water
Article Site Selection of Aquifer Thermal Energy Storage Systems in Shallow Groundwater Conditions
Qais Al-Madhlom 1,2 , Nadhir Al-Ansari 1,* , Jan Laue 1, Bo Nordell 1 and Hussain Musa Hussain 3
1 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Luleå, Sweden 2 College of Engineering/Al-Musaib, University of Babylon, Hillah 51006, Iraq 3 Remote Sensing Center, University of Kufa, Kufa 54003, Iraq * Correspondence: [email protected]; Tel.: +46-725-390-767
Received: 18 April 2019; Accepted: 12 June 2019; Published: 6 July 2019
Abstract: Underground thermal energy storage (UTES) systems are well known applications around the world, due to their relation to heating ventilation and air conditioning (HVAC) applications. There are six kinds of UTES systems, they are tank, pit, aquifer, cavern, tubes, and borehole. Apart from the tank, all other kinds are site condition dependent (hydro-geologically and geologically). The aquifer thermal energy storage (ATES) system is a widespread and desirable system, due to its thermal features and feasibility. In spite of all the advantages which it possesses, it has not been adopted in very shallow groundwater (less than 2 m depth) regions, till now, due to the susceptibility of the storage efficiency of these systems to the in-site parameters. This paper aims to find a reliable method that can be used to find the best location to install ATES systems. The concept of the suggested method is based on integrating three methods. They are, the analytical hierarchy process (AHP), the DRASTIC index method, and ArcMap/GIS software. The results from this method include a criterion that summarizes the best location to install an ATES system. This criterion is depicted by ArcMap/GIS software, producing raster maps that specify the best location for the storage system. The suggested method can be used to find the best location to install the thermal storage, especially in susceptible aquifers.
Keywords: site selection; underground thermal energy storage systems; analytical hierarchy process (AHP); aquifer vulnerability; DRASTIC index; ArcMap/GIS software
1. Introduction Underground thermal energy storage (UTES) systems are widely used around the world, due to their relations to heating ventilating and air conditioning (HVAC) applications [1]. To achieve the required objectives of these systems, the best design of these systems should be accessed first. The process of determining the best design for any UTES system has two stages, the type selection stage and the site selection stage. In the type selection stage, the best sort of UTES system is determined. There are six kinds of UTES systems, they are: boreholes, aquifer, bit, tank, tubes in clay, and cavern [2–5]. The selection of a particular type depends on three groups of parameters. They are: Site specific, design, and operation parameters (Figure1). Apart from site specific parameters, the other two types can be changed through the life time of the system. The site specific parameters, e.g., geological, hydrogeological, and metrological, cannot be changed during the service period of the system. Therefore, the design of the best type should depend, at first consideration, on site specific parameters.
Water 2019, 11, 1393; doi:10.3390/w11071393 www.mdpi.com/journal/water Water 2019, 11, x FOR PEER REVIEW 2 of 19
WaterTherefore,2019, 11 ,the 1393 design of the best type should depend, at first consideration, on site specific2 of 19 parameters.
Figure 1. Factors effecting underground thermal energy storage (UTES) system type and efficiency. Figure 1. Factors effecting underground thermal energy storage (UTES) system type and efficiency. After the type selection stage is finalized, the stage of the best location/site can be initiated. In this After the type selection stage is finalized, the stage of the best location/site can be initiated. In stage, the best location to install the system is determined. It depends essentially on the site specific this stage, the best location to install the system is determined. It depends essentially on the site parameters, since the other two groups of parameters (design and operation) can be changed. The site specific parameters, since the other two groups of parameters (design and operation) can be changed. selection should be based on the best efficiency to the system. The best efficiency can be formulated The site selection should be based on the best efficiency to the system. The best efficiency can be through different approaches. Some of these approaches are energy, cost, return time (payback formulated through different approaches. Some of these approaches are energy, cost, return time time), and environment approaches. All these approaches can be summarized in one combined (payback time), and environment approaches. All these approaches can be summarized in one approach, which is the feasible and objective approach. The decision makers can formulate the different combined approach, which is the feasible and objective approach. The decision makers can formulate approaches, e.g., energy, cost, and environmental, into specific equations, i.e., objective functions, using the different approaches, e.g., energy, cost, and environmental, into specific equations, i.e., objective optimization principles. The different approaches can be represented by many criteria. Considering functions, using optimization principles. The different approaches can be represented by many the environmental approaches, they can be addressed by DRASTIC index [6–8]. DRASTIC index refers criteria. Considering the environmental approaches, they can be addressed by DRASTIC index [6–8]. to the potential