Keywords

Hydrogen energy technologies; LCC; LCA; MCDA; AHP

1 Introduction

Nowadays, one of the most critical and important issues is related to energy production and its use since energy is vital to improve our daily quality of life. Thus, scientists, academics, and researchers worldwide are looking for new and better sources of energy. At the same time, it is crucial to ensure an efficient use of energy, reducing environmental impacts. From this point of view, innovative energy technologies are vital to improve the energy generation, consumption, and to reduce the environment impacts deriving from actions and wrong energy policies. Currently, the use of hydrogen energy technologies seems promising to reducing the anthropogenic impact on climate change. But, there is a great debate on it (Llaria et al., 2011; Afgan and Carvalho, 2004). The main advantage of hydrogen is related to its availability. In fact, it is known that it is the simplest and the most plentiful element on earth, but free hydrogen is scarce. It must be produced from compounds that contain it. The hydrogen forms the water and other organic forms and represents over 70% of all that is located on the earth’s surface. Once isolated, the hydrogen becomes a useful reserve for a variety of industrial activities, as well as a fuel with great potential, enough to provide energy for almost all applications in society, from electrical services, from business to industry transport. Currently, most hydrogen is produced in oil refineries and the chemical industry. But, as stated by Steward et al. (2009), the use of hydrogen for energy storage provides unique opportunities for integration between the transportation and power sectors. Definitively, in the immediate future, hydrogen could represent an important energy carrier. In fact, in the last decade, hydrogen energy technological innovation and changes in the economic and regulatory environment have resulted in increased attention to energy systems (Fu et al., 2011). As a demonstration of this scenario, the European Union puts much emphasis on developing means of dealing with both climate change control and the energy market. In other words, there is still a need for research and development to prove sustainability applicability of innovative energy systems.

For the above reason, energy managers and all stakeholders should be able to make the right decisions in terms of economic and technical feasibility. In general, decision-making requires both objective and subjective perspectives (Mattiussi et al., 2014). This is true above all when decision is complex, such as in energy technological innovations that require a real and substantial measurement and quantification of the parameters. Definitely, it is necessary to pursue the adoption of new hydrogen energy technologies through the measurements of specific indicators. In this way, it is possible to control the variables that influence the state of the economic, social, and environmental impacts.

For economic, social, and environmental evaluations, accurate information based on life cycle analysis is needed to ensure a good policy and proper consumer decisions. Specifically, the life cycle costing analysis (LCCA) method is an evaluation technique for decision-making that evaluates the sum of all costs associated with the entire life cycle of a product/process. It is able to synthesize data and contributes to making a logical decision. However, LCCA does not provide a decision-making tool useful to select the most advantageous parameter among all. To face this disadvantage, the multicriteria decision analysis (MCDA) tools can be useful to compare different results and to balance, e.g., environmental, social, and economic data. The multicriteria analysis uses different technique for solving for the optimum. Among them, the analytic hierarchy process (AHP), developed by Saaty (1980), is one of the most well-known method used by decision makers for planning decision for both technical economical evaluation. Above these considerations are based the present research. In the remainder of this work, the historical development on life cycle costing both in general and in the context of hydrogen energy technologies is provided in section Historical Development and Survey on Life Cycle Costing and Hydrogen Energy Technologies”. In section “Historical Development and Survey on Life Cycle Costing and Hydrogen Energy Technologies”, the main life cycle costing methods are analyzed. Section “Historical Development and Survey on Life Cycle Costing and Hydrogen Energy Technologies” describes the proposed decision support framework, the ABC Analysis (analytic balanced cost analysis) based on LCCA and AHP. Finally, section “Conclusion” presents a summary of research contribution and findings.

2 Historical Development and Survey on Life Cycle Costing and Hydrogen Energy Technologies

Officially, the life cycle costing was coined in 1965 in a report entitled Life Cycle Costing in Equipment Procurement published at the United States Logistics Management Institute (Logistics Management Institute, 1965; Okano, 2001). Afterward, in 1974, the concept of life cycle costing was formally adopted by the United States Department of Health, Education, and Welfare in Florida in a project entitled Life Cycle Budgeting and Costing as an Aid in Decision-Making (Earles, 1978).

