**2. BOCR sub‐nets developments for business electricity sustainability**

For an energy project leader in business electricity sustainability, we have performed about 40 parameters (with some repetitions), classified into 12 groups, and specific for both BOCR merits and sustainability's main pillars. These groups are as follows: positives, characteristics, factors, results (considered twice), choices, policies, financials, resources, constraints, outlays, and sensitivities. The project leader is also requested to conceal growth and profit seen as key measurements of success, with sustainability observed as an obvious key measurement of quality of life and being better. He is asked for prudent management of risks and opportunities, and for optimistic assessment of benefits and costs. In this way, many researchers have proposed various methods to evaluate environmental, economic, and technical benefits. The important one evolved is the cost‐benefit analysis [11, 12]. The BOCR merits toward business electricity sustainability are shown in the flowchart of **Figure 1**. The latter outlines in detail the different interactions between the criteria, the expectations, and the scenarios linked to the nature of the used energy resource. The examination of the hierarchy shows an interesting link between internal and external stakeholders sub‐criterion with the BOCR merits regarding the environment pillar of sustainability. When dealing with the benefits, it is a factor. It is a constraint for costs, a policy for opportunities, and sensitivity for risks. That is to say that for energy sustainability, the project holder should join with the partners, contrary to what is done actually, where the tendency is going with political decisions taken at the head of governments. The second lesson drawn comes from the growth and development, which appears as a benefit to get and an opportunity to use, and concerns both economic and environment pillars. The third point discussed concerns job creation and employment. They are highlighted by costs and opportunities for social aspects; they also remain as a former evaluation index of the governments' performance. Unfortunately, nowadays we can observe that statistics confirm a lack of creation due to the inertia in technological transfer regarding restructuration and decentralization on power systems. In their recent publications, many authors [11–16] have stated that for solving a problem by BOCR analysis, they consider both positive attributes (benefits and opportunities) and negative ones (costs and risks) to determine a preference of alternatives in relation to a specific goal. They have defined for costs the following constituents: the capital cost (investment), operation and maintenance cost, pretreatment cost, land use cost, and finally ecological damage cost.

Five formulas were suggested to calculate the overall priorities of the alternatives by synthe‐ sizing the priority (*Bi* , *Oi* , *Ci* , *Ri* ) of each alternative under each merit with the corresponding priorities (*b*, *o*, *c*, *r*) such as

Multiplicative:

ing the present? No. How about the electricity sustainability? The Electric Power Research Institute (EPRI) describes electricity as a solution, an essential foundation for a sustainable world. Modernization of the electric system will increase productivity, contribute to economic growth, and transition to cleaner technologies and environmental sustainability, and it can also increase the reliability and safety of food while reducing the risk of failure or dangerous electrical disturbances as stated by North American Electric Reliability Corporation [1] and Bauchot and Marcaux [2]. In this chapter, we have investigated the main indicators of electricity sustainability, the multi‐criteria decision‐making methods that allow highlighting decision makers' attitudes and customers' reactions, and finally the merits of some energy resources taking into account simultaneously profitability and quality of life. Nowadays, many companies face the option to expand their business and venture into new global market. For their long‐run growth planning, they are required to consider sustainability as a performance index. Decision makers should combine several indicators such as economic, social, and environmental, which are the three main pillars of sustaina‐ bility. Many opinions were raised on the importance of such an area. Based on the Brundt‐ land report of the United Nations in 1987, some researchers consider equal importance for the three areas [3]. However, Doane and Mac Gillivray [4] have reported that most of the existing sustainability management tools and systems are mainly designed by environmen‐ talists and social scientists. Some do refer to economic sustainability but are so sketchy that they would be inadequate for actually managing a real business. They have shown that maintaining high and stable levels of economic growth is one of the key objectives of sustainable development. Murphy [5] has stated that a relatively limited treatment has been afforded to the social pillar. The author showed the way to expand the parameters of the latter by connecting it empirically to the environmental imperatives. The tilting of the societies in the areas of new technologies and sustainable development is a matter of decision making, knowing that the tools are available with the duty of obtaining the desired results. Today, we should face the decision making on business electricity sustainability in a multi‐criteria context, including various energy resources technologies with their posi‐ tive and negative attributes. Over the past three decades, a considerable progress has been made in multi‐criteria decision analysis (MCDA), and many examples of applications can be found in the literature in different areas, such as design and control of complex sys‐ tems, energy management, environment protection, territory planning and development. To provide knowledge and to assist decision makers, several researchers in sustainability have suggested the use of multi‐criteria decision‐making methods (MCDM) [6–10]. A review of the literature on analytic hierarchy process (AHP) and BOCR combination has generated a great interest in such fields, and it will be generalized to both enterprise long growth and electricity sustainability. The rest of the chapter is organized as follows: The BOCR sub‐ nets development is discussed in Section 2. AHP development is provided in Section 3. It is followed by reviews of its combination with BOCR merits and a concept development in Section 4. Section 5 is devoted to the economic criteria and to cost‐benefit analysis meth‐ ods. The electricity sustainability development with an application to a case study is

