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Subcooled compressed air energy storage system for coproduction of heat, cooling and electricity
Applied Energy 205 (2017) 602–614
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Subcooled compressed air energy storage system for coproduction of heat, cooling and electricity
MARK
⁎
A. Arabkoohsara,b, , M. Dremark-Larsenb, R. Lorentzenb, G.B. Andresenb a b
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands Department of Engineering, Aarhus University, Aarhus, Denmark
H I G H L I G H T S configuration of compressed air energy storage system is proposed and analyzed. • AThisnewsystem, so-called subcooled-CAES, offers cogeneration of electricity, heat and cooling. • A pseudo-dynamic exergy and economic analysis of the system for an entire year is presented. • The annual power, energy, cooling and heat efficiencies of the system are around 31%, 32% and 92%. • The overall energy and exergy performance coefficients of the system are 1.55 and 0.48, respectively. •
A R T I C L E I N F O
A B S T R A C T
Keywords: CAES Energy storage Smart energy systems Cogeneration Coefficient of energy performance District energy systems
Various configurations of compressed air energy storage technology have received attention over the last years due to the advantages that this technology offers relative to other power storage technologies. This work proposes a new configuration of this technology aiming at cogeneration of electricity, heat and cooling. The new system may be very advantageous for locations with high penetration of renewable energy in the electricity grid as well as high heating and cooling demands. The latter would typically be locations with district heating and cooling networks. A thorough design, sizing and thermodynamic analysis of the system for a typical wind farm with 300 MW capacity in Denmark is presented. The results show a great potential of the system to support the local district heating and cooling networks and reserve services in electricity market. The values of power-topower, power-to-cooling and power-to-heat efficiencies of this system are 30.6%, 32.3% and 92.4%, respectively. The exergy efficiency values are 30.6%, 2.5% and 14.4% for power, cooling and heat productions. A techno-economic comparison of this system with two of the most efficient previous designs of compressed air energy storage system proves the firm superiority of the new concept.
1. Introduction The importance of electricity storage is increasing as the awareness about the importance of more utilization of intermittent renewable energy increases. Energy storage technologies are mainly attractive due to their potential in load balancing in the electricity grid as well as storing the surplus power during peak production periods for later use at peak consumption times [1]. Compressed air energy storage (CAES) is one of the promising energy storage technologies being given much attention as it offers a very high energy density, a relatively low cost of capital, a short response time to load change and competitive energy efficiency [2]. The history of CAES dates back to about 70 years ago with a patent application in the USA [3], though this concept was not of
⁎
interest for the following two decades due to the unimportance of electricity storage in absence of renewable power generation systems. Rising the importance of storage technologies over time, the Huntorf plant was built as the first pilot scale CAES of the world in 1978 [4]. In fact, this was the beginning of an increasing practical interest in this technology focusing on two major issues of long-term reservoir stability of CAES operation and second-generation CAES concepts with the main goal of minimizing fuel use [5]. As the results of the R & D efforts on the second-generation of CAES concepts, diabatic-CAES (DCAES) was proposed first. In fact, the D-CAES is the simplest form of the second-generation of this technology. Here, the heat produced in the air stream during the compression process is dissipated into the atmosphere as waste. Then, a massive amount of auxiliary heat is provided,
Corresponding author at: Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. E-mail address: [email protected] (A. Arabkoohsar).
http://dx.doi.org/10.1016/j.apenergy.2017.08.006 Received 2 May 2017; Received in revised form 30 July 2017; Accepted 4 August 2017 0306-2619/ © 2017 Elsevier Ltd. All rights reserved.
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A. Arabkoohsar et al.
ηI ηII α ε λ γ € ϑ Δ
Nomenclature c cp h k m ṁ n P Eb Eḋ Efc Eṡ PTC PTH PTP ̇ Qcold ̇ Qheat r R s Ṡ tc td T U V w Ẇ X
specific heat capacity (W/m2) air specific thermal capacity in constant pressure (kJ/ kg·°C) specific enthalpy (kJ/kg) thermal conductivity (W/m·K) mass (kg) mass flow rate (kg/s) number of compression/expansion stages pressure (kPa or bar) bidding power value (MW) deficit of power (MW) forecasted wind power (MW) surplus power (MW) power-to-cold power-to-heat power-to-power cold production rate (kW) heat production rate (kW) compression/expansion factor gas constant (kJ/kg·K) specific entropy (kJ/kg·K) entropy generation rate (kW/K) charging operation time-steps discharging operation time-steps temperature (°C or K) overall heat transfer coefficient (W/m2·K) volume (m3) specific work (kJ/kg) work rate (kW) traded electricity (€)
Subscriptions a c char cm cs d dam disch e el f g hm hs i idm is o ptc pth ptp r rtm tes t tot
Greek symbols
ψ̇ ψ µ η
energy efficiency exergy efficiency energy price (€/MWh) heat exchanger effectiveness factor 5-min time-step counter hourly time-step counter euro currency 5-min time-step counter in each hour in power market power imbalance value (MW)
exergy rate (kW) exergy (kJ) thermal capacity ratio energy conversion efficiency
typically by a fossil fuel fired system, to heat up the high-pressure air to temperatures in the range of 300–500 °C before expansion in the turbine. The round-trip efficiency of this system ranges from 0.54 to 0.6 [6]. The second pilot-scale CAES unit built ever is a D-CAES plant with 110 MW output power in McIntosh in operation since 1991 [7]. Another type of second-generation CAES is adiabatic-CAES (ACAES) [8]. In this configuration, the heat generated in the compression stage is gathered and stored for reuse in the expansion process. This can be done in two different ways of with or without thermal energy storage (TES) units. For the scheme without any TES, the heat is stored in the air reservoir. In fact, the air reservoir plays the role of pressure heat storages simultaneously. A-CAES without TES is restricted to rather low storage pressures and consequently to low energy densities [9]. This drawback without TES scheme of the A-CAES led to the appearance of TES equipped A-CAES in which the there is an individual storage tank/ reservoir for the heat gathered in the charging process and cold compressed air with much higher pressure level is stored in the cavern. Ancillary heaters provide the rest of the required heat for the air stream [10]. In this way, the round cycle efficiency can increase to around 0.7 [11]. A further introduced schematic of CAES is isothermal-CAES (ICAES) in which higher expansion and compression process efficiencies are aimed by preventing temperature variation of the air in the turbomachinery during the charging and discharging processes. Piston
air compressor charging phase cold market cold storage destruction (exergy) day-ahead market discharging phase external electricity working fluid (industrial oil) electricity generator heat market heat storage internal intra-day market isentropic process ambient conditions power-to-cold power-to-heat power-to-power air reservoir real-time market thermal energy storage turbine total
machinery is recommended to be used in this system as such machines can perform slow compression and expansion processes, facilitating the heat exchange process inside the machinery [12]. Closed cycle and open cycle hydro-pneumatic concepts are two different types of this system [13]. The major drawback of the closed cycle type is its low energy density, restricting its application in laboratory scale only [14]. On the other hand, the open cycle scheme has been developed in utility scale due to not suffering from such technical problems. There is one pilot plant open cycle I-CAES in operation in the USA [15]. The obtainable efficiency of this system in a full charging and discharging process can increase up to 80%. Finally, the low-temperature CAES (LT-CAES), which is a new design of the A-CAES concept, is the last configuration proposed in this regards [16]. The main objective here is to minimize or even eliminate the contribution of fuels in the system. Thus, the air stream is only heated by the stored heat during the compression process before being expanded through the turbines. The maximum temperature of the expanding air in this arrangement is between 90 and 200 °C. Although the round-trip efficiencies of this scheme (in the range of 52–60%) is slightly lower compared to those obtainable in A-CAES, the system benefits from faster startup and wide-ranging part load ability [17]. On the other hand, district energy systems provide energy for the consumers cheaper than decentralized or individual methods. In addition, flexible building design, lower operations and maintenance costs, 603
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reservoir. The heated oil produced in this stage could be stored in another storage tank for heating applications, like district heating balancing reserve. Based on the desired district heating temperature, a high temperature of 120 °C is considered for the hot storage. This temperature could be lowered in modern low-temperature district heating systems where the supply temperature is often below 60 °C. In discharging mode, the compressed air is expanded through air expansion motors. Here, the rotational work is used to drive an electricity generator. However, unlike all of the previous configurations of the CAES, the airflow is not heated before the expansion. In this way, although the power-to-power (PTP) efficiency of the system decreases, no surplus heat is required for the preheating process and more importantly, a large amount of cooling energy may be produced after the airflow expansion. The cooling produced in this phase can be gathered by employing a heat exchanger to be stored for later use of cooling applications. Therefore, with such a configuration of CAES, the system can be used for heat production in the charging state and for cogeneration of cooling and power in discharging mode. The cooling energy can be stored like the heat generated during compression. To this end, two storage tanks are assumed for the expansion stage as well. One tank will be at ambient temperature and the other one is the cold storage with much lower temperature. This tank is supposed to be kept at −30 °C to be able to support low temperature industrial cooling demands as well. Here also, the working fluid is industrial oil. There are some further important notes that should be taken into account for the above system. Multistage compressor and turbine are used in the system to increase the efficiency. The number of stages must be defined based on a techno-economic trade off. As the number of stages increases, the efficiency of the system will increase where the cost picks up too. However, as there is no information about the actual trend of cost increase with the stages number, a triple stage compression/expansion will be assessed in this work. The same can be claimed for the compression and expansion factor of the assets. The compression/expansion factor is considered equal to 5 in each stage. Therefore, the maximum pressure of the air in the cavern is 125 bar. In contrast with the compressors that can be similar to those in other CAES configurations due to the same compression process, the expanders cannot be the same as the air goes to very low temperatures during the expansion. There is a wide range of air expanders that are used in refrigeration systems and can be used in this system [25]. Here, employing multistage expansion, and the heat exchangers in between supporting by water in ambient temperature level, facilitates the process because the airflow can come back to temperatures around ambient level so as not to go down to too low temperatures. Thus, among
better energy delivery, increased price stability, enhanced comfort, reduced carbon footprint are further advantages of such systems [18]. These all are why district cooling and heating are popular and widely distributed in several countries, especially in Northern Europe [19]. For example, distributed heating networks supported by heat production plants supply almost 60% of heat consumers in Denmark [20]. Although district cooling covers only 4% of the total cooling demand of this country, expanding the areas covered by district cooling is receiving increased attention [21]. In Sweden, district cooling already supplies about 40% of the demand as the result of an expansion over the last 20 years [22]. In this study, a new design of CAES, so-called subcooled-CAES (SCCAES), is proposed by which the system will be capable of producing heat, power and cooling at a high overall efficiency. The work presents a detailed design, sizing and thermodynamic modelling of the system. For the sake of giving realistic information and performance values, the system is designed and sized for a typical wind farm with 300 MW in Denmark, i.e. 10% of the whole wind production capacity of the WestDenmark region. Local power and district heating/cooling demands and productions of this region are also further pieces of the database of this study in the simulations. All the simulations have been carried out in MATLAB software. The novelty of this works lies on proposing a new generation of CAES technology with high compatibility with the locations with district energy systems. As both district energy systems and energy storage technologies are important parts of future energy systems worldwide, the proposed configuration can play an important role in the future energy matrixes. It will be shown that the proposed concept outperforms all the previous configurations of the CAES in terms of energy, and economic performance and offers a competitive exergy efficiency as well. 2. The SC-CAES system Fig. 1 illustrates the schematic diagram of the SC-CAES technology. Like all other energy storage systems, there may be two operation states for the systems, i.e. charging and discharging. There is detailed information and explanation about the operation of various CAES designs in [23,24]. In charging mode, the compressors start working like in other CAES systems when surplus power is available. During the compression process, the airflow temperature increases. Thus, heat exchangers are employed to gather this heat after the compression. After exchanging heat with a cold medium (here proposed to be industrial oil at ambient temperature), the cold compressed air is stored in the air storage
Fig. 1. The schematic of the proposed configuration of the SC-CAES system; WF: wind farm, M: motor, C: compressor, HX: heat exchanger, ST: storage tank, T: turbine, G: electricity generator; green arrows: airflow, light-blue arrows: working fluid in ambient temperature, red arrows: hot working fluid, dark-blue arrow: subcooled working flow. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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kg). The subscriptions c and a represent the compressors and the airflow respectively and n is the number of stages. For each of the stages, the specific work is simply calculated by:
all the options, single screw air expander has been selected for the present analysis [26]. The other noteworthy point is that since the system changes from charging to discharging and vice versa frequently, it may be difficult to manage direct supply of heat and cooling for district energy systems. This is why the produced heating and cooling is proposed to be stored and then supplied at a later time. The price of flexible balancing is normally higher in energy markets. It is desirable to change the arrangement of the compressors and the expanders from parallel to series and vice versa as the pressure of the compressed air reservoir changes. Table 1 shows how the arrangement of the turbomachinery of the system changes by the in accordance with the pressure.