In Europe, the LCC methodology has been introduced since the 1970s (UNEP, 2011). LCC is useful to carry out economic evaluation of alternatives which considers all relevant costs and benefits associated with each activity or project over its life. LCC has been developed based on the principal life cycle view (Hoogmartens et al., 2014; Hunkeler et al., 2008).

From a general point of view, there are different definitions of LCC. For example, according to European Commission, LCC is “An approach which evaluates the costs of an asset throughout its life cycle” (EU DG Environment, 2008). While according to ISO15686—Life planning—building and construction assets, LCC is defined as “A methodology for the systematic economic evaluation of the life cycle costs over the period of analysis, as defined in the agreed scope” (ISO15686, 2011). In short, life cycle costing is a technique that allows to assess all costs associated with the entire life cycle of a product.

Historically, the SETAC-Europe Working Group on Life Cycle Costing defines three types of LCC. The first one is the conventional life cycle costing (LCC). LCC aims to assess all costs associated with the life cycle of a product. It is an economic evaluation, considering various stages in the life cycle (Ness et al., 2007). The second one is the environmental LCC that uses system boundaries and functional units equivalent to those of LCA, addressing the complete life cycle. It is a complementary analysis to environmental LCA (Klöpffer, 2003). The last one is the societal LCC. It considers a larger set of costs relevant in the long term for all stakeholders directly affected and for all indirectly affected through externalities (Hunkeler, 2006).

In the last few years, there are interesting studies that analyze the most cost-effective option among different competing alternatives based on LCC. For a comprehensive survey of the phenomenon an investigation on Scopus data base, the largest abstract and citation database of peer-reviewed literature, was carried out. Search string used in the literature survey was life cycle costing. String was defined according to the standards of Scopus database. We applied three main criteria to select articles. Only articles in which the string life cycle costing was found in (1) article title, or in (2) abstract, or in (3) keywords were analyzed. The analysis on Scopus pointed out that from 1996 until January, 2017, a set of 1801 documents has been published divided in 980 articles, 655 conference papers, and the remain part on books, editorials, letters, etc. The research highlighted a growth in the number of publications. The most of them have been published in 2016 (in total 125). Furthermore, it is interesting to note that most of the publications (343) have been published in the USA.

Then, we limited the field of inquiry only to the criterion “keywords.” In this case, the total number of documents was decreased to 926.

Considering our specific field of interest we refined our search applying a preliminary filter. Search string used was life cycle costing AND hydrogen considering the three criteria (1) article title, (2) abstract, and (3) keywords. Out of 926, we identified 18 articles from 1996 to 2017. Then, we limited the field of inquiry only to the criterion “keywords.” In this case, only seven were found.

Similarly, we conducted a deeper analysis applying a second filter. Search string used was life cycle costing AND hydrogen AND technologies. First, considering the three criteria, (1) article title, (2) abstract, and (3) keywords, only four articles were found, while taking into account the criterion “keywords,” only one article was found.

As a result of the previous research analysis, we decided to examine some relevant studies in which the direct relationship between LCC and hydrogen technologies is explored.

The first research in which the LCC and hydrogen are investigated is proposed by Scott et al. (1993). Because of the growing interest for Canadian National Rail, they examine the prospect of deploying fuel cell (FC) locomotives using a life cycle costing analysis. Afterward, Ghosh et al. (2004) examine the life cycle costing of a system that combines an electrolyzer and a high-pressure hydrogen tank for long term energy storage.

While Stanley and Martinez (2008) propose a life cycle costs assessments for scenarios that employ a wide variety of main and auxiliary propellant combinations (among them, hydrogen propellants), interesting studies were proposed by Lee and his colleagues. In 2009, Lee et al. (2009) use a life cycle costing methodology to identify when hydrogen can become economically feasible compared to the conventional fuels. While in 2010, Lee et al. (2010) develop a life cycle environmental and economic analyses of a hydrogen station with wind energy. In another work, Lee et al. (2011) analyze environmental and economic aspects of hydrogen pathways according to plausible production methods and capacity, and distribution options in Korea using life cycle assessment (LCA) and life cycle costing (LCC) methods.

