the air compressors for a similar system is 108 MW. Therefore, the charge section has a much smaller power (108 MW) than the
3
wind farm (270 MW) and the discharge section (240 MW), in order to cope with the wind velocity variations. The compressed air reservoir (CAR) has a volume of about 210,000 m3. The discharge section uses stored compressed air to produce electricity. This section is constituted by an air pre-heater (APH), a first combustion chamber (CC1), a high pressure gas turbine (HP GT) and a low pressure gas turbine (LP GT) with a second combustor (CC2) between them. During the discharge phase, air is extracted from the CAR and pre-heated in an APH where it receives heat from the flue gases that expand in the LP GT. Before the expansion, air reacts with natural gas in CC1 and CC2. Concerning the charge phase, pressure at the final stage of the compressors is kept constant whatever is the power provided to these components. A throttling valve located at the inlet of the CAR adjusts the air pressure according to the pressure in the cavern (point 10), that varies continuously with time. A variable configuration, similar to that suggested in [3], has been considered since pressure reduction caused by the valve is a source of irreversibility. A configuration with only three compressors in series and by-passing the last intercooler and compressor (point 6 reached at the outlet of AC4) is adopted as pressure in the reservoir is between 35 bar (minimum pressure imposed by design) and 55 bar. Pressure in point 9 is 55 bar. When this value is reached in the cavern, a configuration with the four compressors in series is adopted. Air pressure at the final stage (point 8) becomes equal to the maximum pressure allowed in the cavern (80 bar). Concerning the discharge phase, another throttling valve at the outlet adjusts the air pressure at 40 bar (point 12). As a consequence, air from the CAR can be extracted only if the pressure in the reservoir is higher than 40 bar. Pressure losses along the lines and the components are neglected (see for example [10]), therefore pressure at point 12 is equal to that at the inlet of the CC1 (point
Fig. 1. Scheme of HPP-CAES system.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx
13) and HP GT (point 14). This assumption is reasonable since the pressure drops in the heat exchangers are significantly smaller than in the valves at the inlet and outlet sections of the cavern. Combustors are fed with compressed natural gas. The GT is supposed to operate in ultrasonic conditions: this fact permits some simplifications regarding the control mode. At partial load, the natural gas burnt in the first combustor varies according to the power request, while the air mass flow rate extracted from the CAR is nearly constant. The inlet temperatures at each GT, at nominal power, are set to 1100 °C and 1200 °C respectively (point 14 and 16). In this configuration, due to the reduction of temperature of air entering the CAR obtained in the aftercooler (A), the inlet temperature at the APH (point 12) is set in order to obtain an outlet flue gas temperature (point 18) of about 145 °C. Temperature at the outlet of the LP GT (point 17) is fixed to 630 °C, so that air temperature at the outlet of the APH (point 13) permits to reduce the natural gas consumption. Temperatures, pressures, mass flow rates and other required parameters necessary to completely characterize the HPP-CAES at each thermodynamic state are determined by means of a mathematical model, which has been written using the software EES (Engineering equation Solver). The model includes all balance equations (mass, momentum, energy), characteristic equations for heat and work transfer in the components (effectiveness-NTU in the heat exchangers [23], characteristic curves in the compressor and turbine [24], etc.), boundary conditions and set-points. Similar model was applied to the simulation of the operating conditions of a combined cycle. Model verification has been performed through comparison between simulated and measured data [25]. Four different operation modes for the air compressors have been considered and compared in order to investigate the impact of this selection on the amount of annual energy that is elaborated by the compressors. This analysis has been performed by assuming the ratio between the nominal power of compressor section and the installed wind power as a variable parameter. The investigated modes are:
80–100% 50–100% 60–140% 70–110%
of of of of
the the the the
nominal nominal nominal nominal
compressor compressor compressor compressor
power (108 MW). power. power. power.
Maximum energy to the compressors [%]
The first mode is a common operating strategy, while the others correspond to more aggressive control strategies. The second mode has the intent of expanding the operation range toward smaller power, while the third mode has the intent to explore the effects
70% 60% 50%
3. Cost of energy A crucial point in this analysis is the assessment of the cost at which electricity is produced in a HPP-CAES. This analysis should consider the different operating time of the various subsystems. As the discharging process uses compressed air that has been previously stored, a possible solution consists in calculating the cost of compressed air during the charging process and use this piece of information during the discharging process. To assess the production cost of electricity cEl, the results of a thermoeconomic analysis are used [21], both in design and off-design operation mode. Thermoeconomics lays on the concept of exergy in order to define the cost formation process, considering the effects of both the thermodynamic irreversibilities as well as the investment and operating costs. Exergy represents the maximum work that would be obtained from an amount of energy, using it in an ideal (reversible) device which only interacts with the environment. Therefore, the various forms of energy can be properly compared using this quantity. The unit cost of electricity, cEl, is expressed by:
cEl ¼
40% 30% 20% 10% 0%
of larger operating power. Last mode corresponds to a small bilateral extension of the first mode. Wind power production has been calculated on the basis of measured data: these consist in the wind speed at 30 m and 50 m, average wind direction at 48 m and air temperature at 10 m. All data have been measured in 2012 in the Puglia region, with a 10 min interval for an entire year. The power delivered from a specific typology of wind turbine is computed with the aid of the software Windographer. Results are reported in Fig. 2. For almost all the operation modes, the maximum energy elaborated by the compressors corresponds to an installed compressor power between 30% and 50% of the rated power of the wind farm. The assumed value for the installed compressor power is highlighted (39%). This confirms the appropriate size of the compressor section with respect to the nominal wind power. The operation mode corresponding to 60–140% of the rated power has been initially selected in order to achieve larger amount of energy extracted from the wind farm. Nevertheless, the results of the simulations performed in EES have highlighted the limits of this choice. When the power to drive the air compressors is higher than 120 MW, the water mass flow rates flowing through the intercoolers and aftercooler becomes too large and unrealistic (530 kg/s). Moreover, when the power is smaller than 78 MW, the isentropic efficiency becomes lower than 0.72, i.e. about 90% of the value at 108 MW. For this reason, the fourth mode has been selected and further analyzed. Considering the working mode of operation between 78 MW and 120 MW (72–111% respect to the rated power of 108 MW), the maximum amount of energy supplied to the compressors (47.8%) is obtained when the nominal power of the compressors is equal to 40% of the wind farm nominal power. For a nominal power of the wind farm of 270 MW, the global energy provided by the wind farm is 511 GW h, thus, up to 244 GW h are used to power the compressors.
0%
20%
40%
60%
80%
100%
P_comp/P_wind_farm [%] 80%-100%
50%-100%
60%-140%
72%-111%
Fig. 2. Optimal size of the wind farm respect to the compressor size.
ðC 23 þ C 24 Þ W el
ð1Þ
where C23 and C24 are the cost rates associated to the electricity produced by the HP and LP GT respectively. These cost rates vary according to the cost rates associated to the air mass flow rate extracted from the storage, C11. The latter depends on the cost of the electricity provided by the wind farm to the air compressors cs,WT. The exergy cost of compressing air cc,exergy is related to its cost rate C11 by:
cc;exergy ¼
C 11 BCAR;mech
ð2Þ
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx
where BCAR,mech is the mechanical exergy rate supplied to the CAR. A parametric analysis varying the mechanical power provided by the Electric Motors (EM) to the compressors WEM is performed in order to evaluate cc,exergy for the configurations with three and four compressors in series. The unit cost cc,exergy depends on the instantaneous pressure in the reservoir pCAR. As a consequence, a parametric analysis is performed as pCAR varies, taking into account the range of pressure at which each configuration is adopted. The cost of energy from the wind farm has been considered as 50 €/ MW h. This value comes from a cost of 40 €/MW h, which is reported in [26] for electricity used to charge a storage facility. The cost has been increased in order to consider the effects of reduced average speed of wind [27]. The corresponding results are shown in Figs. 3 and 4. The functions in Fig. 4 shows a minimum because the isentropic efficiency is variable, as non-dimensional compressor maps have been used [28]. The minimum cost of mechanical exergy obtained using three compressors is slightly smaller than in the case of four compressors, maily because of the investment cost rate of the fourth compressor, which is charged on a more limited number of operating conditions. The unit cost cEl has been evaluated as the function of cc,exergy, for different values of the net electric power Wel. As a consequence, cEl depends on WEM, pCAR and Wel. Two examples of results obtained considering two different pressures in the reservoir are reported in Figs. 5 and 6. The relation between cEl and cc,exergy is linear at a given pCAR since the specific exergy of the compressed air is constant. In fact, BCAR,mech increases as air is extracted from the reservoir only because of a higher mass flow rate. As Wel increases, the air mass flow rate extracted is nearly constant. To check the cost of electricity obtained from thermoeconomic analysis, the Levelized Cost of Energy (LCOE) is also calculated:
Pn LCOE ¼
1 t¼0 ð1þWACCÞt
1
TCI þ O&M þ FC
=55 bar
=65 bar
=75 bar
Fig. 4. Exergy cost of compressing air as the mechanical power varies for different values of pressure inside CAR. Four compressors in series configuration. R2 = 99.29%. Interpolation with a 3rd order polynomial.