wc = (hi−he ) = cp (Ti−Te )
where, T is temperature, cp is the average specific thermal capacity of air and the subscriptions i and e refer to the inlet and outlet conditions of each compressor stage. Assuming an adiabatic process through each of the stages, the outlet air temperature can be given as: μ−1
( μ ) −1 ⎞ ⎛ rc ⎟ Te = Ti ⎜1 + ηis,c ⎟ ⎜ ⎠ ⎝
This section provides the mathematical modelling of the system, based on the first and the second laws of thermodynamics, as well as the further information required for simulating the system performance including the operation conditions and the energy market details of the case study. 3.1. First law analysis The energy performance model of the system is divided into two different sections, one for the charging and the other one for the discharging mode. Charging mode: In this mode, the surplus electricity (Eṡ ) available for the energy storage system is used to drive the compressor stages. Due to the high mass flow rate and pressure required in the system, the compressors are proposed to be of the centrifugal type [24]. For the multistage compressor set, one could write:
Te,a = Ti,a (1−ε) + εTi,f
ṁ f =
n
∑
(ṁ a wc )j
(4)
ṁ a cp (Ti,a−Te,a) cf (Te,f −Ti,f )
(5)
Note that, in the two above equations, the airflow is the fluid with the Cmin as the temperature drop in the air stream will be higher than the working fluid temperature change through the heat exchanger. Doing the above calculation procedure and having information
(1)
j=1
(3)
in this relation, rc is the compression factor (equal to 5), μ and ηis,c are the ratio of specific heat capacities for the air and isentropic efficiency of the compressors, equal to 1.4 and 0.85, respectively. So far, the specific work of each compressor can be calculated if the inlet temperature of each stage is known. Except the first stage of the compressor for which the inlet air temperature is known, i.e. equal to the ambient temperature, for the next stages, the temperature should be calculated by modelling the performance of the intercooling heat exchangers. For these heat exchangers, the inlet and outlet temperatures of the working fluid are known, i.e. ambient temperature and 120 °C, respectively. The inlet airflow temperature is also known and equal to the outlet temperature of the same stage compressor (Eq. (3)). Designing the counter-flow heat exchangers to offer an effectiveness factor (ɛ) of 0.8, based on ɛ-NTNU method [27], one has the following formulation:
3. Material and methods
Wċ = Eṡ =
(2)
in which, Ẇ is the total work of the compressor set (kW), ṁ is mass flow rate (kg/s), w is the specific work of each stage of the compressor (kW/ Table 1 The arrangement of the compressors and the expanders in various cavern pressure ranges. Cavern pressure
Compressors
Expanders
P < 5 bar
5 bar ≤ P < 25 bar
P ≥ 25 bar
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system as power-to-cooling (PTC) efficiency and PTP efficiency given by the following equations, respectively:
about the amount of surplus power available, one can calculate the airflow rate being compressed and hot oil produced in the charging phase. Considering the compressed air as ideal gas, which is reasonable for ambient temperature and pressure of 125 bar, the pressure of the reservoir can be calculated by:
m RT Prλ = ⎛ r r ⎞ ⎝ Vr ⎠ ⎜
ηI ,ptc =
λ
where: mrλ = mrλ − 1 + ṁ a
⎟
and
Tr = To
ηI ,ptp =
(6)
in this equation, the subscription r refers to the reservoir and the superscription λ counts the time-steps in seconds. P, V and m are respectively pressure, volume and mass and To is ambient temperature. The rate of heat production by the system in charging mode can be calculated by the following equation:
td ̇λ ∑λ = 1 Qcold λ
tc
∑λ = 1 Eṡ
(13)
λ Eḋ λ Eṡ
(14)
td ∑λ = 1 tc ∑λ = 1
The total energy performance coefficient of the system can be defined as below: λ
td
ηI ,tot =
∑
(7)
tc λ=1
λ ̇ ⎛ Qheat ⎟⎞ ̇ ⎝ Es ⎠
There are a number of studies dealing with exergy analysis of various configurations of the CAES. A detailed exergy modelling of heat storage equipped A-CAES is presented in [10]. Having those formulas, one could assess the exergy performance of the understudy configuration of CAES by taking the differences into account. Similar to the energy analysis section, here also the exergy modelling is presented based on the two different operation states of the system, i.e. charging or discharging, just highlighting the key correlations. It should be noted that the variations of kinetic, potential and chemical exergies through all of the control volumes are assumed to be negligible. Charging mode: In this phase, the active instruments are the compressors, the heat exchangers, the heat storage and the air storage reservoir. For the compressors, the exergy balance of each stage of the compressor set can be written as:
⎜
(8)
here, tc refers to the number of time-steps in the charging mode. Discharging mode: In this state, the total required work to be produced by the turbine set is calculated by:
Ė Wṫ = d = ηg
n
∑
(ṁ a wt )j (9)
j=1
here, Eḋ stands for the amount of power to be produced (the deficit of electricity in the wind farm) and ηg is the electricity generator efficiency (0.95). wt is the specific work of each stage of the turbine, which are supposed to be single screw expanders, given by the following equation: (
wt = RTi
μ−1
μ ⎛ 1−rt μ ⎜ μ−1 ⎜ ηis,t ⎝
ψ̇
d Ti,a ⎞ ⎡ ⎞⎤ = T Ṡ Wċ + ṁ a ⎢ (hi,a−he,a)−To ⎛⎜cpln ⎛⎜ ⎟ + R ln(rc )⎟ o gen ⎥ ⎝ Te,a ⎠ (16) ⎝ ⎠⎦ ⎣ where h is the specific enthalpy of airflow and ψḋ is the rate of exergy destruction through the compressor which is a direct functional of the ̇ ). rate of entropy generation (Sgen For the heat exchangers, the exergy balance can be written as:
)⎞
⎟ ⎟ ⎠
(10)
where R is the gas constant of air (0.287 kJ/kg·K) and rt is the expansion factor of the expander (1/5 = 0.2). In addition, ηis,t is the isentropic efficiency of the single screw expander, which is a functional of the intake pressure and rotational speed. Experimental results reported in [28] are used to estimate the isentropic efficiency of the various stages of the expanders and various operational loads. Based on this report, the higher inlet pressure of the expander, the higher adiabatic efficiency it offers. Overall, an average adiabatic efficiency of 0.65 is expected from the single screw expanders working at low temperatures. The outlet temperature of each stage is also given by the same relation as Eq. (3), where rt and ηis,t sit instead of rc and ηis,c , respectively. For an expander, the more efficient it is, the lower discharge temperature it presents. Having the outlet air temperature of the turbines, one can model the heat exchangers between the expanders. Here also the temperature change in the airstream through the expander should be higher than the working fluid. Thus, reusing Eqs. (4) and (5), the outlet air temperature and mass flow rate and the working fluid mass flow rate can be calculated. The mass and the pressure of the cavern can be given by:
m RT Prλ = ⎛ r r ⎞ ⎝ Vr ⎠ ⎜
⎟
⎡ ⎛ Ti,f ⎞ ⎤ ⎛ Ti,a ⎞ ⎤ ̇ ṁ a ⎡ ⎢ (hi,a−he,a)−To cpln ⎜ Te,a ⎟ ⎥ + ṁ f ⎢ (hi,f −he,f )−To cf ln ⎜ Te,f ⎟ ⎥ = To Sgen ⎝ ⎠⎦ ⎝ ⎠⎦ ⎣ ⎣ (17) in which, the pressure drop of the working fluid and the airflow are considered as negligible. For any other storage control volume, such as the air storage reservoir and the heat storage tank, all types of balance equations are written in transient mode. Therefore, for the compressed air reservoir in charging mode, the balance of exergy can be written as below:
dψṙ ⎛ ⎛ Ti,a ⎞ ⎛ Pi,a ⎞ ⎞ ⎤ ̇ = ṁ a ⎡ ⎢ (hi,a−ho)−To ⎜cpln To −Rln Po ⎟ ⎥−To Sgen dt ⎝ ⎠ ⎝ ⎠⎠⎦ ⎝ ⎣ ⎜
j=1
⎜
⎟
(18)
λ
where: mrλ = mrλ − 1−ṁ a
and
Tr = To
P P ψr = Vr Pr ⎡ln ⎛ r ⎞ + o −1⎤ ⎢ ⎝ Po ⎠ Pr ⎥ ⎣ ⎦
(11)
⎜
⎟
(19)
A similar exergy balance correlation can be applied for the heat storage as well while the stored exergy in the form of heat can be calculated by:
n
∑
⎟
where, ho and Po are the enthalpy and pressure of air in dead state condition, i.e. ambient temperature and pressure. The stored exergy in the form of compressed air is calculated by [29]:
The rate of cold production during the discharging mode can be calculated as:
̇ = Qcold
(15)
3.2. The second law analysis
The power-to-heat (PTH) efficiency (ηpth ) of the system is defined as below:
∑
λ
≤ (ṁ f cf (Te,f −Ti,f ))j
j=1
ηI ,pth =
tc
λ tc ∑λ = 1 Eṡ
n
̇ Qheat =
λ
td
̇ + ∑ Qheat ̇ ∑λ = 1 Eḋ + ∑λ = 1 Qcold λ=1
(ṁ f cf (Ti,f −Te,f ))j (12)
Tf ,hs Tf ,hs ⎞ ⎤ −ln ⎛ −1 ψhs = mf ,hs cf To ⎡ ⎢ To To ⎠ ⎥ ⎝ ⎣ ⎦ ⎜
In this phase, two more performance factors can be defined for the 606
⎟
(20)
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electricity and Eb is the bided power amount. In the intra-day market, when the forecast data is more precise, the difference between Eb and the new production forecast value (Efc) is traded for each hourly time-step. Here, if Efc is greater than Eb, the plant may trade for the difference and earn more revenue, and if Eb is greater than Efc, the plant has to procure additional energy. Therefore, the daily traded electricity in this market can be calculated as:
where the subscriptions hs and f, hs refer to the stored heat and the working fluid within the heat storage, sequentially. Having the useful exergy stored as heat during this phase, one could calculate the PTH exergy efficiency of the system as below:
ψhs λ tc ∑λ = 1 Eṡ
ηII ,pth =
(21)
Taking into account the exergy stored in the form of compressed air as well, one may define an index called exergy efficiency of the charging phase as below:
ηII ,char =
24
Xidm =
λ
tc
Discharging mode: In this phase, the active apparatuses are the turbines, the heat exchangers, the cold storage and the air storage reservoir being discharged. For the turbines, the exergy balance is written as follow?