An interesting point of view has been developed by Manzardo et al. (2012). In their study, a gray-based group decision-making methodology for the selection of hydrogen technologies in life cycle sustainability perspective has been analyzed, while in 2014, Meyer and Weiss (2014) use life cycle costs analysis to optimized production of hydrogen and biogas from microalgae.

More recently, Ally and Pryor (2016) propose a life cycle costing of diesel, natural gas, hybrid, and hydrogen FC bus systems in Australia. Lastly, very recently, an interesting study was proposed by Miotti et al. (2017) in which they assess the environmental impacts and costs of a polymer electrolyte membrane FC system through a detailed LCA and cost analysis. The main result of their study shows that FC vehicles can decrease life cycle greenhouse gas emissions by 50% compared to gasoline ICEVs if hydrogen is produced from renewable electricity.

In the context of hydrogen energy technologies, special attention should be given to applications related to solid oxide fuel cell (SOFC). In fact, in Europe, in recent years, remarkable improvements have been achieved in FCs and hydrogen technologies because their high efficiency/performance and low pollution emissions in comparison with traditional energy conversion technologies (EU JRC JRC-IET SETIS, 2011; Elmer et al., 2015).

Although several studies on LCA of SOFCs are analyzed in literature by several authors, such as Mehmeti et al. (2016), Lee et al. (2015), Cánovas et al. (2013), the LCC applications on SOFCs have been evaluated in literature by a limited number of studies. An interesting study is proposed by Strazza et al. (2015) in which are analyzed the sustainability evaluations of SOFCs compared with a conventional technology. In their study, LCA and LCC methodologies are combined.

3 Methods and Models for Life Cycle Costing

The overall cost of a durable good is represented by the purchase price and all expenses that must be supported in the course of its useful life. The main scope of the life cycle costing approach is to support managers to carry out the best decisions, considering future expenditure, comparison between alternative solutions, and evaluation of cost reduction opportunities. The purpose is to “control” the life cycle cost by assessing the financial impacts of the decisions taken about the complete system. The output may be expressed in several ways, but the most used indicator is present worth or present value (PV).

It is important to underline that life cycle costing is not an “exact science.” It is an estimate of the major cost factors and an insight into the magnitude of the costs. Of course, it is essential to define what is to be estimated and understand what the estimates will be used for.

Different methods and models for performing conventional LCC have been described in the literature as analyzed by Dhillon (1989) that outlines the following steps for performing LCCA: (1) define the goal of the analysis; (2) identify the purpose of the system; (3) choose the methodology to perform the LCCA; (4) collect all essential data; (5) conduct sanity checks of outputs and inputs; (6) develop the essential sensitivity analysis; and (7) analyze results.

Over the years, various advantages and disadvantages of life cycle costing have been identified by various authors. Some of the important advantages of life cycle costing are (1) useful to reduce the total cost; (2) useful tool for making decisions associated with budget; (3) useful in comparing the cost of competing projects, and (4) useful to make a selection among the competing contractors/manufacturers. In contrast, some of the main disadvantages of life cycle costing include that it (1) is time consuming; (2) is costly; and (3) has doubtful data accuracy.

In a life cycle cost approach, it is important to generate life cycle cost estimates and to conduct cost analysis. Different methods may be used to estimate costs. Depending on the method’s category, different types of approach can be used which are as follows:

Among the above methods, the most known and applied are two. The first one is the Monte Carlo simulation that is used to generate frequency or probability distributions in order to consider the systematic uncertainties resulting from boundary selection, aggregation of similar environmental impacts, etc. The second done is the AHP method that is used to define a priority ranking or in other words the grade of importance of a number of items.

The quality of the data is the real value of a LCCA. However, one of the most critical aspect in a life cycle cost project is the collection of data (primary data and secondary) in terms of time and effort.

The formula of LCC is different in various literatures but virtually similar as underlined by Eric and Timo (2008). Here, below are some of them.

3.1 Ravenmark’s Approach (2003)

Ravenmark considers several costs as described in Eq. (5.1):

$\mathrm{LCC}=C_{\mathrm{ic}}+C_{\mathrm{in}}+C_{\mathrm{e}}+C_{\mathrm{o}}+C_{\mathrm{m}}+C_{\mathrm{s}}+C_{\mathrm{env}}+C_{\mathrm{d}}$ (5.1)

(5.1)

where

• C_ic initial cost, it is the purchasing price of the component/system.