278 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

discussed in Section 6. Conclusion and future work are provided in Section 7.

$$P\_l = B\_l \mathcal{O}\_l / \mathcal{C}\_l \mathcal{R}\_l \tag{1}$$

Additive:

**Figure 1.** BOCR (benefits, opportunities, costs, and risks) networks of electricity sustainability.

Combining AHP Method with BOCR Merits to Analyze the Outcomes of Business Electricity Sustainability http://dx.doi.org/10.5772/64042 281

$$P\_l = bB\_l + oO\_l + \mathcal{c}(1 \mid C\_l)\_{\text{Normalized}} + r(1 \mid R\_l)\_{\text{Normalized}} \tag{2}$$

Probabilistic additive:

$$P\_i = bB\_i + oO\_i + \mathcal{c}(1 / C\_i)\_{Normalized} + r(1 / R\_i)\_{Normalized} \tag{3}$$

Subtractive:

**Figure 1.** BOCR (benefits, opportunities, costs, and risks) networks of electricity sustainability.

280 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

$$P\_i = bB\_i + oO\_i - cC\_i - rR\_i \tag{4}$$

Multiplicative priority powers:

$$P\_i = B\_i^b \cdot O\_i^o \cdot \left[ \left( 1 / C\_i \right)\_{Normalized} \right]^c \cdot \left[ \left( 1 / R\_i \right)\_{Normalized} \right]^r \tag{5}$$

#### **3. Analytic hierarchy process (AHP) development**

The analytic hierarchy process (AHP) is a method of the first performance aggregation‐based approaches, and it was introduced by Saaty with the aim of evaluating tangible and intangible criteria in relative terms using an absolute scale. A literature review of around 150 applications was performed, and it stated that AHP use is increasing in the developing countries and augurs well with the economic development of emerging countries [17]. In a recent research dealing with renewable energy reviews, it was stated that the AHP method is popular looking to its simplicity, flexibility, and intuitive appeal [6]. The AHP method is judged as a transparent process and an appropriate tool to avoid conflict of interest when acting in a monopoly business environment and in a regulated market. This is the case of several societies today, where a lot of questions around the energy issues are posed. The approach defined by Rafikul and Saaty [18] involves the identification of the goal, the development of potential scenarios that can meet the desired objective, and the identification of the criteria and sub‐criteria that influence the decision, it is summarized as follows:


Sensitivity measures are developed to determine the robustness of the consistency ratio and the principal right eigenvector to perturbation in the group judgments of the pair‐wise comparison matrix, defined as *A*. The elements of this matrix are designated as *aij* , as a quantified judgment.

If *A* is a consistency matrix, the relations between weights *Wi* and judgments *aij* are simply given as *aij* <sup>=</sup> *<sup>W</sup> <sup>j</sup> Wi* (for *i,j* = 1,2,…,*n*). The largest eigenvalue *λ*max is given as

$$\mathcal{A}\_{\text{max}} = \sum\_{j=1}^{n} a\_{lj} \times \frac{W\_j}{W\_i} \tag{6}$$

If *A* is a consistency matrix, the eigenvector *X* can be calculated as

$$(\mathbf{A} - \mathbb{A}\_{\text{max}}\mathbf{I})\mathbf{X} = \mathbf{0} \tag{7}$$

The consistency index *CI* and the consistency ratio (*CR*) were proposed to verify the consistency of the comparison matrix. It is adopted that

$$CI = \frac{\lambda\_{\text{max}} - n}{n - 1} \tag{8}$$

$$CR = \frac{C\text{I}}{CR\text{I}}\tag{9}$$

where the values of *CRI* are varying with the consistency matrix size. In the AHP, the pair‐ wise comparisons in a judgment matrix are considered to be adequately consistent if the corresponding (*CR*) is less than 10%.

