Smart grid technologies represent different ways to enhance the effectiveness of the power distribution and transmission system by making it possible to use existing power infrastructure more efficiently. Implementation of smart grid solutions could for instance, represent an alternative to investment in new power generation capacity or new power lines.
Many new smart grid technologies are available, but not yet deployed. In order to advance implementation, governments and other investors need decision support to evaluate investments in smart grid technologies.

Cost-benefit analysis (CBA) offers a systematic process for comparing the advantages and disadvantages of a smart grid initiative from society perspective.
This report presents a mapping and analysis of existing literature on social costs and benefits of smart grid solutions and identifies gaps in current guidance. The study also includes a review on how network regulation affects incentives to invest in smart grid technologies and an analysis on how CBA constitutes an important input to the design of the network regulation. The report also serves as a basis for selecting models and methods to be used by the Swedish Smart Grid Forum in order to assess different smart grid projects and applications.

Due to the multifaceted and broad nature of smart grid technologies, CBA of smart grid deployment is complex as smart grid technologies provide benefits on a system level as well as on the project level. Smart grid technologies are also under fast development, which lead to a lack of data and uncertainty when extrapolating results from pilot projects to the system level.

Energy and climate goals as those identified on the European level as well as on a national levels aim to increase renewable energy, improve energy efficiency and reduce carbon emissions. Smart grid technologies contribute to all these goals, not only directly but to large extent indirectly, which calls for comprehensive evaluation methodologies on a system level. Comprehensive analyses on the system level can provide input to CBA.
The aim of CBA is to identify all the gains and losses (benefits and costs) created by an initiative. The intention is to express the gains and losses in monetary terms irrespective to whom they accrue.

On a general level, CBA contains three mains steps. These are identification, quantification and valuation of the benefits and costs.

Nowadays, due to the increasing presence of distributed energy resources and the integration of automation and communication technologies the electric power system is evolving towards the smart grid paradigm. Typically, smart grid projects are responsible of wide range impacts which span from the electrical power system to the entire society. Often, these impacts are not easily quantifiable thus an assessment based on their monetary value is not attainable. In this context, traditional approaches as the cost-benefit analysis (CBA) become unfit. The reliable assessment of several planning options can be obtained by using hybrid approaches which combine monetary appraisal tools within a generalised framework based on multi-criteria analysis (MCA). A combined MC-CBA approach preserves the advantages of each methodology while overcoming the respective weaknesses. This report describes the mathematical model of the MC-CBA framework for smart grid projects proposed in previous research activities. This model is exploited by the original software MC-CBA toolkit, which is presented in this document. The MC-CBA toolkit aims at supporting the decision makers by providing an assessment framework which rejects any personal bias by preserving the stakeholders’ interests. In fact, the MC-CBA toolkit allows for an output-based assessment of the smart grid alternatives based on an automated comparison procedure. The MC-CBA toolkit decomposes the decision-making problem by dividing the impacts in three main areas: economic impacts, the contribution towards the smart grid realisation, the externality impacts. The calculation procedure for identifying the best option of the set under analysis relies on the Analytic Hierarchy Process (AHP). In order to illustrate the effectiveness of the MC-CBA toolkit, a case study focused on project selection among different smart grid development plans is presented. More specifically, a set of different upgrading plans based on the Active Distribution Network (ADN) approach and the siting and sizing of distributed energy storage is analysed. The MC-CBA toolkit helps the decision maker to identify the best smart grid investment option; the final aim is to provide a reliable support tool for effectively orienting the investments and the regulatory policies on smart grids.

The world’s electricity systems face challenges, including ageing of infrastructures, continued growth in demand, integration of variable renewable energy sources and plug-in electric vehicles, the need to improve the security of supply as well as the need to lower carbon emissions. Smart grid technologies offer a way to meet these challenges and to develop a cleaner and more efficient energy supply. However, national and regional circumstances, such as available sources of supply, grid structure and legislative and regulatory conditions, will give rise to a substantial diversity in the implementation of different smart grid technologies and system solutions.

In order to be able to disseminate experiences and conclusions regarding costs and benefits of these different projects in an efficient and systematic way, a framework for socioeconomic cost-benefit analyses in relation to smart grid solutions needs to be developed. Knowing ex-ante how the socioeconomic effects are distributed can support the design of new policies, the reformation of the regulatory framework as well as the prioritisation of initiatives, and shed light on gaps in research.