– If the price is paid immediately, the initial cost is expressed as C_ic=purchasing price.

– If the cost is spread over several years, the cost is expressed in net present value, see Eq. (5.2):

$A=\sum _{i=0}^N\frac{C_i}{{\left(1+\mathrm{rate}\right)}^i}$ (5.2)

(5.2)

where C_i is cost year i and rate is the interest rate;

• C_in installation cost. Startup costs that are not included in the purchasing price;

• C_e energy cost. Energy costs are the costs of energy supplied to the system during use, or energy consumption of the system;

• C_o operating cost. Yearly operating cost (excluding energy cost);

• C_m maintenance cost. The maintenance cost is costs of service and repairs and consist of man-hour and spare part costs;

• C_s downtime cost. Downtime costs are costs related to downtime, i.e., stops in operation;

• C_env environmental cost. Environmental costs, C_env, are complex costs, some difficult to estimate and include: potentially hidden costs (regulatory costs, upfront costs, etc.); image and relationship costs and contingent costs; and

• C_d decommissioning cost. decommissioning cost is an estimate of the cost to decommission a unit and can be expressed as a cost occurring at the end of the lifetime.

3.3 National Institute of Standards and Technology’s Approach (1995)

The total life cycle cost is equal, as defined in the following equation:

$\mathrm{LCC}=I+R_{\mathrm{epl}}-R_{\mathrm{es}}+E+W+\mathrm{OM}\&\mathrm{R}+O$ (5.3)

(5.3)

where

• LCC=Total LCC in present value (PV) dollars of a given alternative;

• I=PV investment costs (if incurred at base date, they need not be discounted);

• R_epl=PV capital replacement costs;

• R_es=PV residual value (resale value, salvage value) less disposal costs;

• E=PV of energy costs;

• W=PV of water costs;

• OM&R=PV of non-fuel operating, maintenance and repair costs; and

• O=PV of other costs.

3.4 Swarr et al. (2011) Approach

The total life cycle cost TLCC of a plant or product can be estimated as defined in the following equation:

$T_{\mathrm{LCC}}=\sum _{n=1}^N{c}_n{X}_n$ (5.4)

(5.4)

where c_n represents unit cost of life cycle activity n. For example, the total LCC of a product includes the costs of raw materials and energy, production and packaging costs, transport, and end-of-life management. For a manufacturing plant, it includes costs of construction, operation, and decommissioning of the plant.

3.5 Baldo’s Approach (2000)

Baldo proposes the following formula, see Eq. (5.5):

$\mathrm{LCC}=\mathrm{CI}+\text{pv}(\text{CE})$ (5.5)

(5.5)

where CI represents all initial costs determinable in monetary units at their real value and pv(CE) represents all operating costs discounted at the time of selection of materials, expressed in monetary units.

This approach is based on the principle of cash flow or the costs incurred during the entire life cycle are actualized in order to make comparisons realistic.

The above formula can also be written as follows, see Equation 5.6:

$\mathrm{LCC}=\mathrm{AC}+\mathrm{IC}+\sum _{n=1}^N\frac{\mathrm{OC}}{{(1+i)}^n}+\sum _{n=1}^N\frac{\mathrm{LP}}{{(1+i)}^n}+\sum _{n=1}^N\frac{\mathrm{RC}}{{(1+i)}^n}+\sum _{n=1}^N\frac{\mathrm{EC}}{{(1+i)}^n}$ (5.6)

(5.6)

where N=the useful life of the system; i=real rate of interest; n=year in which occurs the event considered (e.g., maintenance, production losses, etc.) counted from the entry into operation of the system; AC=initial costs; IC=installation costs; OC=operating costs; LP=costs of production losses; RC=replacement costs of materials; and EC=environmental costs derived from LCA.

