*2.5.1 Procedure for the representation of a Smart Grid source in the proposed model*

The representation of a Smart Grid source requires following a basic procedure for modeling in the SGAM architecture. The procedure consists of four steps and can be repeated iteratively to refine the result. Each iteration can cover a different aspect of the class of systems:

Step 1. **Smart Grid source selection**, this step involves the analysis of available sources and the acquisition of design documentation. This step is crucial for obtaining the model and applying it to the reference architecture. The result of this step is a set of design data and specifications.

Step 2. **Identification**, **creation or adoption of indicators for each stage defined in the reference model**, the purpose of this step is to find through different key performance indicators those with the lowest level of abstraction in the representation in the reference model. The use of existing terms (derived, for example, from standardized glossaries) enables generalization in the implementation. The result of this step is a set of reference indicators.

Step 3. **Modeling the reference level,** in this step each KPI is projected onto the layer level and its respective latency. This translation of the KPIs to the layer level allows a better comparison of the individual KPIs, as well as accessibility to the relevant information of each layer. The result of this step is the relation of the KPIs with the layer level and their latency.

Step 4. **Validation of the reference architecture**, the creation of reference indicators and their assignment to the layers of the model may not be valid. Therefore, the task in this step is to present the resulting model to Smart Grid system experts, operations, commercial, financial and engineering managers from the power sector, and Smart Grid users to validate whether the Smart Grid source is represented correctly. The result of this step is a validated reference architecture.

#### **3. Multi-criteria analysis for planning assessments**

Decision making can be considered as a cognitive process resulting from the selection of a belief or a course of action among several possible options. Every day, all people are faced with different alternatives from which they must select and identify the one that seems to be the best alternative or the one that satisfies the greatest number of intended needs. It is common to find circumstances that lead to make decisions that are relevant in a specific context and the fact of facing the choice of one alternative over another, generates several sensations to the decision maker [13]. It is, therefore, an emotional reasoning or process that can be rational or irrational.

A decision can be considered good or safe, if it comes from an appropriate methodology, considering all related aspects. On the other hand, it is not possible to consider a decision as good if it has not provided an optimal result, or the source and the procedure in its adoption are unknown. The process used to decide becomes important now of choosing the best alternative, since in this way it is possible to support that the solution was the best possible within the options and resources available. The three main characteristics for making a good decision can be found in [14]:


In engineering projects, decision making is a daily activity. Therefore, the project leader must be clear about what will be the best decision so that the project can thrive and have the least number of inconveniences. It is common that during the development of engineering projects, complex decisions are made and that these

have direct consequences on the stakeholders and affected by the decision-making process. Therefore, before making any decision, knowledge, facts, and experience must be gathered and evaluated in the context of the problem. The decision-making process usually relies on the experience of the decision maker or on the similarity to decisions previously made that led to good results.

### **3.1 Multi-criteria analysis**

Multi-criteria analysis (MCA) is a type of decision analysis tool that is applicable to cases where mutually conflicting criteria must be assessed, tangible and intangible impacts must be evaluated simultaneously and allows qualitative assessments such as environmental and social impacts to which quantifiable values cannot be assigned [15]. Multicriteria analysis can help individuals to make decisions in complex situations, where the problem can be addressed from different points of view and the interests can be social, political, environmental, technical and financial. This methodology provides support for decision making as it helps to focus on what is most important, it is logical and consistent and easy to use. At its core, multicriteria decision-making analysis is useful for:

Breaking the decision into smaller, more understandable parts.

Analyze each part of the problem.

Integrate the parts to generate a comprehensive solution.

The above, supported by mathematical, analytical, research and experimental foundations of management sciences [16]. The application of this type of techniques has been developed since the 50's of the last century, where the main objective has been to help managers and leaders to make complex decisions. There are several techniques for multi-criteria decision making, among which the Scoring method, the multi-attribute utility, the Analytic Hierarchy Process (AHP), the Analytic Network Process (ANP), among others, stand out. The most widely used for solving problems related to the choice of technologies is the AHP. The AHP method is widely used to choose among certain technological options which would be the best, considering the characteristics of certain areas with their respective particularities.