=195 MW =210 MW
=235 MW =175 MW =240 MW
ð3Þ
AEP
where: TCI is the Total Cost of Investment [€]. O&M are the annual Operation and Maintenance Expenditures [€/y]. FC is the annual cost of fuel (natural gas) [€/y]. AEP is the Annual Energy Production [MW h/y]. WACC is the Weighted Average Capital Cost, assumed equal to the interest rate i = 3%. According with the data reported by the Authority for Electricity and Gas (AEEG) [29], oscillations of the price of natural gas for electricity generation in the last five years have been about 20 €/MW h.
Fig. 5. Cost of electricity produced as the exergy cost of compressing air varies for different values of generating power. Three compressors configuration at 45 bar.
=175 MW =195 MW =210 MW =235 MW
=240 MW
=35 bar
Fig. 6. Cost of electricity produced as the exergy cost of compressing air varies for different values of generating power. Four compressors configuration at 55 bar. =45 bar =55 bar
Fig. 3. Exergy cost of compressing air as the mechanical power varies for different values of pressure inside CAR. Three compressors in series configuration. R2 = 98.23%. Interpolation with a 3rd order polynomial.
An average value of cNG,MW h = 33 €/MW h has been assumed. Considering the density and lower heating value qNG = 0.74 kg/Sm3 and LHV = 34.5 MJ/m3, respectively, the fuel mass flow rate in nominal _ NG;tot ¼ 6:337 kg=s. It is then possible to evaluconditions results m ate the primary energy consumption associated with natural gas ENG as the function of the number of equivalent operating hours of the discharge section heq. FC is expressed as:
FC ½€=y ¼ ENG ½MW h=y cNG;MW h ½€=MW h
ð4Þ
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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FC results equal to 3.453 107 €/y at heq = 3500 h/y. Note that FC is assumed as constant every year. The annual energy production is evaluated as follows:
AEP ½MW h=y ¼ W el;nom ½MW heq ½h=y
ð5Þ
At heq = 3500 h/y, AEP = 8.4 105 MW h/y. Particular care must be taken when calculating LCOE associated to the wind source (LCOEWT). In fact, cs,WT must be weighted according to the energy provided to the electric motors in nominal conditions respect to the generated energy:
LCOEWT ¼ cs;WT
W EM;nom heq;c W el;nom heq
ð6Þ
where heq,c is the number of equivalent operating hours of the charge section, which may be different than that of the discharge section (heq). As heq affects the result of cEl, a parametric analysis is performed as heq varies, with the HPP-CAES working both in design and off-design conditions. Results are reported in Fig. 7. The figure shows that LCOE significantly decreases with increasing operating hours and tends to a constant value of about 77 €/MW h. Assuming a target value of 3500 operating hours, the corresponding LCOE is 80 €/MW h. This result is strongly affected by the price of electricity from the wind farm. If a lower price were considered (e.g. 40 €/MW h, which was the price in [26]) an almost equally reduction would be obtained on the LCOE (about 70 €/MW h). The price of natural gas has a smaller influence on the LCOE. For a variation of 20% of the natural gas price, the LCOE varies less than 10%. In Fig. 7, the thermoeconomic costs are also reported. While LCOE can be obtained only for the design condition, thermoeconomic costs can be calculated in any operating conditions of the plant. The figure shows that thermoeconomic cost in design condition coincides with the LCOE. Two off-design conditions are also shown: one corresponding with reduced electricity production from the discharging section (175 MW) and one corresponding with reduced electricity production combined with increased compressed air production (120 MW). In both cases, the unit cost of electricity increases, but the distances between the curves show that the effects of off-design condition of the compressors is much larger.
4. HPP-CAES economic evaluation The IPEX Market is composed by the Spot Electricity Market, the Forward Electricity Market and the Platform for physical delivery of financial contracts concluded on IDEX – CDE. The Spot Electricity
Market is the place where electricity trading occurs. This consists of three different sessions: Day-Ahead Market (abbreviated as MGP, in Italian), where the main stocks are assigned. Intra-Day Market (abbreviated as MI, in Italian), where adjustments are operated on the basis of the grid capacity. Ancillary Service Market (abbreviated as MSD, in Italian), where some of the plants provide their availability to modify their operation in order to prevent from unexpected request variations [30]. To evaluate the economic convenience of the HPP-CAES, one year operation is simulated on the Spot Electricity Market. Ten minute time-step (WT) data are considered and suitable selling strategies are implemented. To evaluate the success of the strategies, two significant parameters are introduced: the demand met parameter and the spillage parameter. The demand met parameter, Pd, is used to assess how many times the HPP-CAES satisfies the demand in a year. This is defined as follows:
Pd ¼
8760 X
W iel
i¼1
W iD
min floor
!
! ; 1 100
ð7Þ
where: Wel is the power generated by the HPP-CAES. WD is the power demand from the electricity market. This parameter is evaluated on an hourly basis both for the sales on the MGP and MSD. The spillage parameter, Ps, is used to evaluate the energy from the wind farm that can be stored. It represents the percentage of WT power which cannot be stored in the HPP-CAES because of its incapability of operating the compressors. In other terms, it is the available wind power input to the HPP-CAES compressor side, spilled due to insufficient reservoir space or pressure limit hits.
Ps ¼
8 760 X
ðW iWT;in W iEM Þ
i¼1
W iWT
100
ð8Þ
where: WWT,in is the power available from WT within the range of air compressors operation. WEM is the power available from the WT that effectively powers the Electric Motors driving the air compressors. 5. MGP and MSD demand following island mode The HPP-CAES is simulated to operate on MGP and MSD markets in order to satisfy the demand, regardless of the selling prices. The results of this strategy are reported in Figs. 8–10 and in Table 1. In Fig. 8, the share of the total wind energy available (EWT,available) in the components, the energy stored (Estored) and the energy directly
Fig. 7. LCOE vs. cEl at different working modes as the number of equivalent operating hours of the discharge section varies.
Fig. 8. Stored energy. Island mode configuration.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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Fig. 9. Storage limits due to pressure limits. Island mode configuration.
Table 2 Demand on the MGP market. Island mode configuration. DemandMGP (h/y) DemandMGP,accepted (h/y) DemandMGP,refused (h/y) DemandMGP,NOT MET (h/y) DemandMGP,MET (h/y) Pd,MGP (%)
4802 839 3963 9 830 98.9%
Table 3 Economic evaluation of the strategy on the MGP and MSD markets. Island mode configuration. HPP-CAES revenues MGP (k€) HPP-CAES revenues MSD (k€) HPP-CAES fees (k€) Energy from grid (k€) Wind energy HPP-CAES net revenues (k€) HPP-CAES SPBO (y)
Fig. 10. Energy sold and stored. Island mode configuration.
