**1. Introduction**

Smart Grids (SG)refer to advanced energy networks that incorporate the advances, trends and needs of the 21st century.

The current electric power transmission, distribution and commercialization infrastructure allows SG to add the technological potential of electronics, communication and computing, achieving a bidirectional flow between the equipment installed in the user's network area and the service providers. Therefore, SG seek to support a more efficient and reliable electric grid, which improves the security and quality of supply, according to the advances of the digital era [1]. Additionally, SG have been conceived in such a way that they can generate a positive environmental impact in the reduction of the "carbon footprint" due to their implementation through global policies and regulations.

SG emerge in their first generation to solve issues related to building more grids, installing automatic meters, developing a workforce oriented towards new communication technologies, and reducing losses and increasing system reliability. The second generation includes the concepts of stability, market design issues and the setting of hourly tariffs.

The third and fourth generations point towards a smart energy grid, addressing the new needs of the world's sustainable energy system by making full use of new methods for optimization, the penetration of electric mobility with electric vehicles, renewable energies, storage, distributed generation, and distributed, interoperable and secure information systems. These opportunities, which are important for society and new fundamental research challenges, demand a breakthrough in the modernization of SG [2, 3].

The wide range impacts not only monetary aspects are of interest, clearly identify the impact allocation is difficult because indirect/side effects by intangible impacts and quantify all impacts is not possible. Data availability and reliability are necessary for strategic decision-making. One of the main aspects of decision making in planning is identifying the best options. The best option involves the assessment the option performance on several conflicting criteria, making trade-offs, considering the stakeholder perspective and typically achieves a comfortable level of performances by minimizing the related cost. The Smart grid planning calls for effective tools for complex decision- making problems.

Most Smart grid assessment frameworks descend from EPRI approach. Some methods are devised on the specific case study, while others are devised as general frameworks where only qualitative or quantitative criteria are considered [4].

For the majority of the methods for analytical frameworks, large amount of input data and high analyst know-how are main requirements. These make those methods have low replicability due to different context or different assets. Therefore, low comparability of results from different frameworks limited feedbacks from real smart grid projects. The gap between users' requirements and methods for smart grid assessment is great and the lack of unprofitable projects reassessment grows a gap in how to deal with uncertainty and regulation [4].

The proposed methodology is based on the latency model [5]. In this model, every pilot project is considered a Smart Grid information source, where its data can be analyzed in different layers according to latency and storage. After the modeling of the pilot project, Key Performance Indicators (KPI) are defined for each of the layers described in the reference model used. Once the KPIs have been defined with the characteristics of effectiveness, efficiency, quality and economy, the Analytic Hierarchy Process (AHP) is applied for the multi-criteria evaluation including the cost/benefit analysis to obtain the weights relating the indicators to the criteria. This last phase allows the comparison between the different alternatives for prioritization.

#### **2. Good practices in smart grid project assessment**

Several factors at the global level, as well as the emerging technologies needed to establish the criteria and vision of a smart grid, lead electric energy companies to exchange information to ensure the reliability of the operation of interconnected electricity systems [6].

Advances in the integration of SG can be observed in the most important countries and economic groups in the world, among which we can mention:

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


However, the infrastructure of electric grids is generally heterogeneous, i.e., there are different formats, technologies and management and storage systems with proprietary and closed formats that hinder interoperability between companies and even internally. The problem of having a large number of data interfaces, multiple processes for exporting and importing information, as well as diverse requirements for transforming the exchanged data, has become exponential. Likewise, several typical problems arise, such as duplicity of information and functions that occur when two or more systems contain the same data or perform the same function; data inconsistency is evident when two systems have different values for the same data; and incompatibility that occurs when information from two or more systems cannot be combined for technological, political, syntactic or semantic causes [7].

In 2011, the IEEE P2030 [8] international standard proposal for Smart Grid Interoperability was published with the objective of providing common understanding, terminology and definitions for the design and implementation of Smart Grid components and applications. P2030 offers three key viewpoints: the energy systems perspective, the communication technology perspective, and the information technology perspective. In addition, each perspective is composed of seven domains: generation, transmission, distribution, service, markets, control/operations and customers. Each domain is composed of a few entities that are logically connected with interfaces. P2030 is promising reference architecture for the standardization of interfaces. However, there is no evidence to determine whether smart grid concepts are appropriate in this approach and the links between the different perspectives are not presented. Therefore, it is not clear whether this approach is feasible for a rigorous analysis with respect to the functionalities envisioned for the smart grid, and their manifestation within the system. In the following subsections, reference models widely used in smart grid representation are presented.