24
⎛1 Xrtm = − ∑ ⎜ 12 γ=1 ⎝
(23)
For the heat exchangers, the air reservoir and the cold storage, the same formulas as those of the previous section can be applied. Calculating all of these parameters, the value of PTC exergy efficiency of the system can be given by the following equation:
ηII ,ptc =
λ
(24)
where ψcs refers to the cooling exergy stored. It should be noted that the values of PTH exergetic and energetic efficiencies are the same as similar formula applies for the both (Eq. (14)). The exergy efficiency of the whole system in discharging phase is: td
ηII ,disch =
λ
ψcs + ∑λ = 1 Eḋ ψr
(25)
ηII ,tot =
ψcs +
(29)
Qheat ,γ (30)
24
Xcm = α cold
λ Eḋ
∑
Qcold,γ (31)
γ=1
(26)
where Qheat and Qcold are the total hourly heat and cooling productions and αheat and αcold are respectively the heat and cooling production prices. For calculating the levelized cost of heat and cooling production, the same procedure as [31] is used. In this method, one needs to define the heat and cooling prices as functional of electricity price. The electricity production fee is considered as an annual average rate of 30 €/ MWh [32]. For the heat price, the heat is considered to be produced by fuel driven boilers with a fuel-to-heat ratio of 63/56 while the power is produced by conventional power plant with a fuel-to-power ratio of 70/ 28. So far, one has 133 units of fuel (63 + 70) as the inlet energy and 28 units of power plus 56 units of heat as the products. Since district heating systems are mainly supplied by CHP plants with fuel-to-heat and fuel-to-power ratios of 100/56 and 100/28, the heat price is calculated as:
αheat = α el ×
(56−
133 − 100 2
56
) = 30 × 0.705 = 21€/MWh
(32)
For the cooling price, since the district cooling network is mainly supplied by heat driven absorption chillers, the cooling price is calculated as a function of the heat price. For this case, with an average coefficient of performance of 0.75, the cooling production spot price will be 28€/MWh . Having this formulation, one now could develop an algorithm that
24 γ=1
⎞ (|Δ|α el + 13.5Δ)ϑ ⎟ ⎠γ
γ=1
As discussed, the system is designed for a wind farm in the western region of Denmark. It is assumed to operate as part of a portfolio with a maximum production of 300 MW. A critical parameter affecting the performance of an energy storage system is the operation strategy of the unit, i.e. charging and discharging times and amounts. The operation strategy is defined through a bidding market with the main objective of maximizing the revenue of the plant while smooth and reliable power production is insured. Detailed information about Danish power market can be found in [30]. This matter is discussed here briefly as well. In this market, electricity is traded in three different markets, namely, the day-ahead market, the intra-day market and the real-time market. In the day-ahead market, wind farms bid on certain values in hourly resolution taking advantage of their day-ahead wind forecast data. The electricity price is known on an hourly basis. The total daily value of the traded electricity of the wind farm in this market is calculated by:
∑
∑
Xhm = αheat
3.3. Energy market and operation conditions of the case study
Xdam =
ϑ=1
24
Finally, the overall exergy efficiency of the system is defined as the ratio of the total useful exergy produced in both charging and discharging phases to that used for driving the compressors in charging mode as below: td ψhs + ∑λ = 1 λ tc ∑λ = 1 Eṡ
12
∑
where Δ is the value of imbalance in each 5-min time-step and ϑ counts the 5-min time-steps within each hour. In addition to the electricity market, there should be a certain strategy for trading heat and cooling as the storage system here is a cogeneration. Although district heating is widespread in Denmark, specifically in Aarhus, unlike the electricity sector, there is not a coherent market for heat. Evidently, there is no purposive market for cooling either. However, as the working principle of the system is to store the heat and cooling production, the assumption of the average values of levelized cost of heat and cooling production seems reasonable. Based on this assumption, the values made in the heat and cooling markets by the energy storage system are obtained by the following equations, respectively:
ψcs tc
∑λ = 1 Eṡ
(28)
Finally, the real-time market is for balancing in shorter time-scales than one hour. Here, if there is still any imbalance between the demand and the production, the power plants bid for recovering this imbalance. It is assumed that the time-steps in this market are in 5 min. Investigations show that up balancing and down balancing should be priced at about 13.5€ above and below the corresponding hourly electricity price, respectively. Thus, the total daily value in the realtime market may be estimated by:
(22)
Ti,a ⎞ ⎡ ⎞ ⎤ = Ẇ + T S ̇ ṁ a ⎢ (hi,a−he,a)−To ⎛⎜cpln ⎛⎜ ⎟ + R ln(rt )⎟ t o gen ⎥ Te,a ⎠ ⎝ ⎝ ⎠⎦ ⎣
((Efc−Eb) α el ) γ
γ=1
ψhs + ψr ∑λ = 1 Eṡ
∑
(Eb α el ) γ (27)
in which, γ is the counter of hourly time-steps, αel is the hourly price of 607
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early, middle and late three 20-day periods of the year. The production level is in fact the difference between the bidding value and the realized production of wind farm that should be covered by the energy storage system. According to the figure, here again a maximum power production of around 140 MW is seen only once during these sample days and the production level comes rarely above 50 MW for the rest of the shown period. This is also due to the performance of the operation algorithm and can be modified by employing a better operation/bidding strategy. Evidently, when the power production is zero, the storage unit is in charging phase. Fig. 7 shows the rate of unrecovered demand due to the cavern running out of compressed air. This parameter is presented for three 120-day periods over the year. As seen, the performance of the system has been much better during the middle third of the year during which the level of unsuccessful coverage of ramps stay very low and even in shorter time-scales. On the other hand, a sharp and continuous failure in ramp coverage is seen during the last days of the year. Comparing this figure with the cavern pressure graph, one could easily realize the agreement between the results as the reservoir pressure is almost always zero exactly during this period, stipulating empty air storage. Clearly, modifying the algorithm, the rate of successful coverage can also be modified sharply and even it may approach to zero. Fig. 8 shows the total monthly heat production of the storage system in charging phase. As seen, a maximum total monthly heat production of 21 GWh is produced in May while the least production is 7.7 GWh occurring in November. Naturally, the more system is in charging phase, the more heat is generated. Thus, based on the operation strategy defined for the system, it goes more in the charging phase during April and May. As such, the less heat production of the system, the less it has worked in charging mode. Fig. 9 gives information about the total monthly cooling production of the system in discharging phase. According to the figure, expectedly, the same annual trend as that for heat production is seen for the cooling production where maximum monthly cooling production occurs in May (approximately 8 GWh) and the lowest cooling production is in November (about 2.75 GWh). However, comparing the values of heat and cooling productions, one can easily conclude that the heating generation of the system is much higher than its cooling production potential. This is due to the fact that cooling production capacity of the unit depends on its power production level whereas heat production is a functional of the surplus power of the wind farm used for charging the storage unit. The previous figures well reveal that the total amount of surplus power is considerably higher than the power deficit of the wind
maximizes the revenue of the system. For developing an optimal operation strategy for such a multi-parameter problem, complicated multi-objective optimization method is required which is far out of the scope of this work. The operation strategy of this storage unit is based on a simple algorithm developed in [30] for the same case study. Appendix A shows a flowchart of the algorithm. One more case-specific parameter that affects the performance of the system is the local ambient temperature. Fig. 2 presents information about the ambient temperature over the entire year (2015) for Aarhus. Besides affecting the performance of the storage system, this parameter has direct impact on the level of heating and cooling demand of the district energy systems. The effect of ambient temperature variation on the efficiency of the system will be discussed in the next section. 4. Results and discussion In this section, the results of the simulation of the SC-CAES performance are presented. Fig. 3 gives information about the production of the wind farm in 5-min gaps and its hourly bidding values in dayahead market. Taking into account the bidding strategy of the wind farm, one can calculate the value of surplus power and auxiliary power required (power deficit) to balance the demand and production for the wind farm over the entire year. In order to be able to show readable results on the graphs, Fig. 4 shows the value of surplus power and the power deficit in the case study for the first 20 days of the year in 5-min gaps. Based on the data provided, the maximum annual surplus power of the wind farm is 208 MW while the maximum auxiliary electricity demand of the farm is 169 MW. The storage system should be sized based on these critical values. Fig. 5 shows how the pressure of the cavern changes through the continuous and irregular charging and discharging phases over the whole year. As seen, the cavern pressure comes up to around the maximum possible pressure of 125 bar only twice during the year. For reaching this level of pressure within the cavern, the volume of the reservoir should be 250,000 m3. Except the two times that the cavern pressure approaches to 125 bar, it hardly comes up to higher values than 50 bar and this stipulates the unnecessary cost for digging a bigger cavern. This relatively significant inaccuracy in cavern sizing is mainly due to the not efficient enough operation algorithm used in this work resulting to an uneven charging and discharging time and amounts over the year. Fig. 6 shows the rate of power production of the storage system for
Fig. 2. The hourly average ambient temperature in Aarhus in 2015.