Table 1 Compressors working time. Island mode configuration. EWT,available (MW h) Storedmax (%) EWT,com (MW h) Ps (%) Stored (%) Miss (%) TimeWT,available,com (h/y) Timeworking,com (h/y) Timerel,working,com (%)
5.11 E+05 47.8% 1.33 E+05 74.0% 26.0% 21.8% 2171 1240 57.1%
sold to the grid (Esold,wind) are shown. The relation between these quantities is the following:
Etot;wind ¼ Esold;wind þ
Estored
gcharge
ð9Þ
Note that Estored is always much lower than Esold,wind. The annual amount of electricity that can be produced by the wind turbines is about 511 GW h/year. The maximum amount of energy that could be stored using the proposed system is about 244 GW h/year, i.e. 47.8% of the production. Instead, only 133 GW h/year (26.0%) are used to drive the compressors and stored in the cavern. Compressors operate about 57.1% (1240 h/y) of the maximum number of hours (i.e. only 2171 h/y of the 4802 h/y). The reasons why not all the EWT,com is stored are related with the power exceeding the compressor capacity or the too large pressure in the cavern (pCAR), as shown in Fig. 9. About 8.9% of the times energy available from the wind farm is in the range of operation using three compressors in series (78–90 MW) and pCAR is higher than 55 bar, thus energy is
3443 4214 129 0 2547 1905 Not reached
directly supplied to the grid instead of being stored. Similarly, about 8.1% of the times energy available from the wind farm is in the range of operation using four compressors in series (90– 120 MW), but pressure in the cavern is too large. In 25.9% of the cases, wind power is larger than the maximum value. In Fig. 10 the fuel energy that is spent (Efuel), with the stored energy (Estored) and the energy sold from the CAR to the grid (ECAR,sold) are reported. Considering ECAR,sold as the useful effect and Efuel and Estored/gcharge as the expenses, an average monthly efficiency gall can be defined:
gall ¼
ECAR;sold Estored =gcharge þ Efuel
ð10Þ
The average efficiency is about 52.6%. Results concerning the economic evaluation are reported in Tables 2 and 3. In Table 2, the potential hourly requests of energy (DemandMGP,) the demand accepted (DemandMGP,accepted) and that refused (DemandMGP,refused) as well as Pd are reported. Only 17.5% of the demand is accepted (about 839 h/y) and 98.9% of cases the duty is accomplished. As a consequence, the working hours of the generation section of the HPP-CAES is about 830 per year. This value is consistent with the results found by RSE [31]. Almost 2 M€ would be lost in a year if the HPP-CAES were operated under these conditions, as shown in Table 3. In fact, it is not convenient to operate only in MGP because often the cost of electricity produced by the HPP-CAES is higher than the selling price on MGP. In this scenario, the energy bought
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Fig. 11. Stored energy. Grid connected configuration.
from the grid (Grid energy) is equal to zero (HPP-CAES operates in island mode) and the Simple Pay-Back Period (SPBP) is never reached. As shown in Fig. 2, this is not due to wrong size of the compressors, but to the large variability of the wind production. The average wind speed is 6.0 m/s and the standard deviation is 3.0 m/s, with significant number of hours without power production because of the too small wind velocity (about 6600 h). For this reason a different option is considered in next section.
6. MGP and MSD demand following grid connected The HPP-CAES operates both on MGP and MSD markets, but, with respect to the previous strategy, it is possible to buy electricity from the power grid to integrate the energy provided by the wind farm. This strategy does not significantly modify the operating hours of the wind farm, but increases the operating hours of the HPP-CAES. This means that the storage system is truly regarded as a way to solve the zonal unbalances, instead of the wind farm unbalances. This kind of operation stresses the ability of the HPPCAES to operate with fast transients, which is a feature that makes it suitable for the MSD market. The results of this scenario are reported in Figs. 11–13 and in Table 4. A comparison between Figs. 11 and 8 shows that the grid connected option allows one to increase the total amount of energy produced by the system of about 277 GW h. Stored energy also increases, about 265% more than in the island mode, thanks to the electricity bough from the grid. The largest increases are obtained in October, November and March. As shown in Table 4, Ps decreases from 74% to 59.5%, therefore compressors work more hours (4380 h/y compared to 1240 h/y). The percentage of infeasible storage decreases to about 24.5%, as shown in Fig. 12. The average monthly efficiency, gall, using this operation strategy is about 49.9%, as shown in Fig. 13.
Fig. 13. Energy sold and stored. Grid connected configuration.
Table 4 Compressors working time. Grid connected configuration. EWT,available (MW h) Storedmax (%) EWT,com (MW h) Ps (%) Stored (%) Miss (%) TimeWT,available,com (h/y) Timeworking,com (h/y) Timerel,working,com (%)
5.11 E+05 47.8% 4.86 E+05 59.5% 40.5% 7.3% 5800 4380 75.5%
The results of the economic analysis are reported in Tables 5 and 6. The cost of electricity purchased from the network and that associated to the wind generation are considered. The first term is assumed as equal to the National Single Price (PUN), i.e. the purchase price for end customers, computed as the weighted average zonal price. This assumption comes from the following considerations. The value of unbalancing in MSD, that is the difference between the produced electricity and the expected production, depends on the zonal unbalance, i.e. the production of all producers in the zone, which may be positive or negative. Being this information not available, it is assumed that the electricity is bought as MGP price, that corresponds to a positive zonal unbalance. The fact that the electricity is bought at PUN price and not at zonal price, once is bought at MGP price, is always the real case. The demand accepted on the MGP is significantly higher then in the island mode, 3297 h/y instead of 839 h/y), which brings a significant reduction in the demand refused, 1105 h/y instead of 3963 h/y. The demand not met increases, from 9 h/y in the island mode to 247 h/year, therefore the demand met parameter becomes
Fig. 12. Storage limits due to pressure limits. Grid connected configuration.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx Table 5 Demand on the MGP market. Grid connected configuration. DemandMGP (h/y) DemandMGP,accepted (h/y) DemandMGP,refused (h/y) DemandMGP,NOT MET (h/y) DemandMGP,MET (h/y) Pd,MGP (%)
4802 3297 1105 247 3050 92.5%
Table 6 Economic evaluation of the strategy on the MGP and MSD markets. Grid connected configuration. HPP-CAES revenues MGP (k€) HPP-CAES revenues MSD (k€) HPP-CAES fees (k€) Energy from grid (k€) Wind energy HPP-CAES net revenues (k€) HPP-CAES SPBO (y)
9568 26,097 6186 10,167 10,933 8379 9.53
slightly lower (Pd,MGP = 92.5% instead of 98.9%). Due to the higher number of expected full load hours (3500 h/y), cEl is lower than in the previous scenario and revenues are positive also on the MGP, registering an increase of about 13 M€. The revenues on MSD register an increase of about 22 M€. Expenses also increase, particularly because of the energy bought from the grid and the larger amount of wind energy that is stored. The SPBP is reached after about 9.5 years, thus the investment is convenient. This result is not significantly affected by the unit cost of natural gas. If the unit cost of natural gas was considered 20% larger, the pay-back period would increase to about 9.7 years.
7. Conclusion In this paper, thermoeconomic analysis is used for the design and analysis of a Compressed Air Energy Storage (HPP-CAES) integrated with a wind farm. With respect to conventional economic analysis (LCOE) thermoeconomics allows one to obtain the cost assessment in off-design operation, which is crucial in the case of storage systems. The charging and discharging sections have been separately analyzed. In the charging section, the exergetic unit cost of compressed air has been obtained as the function of pressure in the cavern and the mechanical power supplied to the compressors. In the discharging section, instead, the unit cost of electricity is obtained as the function of the exergetic unit cost of compressed air and the electricity production. The HPP-CAES is simulated to operate in the South of Italy, thus wind data and the Italian Power Exchange market considered make reference to that region. The energy system has been designed according to the size required by the Italian electricity transmission grid operator (Terna S.p.A.) to effectively contribute to solve congestion problems in that region (240 MW). A simulation of the system has been performed considering two different operating strategies according to the demand and sale price on the IPEX Market. In the first strategy, the storage system operates in the Day-Ahead Market (MGP) and Ancillary Service Market (MSD) with no possibility to buy electricity from the grid. In the examined scenario, the system results as economically infeasible. This is mainly due to the production of the wind farm, which causes a small number of operating hours of the compressors (about 1240 h/y). As a result, also the generation section of the CAES operates for a small number of hours and about 82.5% of the market energy demand cannot be accepted.