This report analyses the distribution of costs and benefits primarily in relation to decentralized electricity consumption on the residential level. The aim is to discuss whether social imbalances are induced by shifting the burdens of financing the grid towards lower income classes. Such imbalances may be aggravated by the tendency to go off grid, thereby challenging current cost recovery schemes.

Socioeconomic analyses are those that aim at identifying differences between groups of people that share similar characteristics like their level of education, employment status, living condition, occupation and income, among other. When assessing smart technologies and regulatory regimes in the context of smart grids, socioeconomic analyses highlight their associated social impact, thereby looking at how related measures affect energy consumption, income and wealth distribution, equity and participation.

The report especially focuses on the question how own, decentralized electricity production changes pricing and tariffing schemes and which socioeconomic factors should be taken into account when designing new cost and benefits models to analyse and assess investments in smart grids related technologies and smart grid regulation.

Energy consumption (in kWh/a) for different types (left) and sizes (right) of households

The impacts generated by the smart metering infrastructure (or Advanced Metering Infrastructure, AMI) are evaluated by means of a tailored MC-CBA approach. In particular, the state of art in Italy of smart metering for low voltage consumers is presented and analysed. The aim of this document is twofold. Firstly, the proposed MC-CBA methodology is applied to a smart grid asset case study. Secondly, the assessment is made by means of a cross-platform which integrates the MCA approach and the ISGAN CBA toolkits. The decision-making problem of identifying the best AMI alternative is modeled as a hierarchical structure of evaluation criteria. Three different area of interest are investigated: economic effects, enhanced smartness of the grid, and externalities. The most suitable criteria are selected to obtain an effective assessment framework and avoid double counting. Firstly, the AMI case study is evaluated by means of the Analytic Hierarchy Process (AHP) technique. The same MCA approach has been applied by using the ELECTRE III technique and the ELECTRE III technique succeeding a fuzzy-scoring method. Finally, the obtained results are compared and the observed peculiarities of the used MCA techniques are described. In particular, the evolutive AMI alternative is always pointed out as the best. On one hand, the AHP appraisal seems to be suitable for preliminary decision-making analysis. On the other hand, the ELECTRE III method appears to be suitable for a deeper analysis of the decision-making problem.

To meet the required objective of this Annex, a program of work is designed and it includes the following three tasks:

• Task 1: Assess Current Network Maturity Model and Update data
• Subtask 1.1: Trial application of two network maturity analysis tools and results discussion
• Subtask 1.2: Development of the questionnaire for the assessment of the level of smartness of transmission and distribution networks

• Task 2: Analyze Current Benefit-Cost Analytical Methodologies and Tools
  • Subtask 2.1: Analyzing benchmark benefit-cost frameworks and tools
  • Subtask 2.2: Model research to overcome limit of current BCA frameworks and tools
• Task 3: Develop Toolkits to Evaluate Benefit-Costs
  • Subtask 3.1: Development of Simplified cost-benefits analysis tool
  • Subtask 3.2: Technical Analysis of current BCA took-kit and Modification of Simplified tool-kit

For Task I, the report goes through several maturity frameworks available, especially those of Software Engineering Institute (SEI) and Katholieke Universiteit Leuven (KUL). The SEI has developed a management tool that can be used to measure the current state of a smart grid project, aiming to help utilities to identify the target and build proper strategies to reach it. The tool, Smart Grid Maturity
Model (SGMM), utilizes a set of surveys called Smart Grid Compass. The drawback of this tool is the undocumented scoring method of the surveys once a result is obtained. Full assistance of an SGMM Navigator is required for the utility to understand and analyze the SGMM output. Meanwhile, the KUL introduce the characteristics, categories and key performance indicators of a smart electricity grid. The previous report also includes own survey methods developed by Annex III, although there has not much of progress after that.