3.6 Politano and Frohlich’s Approach (2006)

Politano and Frohlich give the calculations for LCC. And normally, four major factors can be considered in the calculations, which are computed on the following formula:

$\mathrm{LCC}=C_{\mathrm{i}}+C_{\mathrm{o}}+C_{\mathrm{m}}+C_{\mathrm{d}}$ (5.7)

(5.7)

where LCC represented the life cycle cost, C_i indicates the initial cost, C_o refers to the operation cost, C_m is the maintenance cost, and C_d indicates the disposal cost.

4 Analytic Balanced Cost Analysis: The Proposed “ABC” Analysis

The basic problems involved in the calculation of LCC are the evaluation of the relative importance of the parameter or variant preferable. To cover this gap, an analytic balanced cost analysis based on LCC and AHP is proposed.

Following an outline of the main features of the model, a simple case study on SOFC is analyzed.

4.1 The Rationale

First of all, it is important to remind that the essence of the AHP process is the decomposition of a complex problem into a hierarchy with a goal (objective) at the top of the hierarchy, criterions, and subcriterions at levels and sublevels of the hierarchy, and decision alternatives at the bottom of the hierarchy (Saaty, 1994). AHP is an operational evaluation and decision support system that is suitable for addressing complex problems featuring high uncertainty, conflicting objectives, different forms of data and information. AHP has long been widely applied to economic, social, and industrial systems (Wang et al., 2009). The modeling process requires a pairwise comparisons of the elements in each level using a scale of 1–9, as suggested by Saaty (Saaty, 1992). The result of the comparison is the so-called dominance coefficient a_ij that represents the relative importance of the component on row (i) over the component on column (j), i.e., a_ij=w_i/w_j. The pairwise comparisons can be represented in the form of a matrix. After all pairwise comparison is completed, the priority weight vector (w) is computed as the unique solution of Aw=λ_maxw, where λ_max is the largest eigenvalue of matrix A. Finally, consistency index (CI) is estimated. CI could then be calculated by CI=(λ_max − n)/n − 1. In general, if CI is less than 0.10, satisfaction of judgments may be derived.

Figure 5.1 shows the main phases and steps characterizing the “ABC” analysis.

It is important to note that the overall performance of a project is significantly affected by the experts team composition. Thus, an experts team was selected in order to put together a winning strategy that covers all the necessary aspects. Experts team was composed by three energy managers, two LCA/LCC experts, one AHP expert (moderator). The experts team worked for 5 months. They defined the ABC model, the relationships between model elements based on his/her knowledge of the problem and expressed pairwise comparison judgments according AHP theory.

4.2 The Scenario Under Study: A Schematic Overview

Below the main phase of the proposed model are summarized.

Phase 1: Environmental Analysis LCA

The systems investigated in this study include: (a) a spark ignition internal combustion engine (ICE), which represents a widespread and mature technology for cogeneration; (b) a microturbine (MT), which is an evolving technology in the early stages of application in commercial buildings; and (c) a SOFC, which represents a new technology that is in development and testing stage, which, because of its modular design, can be used in commercial building applications. The objective functions used in the formulation of the problems include: minimizing global warming potential (GWP); minimizing acidification potential (AP); and minimizing tropospheric ozone precursor potential (TOPP). The functional unit chosen for the current study is 1 kW h of electricity delivered for domestic consumption. Figs. 5.2 and 5.3 shows, respectively, GWP emission factors for energy systems producing unit power output and AP emission factors for energy systems producing unit power output. In the first case, result point out that both MT and ICE have the lowest GWP impacts when operated at full load, while in the second case, it means that both the MT and ICE have the lowest AP impacts when operated.

Similarly, LCTOPP emission factors from energy systems producing unit power output have been analyzed.

Phase 2: Economic Analysis LCC

Systems are defined in Table 5.1, according to literature review (Karni et al. 2000; Simander and Hasslacher, 2001) and the authors’ elaboration.