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Fig. 3. The realistic 5-min data of production and the hourly bidding values proposed for the case study.
Fig. 11 shows how changing heat and cold storage temperatures may affect the corresponding values of exergy efficiencies while keeping the other temperatures constant. For assessing this factor, in order to speed up the calculation process, the performance of the system was assessed over 12 h full-load charging and 12 h full-load discharging instead of one entire year of operation. As seen in Fig. 11a, higher heat production exergy efficiencies can be obtained for higher heat storage temperatures. If the storage temperature is selected as 300 °C, the PTH exergy efficiency of 28% is achieved. Fig. 11b shows that the lower cold storage temperatures, the better PTC exergy efficiency can be achieved. However, it should be noted that increasing the heat storage and decreasing the cold storage temperatures may increase the rate of energy losses from the storage as well. Fig. 12 shows the effect of ambient temperature on various exergy performance indexes. Here also a one-day charging/discharging operation of the system is assessed. As seen, ambient temperature doesn’t affect the PTP exergy efficiency of the system that much while it has considerable effect on the PTC and PTH exergy efficiencies. For cold production exergy efficiency this effect is direct (it comes up with ambient temperature increase) whereas for heat production this effect is reverse so that higher ambient temperatures result to lower PTH exergy efficiency. Therefore, ambient temperature variation is not
farm. This is reasonable because in an energy storage system, regardless of the technology, the received electricity is higher than the generated electricity due to the losses in the system. Comparing Figs. 8 and 9 and taking the descriptions given for them into account, one can conclude that the performance of the system in terms of heat production will definitely be much better than its cooling production. Fig. 10 gives information about the energetic and exergetic efficiencies of the system for an entire year of operation. According to the figure, PTP energy efficiency of the SC-CAES is 30.6%, the PTH energy efficiency is 92.4% and PTC energy efficiency is 32.3%. Thus, the overall energy performance factor of the system is 1.55. The reason for close values of cooling and power energy efficiencies is that nearly the same amount of enthalpy as that reduced through each turbine is received by the air through the cold heat exchangers. On the other hand, although still acceptable value for power production exergy efficiency is obtained, the values of exergy efficiencies for heat and cold production are not comparable with those obtained in energy performance assessment. The main reason of this fact is that firstly, heat and cold are inherently low exergy streams compared to electricity and secondly, the cold and heat storage temperatures are not that far different from the ambient temperature that result to high exergy destruction rates. The total exergy efficiency is 47.5%.
Fig. 4. The 5-min data of the surplus and the deficit of power in the case study during the first 20 days of the year.
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Fig. 5. The reservoir pressure variation over the year.
Fig. 6. The power production of the system for three 20-day periods during the year (each period in a separate color).
Fig. 7. The unrecovered power demand of the wind farm for the three third of the year.
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Fig. 8. Total monthly heat production of the system.
Fig. 9. Total monthly cooling production of the system.
reduces the work of both compressors and turbines without affecting the level of heat and cold production considerably. Table 2 makes a comparison of the SC-CAES and two of the most efficient designs of CAES proposed before, i.e. the A-CAES and the ICAES. This comparison is between the energy and exergy efficiencies as well as the total annual benefit of each of these systems for the case study. According to the table, the overall energy performance of the system is far better than the other two cases, resulting to a much higher annual economic benefit. However, the exergy efficiency of the system is slightly lower than the A-CAES and considerably lower than the ICAES. The main reason of this is that heat and cold are low quality energy streams and their exergy values are considerably low. This could be improved by bigger difference between the heat and cold storage temperatures and the ambient temperature. It bears mentioning that the comparison of capital and O & M costs for these systems is out of scope of this work, however, it can be claimed that the SC-CAES is among the lowest-cost options among all due to the simpler technology, not having airflow preheaters, etc. Another key advantage of the SCCAES is the shorter startup time due to the direct expansion (without preheating process) of the compressed air.
expected to affect the exergy performance of the whole system as variation in PTC exergy efficiency compensates the change in PTH exergy efficiency while the PTP exergy efficiency is almost constant. The same can be claimed for the effect of this parameter on the overall energy efficiency of the system, where decreasing ambient temperature 100
92.4
Efficiency Values (%)
PTP
PTH
PTC
80 60 40 20 0
30.6
30.6
32.3
14.4 2.5
Exergy efficienies
Energy Efficiencies
Fig. 10. Annual energy conversion efficiency of the system.
5. Conclusion In this work, a new configuration of CAES technology is developed 611
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10 8
25
6
20
4
15
2
10 100
a
200
250
0
300
-100
Heat Storage Temperature (oC)
PTP Ex. Efficiency
35
Exergy Efficiencies (%)
150
-80
-60
-40
-20
Cold Storage Temperature (oC)
PTH Ex. Efficiency
PTC Ex. Efficiency
0
PTC Exergy Efficiency (%)
PTH Exergy Efficiency (%)
30
Fig. 11. The effect of cold and heat storage temperatures on the obtainable exergy efficiencies.
b
Fig. 12. The effect ambient temperature on the obtainable exergy efficiencies.
28 21 14 7 0 10-
0
10
20
30
40
Ambient Temperature (oC)
the pseudo-dynamic modelling of the system show that a PTH efficiency of 92.4% is expected from the system in charging mode. In discharging phase, the PTP efficiency of 30.6% and PTC efficiency of 32.3% could be achieved. Comparing this system with the most efficient designs of CAES, one may notice that although the PTP efficiency is considerably lower (70% in the A-CAES and 80% in the I-CAES), the potential of this system to support the demand of district energy systems, makes it significantly impressive in terms of economic performance. The comparative exergy analysis carried out reveals that in spite of offering low exergy heat and cold storages, the overall exergy efficiency of the system is 47.5%. This value is only slightly lower than the exergy efficiency of A-CAES but it has a relatively high distance from that of the I-CAES. Further important advantages of this system are the higher pace of startup, faster reaction to load changes and the lower capital cost. This system is a very good candidate for future energy systems where the share of renewable energy is much higher and there is a solid integration between energy sectors.
Table 2 Comparison of SC-CAES with A-CAES and I-CAES designs. Item
SC-CAES
A-CAES
I-CAES
PTP Energy Efficiency (%) PTH Energy Efficiency (%) PTC Energy Efficiency (%)
30.6 92.4 32.3
70 – –
80 – –
Overall Energy Performance Coefficient/ Efficiency (%)
155
70
80 [33]
PTP Exergy Efficiency (%) PTH Exergy Efficiency (%) PTC Exergy Efficiency (%)
30.6 14.4 2.5
50 – –
70 – –
Overall Exergy Efficiency (%)
47.5
50
70 [33]
Annual Power Production Benefit (1000€) Annual Heating Production Benefit (1000€) Annual Cooling Production Benefit (1000€)
1677 3303 1565
3669 – –
4193 – –
Net Annual Benefit (1000€)
6545
3669
4193
Acknowledgements This study is part of the READY project (Resource Efficient cities implementing Advanced smart CitY solutions) Task 4.7. The project is partly financed by the EU’s Research and Innovation program FP7.
and designed. This system is capable for cogeneration of heat, cooling and power. The proposed system is designed, sized and simulated for a large-scale wind farm with 300 MW capacity in Denmark. The results of
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Appendix A
Cheap Electricity
Fair Electricity Price
Expensive Electricity
The algorithm for biding in the day-ahead market for the combined wind turbine and energy storage system; DA: Forecast of the daily average wind power generation, HA: Forecast of the hourly average wind power generation, N and M: constant coefficients, AWP: actual wind power generation at 5 min resolution.