For Task II, an extensive update of the BCA survey has been provided in the previous report. It started with various frameworks related to BCA, which include Frontier Economics and the Smart Grid Forum (SGF) in UK, Smart Grid Investment Model (SGIM) of SGRC, I
MPLAN Model, McKinsey Tool, and general overviews of EPRI’s methodology to BCA and its subsequent developments by DOE and JRC. After that, several BCA applications to country-specific or states cases are summarized. Some of the surveyed countries are Czech Republic, Netherland, Lithuania, Denmark, and USA states. For the comparison purpose, the summary for each case is carried out following some key points: background of the smart grid project, the methodology or toolkits used, the scope of the project (location, period, technologies), the list and definition of benefits and costs, and deliverables (results, recommendations, policy andregulations). The 1st year’s work of Task II can be compared with the previous year’s work in the sense that how EPRI guideline has any impact on the work development of JRC and DOE frameworks, especially for the Smart Grid Computational Tool (SGCT), a BCA toolkit that is developed by US DOE. This report summarizes the findings from the previous works with the focus of selecting the benchmark smart grid tool kit for the development of own ISGAN tool kit for member countries.

For Task III, a simplified cost-benefit analysis tool is being developed taking SGCT of DOE as a benchmark tool kit, based on the previous year report on the development plan of ISGAN member countries’ toolkit. A standalone program based on Object Oriented Programming (OOP) is now being developed replicating, revising and upgrading the currently available excel-based SGCT. As will be discussed, this tool kit has various advantages over other tools: First, this tool is open to public and anyone can take a look inside of the model deep enough to examine the visual basic application modules. JRCEU, McKinsey models were once discussed in Annex III before for any potential utilization for ISGAN member countries’ tool kit. However, members acknowledge the fact that JRC works on excel based format and there seems to be not much difference between JRC’s work and DOE. The difference lies in the fact that JRC never opened up the details of the functionalities and sample calculation of BC in their whole work process. McKinsey software was discussed but it is not open to public. Rather it is a commercial package with no specific advantage over to SGCT of DOE. Detailed engine is not fully explained and the scope of the analysis the tool kit provides does not seem to be very useful (Nigris 2012, Kim 2013). The new tool kit being developed is named for the time being as ‘Replicated Tool Kit’ for convenience. Through the replication process, a lot of details have been identified, which, otherwise, would not have been known to us. Many of the parameters utilized in the process of benefitcalculation may be required to be collected from outside, reflecting the region specific characteristics. Some of the default values provided by SGCT, although they are from USA case (refer to Appendix), may also be useful until those detailed information becomes available for ISGAN member countries even when they don’t have them.

In addition, there a at least 12 smart grid projects currently being conducted in USA (refer to III.2.24), and those projects are starting to produce some detailed information which might be potentially utilized by current SGCT. Not only those advantages, there are many interesting researches being conducted around the world and the work results could be very useful sources of updating this replication effort in the future, once this replication process allows us to identify the pros and cons of the current model. The last chapter of the Expansion of Smart Grid Computational Tool is the wild idea of what could be accomplished in this whole process of simplified own ISGAN tool kit for member countries. Some of the ideas for the tool kit development become clearer as the process of the replication progresses. By the time of the completion of this year’s work, we hope to have a very concrete idea on how to proceed to further develop this current work in the future for the benefit of every member country in ISGAN.

Smart grid projects are responsible of wide range impacts, which span from the electrical power system to the entire society. In general, the investment projects are assessed with a Cost-Benefit Analysis (CBA), which requires quantifying the impacts for converting them in monetary terms. In the smart grid context, not all impacts are quantifiable and/or monetizable; therefore, the CBA lacks in describing completely the smart grid potential. With the aim to outclass the CBA shortcomings, this discussion paper proposes to integrate the CBA into a Multi-Criteria Analysis (MCA) framework. The combined approach preserves the strengths of both CBA and MCA and identifies the best alternative according to its monetary and non-monetary performances. Furthermore, the stakeholders’ point of view is directly collected and the preferences are explicitly related to the decision-making problem under analysis. To achieve a common smart grid assessment framework, the MC-CBA methodology relies on acknowledged guidelines on project analysis. The assessment approach described in this report decomposes the decision-problem by analysing the impacts in three main areas: the economic area, the smart grid development merit area, and the externalities area. The MC-CBA methodology helps the decision maker identify the best smart grid investment option; the final aim is to provide a reliable support tool for orienting effectively the investments and the regulatory policies on smart grids.