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In a second strategy, the possibility of meeting the energy market demands by integrating the energy production from the wind farm with electricity bought from the grid has been considered. This means that the CAES is truly used to solve the local imbalances of the grid. In this case the number of operating hours of the compressors becomes about 4380 h/y. The generation section operates for about 3297 h/y, which means that the percentage of market energy demand that cannot be accepted is reduced to about 25.1%. Although the project is focused on the analysis of a specific plant, the thermoeconomic methodology has a general validity that can be easily applied to the optimal design and operation of different installations of HPP-CAES systems. Further work is needed on the implementation of a model able to predict the prices and the quantity sold on the IPEX, taking into account the utilities participating to the bids. In addition, a detailed analysis of the effects of CAES systems on the electricity grid should be performed. Acknowledgements This work was developed with the help of the Enel personnels, who have assisted Federico de Bosio during his Master’s thesis. Authors are particularly grateful to Juri Riccardi of Enel Research and Innovation, Franco Sansone and his collaborators. References [1] International Energy Agency. Renewable energy medium-term market report 2013; 2013. [2] Keles D, Hartel R, Möst D, Fichtner W. Compressed-air energy storage power plant investments under uncertain electricity prices: an evaluation of compressed-air energy storage plants in liberalized energy markets. J Energy Markets 2012;5(1):53–84. [3] Milazzo A, Grazzini G. Thermodynamic analysis of CAES/TES systems for renewable energy plants. Renewable Energy 2008;33(9):1998–2006. [4] Salgi G, Lund H. System behavior of compressed air energy storage in Denmark with a high penetration of renewable energy sources. Appl Energy 2008;85:182–9. [5] Cavallo A. Controllable and affordable utility-scale electricity from intermittent wind resources and compressed air energy storage (CAES). Energy 2007;32:120–7. [6] Lund H, Salgi G, Elmegaard B, Andersen AN. Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices. Appl Therm Eng 2009;29:799–806. [7] Zafirakis D, Kaldellis JK. Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks. Energy Policy 2009;37:1958–69. [8] Mason JE, Archer CL. Baseload electricity from wind via compressed air energy storage (CAES). Renew Sustain Energy Rev 2012;16:1099–109. [9] Succar S, Denkenberger DC, Williams RH. Optimization of specific rating for wind turbine arrays coupled to compressed air energy storage. Appl Energy 2012;96:222–34. [10] Hartmann N, Vöhringer O, Kruck C, Eltrop L. Simulation and analysis of different adiabatic compressed air energy storage plant configurations. Appl Energy 2012;93:541–8. [11] Marano V, Rizzo G, Tiano FA. Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage. Appl Energy 2012;97:849–59. [12] Wang SY, Yu JL. Optimal sizing of the CAES system in a power system with high wind power penetration. Electrical Power Energy Syst 2012;37:117–25. [13] Mauch B, Carvalho PMS, Apt J. Can a wind farm with CAES survive in the dayahead market? Energy Policy 2012;48:584–93. [14] Safaei H, Keith DW, Hugo RJ. Compressed air energy storage (CAES) with compressors distributed at heat loads to enable waste heat utilization. Appl Energy 2013;103:165–79. [15] Madlener R, Latz J. Economics of centralized and decentralized compressed air energy storage for enhanced grid integration of wind power. Appl Energy 2013;101:299–309. [16] Manchester S, Swan L. Compressed air storage and wind energy for time-ofday. Proc Comput Sci 2013;19:720–7. [17] Abbaspour M, Satkin M, Mohammadi-Ivatloo B, Hoseinzadeh Lotfi F, Noorollahi F. Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES). Renewable Energy 2013;51:53–9. [18] Yucekaya A. The operational economics of compressed air energy storage systems under uncertainty. Renew Sustain Energy Rev 2013;22:298–305. [19] Wolf D, Budt M. LTA-CAES – a low-temperature approach to adiabatic compressed air energy storage. Appl Energy 2014;125:158–64.
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[20] Italian authority for the electricity and gas. Selection procedure and criteria for the incentives to pilote projects of storage systems. 288/2012/R/EEL; 2012. [21] Gaggioli RA, Wepfer WJ. Exergy economics. Energy Intl J 1980;5:823–37. [22] Augello L. Analysis of a CAES system. Internal report RSE S.p.A.; 2011 [in Italian]. [23] Kays WM, London AL. Compact heat exchangers. Krieger Publishing Company; 1984. [24] Kurzke J. How to get component maps for aircraft gas turbine performance calculations. ASME Paper 96-GT-164; 1996. [25] Valero A, Correas L, Zaleta A, Lazzaretto A, Verda V, Reini M, et al. On the thermoeconomic approach to the diagnosis of energy system malfunctions Part 1: The TADEUS problem. Energy 2004;29:1875–87.
[26] The Boston Consulting Group – BCG. Electricity storage: making large-scale adoption of wind and solar energies a reality, Boston; 2010. [27] Lantz E, Hand M, Wiser R. The Past and Future Cost of Wind Energy. World Renewable Energy Forum. Colorado: Denver; 2012. [28] Pizzo D. Design of a multi-stage axial compressor. Thesis, University of Padova; 2013 [in Italian]. [29] Autorità per l’energia elettrica il gas e il sistema idrico. [accessed 07.09.14]. [30] GME. [accessed 05.07.13]. [31] Augello L, Cernuschi F, Agate G. Technical and economical feasibility of a CAES system, through geological storage of compressed air and submersed tanks. Research on the Electric System – RSE S.p.A.; 2012 [in Italian].
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market q Federico de Bosio, Vittorio Verda ⇑ Energy Department, Politecnico di Torino, Italy
h i g h l i g h t s Thermoeconomic analysis of the off-design operation of a HPP-CAES is performed. An optimal strategy for air compressor operation is selected. Island mode and grid connected operations are compared. Economic analysis of the systems are performed considering the IPEX Market.
a r t i c l e
i n f o
Article history: Received 9 May 2014 Received in revised form 13 January 2015 Accepted 13 January 2015 Available online xxxx Keywords: Energy storage CAES Compressed air Thermoeconomics
a b s t r a c t Energy storage is regarded as a key factor to allow significant increase in the percentage of electricity generation from renewables. One of the most critical aspects related with energy storage is its economic feasibility, which intrinsically involves the analysis of the off-design conditions and the evaluation of the operating strategies using proper methodologies. This paper considers a promising system for mechanical energy storage constituted by a Compressed Air Energy Storage (CAES) integrated with a Hybrid Power Plant (HPP) and coupled with a wind farm. This system is modeled considering the South of Italy as the possible location. The HPP-CAES is simulated to operate on the Italian Power Exchange market, for one year, implementing suitable selling strategies. Cost analysis is performed using a thermoeconomic approach. Results show that reduced operating hours and large variations in the electricity production of the wind farm make the HPP-CAES cost-effective only when it is operated with the goal of solving local imbalances of the grid. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The share of renewable energy has significantly increased in the last few years, reaching a percentage of about 20% in the global electricity production. Similar trend is expected in the next years and a percentage of about 25% is foreseen in 2018 [1]. Hydro is the main renewable power source, with about 1138 GW installed, which is about 67% of the installed renewable power. Nevertheless the fastest-growing sources are wind and solar PV. Wind is expected to increase its installed capacity from the current 321 GW to about 559 GW, while solar PV from the current
q This paper is included in the Special Issue of Energy Storage edited by Prof. Anthony Roskilly, Prof. Phil Taylor and Prof. Yan. ⇑ Corresponding author. Tel.: +39 011 564 4449; fax: +39 011 564 4499. E-mail addresses: [email protected] (F. de Bosio), vittorio.verda@polito. it (V. Verda).