[14] Lemofouet-Gatsi S. Investigation and optimisation of hybrid electricity storage systems based on compressed air and supercapacitors. Dissertation, Lausanne; 2006. [15] Marcus D. Fuel-free geologic compressed air energy storage from renewable power: task # 1. Deliverable report; 2011. [16] Arabkoohsar A, Machado L, Koury RNN. Operation analysis of a photovoltaic plant integrated with a compressed air energy storage system and a city gate station. Energy 2016;98:78–91. [17] Wolf D, Budt M. LTA-CAES – a low-temperature approach to adiabatic compressed air energy storage. Appl Energy 2014;125:158–64. [18] Mathiesen BV, Lund H, Connolly D, Wenzel H, Østergaard PA, Möller B, Nielsen S, Ridjan I, Karnøe P, Sperling K, Hvelplund FK. Smart energy systems for coherent 100% renewable energy and transport solutions. Appl Energy 2015;145:139–54. [19] Thellufsen JZ, Lund H. Roles of local and national energy systems in the integration of renewable energy. Appl Energy 2016;183:419–29. [20] Čulig-Tokić D, Krajačić G, Doračić B, Vad Mathiesen B, Krklec R, Larsen JM. Comparative analysis of the district heating systems of two towns in Croatia and Denmark. Energy 2015;92(3):435–43. [21] Pol O, Schmidt RR. 15 - Development of district heating and cooling in the urban planning context advanced district heating and cooling (DHC) systems; 2016. p. 319–37. [22] Werner S. District heating and cooling in Sweden. Energy 2017;126:419–29. [23] Arabkoohsar A, Ismail KAR, Machado L, Koury RNN. Energy consumption minimization in an innovative hybrid power production station by employing PV and evacuated tube collector solar thermal systems. Renewable Energy 2016;93:424–41. [24] Arabkoohsar A, Machado L, Farzaneh-Gord M, Koury RNN. Thermo-economic analysis and sizing of a PV plant equipped with a compressed air energy storage system. Renewable Energy 2015;83:491–509. [25] Zhang X, Xu Y, Xu J, Sheng Y, Zuo Z, Liu J, Chen H, Wang Y, Huang Y. Study on the performance and optimization of a scroll expander driven by compressed air. Appl Energy 2017;186(3):347–58. [26] He W, Wu Y, Peng Y, Zhang Y, Ma C, Ma G. Influence of intake pressure on the performance of single screw expander working with compressed air. Appl Therm
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Subcooled compressed air energy storage system for coproduction of heat, cooling and electricity
MARK
⁎
A. Arabkoohsara,b, , M. Dremark-Larsenb, R. Lorentzenb, G.B. Andresenb a b
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands Department of Engineering, Aarhus University, Aarhus, Denmark
H I G H L I G H T S configuration of compressed air energy storage system is proposed and analyzed. • AThisnewsystem, so-called subcooled-CAES, offers cogeneration of electricity, heat and cooling. • A pseudo-dynamic exergy and economic analysis of the system for an entire year is presented. • The annual power, energy, cooling and heat efficiencies of the system are around 31%, 32% and 92%. • The overall energy and exergy performance coefficients of the system are 1.55 and 0.48, respectively. •
A R T I C L E I N F O
A B S T R A C T
Keywords: CAES Energy storage Smart energy systems Cogeneration Coefficient of energy performance District energy systems
Various configurations of compressed air energy storage technology have received attention over the last years due to the advantages that this technology offers relative to other power storage technologies. This work proposes a new configuration of this technology aiming at cogeneration of electricity, heat and cooling. The new system may be very advantageous for locations with high penetration of renewable energy in the electricity grid as well as high heating and cooling demands. The latter would typically be locations with district heating and cooling networks. A thorough design, sizing and thermodynamic analysis of the system for a typical wind farm with 300 MW capacity in Denmark is presented. The results show a great potential of the system to support the local district heating and cooling networks and reserve services in electricity market. The values of power-topower, power-to-cooling and power-to-heat efficiencies of this system are 30.6%, 32.3% and 92.4%, respectively. The exergy efficiency values are 30.6%, 2.5% and 14.4% for power, cooling and heat productions. A techno-economic comparison of this system with two of the most efficient previous designs of compressed air energy storage system proves the firm superiority of the new concept.
1. Introduction The importance of electricity storage is increasing as the awareness about the importance of more utilization of intermittent renewable energy increases. Energy storage technologies are mainly attractive due to their potential in load balancing in the electricity grid as well as storing the surplus power during peak production periods for later use at peak consumption times [1]. Compressed air energy storage (CAES) is one of the promising energy storage technologies being given much attention as it offers a very high energy density, a relatively low cost of capital, a short response time to load change and competitive energy efficiency [2]. The history of CAES dates back to about 70 years ago with a patent application in the USA [3], though this concept was not of
⁎
interest for the following two decades due to the unimportance of electricity storage in absence of renewable power generation systems. Rising the importance of storage technologies over time, the Huntorf plant was built as the first pilot scale CAES of the world in 1978 [4]. In fact, this was the beginning of an increasing practical interest in this technology focusing on two major issues of long-term reservoir stability of CAES operation and second-generation CAES concepts with the main goal of minimizing fuel use [5]. As the results of the R & D efforts on the second-generation of CAES concepts, diabatic-CAES (DCAES) was proposed first. In fact, the D-CAES is the simplest form of the second-generation of this technology. Here, the heat produced in the air stream during the compression process is dissipated into the atmosphere as waste. Then, a massive amount of auxiliary heat is provided,
Corresponding author at: Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. E-mail address: [email protected] (A. Arabkoohsar).
http://dx.doi.org/10.1016/j.apenergy.2017.08.006 Received 2 May 2017; Received in revised form 30 July 2017; Accepted 4 August 2017 0306-2619/ © 2017 Elsevier Ltd. All rights reserved.
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ηI ηII α ε λ γ € ϑ Δ
Nomenclature c cp h k m ṁ n P Eb Eḋ Efc Eṡ PTC PTH PTP ̇ Qcold ̇ Qheat r R s Ṡ tc td T U V w Ẇ X
specific heat capacity (W/m2) air specific thermal capacity in constant pressure (kJ/ kg·°C) specific enthalpy (kJ/kg) thermal conductivity (W/m·K) mass (kg) mass flow rate (kg/s) number of compression/expansion stages pressure (kPa or bar) bidding power value (MW) deficit of power (MW) forecasted wind power (MW) surplus power (MW) power-to-cold power-to-heat power-to-power cold production rate (kW) heat production rate (kW) compression/expansion factor gas constant (kJ/kg·K) specific entropy (kJ/kg·K) entropy generation rate (kW/K) charging operation time-steps discharging operation time-steps temperature (°C or K) overall heat transfer coefficient (W/m2·K) volume (m3) specific work (kJ/kg) work rate (kW) traded electricity (€)
Subscriptions a c char cm cs d dam disch e el f g hm hs i idm is o ptc pth ptp r rtm tes t tot
Greek symbols
ψ̇ ψ µ η
energy efficiency exergy efficiency energy price (€/MWh) heat exchanger effectiveness factor 5-min time-step counter hourly time-step counter euro currency 5-min time-step counter in each hour in power market power imbalance value (MW)
exergy rate (kW) exergy (kJ) thermal capacity ratio energy conversion efficiency
typically by a fossil fuel fired system, to heat up the high-pressure air to temperatures in the range of 300–500 °C before expansion in the turbine. The round-trip efficiency of this system ranges from 0.54 to 0.6 [6]. The second pilot-scale CAES unit built ever is a D-CAES plant with 110 MW output power in McIntosh in operation since 1991 [7]. Another type of second-generation CAES is adiabatic-CAES (ACAES) [8]. In this configuration, the heat generated in the compression stage is gathered and stored for reuse in the expansion process. This can be done in two different ways of with or without thermal energy storage (TES) units. For the scheme without any TES, the heat is stored in the air reservoir. In fact, the air reservoir plays the role of pressure heat storages simultaneously. A-CAES without TES is restricted to rather low storage pressures and consequently to low energy densities [9]. This drawback without TES scheme of the A-CAES led to the appearance of TES equipped A-CAES in which the there is an individual storage tank/ reservoir for the heat gathered in the charging process and cold compressed air with much higher pressure level is stored in the cavern. Ancillary heaters provide the rest of the required heat for the air stream [10]. In this way, the round cycle efficiency can increase to around 0.7 [11]. A further introduced schematic of CAES is isothermal-CAES (ICAES) in which higher expansion and compression process efficiencies are aimed by preventing temperature variation of the air in the turbomachinery during the charging and discharging processes. Piston
air compressor charging phase cold market cold storage destruction (exergy) day-ahead market discharging phase external electricity working fluid (industrial oil) electricity generator heat market heat storage internal intra-day market isentropic process ambient conditions power-to-cold power-to-heat power-to-power air reservoir real-time market thermal energy storage turbine total
machinery is recommended to be used in this system as such machines can perform slow compression and expansion processes, facilitating the heat exchange process inside the machinery [12]. Closed cycle and open cycle hydro-pneumatic concepts are two different types of this system [13]. The major drawback of the closed cycle type is its low energy density, restricting its application in laboratory scale only [14]. On the other hand, the open cycle scheme has been developed in utility scale due to not suffering from such technical problems. There is one pilot plant open cycle I-CAES in operation in the USA [15]. The obtainable efficiency of this system in a full charging and discharging process can increase up to 80%. Finally, the low-temperature CAES (LT-CAES), which is a new design of the A-CAES concept, is the last configuration proposed in this regards [16]. The main objective here is to minimize or even eliminate the contribution of fuels in the system. Thus, the air stream is only heated by the stored heat during the compression process before being expanded through the turbines. The maximum temperature of the expanding air in this arrangement is between 90 and 200 °C. Although the round-trip efficiencies of this scheme (in the range of 52–60%) is slightly lower compared to those obtainable in A-CAES, the system benefits from faster startup and wide-ranging part load ability [17]. On the other hand, district energy systems provide energy for the consumers cheaper than decentralized or individual methods. In addition, flexible building design, lower operations and maintenance costs, 603
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reservoir. The heated oil produced in this stage could be stored in another storage tank for heating applications, like district heating balancing reserve. Based on the desired district heating temperature, a high temperature of 120 °C is considered for the hot storage. This temperature could be lowered in modern low-temperature district heating systems where the supply temperature is often below 60 °C. In discharging mode, the compressed air is expanded through air expansion motors. Here, the rotational work is used to drive an electricity generator. However, unlike all of the previous configurations of the CAES, the airflow is not heated before the expansion. In this way, although the power-to-power (PTP) efficiency of the system decreases, no surplus heat is required for the preheating process and more importantly, a large amount of cooling energy may be produced after the airflow expansion. The cooling produced in this phase can be gathered by employing a heat exchanger to be stored for later use of cooling applications. Therefore, with such a configuration of CAES, the system can be used for heat production in the charging state and for cogeneration of cooling and power in discharging mode. The cooling energy can be stored like the heat generated during compression. To this end, two storage tanks are assumed for the expansion stage as well. One tank will be at ambient temperature and the other one is the cold storage with much lower temperature. This tank is supposed to be kept at −30 °C to be able to support low temperature industrial cooling demands as well. Here also, the working fluid is industrial oil. There are some further important notes that should be taken into account for the above system. Multistage compressor and turbine are used in the system to increase the efficiency. The number of stages must be defined based on a techno-economic trade off. As the number of stages increases, the efficiency of the system will increase where the cost picks up too. However, as there is no information about the actual trend of cost increase with the stages number, a triple stage compression/expansion will be assessed in this work. The same can be claimed for the compression and expansion factor of the assets. The compression/expansion factor is considered equal to 5 in each stage. Therefore, the maximum pressure of the air in the cavern is 125 bar. In contrast with the compressors that can be similar to those in other CAES configurations due to the same compression process, the expanders cannot be the same as the air goes to very low temperatures during the expansion. There is a wide range of air expanders that are used in refrigeration systems and can be used in this system [25]. Here, employing multistage expansion, and the heat exchangers in between supporting by water in ambient temperature level, facilitates the process because the airflow can come back to temperatures around ambient level so as not to go down to too low temperatures. Thus, among