vulnerability of the aquifers to be contaminated by the surface contaminants which are DRASTIC index refers to the potential vulnerability of the aquifers to be contaminated by the surface leached to the aquifer by the water infiltration from rainfall [6,9,10]. By optimization, the objective contaminants which are leached to the aquifer by the water infiltration from rainfall [6,9,10]. By function can be solved to find the solution, maximum or minimum value. optimization, the objective function can be solved to find the solution, maximum or minimum value. As aforementioned, in spite of the importance of site conditions upon the efficiency to the storage As aforementioned, in spite of the importance of site conditions upon the efficiency to the storage system, there is no particular methodology that can be used to specify the best location to install the system, there is no particular methodology that can be used to specify the best location to install the storage system. Al-Madhlom et al. [11] suggested a method that has not been adopted before. storage system. Al-Madhlom et al. [11] suggested a method that has not been adopted before. This paper suggests a new method that can be used to find the best location to install aquifer thermal This paper suggests a new method that can be used to find the best location to install aquifer energy storage (ATES) system. The suggested method has not been adopted till now. The invented thermal energy storage (ATES) system. The suggested method has not been adopted till now. The method presents the best location as geographic information system (GIS) maps. invented method presents the best location as geographic information system (GIS) maps. The new method requires combining three types of fields of knowledge. They are heat transfer, The new method requires combining three types of fields of knowledge. They are heat transfer, hydro-geology, and geographic information systems (GIS). In the case that one field is neglected, hydro-geology, and geographic information systems (GIS). In the case that one field is neglected, the the method cannot be developed. The installed aquifer thermal energy storage (ATES) systems, method cannot be developed. The installed aquifer thermal energy storage (ATES) systems, till now, till now, did not consider the hydro-geological in-site properties in explicit criterion. The new method did not consider the hydro-geological in-site properties in explicit criterion. The new method summarizes all the effecting parameters in one criterion that can be used to determine the best location summarizes all the effecting parameters in one criterion that can be used to determine the best to install the thermal storage system. location to install the thermal storage system. Underground thermal energy storage (UTES) systems, including aquifer systems, are well known Underground thermal energy storage (UTES) systems, including aquifer systems, are well in Europe and in the USA where groundwater is relatively deep. They are used mainly in heating known in Europe and in the USA where groundwater is relatively deep. They are used mainly in applications (in winter) [12–14]. In spite of collected knowledge about these systems, they have heating applications (in winter) [12–14]. In spite of collected knowledge about these systems, they not, till now, been used in Middle East. Middle Eastern countries experience high temperatures in have not, till now, been used in Middle East. Middle Eastern countries experience high temperatures summer, which can reach more than 50 C, and face serious problems in cooling (during summer). in summer, which can reach more than 50◦ °C, and face serious problems in cooling (during summer). This paper suggests a new method that can be used to find the best location to install an aquifer thermal This paper suggests a new method that can be used to find the best location to install an aquifer energy storage system. The suggested method can be used efficiently for regions that have shallow
Water 2019, 11, 1393 3 of 19 groundwater, since the efficiency of the thermal storage in these cases is very affected by the in-site hydro-geological properties. The objectives of this article are: First, to find the best location to install an aquifer thermal energy storage (ATES) system (main objective). The significance of the suggested method is manifested in shallow groundwater conditions, due to the high susceptibility of storage efficiency of the thermal storage in these conditions. This study considered Babylon province (middle of Iraq), which has shallow groundwater, as a case study area. The second objective is to highlight the effect of depth to groundwater on the results. The third objective is to compare the results of site selection for two regions having different groundwater conditions (shallow and deep). The study considered Karbala province, bordering the Babylon province, as a deep groundwater region.
2. Methodology As aforementioned, one of the aims of this article is to present a suggested methodology that can be used to find the best location to install an ATES system. The suggested approach is based on two well-known methods, the analytical hierarchy process (AHP) [15], and the DRASTIC index [6]. Furthermore, the process of site selection has four main steps: First, defining objective function with all its weighting factors by using the analytical hierarchy process method; second, checking the validity of all weighting parameters that are found in the first step; third, using DRASTIC index to formulate the in-site conditions; fourth, using ArcMap/GIS to depict the results. The process is described in more details as follows.