128 GW to about 308 GW. This evolution in the power mix brings some management issues. Electricity generation depends in fact on the availability of the wind or solar energy resources. Production may be intrinsically shifted with respect to the demand, therefore a lag of several hours may emerge between supply and demand. In addition production may be intermittent. The electric system is balanced by Gas Turbines (GT) or Pumped Hydropower Storage (PHS) plants, capable of reacting promptly to load variations and variable production of wind or solar plants [2]. Emerging technologies like hydrogen storage or new large-scale batteries are expected to contribute to balance Supply and Demand, however, at present they are not yet economically viable and often impaired by a low number of charge/discharge cycles. Other systems, such as flywheels or super capacitors, have limited capacity. The gap between discontinuous production and demand can be bridged with the help of other energy storage technologies, such as Compressed Air Energy Storage (CAES) plants, which are smaller
http://dx.doi.org/10.1016/j.apenergy.2015.01.052 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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Nomenclature
Abbreviations A aftercooler AC air compressor AEP Annual Energy Production (MW h/y) APH air pre-heater B exergy flow (MW) c unit cost of exergy (€/MW h) C cost rate (€/s) CAR compressed air reservoir CC combustion chamber E energy (MW h) ENG primary energy consumption (MW h/y) EM Electric Motors FC annual cost of fuel (€/y) GT Gas Turbine h annual operating hours (h/y) HPP-CAES Compressed Air Energy Storage integrated with a Hybrid Power Plant I intercooler IPEX Italian Power Exchange
than PHS systems in terms of power size. CAES plants can contribute to the successful integration of large amounts of wind electricity production into the energy system [3,4]. The advantage of introducing CAES plants into an existing energy system is mainly dependent on their economic convenience. As a consequence, most analyses available in the literature are focused on the economic feasibility and the optimal operation. Cavallo [5] has analyzed the cost of electricity produced by hybrid wind/compressed air energy storage, showing that it is affordable in various economic contexts. CAES systems make wind energy competitive on the long term, as the problems related with uncertainty and transmission costs are significantly reduced. In [6], three different strategies for optimizing the operation of a CAES plant on the spot market are presented. The maximum net earnings can be achieved using an ideal strategy, that is not applicable in real operation. In contrast, the simplified strategies allow one to achieve slightly smaller net earnings but can be easily implemented. Zafirakis and Kaldellis [7] have analyzed a dual mode wind park-CAES operating in autonomous island grids. The analysis shows that, with respect to conventional peak demand power plants, this kind of systems allows lower electricity production costs and fuel consumption. Mason and Archer [8] have compared the combination of wind with natural gas combined cycles and with CAES. The first system is currently characterized by the lowest cost, but the second option becomes more convenient for higher cost of the natural gas. In [9], the wind farm and CAES system are optimized together, using the levelized cost of electricity as the objective function, while in [10], various configurations for a CAES system are analyzed and the corresponding energy efficiency is obtained. Marano and co-workers [11] performed the optimal management of a system composed by a CAES coupled with a wind farm and a photovoltaic plant using dynamic programming. The total operating cost is considered as the objective function to minimize. In [12], the rated power and capacity of a CAES system are selected in order to maximize the economical profits. Mauch and co-workers [13] have modeled a wind farm coupled with a CAES system operating in the day-ahead electricity market. The analysis shows that revenues are not sufficient to pay the investment costs
LCOE MGP MI MSD _ m n NG O&M p Pd Ps TCI W WACC Wel WT
Levelized Cost of Energy (€/MW h) Day-Ahead Market Intra-Day Market Ancillary Service Market mass flow rate (kg/s) lifetime (y) natural gas Annual Operation and Maintenance Expenditures (€/y) pressure (bar) demand met parameter spillage parameter Total Cost of Investment (€) mechanical power (MW) Weighted Average Capital Cost electric power (MW) wind turbines
Greek symbols g efficiency q density (kg/m3)
back. In [14], energy and economic analysis of a distributed CAES (DCAES) system is considered. The compressors are located close to a district heating network, in order to recover the heat available from compressed air cooling. The DCAES is more efficient than conventional CAES, but compressed air must be supplied to the cavern through a pipeline, therefore the economic performance strongly depends on the distance. In [15], an economic analysis of a Compressed Air Energy Storage (CAES) is conducted, with the aim of maximizing the profits from peak power sales. A central CAES system and a distributed CAES system are considered. Manchester and Swan [16] have analyzed the possibility to operate for one year a CAES system of small power size according to the regulation in force in Canada, showing that an increase of 30% in the incomes is obtained. In [17], the optimal operation of a conventional gasfired power system combined with a wind farm and a CAES is investigated using a mixed integer non-linear programming approach. Maximum profit and minimum cost are considered as the objective functions in the analysis. Yucekaya [18] has proposed a mixed integer programming approach to investigate the optimal operation of a CAES. Uncertainties in the forward prices and profits are considered in the analysis. In [19], a Low-Temperature Adiabatic Compressed Air Energy Storage system is introduced. With respect to high temperature CAES, this system is characterized by lower efficiency, but it is able to operate in a larger range of part load. As discussed in the present paper, partial load operation has an important economic impact on CAES systems. The convenience of installing a Hybrid Power Plant capable to store electricity by means of air compression (HPP-CAES) is analyzed in this paper, considering the regulation currently in force in Italy. A HPP-CAES combined with a wind farm is simulated to operate in the South of Italy, thus both wind and market data refer to this geographical region. The energy system has been modeled according to the size (240 MW) considered by the Italian electricity transmission grid operator (Terna S.p.A.) as able to effectively contribute to solving congestion problems in the South of Italy [20]. Therefore this is the nominal power assumed for the discharge section of the HPP-CAES. Such an analysis requires considering offdesign operations due to variable production of the wind turbines
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and the level of storage as well as time shift between storage and generation. To incorporate these features, cos analysis is performed using thermoeconomics [21]. To evaluate the economic convenience of the HPP-CAES, the plant is simulated to operate on the Italian Power Exchange market, for one year, implementing suitable selling strategies. Firstly, a scenario in which the energy storage device operates in the Day-Ahead and Ancillary Service Markets to satisfy the demand regardless of the price of sale and with no possibility to integrate the energy provided by the wind farm with electricity bought from the grid, has been considered. Subsequently, the possibility of expanding the contribution to the reduction of local imbalances between production and demand has been investigated, considering the option to buy electricity from the grid on the MGP market. 2. HPP-CAES architecture and operation The architecture of the HPP-CAES system considered in the present analysis is shown in Fig. 1. It consists of three sections: (1) charge section, (2) air compressed reservoir and (3) discharge section. The charge section is constituted by four air compressors (AC1–AC4) with three intercoolers (I1–I3) and one aftercooler (A). Compressors are powered with electricity provided by a wind farm with a nominal power of 270 MW. The use of intercoolers is convenient in order to reduce the compression work required in each compressor stage. Water, flowing in an open loop circuit, is used as the air coolant. After the compressors stages, an aftercooler cools down the air before entering the storage reservoir. According to a previous analysis performed by RSE [22], the optimal size of the air compressors for a similar system is 108 MW. Therefore, the charge section has a much smaller power (108 MW) than the
3
wind farm (270 MW) and the discharge section (240 MW), in order to cope with the wind velocity variations. The compressed air reservoir (CAR) has a volume of about 210,000 m3. The discharge section uses stored compressed air to produce electricity. This section is constituted by an air pre-heater (APH), a first combustion chamber (CC1), a high pressure gas turbine (HP GT) and a low pressure gas turbine (LP GT) with a second combustor (CC2) between them. During the discharge phase, air is extracted from the CAR and pre-heated in an APH where it receives heat from the flue gases that expand in the LP GT. Before the expansion, air reacts with natural gas in CC1 and CC2. Concerning the charge phase, pressure at the final stage of the compressors is kept constant whatever is the power provided to these components. A throttling valve located at the inlet of the CAR adjusts the air pressure according to the pressure in the cavern (point 10), that varies continuously with time. A variable configuration, similar to that suggested in [3], has been considered since pressure reduction caused by the valve is a source of irreversibility. A configuration with only three compressors in series and by-passing the last intercooler and compressor (point 6 reached at the outlet of AC4) is adopted as pressure in the reservoir is between 35 bar (minimum pressure imposed by design) and 55 bar. Pressure in point 9 is 55 bar. When this value is reached in the cavern, a configuration with the four compressors in series is adopted. Air pressure at the final stage (point 8) becomes equal to the maximum pressure allowed in the cavern (80 bar). Concerning the discharge phase, another throttling valve at the outlet adjusts the air pressure at 40 bar (point 12). As a consequence, air from the CAR can be extracted only if the pressure in the reservoir is higher than 40 bar. Pressure losses along the lines and the components are neglected (see for example [10]), therefore pressure at point 12 is equal to that at the inlet of the CC1 (point
Fig. 1. Scheme of HPP-CAES system.
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13) and HP GT (point 14). This assumption is reasonable since the pressure drops in the heat exchangers are significantly smaller than in the valves at the inlet and outlet sections of the cavern. Combustors are fed with compressed natural gas. The GT is supposed to operate in ultrasonic conditions: this fact permits some simplifications regarding the control mode. At partial load, the natural gas burnt in the first combustor varies according to the power request, while the air mass flow rate extracted from the CAR is nearly constant. The inlet temperatures at each GT, at nominal power, are set to 1100 °C and 1200 °C respectively (point 14 and 16). In this configuration, due to the reduction of temperature of air entering the CAR obtained in the aftercooler (A), the inlet temperature at the APH (point 12) is set in order to obtain an outlet flue gas temperature (point 18) of about 145 °C. Temperature at the outlet of the LP GT (point 17) is fixed to 630 °C, so that air temperature at the outlet of the APH (point 13) permits to reduce the natural gas consumption. Temperatures, pressures, mass flow rates and other required parameters necessary to completely characterize the HPP-CAES at each thermodynamic state are determined by means of a mathematical model, which has been written using the software EES (Engineering equation Solver). The model includes all balance equations (mass, momentum, energy), characteristic equations for heat and work transfer in the components (effectiveness-NTU in the heat exchangers [23], characteristic curves in the compressor and turbine [24], etc.), boundary conditions and set-points. Similar model was applied to the simulation of the operating conditions of a combined cycle. Model verification has been performed through comparison between simulated and measured data [25]. Four different operation modes for the air compressors have been considered and compared in order to investigate the impact of this selection on the amount of annual energy that is elaborated by the compressors. This analysis has been performed by assuming the ratio between the nominal power of compressor section and the installed wind power as a variable parameter. The investigated modes are:
80–100% 50–100% 60–140% 70–110%
of of of of
the the the the
nominal nominal nominal nominal
compressor compressor compressor compressor
power (108 MW). power. power. power.