better energy delivery, increased price stability, enhanced comfort, reduced carbon footprint are further advantages of such systems [18]. These all are why district cooling and heating are popular and widely distributed in several countries, especially in Northern Europe [19]. For example, distributed heating networks supported by heat production plants supply almost 60% of heat consumers in Denmark [20]. Although district cooling covers only 4% of the total cooling demand of this country, expanding the areas covered by district cooling is receiving increased attention [21]. In Sweden, district cooling already supplies about 40% of the demand as the result of an expansion over the last 20 years [22]. In this study, a new design of CAES, so-called subcooled-CAES (SCCAES), is proposed by which the system will be capable of producing heat, power and cooling at a high overall efficiency. The work presents a detailed design, sizing and thermodynamic modelling of the system. For the sake of giving realistic information and performance values, the system is designed and sized for a typical wind farm with 300 MW in Denmark, i.e. 10% of the whole wind production capacity of the WestDenmark region. Local power and district heating/cooling demands and productions of this region are also further pieces of the database of this study in the simulations. All the simulations have been carried out in MATLAB software. The novelty of this works lies on proposing a new generation of CAES technology with high compatibility with the locations with district energy systems. As both district energy systems and energy storage technologies are important parts of future energy systems worldwide, the proposed configuration can play an important role in the future energy matrixes. It will be shown that the proposed concept outperforms all the previous configurations of the CAES in terms of energy, and economic performance and offers a competitive exergy efficiency as well. 2. The SC-CAES system Fig. 1 illustrates the schematic diagram of the SC-CAES technology. Like all other energy storage systems, there may be two operation states for the systems, i.e. charging and discharging. There is detailed information and explanation about the operation of various CAES designs in [23,24]. In charging mode, the compressors start working like in other CAES systems when surplus power is available. During the compression process, the airflow temperature increases. Thus, heat exchangers are employed to gather this heat after the compression. After exchanging heat with a cold medium (here proposed to be industrial oil at ambient temperature), the cold compressed air is stored in the air storage
Fig. 1. The schematic of the proposed configuration of the SC-CAES system; WF: wind farm, M: motor, C: compressor, HX: heat exchanger, ST: storage tank, T: turbine, G: electricity generator; green arrows: airflow, light-blue arrows: working fluid in ambient temperature, red arrows: hot working fluid, dark-blue arrow: subcooled working flow. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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kg). The subscriptions c and a represent the compressors and the airflow respectively and n is the number of stages. For each of the stages, the specific work is simply calculated by:
all the options, single screw air expander has been selected for the present analysis [26]. The other noteworthy point is that since the system changes from charging to discharging and vice versa frequently, it may be difficult to manage direct supply of heat and cooling for district energy systems. This is why the produced heating and cooling is proposed to be stored and then supplied at a later time. The price of flexible balancing is normally higher in energy markets. It is desirable to change the arrangement of the compressors and the expanders from parallel to series and vice versa as the pressure of the compressed air reservoir changes. Table 1 shows how the arrangement of the turbomachinery of the system changes by the in accordance with the pressure.
wc = (hi−he ) = cp (Ti−Te )
where, T is temperature, cp is the average specific thermal capacity of air and the subscriptions i and e refer to the inlet and outlet conditions of each compressor stage. Assuming an adiabatic process through each of the stages, the outlet air temperature can be given as: μ−1
( μ ) −1 ⎞ ⎛ rc ⎟ Te = Ti ⎜1 + ηis,c ⎟ ⎜ ⎠ ⎝
This section provides the mathematical modelling of the system, based on the first and the second laws of thermodynamics, as well as the further information required for simulating the system performance including the operation conditions and the energy market details of the case study. 3.1. First law analysis The energy performance model of the system is divided into two different sections, one for the charging and the other one for the discharging mode. Charging mode: In this mode, the surplus electricity (Eṡ ) available for the energy storage system is used to drive the compressor stages. Due to the high mass flow rate and pressure required in the system, the compressors are proposed to be of the centrifugal type [24]. For the multistage compressor set, one could write:
Te,a = Ti,a (1−ε) + εTi,f
ṁ f =
n
∑
(ṁ a wc )j
(4)
ṁ a cp (Ti,a−Te,a) cf (Te,f −Ti,f )
(5)
Note that, in the two above equations, the airflow is the fluid with the Cmin as the temperature drop in the air stream will be higher than the working fluid temperature change through the heat exchanger. Doing the above calculation procedure and having information
(1)
j=1
(3)
in this relation, rc is the compression factor (equal to 5), μ and ηis,c are the ratio of specific heat capacities for the air and isentropic efficiency of the compressors, equal to 1.4 and 0.85, respectively. So far, the specific work of each compressor can be calculated if the inlet temperature of each stage is known. Except the first stage of the compressor for which the inlet air temperature is known, i.e. equal to the ambient temperature, for the next stages, the temperature should be calculated by modelling the performance of the intercooling heat exchangers. For these heat exchangers, the inlet and outlet temperatures of the working fluid are known, i.e. ambient temperature and 120 °C, respectively. The inlet airflow temperature is also known and equal to the outlet temperature of the same stage compressor (Eq. (3)). Designing the counter-flow heat exchangers to offer an effectiveness factor (ɛ) of 0.8, based on ɛ-NTNU method [27], one has the following formulation:
3. Material and methods
Wċ = Eṡ =
(2)
in which, Ẇ is the total work of the compressor set (kW), ṁ is mass flow rate (kg/s), w is the specific work of each stage of the compressor (kW/ Table 1 The arrangement of the compressors and the expanders in various cavern pressure ranges. Cavern pressure
Compressors
Expanders
P < 5 bar
5 bar ≤ P < 25 bar
P ≥ 25 bar
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system as power-to-cooling (PTC) efficiency and PTP efficiency given by the following equations, respectively:
about the amount of surplus power available, one can calculate the airflow rate being compressed and hot oil produced in the charging phase. Considering the compressed air as ideal gas, which is reasonable for ambient temperature and pressure of 125 bar, the pressure of the reservoir can be calculated by:
m RT Prλ = ⎛ r r ⎞ ⎝ Vr ⎠ ⎜
ηI ,ptc =
λ
where: mrλ = mrλ − 1 + ṁ a
⎟
and
Tr = To
ηI ,ptp =
(6)
in this equation, the subscription r refers to the reservoir and the superscription λ counts the time-steps in seconds. P, V and m are respectively pressure, volume and mass and To is ambient temperature. The rate of heat production by the system in charging mode can be calculated by the following equation:
td ̇λ ∑λ = 1 Qcold λ
tc
∑λ = 1 Eṡ
(13)
λ Eḋ λ Eṡ
(14)
td ∑λ = 1 tc ∑λ = 1
The total energy performance coefficient of the system can be defined as below: λ
td
ηI ,tot =
∑
(7)
tc λ=1
λ ̇ ⎛ Qheat ⎟⎞ ̇ ⎝ Es ⎠
There are a number of studies dealing with exergy analysis of various configurations of the CAES. A detailed exergy modelling of heat storage equipped A-CAES is presented in [10]. Having those formulas, one could assess the exergy performance of the understudy configuration of CAES by taking the differences into account. Similar to the energy analysis section, here also the exergy modelling is presented based on the two different operation states of the system, i.e. charging or discharging, just highlighting the key correlations. It should be noted that the variations of kinetic, potential and chemical exergies through all of the control volumes are assumed to be negligible. Charging mode: In this phase, the active instruments are the compressors, the heat exchangers, the heat storage and the air storage reservoir. For the compressors, the exergy balance of each stage of the compressor set can be written as:
⎜
(8)
here, tc refers to the number of time-steps in the charging mode. Discharging mode: In this state, the total required work to be produced by the turbine set is calculated by:
Ė Wṫ = d = ηg
n
∑
(ṁ a wt )j (9)
j=1
here, Eḋ stands for the amount of power to be produced (the deficit of electricity in the wind farm) and ηg is the electricity generator efficiency (0.95). wt is the specific work of each stage of the turbine, which are supposed to be single screw expanders, given by the following equation: (
wt = RTi
μ−1
μ ⎛ 1−rt μ ⎜ μ−1 ⎜ ηis,t ⎝
ψ̇
d Ti,a ⎞ ⎡ ⎞⎤ = T Ṡ Wċ + ṁ a ⎢ (hi,a−he,a)−To ⎛⎜cpln ⎛⎜ ⎟ + R ln(rc )⎟ o gen ⎥ ⎝ Te,a ⎠ (16) ⎝ ⎠⎦ ⎣ where h is the specific enthalpy of airflow and ψḋ is the rate of exergy destruction through the compressor which is a direct functional of the ̇ ). rate of entropy generation (Sgen For the heat exchangers, the exergy balance can be written as:
)⎞
⎟ ⎟ ⎠
(10)
where R is the gas constant of air (0.287 kJ/kg·K) and rt is the expansion factor of the expander (1/5 = 0.2). In addition, ηis,t is the isentropic efficiency of the single screw expander, which is a functional of the intake pressure and rotational speed. Experimental results reported in [28] are used to estimate the isentropic efficiency of the various stages of the expanders and various operational loads. Based on this report, the higher inlet pressure of the expander, the higher adiabatic efficiency it offers. Overall, an average adiabatic efficiency of 0.65 is expected from the single screw expanders working at low temperatures. The outlet temperature of each stage is also given by the same relation as Eq. (3), where rt and ηis,t sit instead of rc and ηis,c , respectively. For an expander, the more efficient it is, the lower discharge temperature it presents. Having the outlet air temperature of the turbines, one can model the heat exchangers between the expanders. Here also the temperature change in the airstream through the expander should be higher than the working fluid. Thus, reusing Eqs. (4) and (5), the outlet air temperature and mass flow rate and the working fluid mass flow rate can be calculated. The mass and the pressure of the cavern can be given by:
m RT Prλ = ⎛ r r ⎞ ⎝ Vr ⎠ ⎜
⎟
⎡ ⎛ Ti,f ⎞ ⎤ ⎛ Ti,a ⎞ ⎤ ̇ ṁ a ⎡ ⎢ (hi,a−he,a)−To cpln ⎜ Te,a ⎟ ⎥ + ṁ f ⎢ (hi,f −he,f )−To cf ln ⎜ Te,f ⎟ ⎥ = To Sgen ⎝ ⎠⎦ ⎝ ⎠⎦ ⎣ ⎣ (17) in which, the pressure drop of the working fluid and the airflow are considered as negligible. For any other storage control volume, such as the air storage reservoir and the heat storage tank, all types of balance equations are written in transient mode. Therefore, for the compressed air reservoir in charging mode, the balance of exergy can be written as below:
dψṙ ⎛ ⎛ Ti,a ⎞ ⎛ Pi,a ⎞ ⎞ ⎤ ̇ = ṁ a ⎡ ⎢ (hi,a−ho)−To ⎜cpln To −Rln Po ⎟ ⎥−To Sgen dt ⎝ ⎠ ⎝ ⎠⎠⎦ ⎝ ⎣ ⎜
j=1
⎜
⎟
(18)
λ
where: mrλ = mrλ − 1−ṁ a
and
Tr = To
P P ψr = Vr Pr ⎡ln ⎛ r ⎞ + o −1⎤ ⎢ ⎝ Po ⎠ Pr ⎥ ⎣ ⎦
(11)
⎜
⎟
(19)
A similar exergy balance correlation can be applied for the heat storage as well while the stored exergy in the form of heat can be calculated by:
n
∑
⎟
where, ho and Po are the enthalpy and pressure of air in dead state condition, i.e. ambient temperature and pressure. The stored exergy in the form of compressed air is calculated by [29]:
The rate of cold production during the discharging mode can be calculated as:
̇ = Qcold
(15)
3.2. The second law analysis
The power-to-heat (PTH) efficiency (ηpth ) of the system is defined as below:
∑
λ
≤ (ṁ f cf (Te,f −Ti,f ))j
j=1
ηI ,pth =
tc
λ tc ∑λ = 1 Eṡ
n
̇ Qheat =
λ
td
̇ + ∑ Qheat ̇ ∑λ = 1 Eḋ + ∑λ = 1 Qcold λ=1
(ṁ f cf (Ti,f −Te,f ))j (12)
Tf ,hs Tf ,hs ⎞ ⎤ −ln ⎛ −1 ψhs = mf ,hs cf To ⎡ ⎢ To To ⎠ ⎥ ⎝ ⎣ ⎦ ⎜
In this phase, two more performance factors can be defined for the 606
⎟
(20)
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electricity and Eb is the bided power amount. In the intra-day market, when the forecast data is more precise, the difference between Eb and the new production forecast value (Efc) is traded for each hourly time-step. Here, if Efc is greater than Eb, the plant may trade for the difference and earn more revenue, and if Eb is greater than Efc, the plant has to procure additional energy. Therefore, the daily traded electricity in this market can be calculated as:
where the subscriptions hs and f, hs refer to the stored heat and the working fluid within the heat storage, sequentially. Having the useful exergy stored as heat during this phase, one could calculate the PTH exergy efficiency of the system as below:
ψhs λ tc ∑λ = 1 Eṡ
ηII ,pth =
(21)
Taking into account the exergy stored in the form of compressed air as well, one may define an index called exergy efficiency of the charging phase as below:
ηII ,char =
24
Xidm =
λ
tc
Discharging mode: In this phase, the active apparatuses are the turbines, the heat exchangers, the cold storage and the air storage reservoir being discharged. For the turbines, the exergy balance is written as follow?
24
⎛1 Xrtm = − ∑ ⎜ 12 γ=1 ⎝
(23)
For the heat exchangers, the air reservoir and the cold storage, the same formulas as those of the previous section can be applied. Calculating all of these parameters, the value of PTC exergy efficiency of the system can be given by the following equation:
ηII ,ptc =
λ
(24)
where ψcs refers to the cooling exergy stored. It should be noted that the values of PTH exergetic and energetic efficiencies are the same as similar formula applies for the both (Eq. (14)). The exergy efficiency of the whole system in discharging phase is: td
ηII ,disch =
λ
ψcs + ∑λ = 1 Eḋ ψr
(25)
ηII ,tot =
ψcs +
(29)
Qheat ,γ (30)
24
Xcm = α cold
λ Eḋ
∑
Qcold,γ (31)
γ=1
(26)
where Qheat and Qcold are the total hourly heat and cooling productions and αheat and αcold are respectively the heat and cooling production prices. For calculating the levelized cost of heat and cooling production, the same procedure as [31] is used. In this method, one needs to define the heat and cooling prices as functional of electricity price. The electricity production fee is considered as an annual average rate of 30 €/ MWh [32]. For the heat price, the heat is considered to be produced by fuel driven boilers with a fuel-to-heat ratio of 63/56 while the power is produced by conventional power plant with a fuel-to-power ratio of 70/ 28. So far, one has 133 units of fuel (63 + 70) as the inlet energy and 28 units of power plus 56 units of heat as the products. Since district heating systems are mainly supplied by CHP plants with fuel-to-heat and fuel-to-power ratios of 100/56 and 100/28, the heat price is calculated as:
αheat = α el ×
(56−
133 − 100 2
56
) = 30 × 0.705 = 21€/MWh
(32)
For the cooling price, since the district cooling network is mainly supplied by heat driven absorption chillers, the cooling price is calculated as a function of the heat price. For this case, with an average coefficient of performance of 0.75, the cooling production spot price will be 28€/MWh . Having this formulation, one now could develop an algorithm that
24 γ=1
⎞ (|Δ|α el + 13.5Δ)ϑ ⎟ ⎠γ
γ=1
As discussed, the system is designed for a wind farm in the western region of Denmark. It is assumed to operate as part of a portfolio with a maximum production of 300 MW. A critical parameter affecting the performance of an energy storage system is the operation strategy of the unit, i.e. charging and discharging times and amounts. The operation strategy is defined through a bidding market with the main objective of maximizing the revenue of the plant while smooth and reliable power production is insured. Detailed information about Danish power market can be found in [30]. This matter is discussed here briefly as well. In this market, electricity is traded in three different markets, namely, the day-ahead market, the intra-day market and the real-time market. In the day-ahead market, wind farms bid on certain values in hourly resolution taking advantage of their day-ahead wind forecast data. The electricity price is known on an hourly basis. The total daily value of the traded electricity of the wind farm in this market is calculated by:
∑
∑
Xhm = αheat
3.3. Energy market and operation conditions of the case study
Xdam =
ϑ=1
24
Finally, the overall exergy efficiency of the system is defined as the ratio of the total useful exergy produced in both charging and discharging phases to that used for driving the compressors in charging mode as below: td ψhs + ∑λ = 1 λ tc ∑λ = 1 Eṡ
12
∑
where Δ is the value of imbalance in each 5-min time-step and ϑ counts the 5-min time-steps within each hour. In addition to the electricity market, there should be a certain strategy for trading heat and cooling as the storage system here is a cogeneration. Although district heating is widespread in Denmark, specifically in Aarhus, unlike the electricity sector, there is not a coherent market for heat. Evidently, there is no purposive market for cooling either. However, as the working principle of the system is to store the heat and cooling production, the assumption of the average values of levelized cost of heat and cooling production seems reasonable. Based on this assumption, the values made in the heat and cooling markets by the energy storage system are obtained by the following equations, respectively:
ψcs tc
∑λ = 1 Eṡ
(28)
Finally, the real-time market is for balancing in shorter time-scales than one hour. Here, if there is still any imbalance between the demand and the production, the power plants bid for recovering this imbalance. It is assumed that the time-steps in this market are in 5 min. Investigations show that up balancing and down balancing should be priced at about 13.5€ above and below the corresponding hourly electricity price, respectively. Thus, the total daily value in the realtime market may be estimated by:
(22)
Ti,a ⎞ ⎡ ⎞ ⎤ = Ẇ + T S ̇ ṁ a ⎢ (hi,a−he,a)−To ⎛⎜cpln ⎛⎜ ⎟ + R ln(rt )⎟ t o gen ⎥ Te,a ⎠ ⎝ ⎝ ⎠⎦ ⎣
((Efc−Eb) α el ) γ
γ=1
ψhs + ψr ∑λ = 1 Eṡ
∑
(Eb α el ) γ (27)
in which, γ is the counter of hourly time-steps, αel is the hourly price of 607
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early, middle and late three 20-day periods of the year. The production level is in fact the difference between the bidding value and the realized production of wind farm that should be covered by the energy storage system. According to the figure, here again a maximum power production of around 140 MW is seen only once during these sample days and the production level comes rarely above 50 MW for the rest of the shown period. This is also due to the performance of the operation algorithm and can be modified by employing a better operation/bidding strategy. Evidently, when the power production is zero, the storage unit is in charging phase. Fig. 7 shows the rate of unrecovered demand due to the cavern running out of compressed air. This parameter is presented for three 120-day periods over the year. As seen, the performance of the system has been much better during the middle third of the year during which the level of unsuccessful coverage of ramps stay very low and even in shorter time-scales. On the other hand, a sharp and continuous failure in ramp coverage is seen during the last days of the year. Comparing this figure with the cavern pressure graph, one could easily realize the agreement between the results as the reservoir pressure is almost always zero exactly during this period, stipulating empty air storage. Clearly, modifying the algorithm, the rate of successful coverage can also be modified sharply and even it may approach to zero. Fig. 8 shows the total monthly heat production of the storage system in charging phase. As seen, a maximum total monthly heat production of 21 GWh is produced in May while the least production is 7.7 GWh occurring in November. Naturally, the more system is in charging phase, the more heat is generated. Thus, based on the operation strategy defined for the system, it goes more in the charging phase during April and May. As such, the less heat production of the system, the less it has worked in charging mode. Fig. 9 gives information about the total monthly cooling production of the system in discharging phase. According to the figure, expectedly, the same annual trend as that for heat production is seen for the cooling production where maximum monthly cooling production occurs in May (approximately 8 GWh) and the lowest cooling production is in November (about 2.75 GWh). However, comparing the values of heat and cooling productions, one can easily conclude that the heating generation of the system is much higher than its cooling production potential. This is due to the fact that cooling production capacity of the unit depends on its power production level whereas heat production is a functional of the surplus power of the wind farm used for charging the storage unit. The previous figures well reveal that the total amount of surplus power is considerably higher than the power deficit of the wind
maximizes the revenue of the system. For developing an optimal operation strategy for such a multi-parameter problem, complicated multi-objective optimization method is required which is far out of the scope of this work. The operation strategy of this storage unit is based on a simple algorithm developed in [30] for the same case study. Appendix A shows a flowchart of the algorithm. One more case-specific parameter that affects the performance of the system is the local ambient temperature. Fig. 2 presents information about the ambient temperature over the entire year (2015) for Aarhus. Besides affecting the performance of the storage system, this parameter has direct impact on the level of heating and cooling demand of the district energy systems. The effect of ambient temperature variation on the efficiency of the system will be discussed in the next section. 4. Results and discussion In this section, the results of the simulation of the SC-CAES performance are presented. Fig. 3 gives information about the production of the wind farm in 5-min gaps and its hourly bidding values in dayahead market. Taking into account the bidding strategy of the wind farm, one can calculate the value of surplus power and auxiliary power required (power deficit) to balance the demand and production for the wind farm over the entire year. In order to be able to show readable results on the graphs, Fig. 4 shows the value of surplus power and the power deficit in the case study for the first 20 days of the year in 5-min gaps. Based on the data provided, the maximum annual surplus power of the wind farm is 208 MW while the maximum auxiliary electricity demand of the farm is 169 MW. The storage system should be sized based on these critical values. Fig. 5 shows how the pressure of the cavern changes through the continuous and irregular charging and discharging phases over the whole year. As seen, the cavern pressure comes up to around the maximum possible pressure of 125 bar only twice during the year. For reaching this level of pressure within the cavern, the volume of the reservoir should be 250,000 m3. Except the two times that the cavern pressure approaches to 125 bar, it hardly comes up to higher values than 50 bar and this stipulates the unnecessary cost for digging a bigger cavern. This relatively significant inaccuracy in cavern sizing is mainly due to the not efficient enough operation algorithm used in this work resulting to an uneven charging and discharging time and amounts over the year. Fig. 6 shows the rate of power production of the storage system for
Fig. 2. The hourly average ambient temperature in Aarhus in 2015.