2.1. Analytical Hierarchy Process (Weighting Step) In this stage, the objective equation is specified according to the best efficiency to the storage system. There are different approaches that can be used to define the efficiency to the system. Examples are, storage efficiency, cost, return period, and environmental effect. The objective function can be compounded to represent/satisfy two or more different approaches [16]. The decision makers can list the approaches according to their priorities. Then, they can establish the objective function according to that order of priorities. The different approaches are then assembled and written in terms of controlling parameters (site specific factors). Depending on the resultant equation the best location to install the storage system can be specified. The resultant objective function can be written as follows: X n S = i WiRi (1) where S is the site suitability index factor (dimensionless; its values rate from 0 to 10), Wi is the normalized weight of the ith in-site factor (dimensionless; its values rate from 0 to 1), and Ri is the rating of the ith factor (dimensionless; its values rate from 0 to 10). The AHP stage can be subdivided into the subsequent steps as follows [16,17].
2.1.1. Defining the Problem The type of underground thermal energy storage (UTES) system, which is required to be installed, should be defined. In this study, it is the aquifer ATES system. The controlling factors (in-site parameters) that are involved within the objective function are to be deduced. The importance of each in-site factor is decided according to the decision makers’ judgements. Depth to groundwater, seepage velocity, transmissivity, aquifer thickness, type of aquifer, volume of the aquifer, types of groundwater, and temperatures of groundwater are some examples of site specific parameters.
2.1.2. Arranging/Ranking Site specific parameters should be arranged according to their importance on the site selection. The parameters are ordered from high to low effect. An integer value (rates from 1 to 9) is to be assigned to each parameter (Table1). Note that, if a parameter has zero influences, it means that the parameter Water 2019, 11, 1393 4 of 19 Water 2019, 11, x FOR PEER REVIEW 4 of 19 is not comparableassigned to with each the parameter considered (Table parameters 1). Note that, [16 if]. a The parameter high e ffhasective zero parameter influences, isit means given athat larger the integer valueparameter such asis not 8 or comparable 9. The parameters with the considered that have lessparameters influence [16]. are The assigned high effective to a small parameter integer is value likegiven 1 or a 2. larger The parametersinteger value whichsuch as have 8 or 9. intermediate The parameters influences that have areless assignedinfluence are to intermediateassigned to a asset values.small In integer reality, value there like are 1 many or 2. applicationsThe parameters that which can satisfyhave intermediate this ranking influences [10]. But, are this assigned scale can to intermediate asset values. In reality, there are many applications that can satisfy this ranking [10]. be changed according to the judgment of the decision makers in the hydro-geo-energy mapping field, But, this scale can be changed according to the judgment of the decision makers in the hydro-geo- i.e., this scale has the flexibility to assign fractional “tad” values in order to refine the scale [9]. energy mapping field, i.e., this scale has the flexibility to assign fractional “tad” values in order to refine the scale [9]. Table 1. Fundamental scale/parameters influence ranking [15]. Table 1. Fundamental scale/parameters influence ranking [15]. Intensity of Importance Description Intensity of1 Importance Equal importance Description 31 ModerateEqual importance importance 53 StrongModerate importance importance 75 Very strongStrong importance importance 97 ExtremeVery strong importance importance Intermediate values between the two adjacent 2,4,6,89 Extreme importance 2,4,6,8 Intermediate valuesjudgments between the two adjacent judgments
2.1.3. Pairwise2.1.3. Pairwise Matrix Matrix A pairwiseA pairwise comparison comparison matrix matrix should should be constructed, be constructed, which which depicts depicts the the relationships relationships betweenbetween the parameters (Figure 2a,b). Priority matrix (𝑤) (Figure 2a and Equation (2)) can be written in terms the parameters (Figure2a,b). Priority matrix ( w) (Figure2a and Equation (2)) can be written in terms of of matrix A (Figure 2b and Equation (2)) [16]. matrix A (Figure2b and Equation (2)) [16]. There is an infinite number of methods, that can be used to derive the vector of priorities from Therematrix is an 𝐴 infinite. However, number since ofthe methods, emphasis thatis on canthe cons be usedistency, to derivethe eigenvalue the vector formulation of priorities (Equation from matrix A.(2)) However, is to be considered. since the emphasis Consequently, is on thematrix consistency, 𝐴 (Figure the 2b) eigenvaluecan be written formulation in terms of (Equationeigenvalue (2))as is to be considered.follows [16]: Consequently, matrix A (Figure2b) can be written in terms of eigenvalue as follows [16]: 𝐴𝑤=𝑛𝑤 (2) Aw = nw (2) and: and: w = (w𝑤=1, w2(,𝑤... ,𝑤, w n,…,𝑤) ) (3)(3) where w(wwherei matrix 𝑤(𝑤) is 𝑚𝑎𝑡𝑟𝑖𝑥) the priority is the of thepriority element of thei (Figure element2a), 𝑖 (FigureA is the 2a), matrix 𝐴 is of the elements matrix of (a i,elementsj) (Figure (2ab), , ) n is the number(Figure of 2b), rows n is or the columns number in of the rows matrix or columnsA which in equalsthe matrix the numberA which ofequals the considered the number factors, of the i is the indexconsidered of the row,factors,j is i the is the index index of of the the column row, j (Figure is the index2b). of the column (Figure 2b).