In [17, 18] the method is used for the prioritization of microgrid generation plans considering resource uncertainties and efficient energy dispatch in smart microgrids. Finally, in [19, 20] AHP is used to obtain the best data information process of an energy metering system and the selection of a smart metering infrastructure according to the needs of an energy meter developer company.

#### *3.1.1 Analytical hierarchical process*

The Analytical Hierarchical Process (AHP) is a theory of measurement through pairwise comparison and subsequently organized by expert judgments to obtain priority scales. Developed by Thomas L. Saaty between 1971 and 1975 [21], it has been extensively studied and refined since then. This technique is applied in a wide variety of situations associated with the fields of public administration, industry, business, health, and education. To perform an AHP analysis, those who are involved require a thorough knowledge of the issue to be solved because the construction of the hierarchical structure must include enough relevant details to fully describe the problem. After being clear about the main objective of the problem, the first thing to do is to decompose it in a top-down hierarchy, position it at the top vertex and from there, place the criteria first, to make the selection of alternatives, the constituent parts of the problem, the sub-criteria and their fundamental relationships, as shown in **Figure 3**.

#### **Figure 3.**

*AHP hierarchy. Goal, criteria and alternatives.*

When the hierarchy is established, the decision-makers (panel of experts) methodically evaluate each of the elements to compare them with each other; these comparisons are made at each hierarchical level in pairs, as they seek to determine the importance of each of them to the higher-level element to which it is related. When making the comparisons, the experts can use concrete (quantifiable) data on the elements that are necessary, or they can use their own judgments according to their level of relevance. It is fundamental to the AHP method that judgments are used to make the assessments [22]. The comparisons assessments made by means of pairs are evaluated by preference indices if alternatives are compared, or importance indices if criteria are compared, which are subsequently evaluated according to a numerical scale proposed by Saaty, the scales for direct assignment are given in **Table 2**.

The intermediate integer values (2, 4, 6, 8) can be used to express a preference between two adjacent judgments. Humans could establish relationships between objects or ideas so that they are consistent. For this reason, it is important to review the logical consistency of the resulting matrix, to verify whether a contradiction is generated between the values stipulated to the criteria, as a result of the pairwise


*Smart Grid Project Planning and Cost/Benefit Evaluation DOI: http://dx.doi.org/10.5772/intechopen.96315*

comparisons. The number of required pairwise comparisons for AHP increases as the number of the criteria and/or of the alternatives increase by performing the process of paired comparison between criteria and alternatives, this leads to a relative scale of measurement of the priorities given to the problem. The AHP converts these evaluations into numerical values that must add up to the unit, giving them a respective weight, in order to be able to compare them with each other in a rational and consistent way. This is how the AHP distinguishes itself from other decision-making techniques. In the final part of the evaluation, numerical priorities are calculated for each of the decision alternatives. The numerical values obtained (see **Table 3**) represent which of the alternatives has a higher weight to achieve all the criteria of the main objective of the problem [23].

#### **3.2 Multicriteria analysis & cost/benefit analysis for smart grid project**

Strengths of a combined evaluation approach in smart grid project is to select profit each one. The cba is reliable tool for an economical/financial evaluation of tangible impacts, shows some fundamental shortcomings when a large share of intangible impacts is involved. The AHP allows for considering multiple heterogenous, even conflicting criteria, soft effects are directly evaluable and monetization for all impact is not required.

The transformation of traditional power grids to SG demands significant investment in technological infrastructure, certainly, the ability to effectively monitor and manage these technologies will determine the performance of SG and will be critical to the success of those involved in the energy sector. Specifically, in the United States and Europe, regulatory and governmental bodies have for some years defined tools to measure how "smart" current power infrastructures are [24, 25]. In the rest of the world, utilities and government agencies are beginning to work on quantifying and implementing them in the context of each region. These tools help to make important decisions at the organizational level, by allowing to have a clear vision of an implementation towards the future through the results obtained, and to execute in a reliable way, in order to increase the satisfaction of the company itself and its users.