#### **4. An overview on AHP‐BOCR concept development**

In a recent publication [13], authors have reviewed the MCDM methods of sustainability and have highlighted their great potential in the field of energy. AHP is the first popular method, and it was found that AHP and its associated family of methods account for 65% of the published papers. They have stated that to deal with the bipolarity of decision attributes more comprehensively, BOCR merits can be introduced into the AHP method to solve a problem. Saaty [19] asserts that the integration of BOCR into AHP allows for more comprehensive way to achieve meaningful preference scores, and it is well suited for the purpose of comparing and assessing energy technologies. It is also regarded as a suitable method to perform sustainability evaluation owing to its flexibility and the possibility of facilitating the dialog between stakeholders, analysts, and scientists [20]. Recent advances in the literature dealing with the combination of AHP with BOCR have treated the issues, such as AHP‐BOCR for energy planning including strategic analysis of wind projects in China [21], electricity supply chain analysis and multi‐criteria, multi‐actors high‐tech selection problem in Turkey [15, 22], analysis of hybrids in renewable power energy generation in China [23] and on the evaluation of sustainable energy based on the view of different stakeholders for North Korea [24]. Other works dealing with AHP‐BOCR combination were investigated to help select the suitable wind firm project [21] and the optimal hydrogen production method [25]. AHP‐BOCR models have found their applications in economics, industry, and manufacturing, such as evaluation model of buyer‐supplier relationships in high‐tech industry [26], evaluation of the optimal recycling strategy in upstream of solar energy industry [27], and revitalization strategies in historic transport [28].

Sensitivity measures are developed to determine the robustness of the consistency ratio and the principal right eigenvector to perturbation in the group judgments of the pair‐wise comparison matrix, defined as *A*. The elements of this matrix are designated as *aij*

*Wi* (for *i,j* = 1,2,…,*n*). The largest eigenvalue *λ*max is given as

1

=

max ( )0 A- I C= l

The consistency index *CI* and the consistency ratio (*CR*) were proposed to verify the consistency

where the values of *CRI* are varying with the consistency matrix size. In the AHP, the pair‐ wise comparisons in a judgment matrix are considered to be adequately consistent if the

In a recent publication [13], authors have reviewed the MCDM methods of sustainability and have highlighted their great potential in the field of energy. AHP is the first popular method, and it was found that AHP and its associated family of methods account for 65% of the published papers. They have stated that to deal with the bipolarity of decision attributes more comprehensively, BOCR merits can be introduced into the AHP method to solve a problem. Saaty [19] asserts that the integration of BOCR into AHP allows for more comprehensive way to achieve meaningful preference scores, and it is well suited for the purpose of comparing and assessing energy technologies. It is also regarded as a suitable method to perform

max 1 *<sup>n</sup> <sup>C</sup> <sup>n</sup>* l

> *<sup>C</sup> CR CR*

*<sup>n</sup> <sup>j</sup> ij i j <sup>W</sup> <sup>a</sup> <sup>W</sup>*

max

l

If *A* is a consistency matrix, the eigenvector *X* can be calculated as

**4. An overview on AHP‐BOCR concept development**

of the comparison matrix. It is adopted that

corresponding (*CR*) is less than 10%.

If *A* is a consistency matrix, the relations between weights *Wi*

282 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

quantified judgment.

given as *aij* <sup>=</sup> *<sup>W</sup> <sup>j</sup>*

, as a

are simply

and judgments *aij*

= ´ å (6)

(7)


<sup>I</sup> <sup>=</sup> <sup>I</sup> (9)

The objective of the present investigation is to select the appropriate technology of energy production (from three types of energy sources such as fossil, nuclear and renewable technol‐ ogies), depending on the scenarios reflecting the behavior of decision makers. BOCR merits are often considered as control criteria as shown in **Figure 2**. For benefits are selected four main sub‐criteria such as profitability, power customer satisfaction, life quality, and reduction of vulnerability of energy dependences. The first two criteria can be satisfied simultaneously. The enterprise can enhance performances and realize profits while satisfying the customer wants in terms of quality of service, reliability, and a minimal cost of the kWh delivered [17]. As for life quality, some researchers have measured it using life quality index (LQI) defined as a marginal function [29]. However, the reduction of vulnerability of energy dependences is an issue that concerns economists, politics, and managers. The entities doing forecasts to leave nuclear plants and jump to clean energies in the horizon 2020 or 2025 should take into account the operation feasibility as a function of financial resources needed to uninstall nuclear power plants. This allows us to introduce the costs side, where the cost‐benefit analysis method is suggested associated with the economic criteria in uncertain future. It is dependent on the attitudes of decision makers summarized in four scenarios, such as optimistic, pessimistic, prudent, and gambler. Opportunities are also gathered in four clusters such as global em‐ ployment, growth and development, external and internal stakeholders, and Enhance political stability. They are largely explicated in **Figure 2**, and their impacts are visible in medium‐ and long‐term periods planning. The fourth merit describes the risks such as economic volatility, consumer demand, government regulation, and potential of conflicts. Profitability can be modeled as the gain that the enterprise has to procure; this is why we have introduced in the goal the term business. For costs, two groups are defined: external and internal costs with many constituents, namely capital cost (investment), operation and maintenance cost, pretreatment cost, land use cost, and ecological damage cost. We can define for each alternative a total cost as the sum of those of the constituents. It is to say that usually the cost can be defined as a sum of two costs: fixed cost and variable cost. This question is largely defined in reference [17] as well as for the attitudes of decision makers.

**Figure 2.** An explicit hierarchy to determination of business electricity sustainability.