𝑤 𝑤 𝑤 𝑤 …… 𝑊 𝑊 ⎧ 𝑤 𝑤 𝑤 𝑤 ⎫ 𝑤 𝑤 𝑤 𝑤 ⎧ ⎫ ⎧ ⎫ ⎪ …… ⎪ 𝑊 𝑊 ⎪ 𝑤 𝑤 𝑤 𝑤 ⎪ ⎪ ⎪ ⎪ ⎪ 𝑤 𝑤 𝑤 𝑤 ⎪ ⎪ ⎪ ⎪ ⎪ …… ⎪ ⎪ ⎪ ⎪ ⎪ 𝑤 𝑤 𝑤 𝑤 𝑊 𝑊 𝑁 ⎨ ………………⎬ ⎨ … ⎬ = ⎨ … ⎬ ⎪ 𝑤 ⎪ ⎪ ⎪ ⎪ ⎪ ………… …… ⎪ … ⎪ ⎪ … ⎪ ⎪ 𝑤 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪𝑤 𝑤 𝑤 𝑤 ⎪ …… ⎩𝑊 ⎭ ⎩𝑊 ⎭ ⎩ 𝑤 𝑤 𝑤 𝑤 ⎭
(a)
Figure 2. Cont.
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Such that
(b)
FigureFigure 2. Pairwise 2. Pairwise comparison comparison matrix: matrix: (a)) Priority Priority matrix matrix in interms terms of 𝑤 of wandi and 𝑤 ; w(bj);( matrixb) matrix A in termsA in terms of ai,ofj. 𝑎 , .
2.1.4.2.1.4. Weighting Weighting TheThe weight weight for for each each factor factor is is determined determined by by using using the the pairwise pairwise matrix matrix (matrix (matrix 𝐴) seeA )Figure see Figure 2b. 2b. 𝑊 ) The weightThe weight of of any any factor factor (W i) isis the the relative relative importance importance or degree or degree of relative of relativeimportance importance of that factor of that compared to the other elements [18]. The weight (𝑊 ) can be found by applying the following factor compared to the other elements [18]. The weight (Wi) can be found by applying the following equationequation [19]: [19]: n W = √ P (4) 𝑊i = √𝑃 (4) wherewhereWi is 𝑊 the is weight the weight of the of theith factor,𝑖𝑡ℎ factor,P is 𝑃 the is productthe product of i ofth row𝑖th row elements, elements,n is 𝑛 the is the number number of of factors. It equalsfactors. to theIt equals number to the of number the columns of the columns or rows or within rows thewithin matrix the matrixA (Figure 𝐴 (Figure2b). 2b). To normalizeTo normalize the the weights weights produced produced by by EquationEquation (4), (4), Equation Equation (5) (5) is iscalled called [15]: [15 ]: 𝑊 𝑊 = Wi Win = (5) (5) P∑n 𝑊 i Wi
where 𝑊 is the normalized weight of the 𝑖𝑡ℎ factor. where Win is the normalized weight of the ith factor.
2.2. Analytical2.2. Analytical Hierarchy Hierarchy Process Process (Check (Check Step) Step) The consistency of the matrix must be evaluated and verified. To verify the consistency of the The consistency of the matrix must be evaluated and verified. To verify the consistency of the matrix, the following steps are applied [15,16]. matrix, the following steps are applied [15,16].
2.2.1. Lambda ( 𝜆 ) 2.2.1. Lambda (λmax) Lambda (𝜆 , maximum eigenvalue) is an essential verifying parameter in AHP. It is used for calculatingLambda ( λthemax consistency, maximum ratio eigenvalue) (CR) of the is estimate an essentiald vector verifying in order parameterto validate the in AHP.consistency It is used of for calculatingthe pairwise the consistency comparison ratio matrix (CR) [18]. of In the other estimated words, vector it verifies in order the rational to validate relations the consistencybetween theof the pairwisematrix comparison 𝐴 elements matrix [15,16]. [ 18Equation]. In other (2) can words, be written it verifies in terms the rationalof 𝜆 asrelations follows between[16]: the matrix A elements [15,16]. Equation (2) can be written in terms of λmax as follows [16]: 𝐴𝑤=𝜆 𝑤 (6) Aw = λ w (6) 𝜆 is calculated as follows [18]: max