SG are considered as a model that seeks to optimize energy supply, helping to improve efficiency, reliability factors and availability and security from its generation to its delivery to consumers [26].

Some authors are working into integrate MCA and CBA in the evaluation smart grit project, Celli et al. 2017, present a sequential MCA-CBA funded by the Italian Regulator to define the condition for remunerating DSOs which own and operates storage for network issues. Many plans involving storage devices are devised by using a multi-objective optimization approach. Then, the economic sustainability of the alternatives pertaining to the Pareto frons is assessed by a CBA [27].


**Table 3.** *Decision matrix.*

Another job presented in 2016 for Marnay et al. [4], an MCA is used for evaluation the TEC smart grid demonstration project which is divided in three subprojects: distributed automation, microgrid, and smart substation. Four different evaluation domains are considered: technological, economic, social, and practical. An index is assigned to each subproject according to the performances on each domain, an overall score is computed by using the proposed SG-MCA method which combines AHP and fuzzy evaluation method [4].

The upgrading plan of the Italian smart metering infrastructure is evaluated by means of MC-CBA approach. Three different areas of interest are investigated: economic, enhanced smartness of the grid, and externalities. Three different MCA techniques to investigate the effects on the provided result [28].

It will show the construction of the approach in smart grid project assessment based on MCA and CBA methods.

#### *3.2.1 Key performance indicators*

Key performance indicators (KPI) are tools that provides information on the measurement (management or results) in the delivery of products (goods or services) generated by an institution, covering quantitative or qualitative aspects [29, 30]. Indicators are measurable factors that facilitate decision making.

KPIs are the final phase of strategic planning, which implies an adequate evaluation, selection and definition in the context of SG. In the construction of the KPIs for SG projects, a review was made of the most widely used indicators and those that generate value and impact for the pilot projects evaluated were included [24].

**Table 4** describes a KPI which is related to the impact of the alternatives on the quality of the electrical power supply service. The electrical power supply service.

#### *3.2.2 Normalization for quantitative KPI*

The normalization of the KPIs is used to obtain the weights when they are quantitative, since for qualitative KPIs the weights are obtained by means of paired comparison. The objective of normalization is to ensure that the sum of the KPIs for each alternative is 1. The ideal value for each KPI can be for its maximum or minimum value, taking into account this characteristic, it is necessary to select between Eq. (1) or (2).


**Table 4.** *KPI description table.* *Smart Grid Project Planning and Cost/Benefit Evaluation DOI: http://dx.doi.org/10.5772/intechopen.96315*

$$\text{KPI}\_{\text{Minimization}} = \frac{m\omega - \text{KPI}\_i}{m\omega - min} \tag{2}$$

where min and max are the lowest and the highest values of the KPI for each criterion, respectively.

### *3.2.3 Cost/benefit analysis*

Cost/benefit analysis (CBA) is one the most acknowledged tool for assessing the financial viability of industrial projects. It aims to an optimal resource allocation in which the monetary benefic outclass cost, and for the most profitable investment alternative. It also provides an incremental analysis regarding a particular scenario and produces easy to read economic indicators. The economic performance indicators are the indexes obtained from a CBA:


Electric utilities invest large sums in dedicated utility equipment to review compliance with their regulatory or statutory obligations. For example, the benefits of extending service to new regions and planning for continued growth are generally accepted and implicit in mandatory regulations. These companies routinely meet these non-discretionary obligations and minimize their execution costs. Moreover, they are often well prepared to defend their decisions within this costminimization framework. Smart Grid projects, on the other hand, may not fit into this time-tested paradigm of cost minimization because they may be discretionary. For example, the decision to invest in a Smart Grid project to improve reliability beyond currently acceptable levels depends on how much to invest to obtain the improvement, and whether the improvement gained justifies the amount of money to be invested. This goes far beyond mere regulation, maintaining the stringent nature imposed by the regulation itself.