Table 5.1

Systems Under Study

	ICE	Microturbines	SOFC
Capacity range	<5 MW in DG applications	30–250 kW	5 kW to 2 MW
Electrical efficiency (%)	35–45%	18%–27%	30–63%
Power-to-heat ratio	0.8–2.4	0.4–0.7	1–2
Noise	High	Moderate	Low
NOx emissions (kg/MW h)	0.2–10	0.015–0.036	0.0025–0.004
Availability (%)	92%–97%	90%–98%	>95%
Part load performance	Good	Fair	Good
Hours to overhaul	25,000–50,000	20,000–40,000	32,000–64,000
Initial cost	340–1000$	2400–3000$	5000–6500$
Maintenance cost	0.006–0.013$/h	0.005–0.01$/h	0.0135–0.015$/h
Operation cost	0.0075$/kW h	0.005–0.016$/kW	0.0049$/kW h

Phase 3: AHP Model Definition

For this application, the AHP takes as input the results of LCA/LCC and from Table 5.1.

The AHP model aims to define a LCC Performance Index (LCC_index) in order to integrate the LCC analysis. LCC_index represents a single index that aggregates weights of each criterion taking into account that the criterion could have different importance depending on the managerial point of view and strategy. Figure 5.4 shows the AHP model.

As explained above, AHP is a multicriteria decision-making tool that enables the user to establish weights for selected criteria by means of a series of pairwise comparisons, according to the hierarchical structure proposed and according to the 1–9 point Saaty’s scale. Thus, Table 5.2 shows an example of pairwise comparison.

Table 5.2

Pairwise Comparison Example

	Initial Costs	Maintenance Costs	Operation Costs	Weights (%)
	C1	C2	C3
Initial costs C1	1	5	4	10
Maintenance costs C2	1/5	1	2	36
Operation costs C3	1/4	1/2	1	54
Consistency Index: 0.090

The strength of AHP is that it allows to perform a sensitive analysis, as shown in Fig. 5.5.

The vertical dotted line is initially set at 0.5 on the X-axis for the priority of the C3 “Operation costs node.” The respective priorities of the alternatives are indicated by the Y-axis values where their lines intersect the vertical line: A1=0.332; A2=0.310 and A3=0.359. It means that in this case, A3 is preferable.

If we drag left to a priority of 0.6, at which point, the priorities of the alternatives are A1=0.288; A2=0.310, and A3=0.404, avalon is 0.311. It means that after 0.50, A3 becomes the best choice considering C3 “Operation costs node,” A2 is unchanged.

Results are promising since the “system” alternatives can be compared using the “ABC” analysis that integrates LCC approach and multicriteria analysis.

In some circumstances, it could be useful to carry out an integrated analysis to compare different results and to balance environmental, social, and economic data. In these cases, the AHP model proposed in Fig. 5.4 becomes a more complex model, as shown in Fig. 5.6. The final output is the definition of a Global_index that integrates LCA, SLCA, and LCC issues.

5 Conclusion

In conclusion, we can affirm that LCC provides an useful tool to assess the cumulative potential environmental impacts for energy technologies and a fortiori for innovative technologies such as hydrogen energy technologies. However, some disadvantages characterize the method. One of the most limiting aspects is related to its inability to assign a different weight to all analyzed factors. Thus, in the present work, an integrated methodological approach, ABC analysis, was proposed. The main strength of the proposed model is the integration of LCC approach with multicriteria approach, combining the advantages of both methodologies. With the proposed approach alternatives can be compared using a weighted analysis, based on LCA/LCC. The results obtained are promising since the model allows to evaluate the sensitivity of the decisions made. However, making environmental decisions based on cost analysis is complex, even though several tools are available. In our opinion, to solve this problem, it is desirable to develop new research areas. One of this could be to develop a decision support tool that integrates environmental and economic dimensions. Another interesting point could be to define a specific practice guideline in order to standardized the procedure and to ensure a timely and accurate technical analysis.

Specifically, our future efforts aim to extend the use of LCCA in the hydrogen energy technologies sector and hence improve the decision-making process toward more sustainable technologies. In more practical terms, the aim of our future research will be twofold. First, to develop a framework for data collection for LCCA. Second, to develop a web-based database for benchmarking technologies in costs (operation, maintenance, etc.) in order to ease LCC calculations. In addition, the Monte Carlo simulation approach will be investigated to develop a stochastic life cycle cost model in order to compare AHP results and different scenarios.

Acknowledgments

This research represents a result of research activity carried out with the financial support of University of Naples “Parthenope,” namely “Ricerca individuale per il triennio 2015–2017.”