### **2.1 Smart grid architecture model**

The Smart Grid Architecture Model (SGAM) is a reference model for analyzing and visualizing the use of SG in a neutral way [9]. In addition, it supports the comparison between different approaches to smart grid solutions so that differences and similarities between different paradigms, roadmaps, and viewpoints can be identified.

The SGAM provides a systematic approach to deal with the complexity of SG, allowing the representation of the current state of implementations in the power grid, as well as the evolution of future scenarios of SG by supporting the principles of universality, localization, consistency, flexibility and interoperability. The current trends take as a reference model the one proposed by the European community called SGAM, which is shown in **Figure 1**.

The ease of representation in this architecture allows the SG structure to be extended in one dimension of the complete electrical energy conversion chain, divided into 5 domains: generation, transmission, distribution, distributed energy resources (DER) and local customers and in the other dimension of the hierarchical levels of the power management system, divided into 6 zones: process, field, station, operation, enterprise and market. Interoperability as a key factor for SG is intrinsically addressed in SGAM by the overlapping of the 5 layers: components, communication, information, functions and business. The SGAM layers allow modeling the different business views, which are described below:

In the **business layer**, economic and political regulatory structures are mapped onto the models related to enterprises, business possibilities and the market players involved. Business processes can also be represented in this layer. Thus, the management group is supported in making decisions related to (new) business models and specific business projects (business case) as well as regulatory agents in defining new market models. The SGAM technical views are modeled in the four lower layers.

The **function layer** describes the functions and services that the business needs. The functions are represented independently of their physical implementation (represented by elements in the component layer).

The **information layer** contains the objects and information data models, their usage and the exchange mechanism between functions.

The emphasis of the **communication layer** is to describe the mechanisms and protocols for the interoperable exchange of information between functions.

**Figure 1.** *Model architecture for smart grid [9].*

The **component layer** describes all the elements involved. This includes the power system equipment (typically found in process and in the field), the teleoperation protection and control devices, the network infrastructure (wired/ wireless communication connections, routers, switches) and any computers. For a specific use case implementation of the identified functions, they can be mapped to components that complement the relationships between all layers [9].

### **2.2 GridWise architecture council**

The GridWise Architecture Council (GWAC) emerges as a conceptual reference model for the identification of standards and protocols needed to ensure interoperability, IT security and define architectures for systems and subsystems in a Smart Grid [10].

**Technical** interoperability: it covers physical connections and communications between devices or systems (electrical contacts, USB ports).

**Informational** interoperability: it covers the content, semantics and format of data or instruction streams (such as the accepted meaning of human and programming languages). It focuses on what kind of information is exchanged and its meaning.

**Organizational** interoperability: it covers the relationships between organizations and individuals and their parts of the system, including business relationships (contracts, properties, and market structures) and legal relationships (regulations, requirements, protection of physical and intellectual property). It emphasizes pragmatic aspects (context, regulations, laws), especially management and the electricity market.

## **2.3 Smart grid compass**

The central objective of the Smart Grid Compass is to redefine the approach to Smart Grid planning that ensures successful technology deployment and maximizes operational resource efficiency. The framework created by the compass is based on an assessment of the key challenges of Smart Grid planning and the associated causes of failure in implementation [11]. Due to the complexity of the environment and market change, utilities face a myriad of business planning challenges. The approach to building and expanding a smart grid provides a 360o view across the entire core service domains:


#### **2.4 Smart grid maturity model**

The Smart Grid Maturity Model (SGMM) proposed by the Software Engineering Institute of Carnegie Mellon University [12], is a management tool that allows

## *Electric Grid Modernization*

planning functions, a quantifiable measurement of evolution and a prioritization of strategies on the way to the implementation of SG.

The SGMM has the following characteristics: it provides a framework for analyzing and addressing modernization needs with a systemic and integrative approach, and with a balance between domains involving processes, people and technology.

This model uses domains and levels to assess and set aspirations for achieving smart grid maturity. The 8 domains of the SGMM represent logical groups of smart grid capabilities and characteristics:


In the SGMM there are 5 levels, which represent the stages that evaluate the maturity in each domain (see, **Table 1**).