Many Smart Grid investments require going beyond cost minimization. In addition to their novelty, Smart Grid applications offer new benefits beyond basic or least-cost service. They can improve reliability and quality of service beyond


#### **Table 5.**

*Evaluation economics KPI [4].*

currently accepted levels, in addition to providing customers with options and services never before experienced. Consequently, they are discretionary for the utility, and a feasible/positive scenario is needed to incorporate such innovations into the regulated business. Eventually, Smart Grid technologies are the only realistic alternatives to address technical issues that may arise when services such as distributed generation or electric vehicle charging become commonplace on distribution systems. However, these technical issues are mostly in the future, so today it remains the responsibility to devise a business and economically positive scenario, showing sufficient benefits to offset the costs [31].

The CBA is a methodology proposed by the Electric Power Research Institute [31], it aims to determine whether the benefits of a project or decision outweigh its costs. However, CBA analyzes costs and benefits from a particular point of view, which can range from the broad and societally impactful (public perspective) to the particular and focused (private perspective). General economic analyses adopt a social perspective, determining whether a project is a good allocation of social resources, without considering the distribution of benefits. This contrasts with financial analysis, as performed in private companies, which generally focuses on investment returns. This tool allows for a mid-point analysis, as the focus is on the costs incurred by the company, which are borne by the customers. The planning analysis of regulated companies minimizes the cost of reliable service while assuming the return on investment. When minimizing the cost of service is inconsistent with public policy goals, legislators and regulators can impose conditions designed to address those goals, in the hope of stimulating decisions that benefit society.

The methodological approach of the EPRI-generated guidance sets out a CBA methodology that is compatible with either the societal or customer approaches to weighing costs and benefits. This concept is more comfortably suited to fully integrated companies, as costs and benefits are easily aligned, and all are contained within a corporate environment (except for externalities that fall outside the electricity sector). Costs in one part of an enterprise can be offset by savings in another part of the same enterprise, minimizing or even eliminating the need for additional cost recovery. In addition, users are recognized as a variety of utility entity types, many of which participate in some of the functions of a generation, operation,

transmission and/or distribution utility. Costs incurred within one entity may produce offsetting savings in a separate corporate entity. Although consumers may be indifferent to where costs and savings occur, the various corporate entities involved face varying levels of cost recovery risk depending on their regulatory situations and their position in the cost and savings chain. The latter is important from a private enterprise perspective. **Figure 4** shows the steps to its implementation [32].

#### *3.2.4 Sensitivity analysis*

Factors of high uncertainty among costs and benefits are analyzed, the impact on the benefits of the project is analyzed quantitatively, and sensitive factors are identified to enable control and avoidance of risks. Multi-factor sensitivity analysis is carried out as required. The risks of not realizing the project's benefits are determined according to the sensitivity analysis.

The AHP sensitivity analysis for each KPI(1..n) or sub-criterion is obtained from the decision matrix. Each alternative for the sensitivity analysis is calculated with the values of the lines obtained from the relative weights of each KPI and the overall score of the decision matrix, Eqs (3)–(5) is used for calculus. The values to calculate the KPI line are:

$$\mathbf{u}(\mathbf{x}\_2, \mathbf{y}\_2) = \left(\mathbf{100\%, \frac{w(i, j)}{b\_j}}\right) \tag{3}$$

$$(\mathbf{x}\_1, \mathbf{y}\_1) = (b\_j, w\_i) \tag{4}$$

$$S\_i(X) = w\_i + \frac{(X - b\_j)\left(\frac{w(i,j)}{b\_j - w\_i}\right)}{w\_i - b\_j} \tag{5}$$

**Figure 4.** *Steps in describing the technology.*


**Table 6.**

*Sensitivity matrix for KPIi.*

where *b <sup>j</sup>* is sum of the relative weigh of j-th KPI, *w i*ð Þ , *j* is weight of j-th KPI and i-th alternative, and *wi* is overall score for i-th alternative, *Si* is value for i-th alternative, X is percentage for KPI value (see **Table 6**).