Maximum energy to the compressors [%]
The first mode is a common operating strategy, while the others correspond to more aggressive control strategies. The second mode has the intent of expanding the operation range toward smaller power, while the third mode has the intent to explore the effects
70% 60% 50%
3. Cost of energy A crucial point in this analysis is the assessment of the cost at which electricity is produced in a HPP-CAES. This analysis should consider the different operating time of the various subsystems. As the discharging process uses compressed air that has been previously stored, a possible solution consists in calculating the cost of compressed air during the charging process and use this piece of information during the discharging process. To assess the production cost of electricity cEl, the results of a thermoeconomic analysis are used [21], both in design and off-design operation mode. Thermoeconomics lays on the concept of exergy in order to define the cost formation process, considering the effects of both the thermodynamic irreversibilities as well as the investment and operating costs. Exergy represents the maximum work that would be obtained from an amount of energy, using it in an ideal (reversible) device which only interacts with the environment. Therefore, the various forms of energy can be properly compared using this quantity. The unit cost of electricity, cEl, is expressed by:
cEl ¼
40% 30% 20% 10% 0%
of larger operating power. Last mode corresponds to a small bilateral extension of the first mode. Wind power production has been calculated on the basis of measured data: these consist in the wind speed at 30 m and 50 m, average wind direction at 48 m and air temperature at 10 m. All data have been measured in 2012 in the Puglia region, with a 10 min interval for an entire year. The power delivered from a specific typology of wind turbine is computed with the aid of the software Windographer. Results are reported in Fig. 2. For almost all the operation modes, the maximum energy elaborated by the compressors corresponds to an installed compressor power between 30% and 50% of the rated power of the wind farm. The assumed value for the installed compressor power is highlighted (39%). This confirms the appropriate size of the compressor section with respect to the nominal wind power. The operation mode corresponding to 60–140% of the rated power has been initially selected in order to achieve larger amount of energy extracted from the wind farm. Nevertheless, the results of the simulations performed in EES have highlighted the limits of this choice. When the power to drive the air compressors is higher than 120 MW, the water mass flow rates flowing through the intercoolers and aftercooler becomes too large and unrealistic (530 kg/s). Moreover, when the power is smaller than 78 MW, the isentropic efficiency becomes lower than 0.72, i.e. about 90% of the value at 108 MW. For this reason, the fourth mode has been selected and further analyzed. Considering the working mode of operation between 78 MW and 120 MW (72–111% respect to the rated power of 108 MW), the maximum amount of energy supplied to the compressors (47.8%) is obtained when the nominal power of the compressors is equal to 40% of the wind farm nominal power. For a nominal power of the wind farm of 270 MW, the global energy provided by the wind farm is 511 GW h, thus, up to 244 GW h are used to power the compressors.
0%
20%
40%
60%
80%
100%
P_comp/P_wind_farm [%] 80%-100%
50%-100%
60%-140%
72%-111%
Fig. 2. Optimal size of the wind farm respect to the compressor size.
ðC 23 þ C 24 Þ W el
ð1Þ
where C23 and C24 are the cost rates associated to the electricity produced by the HP and LP GT respectively. These cost rates vary according to the cost rates associated to the air mass flow rate extracted from the storage, C11. The latter depends on the cost of the electricity provided by the wind farm to the air compressors cs,WT. The exergy cost of compressing air cc,exergy is related to its cost rate C11 by:
cc;exergy ¼
C 11 BCAR;mech
ð2Þ
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx
where BCAR,mech is the mechanical exergy rate supplied to the CAR. A parametric analysis varying the mechanical power provided by the Electric Motors (EM) to the compressors WEM is performed in order to evaluate cc,exergy for the configurations with three and four compressors in series. The unit cost cc,exergy depends on the instantaneous pressure in the reservoir pCAR. As a consequence, a parametric analysis is performed as pCAR varies, taking into account the range of pressure at which each configuration is adopted. The cost of energy from the wind farm has been considered as 50 €/ MW h. This value comes from a cost of 40 €/MW h, which is reported in [26] for electricity used to charge a storage facility. The cost has been increased in order to consider the effects of reduced average speed of wind [27]. The corresponding results are shown in Figs. 3 and 4. The functions in Fig. 4 shows a minimum because the isentropic efficiency is variable, as non-dimensional compressor maps have been used [28]. The minimum cost of mechanical exergy obtained using three compressors is slightly smaller than in the case of four compressors, maily because of the investment cost rate of the fourth compressor, which is charged on a more limited number of operating conditions. The unit cost cEl has been evaluated as the function of cc,exergy, for different values of the net electric power Wel. As a consequence, cEl depends on WEM, pCAR and Wel. Two examples of results obtained considering two different pressures in the reservoir are reported in Figs. 5 and 6. The relation between cEl and cc,exergy is linear at a given pCAR since the specific exergy of the compressed air is constant. In fact, BCAR,mech increases as air is extracted from the reservoir only because of a higher mass flow rate. As Wel increases, the air mass flow rate extracted is nearly constant. To check the cost of electricity obtained from thermoeconomic analysis, the Levelized Cost of Energy (LCOE) is also calculated:
Pn LCOE ¼
1 t¼0 ð1þWACCÞt
1
TCI þ O&M þ FC
=55 bar
=65 bar
=75 bar
Fig. 4. Exergy cost of compressing air as the mechanical power varies for different values of pressure inside CAR. Four compressors in series configuration. R2 = 99.29%. Interpolation with a 3rd order polynomial.
=195 MW =210 MW
=235 MW =175 MW =240 MW
ð3Þ
AEP
where: TCI is the Total Cost of Investment [€]. O&M are the annual Operation and Maintenance Expenditures [€/y]. FC is the annual cost of fuel (natural gas) [€/y]. AEP is the Annual Energy Production [MW h/y]. WACC is the Weighted Average Capital Cost, assumed equal to the interest rate i = 3%. According with the data reported by the Authority for Electricity and Gas (AEEG) [29], oscillations of the price of natural gas for electricity generation in the last five years have been about 20 €/MW h.
Fig. 5. Cost of electricity produced as the exergy cost of compressing air varies for different values of generating power. Three compressors configuration at 45 bar.
=175 MW =195 MW =210 MW =235 MW
=240 MW
=35 bar
Fig. 6. Cost of electricity produced as the exergy cost of compressing air varies for different values of generating power. Four compressors configuration at 55 bar. =45 bar =55 bar
Fig. 3. Exergy cost of compressing air as the mechanical power varies for different values of pressure inside CAR. Three compressors in series configuration. R2 = 98.23%. Interpolation with a 3rd order polynomial.
An average value of cNG,MW h = 33 €/MW h has been assumed. Considering the density and lower heating value qNG = 0.74 kg/Sm3 and LHV = 34.5 MJ/m3, respectively, the fuel mass flow rate in nominal _ NG;tot ¼ 6:337 kg=s. It is then possible to evaluconditions results m ate the primary energy consumption associated with natural gas ENG as the function of the number of equivalent operating hours of the discharge section heq. FC is expressed as:
FC ½€=y ¼ ENG ½MW h=y cNG;MW h ½€=MW h
ð4Þ
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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FC results equal to 3.453 107 €/y at heq = 3500 h/y. Note that FC is assumed as constant every year. The annual energy production is evaluated as follows:
AEP ½MW h=y ¼ W el;nom ½MW heq ½h=y
ð5Þ
At heq = 3500 h/y, AEP = 8.4 105 MW h/y. Particular care must be taken when calculating LCOE associated to the wind source (LCOEWT). In fact, cs,WT must be weighted according to the energy provided to the electric motors in nominal conditions respect to the generated energy:
LCOEWT ¼ cs;WT
W EM;nom heq;c W el;nom heq
ð6Þ
where heq,c is the number of equivalent operating hours of the charge section, which may be different than that of the discharge section (heq). As heq affects the result of cEl, a parametric analysis is performed as heq varies, with the HPP-CAES working both in design and off-design conditions. Results are reported in Fig. 7. The figure shows that LCOE significantly decreases with increasing operating hours and tends to a constant value of about 77 €/MW h. Assuming a target value of 3500 operating hours, the corresponding LCOE is 80 €/MW h. This result is strongly affected by the price of electricity from the wind farm. If a lower price were considered (e.g. 40 €/MW h, which was the price in [26]) an almost equally reduction would be obtained on the LCOE (about 70 €/MW h). The price of natural gas has a smaller influence on the LCOE. For a variation of 20% of the natural gas price, the LCOE varies less than 10%. In Fig. 7, the thermoeconomic costs are also reported. While LCOE can be obtained only for the design condition, thermoeconomic costs can be calculated in any operating conditions of the plant. The figure shows that thermoeconomic cost in design condition coincides with the LCOE. Two off-design conditions are also shown: one corresponding with reduced electricity production from the discharging section (175 MW) and one corresponding with reduced electricity production combined with increased compressed air production (120 MW). In both cases, the unit cost of electricity increases, but the distances between the curves show that the effects of off-design condition of the compressors is much larger.