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Fig. 3. The realistic 5-min data of production and the hourly bidding values proposed for the case study.
Fig. 11 shows how changing heat and cold storage temperatures may affect the corresponding values of exergy efficiencies while keeping the other temperatures constant. For assessing this factor, in order to speed up the calculation process, the performance of the system was assessed over 12 h full-load charging and 12 h full-load discharging instead of one entire year of operation. As seen in Fig. 11a, higher heat production exergy efficiencies can be obtained for higher heat storage temperatures. If the storage temperature is selected as 300 °C, the PTH exergy efficiency of 28% is achieved. Fig. 11b shows that the lower cold storage temperatures, the better PTC exergy efficiency can be achieved. However, it should be noted that increasing the heat storage and decreasing the cold storage temperatures may increase the rate of energy losses from the storage as well. Fig. 12 shows the effect of ambient temperature on various exergy performance indexes. Here also a one-day charging/discharging operation of the system is assessed. As seen, ambient temperature doesn’t affect the PTP exergy efficiency of the system that much while it has considerable effect on the PTC and PTH exergy efficiencies. For cold production exergy efficiency this effect is direct (it comes up with ambient temperature increase) whereas for heat production this effect is reverse so that higher ambient temperatures result to lower PTH exergy efficiency. Therefore, ambient temperature variation is not
farm. This is reasonable because in an energy storage system, regardless of the technology, the received electricity is higher than the generated electricity due to the losses in the system. Comparing Figs. 8 and 9 and taking the descriptions given for them into account, one can conclude that the performance of the system in terms of heat production will definitely be much better than its cooling production. Fig. 10 gives information about the energetic and exergetic efficiencies of the system for an entire year of operation. According to the figure, PTP energy efficiency of the SC-CAES is 30.6%, the PTH energy efficiency is 92.4% and PTC energy efficiency is 32.3%. Thus, the overall energy performance factor of the system is 1.55. The reason for close values of cooling and power energy efficiencies is that nearly the same amount of enthalpy as that reduced through each turbine is received by the air through the cold heat exchangers. On the other hand, although still acceptable value for power production exergy efficiency is obtained, the values of exergy efficiencies for heat and cold production are not comparable with those obtained in energy performance assessment. The main reason of this fact is that firstly, heat and cold are inherently low exergy streams compared to electricity and secondly, the cold and heat storage temperatures are not that far different from the ambient temperature that result to high exergy destruction rates. The total exergy efficiency is 47.5%.
Fig. 4. The 5-min data of the surplus and the deficit of power in the case study during the first 20 days of the year.
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Fig. 5. The reservoir pressure variation over the year.
Fig. 6. The power production of the system for three 20-day periods during the year (each period in a separate color).
Fig. 7. The unrecovered power demand of the wind farm for the three third of the year.
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Fig. 8. Total monthly heat production of the system.
Fig. 9. Total monthly cooling production of the system.
reduces the work of both compressors and turbines without affecting the level of heat and cold production considerably. Table 2 makes a comparison of the SC-CAES and two of the most efficient designs of CAES proposed before, i.e. the A-CAES and the ICAES. This comparison is between the energy and exergy efficiencies as well as the total annual benefit of each of these systems for the case study. According to the table, the overall energy performance of the system is far better than the other two cases, resulting to a much higher annual economic benefit. However, the exergy efficiency of the system is slightly lower than the A-CAES and considerably lower than the ICAES. The main reason of this is that heat and cold are low quality energy streams and their exergy values are considerably low. This could be improved by bigger difference between the heat and cold storage temperatures and the ambient temperature. It bears mentioning that the comparison of capital and O & M costs for these systems is out of scope of this work, however, it can be claimed that the SC-CAES is among the lowest-cost options among all due to the simpler technology, not having airflow preheaters, etc. Another key advantage of the SCCAES is the shorter startup time due to the direct expansion (without preheating process) of the compressed air.
expected to affect the exergy performance of the whole system as variation in PTC exergy efficiency compensates the change in PTH exergy efficiency while the PTP exergy efficiency is almost constant. The same can be claimed for the effect of this parameter on the overall energy efficiency of the system, where decreasing ambient temperature 100
92.4
Efficiency Values (%)
PTP
PTH
PTC
80 60 40 20 0
30.6
30.6
32.3
14.4 2.5
Exergy efficienies
Energy Efficiencies
Fig. 10. Annual energy conversion efficiency of the system.
5. Conclusion In this work, a new configuration of CAES technology is developed 611
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10 8
25
6
20
4
15
2
10 100
a
200
250
0
300
-100
Heat Storage Temperature (oC)
PTP Ex. Efficiency
35
Exergy Efficiencies (%)
150
-80
-60
-40
-20
Cold Storage Temperature (oC)
PTH Ex. Efficiency
PTC Ex. Efficiency
0
PTC Exergy Efficiency (%)
PTH Exergy Efficiency (%)
30
Fig. 11. The effect of cold and heat storage temperatures on the obtainable exergy efficiencies.
b
Fig. 12. The effect ambient temperature on the obtainable exergy efficiencies.
28 21 14 7 0 10-
0
10
20
30
40
Ambient Temperature (oC)
the pseudo-dynamic modelling of the system show that a PTH efficiency of 92.4% is expected from the system in charging mode. In discharging phase, the PTP efficiency of 30.6% and PTC efficiency of 32.3% could be achieved. Comparing this system with the most efficient designs of CAES, one may notice that although the PTP efficiency is considerably lower (70% in the A-CAES and 80% in the I-CAES), the potential of this system to support the demand of district energy systems, makes it significantly impressive in terms of economic performance. The comparative exergy analysis carried out reveals that in spite of offering low exergy heat and cold storages, the overall exergy efficiency of the system is 47.5%. This value is only slightly lower than the exergy efficiency of A-CAES but it has a relatively high distance from that of the I-CAES. Further important advantages of this system are the higher pace of startup, faster reaction to load changes and the lower capital cost. This system is a very good candidate for future energy systems where the share of renewable energy is much higher and there is a solid integration between energy sectors.
Table 2 Comparison of SC-CAES with A-CAES and I-CAES designs. Item
SC-CAES
A-CAES
I-CAES
PTP Energy Efficiency (%) PTH Energy Efficiency (%) PTC Energy Efficiency (%)
30.6 92.4 32.3
70 – –
80 – –
Overall Energy Performance Coefficient/ Efficiency (%)
155
70
80 [33]
PTP Exergy Efficiency (%) PTH Exergy Efficiency (%) PTC Exergy Efficiency (%)
30.6 14.4 2.5
50 – –
70 – –
Overall Exergy Efficiency (%)
47.5
50
70 [33]
Annual Power Production Benefit (1000€) Annual Heating Production Benefit (1000€) Annual Cooling Production Benefit (1000€)
1677 3303 1565
3669 – –
4193 – –
Net Annual Benefit (1000€)
6545
3669
4193
Acknowledgements This study is part of the READY project (Resource Efficient cities implementing Advanced smart CitY solutions) Task 4.7. The project is partly financed by the EU’s Research and Innovation program FP7.
and designed. This system is capable for cogeneration of heat, cooling and power. The proposed system is designed, sized and simulated for a large-scale wind farm with 300 MW capacity in Denmark. The results of
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Appendix A
Cheap Electricity
Fair Electricity Price
Expensive Electricity
The algorithm for biding in the day-ahead market for the combined wind turbine and energy storage system; DA: Forecast of the daily average wind power generation, HA: Forecast of the hourly average wind power generation, N and M: constant coefficients, AWP: actual wind power generation at 5 min resolution.
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