4. HPP-CAES economic evaluation The IPEX Market is composed by the Spot Electricity Market, the Forward Electricity Market and the Platform for physical delivery of financial contracts concluded on IDEX – CDE. The Spot Electricity
Market is the place where electricity trading occurs. This consists of three different sessions: Day-Ahead Market (abbreviated as MGP, in Italian), where the main stocks are assigned. Intra-Day Market (abbreviated as MI, in Italian), where adjustments are operated on the basis of the grid capacity. Ancillary Service Market (abbreviated as MSD, in Italian), where some of the plants provide their availability to modify their operation in order to prevent from unexpected request variations [30]. To evaluate the economic convenience of the HPP-CAES, one year operation is simulated on the Spot Electricity Market. Ten minute time-step (WT) data are considered and suitable selling strategies are implemented. To evaluate the success of the strategies, two significant parameters are introduced: the demand met parameter and the spillage parameter. The demand met parameter, Pd, is used to assess how many times the HPP-CAES satisfies the demand in a year. This is defined as follows:
Pd ¼
8760 X
W iel
i¼1
W iD
min floor
!
! ; 1 100
ð7Þ
where: Wel is the power generated by the HPP-CAES. WD is the power demand from the electricity market. This parameter is evaluated on an hourly basis both for the sales on the MGP and MSD. The spillage parameter, Ps, is used to evaluate the energy from the wind farm that can be stored. It represents the percentage of WT power which cannot be stored in the HPP-CAES because of its incapability of operating the compressors. In other terms, it is the available wind power input to the HPP-CAES compressor side, spilled due to insufficient reservoir space or pressure limit hits.
Ps ¼
8 760 X
ðW iWT;in W iEM Þ
i¼1
W iWT
100
ð8Þ
where: WWT,in is the power available from WT within the range of air compressors operation. WEM is the power available from the WT that effectively powers the Electric Motors driving the air compressors. 5. MGP and MSD demand following island mode The HPP-CAES is simulated to operate on MGP and MSD markets in order to satisfy the demand, regardless of the selling prices. The results of this strategy are reported in Figs. 8–10 and in Table 1. In Fig. 8, the share of the total wind energy available (EWT,available) in the components, the energy stored (Estored) and the energy directly
Fig. 7. LCOE vs. cEl at different working modes as the number of equivalent operating hours of the discharge section varies.
Fig. 8. Stored energy. Island mode configuration.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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Fig. 9. Storage limits due to pressure limits. Island mode configuration.
Table 2 Demand on the MGP market. Island mode configuration. DemandMGP (h/y) DemandMGP,accepted (h/y) DemandMGP,refused (h/y) DemandMGP,NOT MET (h/y) DemandMGP,MET (h/y) Pd,MGP (%)
4802 839 3963 9 830 98.9%
Table 3 Economic evaluation of the strategy on the MGP and MSD markets. Island mode configuration. HPP-CAES revenues MGP (k€) HPP-CAES revenues MSD (k€) HPP-CAES fees (k€) Energy from grid (k€) Wind energy HPP-CAES net revenues (k€) HPP-CAES SPBO (y)
Fig. 10. Energy sold and stored. Island mode configuration.
Table 1 Compressors working time. Island mode configuration. EWT,available (MW h) Storedmax (%) EWT,com (MW h) Ps (%) Stored (%) Miss (%) TimeWT,available,com (h/y) Timeworking,com (h/y) Timerel,working,com (%)
5.11 E+05 47.8% 1.33 E+05 74.0% 26.0% 21.8% 2171 1240 57.1%
sold to the grid (Esold,wind) are shown. The relation between these quantities is the following:
Etot;wind ¼ Esold;wind þ
Estored
gcharge
ð9Þ
Note that Estored is always much lower than Esold,wind. The annual amount of electricity that can be produced by the wind turbines is about 511 GW h/year. The maximum amount of energy that could be stored using the proposed system is about 244 GW h/year, i.e. 47.8% of the production. Instead, only 133 GW h/year (26.0%) are used to drive the compressors and stored in the cavern. Compressors operate about 57.1% (1240 h/y) of the maximum number of hours (i.e. only 2171 h/y of the 4802 h/y). The reasons why not all the EWT,com is stored are related with the power exceeding the compressor capacity or the too large pressure in the cavern (pCAR), as shown in Fig. 9. About 8.9% of the times energy available from the wind farm is in the range of operation using three compressors in series (78–90 MW) and pCAR is higher than 55 bar, thus energy is
3443 4214 129 0 2547 1905 Not reached
directly supplied to the grid instead of being stored. Similarly, about 8.1% of the times energy available from the wind farm is in the range of operation using four compressors in series (90– 120 MW), but pressure in the cavern is too large. In 25.9% of the cases, wind power is larger than the maximum value. In Fig. 10 the fuel energy that is spent (Efuel), with the stored energy (Estored) and the energy sold from the CAR to the grid (ECAR,sold) are reported. Considering ECAR,sold as the useful effect and Efuel and Estored/gcharge as the expenses, an average monthly efficiency gall can be defined:
gall ¼
ECAR;sold Estored =gcharge þ Efuel
ð10Þ
The average efficiency is about 52.6%. Results concerning the economic evaluation are reported in Tables 2 and 3. In Table 2, the potential hourly requests of energy (DemandMGP,) the demand accepted (DemandMGP,accepted) and that refused (DemandMGP,refused) as well as Pd are reported. Only 17.5% of the demand is accepted (about 839 h/y) and 98.9% of cases the duty is accomplished. As a consequence, the working hours of the generation section of the HPP-CAES is about 830 per year. This value is consistent with the results found by RSE [31]. Almost 2 M€ would be lost in a year if the HPP-CAES were operated under these conditions, as shown in Table 3. In fact, it is not convenient to operate only in MGP because often the cost of electricity produced by the HPP-CAES is higher than the selling price on MGP. In this scenario, the energy bought
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
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F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx
Fig. 11. Stored energy. Grid connected configuration.
from the grid (Grid energy) is equal to zero (HPP-CAES operates in island mode) and the Simple Pay-Back Period (SPBP) is never reached. As shown in Fig. 2, this is not due to wrong size of the compressors, but to the large variability of the wind production. The average wind speed is 6.0 m/s and the standard deviation is 3.0 m/s, with significant number of hours without power production because of the too small wind velocity (about 6600 h). For this reason a different option is considered in next section.
6. MGP and MSD demand following grid connected The HPP-CAES operates both on MGP and MSD markets, but, with respect to the previous strategy, it is possible to buy electricity from the power grid to integrate the energy provided by the wind farm. This strategy does not significantly modify the operating hours of the wind farm, but increases the operating hours of the HPP-CAES. This means that the storage system is truly regarded as a way to solve the zonal unbalances, instead of the wind farm unbalances. This kind of operation stresses the ability of the HPPCAES to operate with fast transients, which is a feature that makes it suitable for the MSD market. The results of this scenario are reported in Figs. 11–13 and in Table 4. A comparison between Figs. 11 and 8 shows that the grid connected option allows one to increase the total amount of energy produced by the system of about 277 GW h. Stored energy also increases, about 265% more than in the island mode, thanks to the electricity bough from the grid. The largest increases are obtained in October, November and March. As shown in Table 4, Ps decreases from 74% to 59.5%, therefore compressors work more hours (4380 h/y compared to 1240 h/y). The percentage of infeasible storage decreases to about 24.5%, as shown in Fig. 12. The average monthly efficiency, gall, using this operation strategy is about 49.9%, as shown in Fig. 13.
Fig. 13. Energy sold and stored. Grid connected configuration.
Table 4 Compressors working time. Grid connected configuration. EWT,available (MW h) Storedmax (%) EWT,com (MW h) Ps (%) Stored (%) Miss (%) TimeWT,available,com (h/y) Timeworking,com (h/y) Timerel,working,com (%)
5.11 E+05 47.8% 4.86 E+05 59.5% 40.5% 7.3% 5800 4380 75.5%
The results of the economic analysis are reported in Tables 5 and 6. The cost of electricity purchased from the network and that associated to the wind generation are considered. The first term is assumed as equal to the National Single Price (PUN), i.e. the purchase price for end customers, computed as the weighted average zonal price. This assumption comes from the following considerations. The value of unbalancing in MSD, that is the difference between the produced electricity and the expected production, depends on the zonal unbalance, i.e. the production of all producers in the zone, which may be positive or negative. Being this information not available, it is assumed that the electricity is bought as MGP price, that corresponds to a positive zonal unbalance. The fact that the electricity is bought at PUN price and not at zonal price, once is bought at MGP price, is always the real case. The demand accepted on the MGP is significantly higher then in the island mode, 3297 h/y instead of 839 h/y), which brings a significant reduction in the demand refused, 1105 h/y instead of 3963 h/y. The demand not met increases, from 9 h/y in the island mode to 247 h/year, therefore the demand met parameter becomes
Fig. 12. Storage limits due to pressure limits. Grid connected configuration.
Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052
F. de Bosio, V. Verda / Applied Energy xxx (2015) xxx–xxx Table 5 Demand on the MGP market. Grid connected configuration. DemandMGP (h/y) DemandMGP,accepted (h/y) DemandMGP,refused (h/y) DemandMGP,NOT MET (h/y) DemandMGP,MET (h/y) Pd,MGP (%)
4802 3297 1105 247 3050 92.5%
Table 6 Economic evaluation of the strategy on the MGP and MSD markets. Grid connected configuration. HPP-CAES revenues MGP (k€) HPP-CAES revenues MSD (k€) HPP-CAES fees (k€) Energy from grid (k€) Wind energy HPP-CAES net revenues (k€) HPP-CAES SPBO (y)
9568 26,097 6186 10,167 10,933 8379 9.53
slightly lower (Pd,MGP = 92.5% instead of 98.9%). Due to the higher number of expected full load hours (3500 h/y), cEl is lower than in the previous scenario and revenues are positive also on the MGP, registering an increase of about 13 M€. The revenues on MSD register an increase of about 22 M€. Expenses also increase, particularly because of the energy bought from the grid and the larger amount of wind energy that is stored. The SPBP is reached after about 9.5 years, thus the investment is convenient. This result is not significantly affected by the unit cost of natural gas. If the unit cost of natural gas was considered 20% larger, the pay-back period would increase to about 9.7 years.
7. Conclusion In this paper, thermoeconomic analysis is used for the design and analysis of a Compressed Air Energy Storage (HPP-CAES) integrated with a wind farm. With respect to conventional economic analysis (LCOE) thermoeconomics allows one to obtain the cost assessment in off-design operation, which is crucial in the case of storage systems. The charging and discharging sections have been separately analyzed. In the charging section, the exergetic unit cost of compressed air has been obtained as the function of pressure in the cavern and the mechanical power supplied to the compressors. In the discharging section, instead, the unit cost of electricity is obtained as the function of the exergetic unit cost of compressed air and the electricity production. The HPP-CAES is simulated to operate in the South of Italy, thus wind data and the Italian Power Exchange market considered make reference to that region. The energy system has been designed according to the size required by the Italian electricity transmission grid operator (Terna S.p.A.) to effectively contribute to solve congestion problems in that region (240 MW). A simulation of the system has been performed considering two different operating strategies according to the demand and sale price on the IPEX Market. In the first strategy, the storage system operates in the Day-Ahead Market (MGP) and Ancillary Service Market (MSD) with no possibility to buy electricity from the grid. In the examined scenario, the system results as economically infeasible. This is mainly due to the production of the wind farm, which causes a small number of operating hours of the compressors (about 1240 h/y). As a result, also the generation section of the CAES operates for a small number of hours and about 82.5% of the market energy demand cannot be accepted.
9
In a second strategy, the possibility of meeting the energy market demands by integrating the energy production from the wind farm with electricity bought from the grid has been considered. This means that the CAES is truly used to solve the local imbalances of the grid. In this case the number of operating hours of the compressors becomes about 4380 h/y. The generation section operates for about 3297 h/y, which means that the percentage of market energy demand that cannot be accepted is reduced to about 25.1%. Although the project is focused on the analysis of a specific plant, the thermoeconomic methodology has a general validity that can be easily applied to the optimal design and operation of different installations of HPP-CAES systems. Further work is needed on the implementation of a model able to predict the prices and the quantity sold on the IPEX, taking into account the utilities participating to the bids. In addition, a detailed analysis of the effects of CAES systems on the electricity grid should be performed. Acknowledgements This work was developed with the help of the Enel personnels, who have assisted Federico de Bosio during his Master’s thesis. Authors are particularly grateful to Juri Riccardi of Enel Research and Innovation, Franco Sansone and his collaborators. References [1] International Energy Agency. Renewable energy medium-term market report 2013; 2013. [2] Keles D, Hartel R, Möst D, Fichtner W. Compressed-air energy storage power plant investments under uncertain electricity prices: an evaluation of compressed-air energy storage plants in liberalized energy markets. J Energy Markets 2012;5(1):53–84. [3] Milazzo A, Grazzini G. Thermodynamic analysis of CAES/TES systems for renewable energy plants. Renewable Energy 2008;33(9):1998–2006. [4] Salgi G, Lund H. System behavior of compressed air energy storage in Denmark with a high penetration of renewable energy sources. Appl Energy 2008;85:182–9. [5] Cavallo A. Controllable and affordable utility-scale electricity from intermittent wind resources and compressed air energy storage (CAES). Energy 2007;32:120–7. [6] Lund H, Salgi G, Elmegaard B, Andersen AN. Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices. Appl Therm Eng 2009;29:799–806. [7] Zafirakis D, Kaldellis JK. Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks. Energy Policy 2009;37:1958–69. [8] Mason JE, Archer CL. Baseload electricity from wind via compressed air energy storage (CAES). Renew Sustain Energy Rev 2012;16:1099–109. [9] Succar S, Denkenberger DC, Williams RH. Optimization of specific rating for wind turbine arrays coupled to compressed air energy storage. Appl Energy 2012;96:222–34. [10] Hartmann N, Vöhringer O, Kruck C, Eltrop L. Simulation and analysis of different adiabatic compressed air energy storage plant configurations. Appl Energy 2012;93:541–8. [11] Marano V, Rizzo G, Tiano FA. Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage. Appl Energy 2012;97:849–59. [12] Wang SY, Yu JL. Optimal sizing of the CAES system in a power system with high wind power penetration. Electrical Power Energy Syst 2012;37:117–25. [13] Mauch B, Carvalho PMS, Apt J. Can a wind farm with CAES survive in the dayahead market? Energy Policy 2012;48:584–93. [14] Safaei H, Keith DW, Hugo RJ. Compressed air energy storage (CAES) with compressors distributed at heat loads to enable waste heat utilization. Appl Energy 2013;103:165–79. [15] Madlener R, Latz J. Economics of centralized and decentralized compressed air energy storage for enhanced grid integration of wind power. Appl Energy 2013;101:299–309. [16] Manchester S, Swan L. Compressed air storage and wind energy for time-ofday. Proc Comput Sci 2013;19:720–7. [17] Abbaspour M, Satkin M, Mohammadi-Ivatloo B, Hoseinzadeh Lotfi F, Noorollahi F. Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES). Renewable Energy 2013;51:53–9. [18] Yucekaya A. The operational economics of compressed air energy storage systems under uncertainty. Renew Sustain Energy Rev 2013;22:298–305. [19] Wolf D, Budt M. LTA-CAES – a low-temperature approach to adiabatic compressed air energy storage. Appl Energy 2014;125:158–64.
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Please cite this article in press as: de Bosio F, Verda V. Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market. Appl Energy (2015), http://dx.doi.org/10.1016/j.apenergy.2015.01.052