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Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system

Abstract

Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence simulation quality, but no methodology available to support the investigation. This paper proposes a method for identifying technical details of smart energy solutions in the energy system and identifying research gaps in the smart grid context with EV solutions as an example. The method includes the investigation of the state-of-the-art EV solutions by scoping review and the allocation of the scoping review results into the Smart Grid Architecture Model framework with three dimensions (Domains, Zones, and interoperability layers). The quantitative scoping review results in a total number of 240 references and 10 references match the criteria based on the qualitative scoping review. The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs’ potentials to work as energy storage. Seventeen features are identified by mapping the EV use cases (solutions and business models) into the three dimensions (domain, zone, and interoperability layers) of the SGAM framework. The process at the Zone layer is the most popularly covered (mentioned 64 times), and enterprise at the Zone layer and communication in the interoperability layer are the least covered (mentioned 4 times each).

Introduction

The increasing number of renewable energy sources in the energy system can contribute significantly to reduce the impacts caused by climate change. However, it will create the challenge of grid imbalance, and demand response is a promising solution (Ma et al. 2017a). Electric Vehicles (EVs) are expected to play an important role in balancing the integration of renewable energy resources in the Smart Grid. The Danish Energy Agency estimates from an extrapolation that EVs and plug-in hybrid vehicles will account for 9% of the total Danish vehicle stocks by 2030 (Energistyrelsen 2019). EV smart charging has been well discussed together with flexible electricity prices as a means to provide demand response (Fatras et al. 2021). Moreover, in a Smart Grid with two-way power- and communication-flow between consumers and the grid it is possible to enable Vehicle-to-Grid (V2G), i.e. allowing EVs to provide power to the grid from their battery (Fatras et al. 2020). V2G can utilize EVs’ battery capacity in the grid as energy storage (Li et al. 2012; McGee et al. n.d.). Especially, hourly electricity prices (Ma and Jørgensen 2018) and dynamic tariffs (Ma et al. 2021) provide significant financial benefits for EVs to provide implicit demand response, and the unbundling electricity markets (e.g., the Nordic spot markets (Zheng et al. 2016)) provides more opportunities for the explicit demand response (Ma et al. 2017b).

However, the increasing number of EVs in the distribution grid and smart charging, especially simultaneously EV charging, might cause grid overload. Meanwhile, various stakeholders are involved in different scenarios, e.g., EV owners, distribution system operators (DSO), charging box providers, and electricity suppliers are involved in EV home charging, their benefits and barriers remain unclear (Ma et al. 2018). Therefore, it is important to investigate EV-related smart energy solutions and business models, and their impacts on the energy system and involved stakeholders.

The studies in energy flexibility based on the agent-based simulation, e.g., commercial greenhouses (Howard et al. 2020a; Christensen et al. 2020a; Christensen et al. 2020b), water supply (Værbak et al. 2019), and breweries (Howard et al. 2020b), show that the agent-based simulation can serve this purpose, especially support the investigation of what-if scenarios (Ma et al. 2019). Meanwhile, related stakeholders and their interactions can be identified with the energy ecosystem modeling methodology proposed by (Ma 2019a). Furthermore, the barriers and opportunities and barriers for adopting energy flexibility have been investigated in the literature, e.g., (Ma et al., n.d.; Billanes et al. 2017).

However, technical details influence the simulation quality, and little literature has provided a methodology for identifying technical details of EVs in the energy system. Meanwhile, simulations for the what-if scenarios should be based on alternatives and multiple options, and overviews and research gaps of the related domains are essential. However, the majority of the literature is mainly case-based.

Therefore, this paper aims to propose a method for identifying technical details of smart energy solutions in the energy system and identifying research gaps in the smart grid context with EV solutions as an example. The method includes the investigation of the state-of-the-art EV solutions by scoping review and the allocation of the scoping review results into the Smart Grid Architecture Model (SGAM) framework (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012). The SGAM framework is an EU standard for the smart grid, and it not only can provide an architectural viewpoint but also technical details with its three dimensions and multiple layers (Ma et al. 2020).

This paper firstly introduces the methodologies of the scoping review method for identifying relevant articles, and the SGAM framework method for analyzing and reviewing the identified articles. The results are presented after the Methodology Section, which consists of a mapping and analysis of the identified articles in the SGAM framework. Following the Result Section, the Discussion Section discusses the findings and limitations of the presented methodology. A summary and conclusion are presented in the conclusion section.

Methodology

Literature search and collection

This paper uses the scoping review method for the literature search and collection regarding EV smart energy solutions and business models in the energy system. A scoping review aims to map rapidly the key concepts underpinning a research area (Ma et al. 2019; Arksey and O'Malley 2005). A scoping review can be used to: examine the extent, range, and nature of research activity; determining the value of undertaking a full systematic review; summarize and disseminate research findings; identify research gaps in the existing literature (Arksey and O'Malley 2005). This study uses the scoping review to summarize and disseminate the research findings. The used approach starts by designing a search string for relevant databases. The overall purpose is to find EV-related smart energy solutions and business models. Hence, five databases are chosen based on the relevance of the domain:

  • Academic Search Premier EBSCO

  • ACM Digital Library

  • IEEE Xplore

  • Web of Science

  • Science Direct

Academic Search Premier EBSCO, Web of Science, and Science Direct include a wide range of research fields including engineering and social science. They are included in the paper to make sure that business-related articles are covered. Many smart energy solutions are related to information technology and computing, in particular, EVs’ charging solutions. Therefore, the ACM Digital Library database is included. IEEE Xplore is included due to its focus on electrical engineering, computer science, and electronics.

The following search string is an example of a string used for the database of Web of Science.

(TI=((smart OR intelligent) AND (energ* OR electric* OR heat*) AND (solution* OR technolog* OR product* OR service*)))

The search string is modified for the other databases following their searching-system syntax. From the search results, the keywords “business model” and “solution” are used to reduce the results.

Literature analysis

In the literature search results, all titles or abstracts including EVs are chosen to be implemented in the SGAM framework. The SGAM framework provides a smart grid architecture and illustrates the complexity of the smart grid (Ma 2019b). The SGAM framework methodology consists of seven principles: universality, localization, consistency, flexibility, scalability, extensibility, and interoperability (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012). The principles build the framework that is visualized in Fig. 1.

Fig. 1
figure1

Smart Grid Architecture Model framework (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012)

The SGAM framework represents smart grid architectures in a common and neutral view. Placing entities to the appropriate location and thereby mapping them in the framework by their domains, zones, and interoperability layers. Consistent mapping means that all layers are covered with an appropriate entity. Inconsistency (missing layers) shows that there is a need for specification or standard in order to realize the solution. By mapping the entities for a given smart energy solution, it is possible to validate the support by standards; identify gaps in respect to standards, map existing architectures into a general view, and develop smart grid architectures (Ma et al. 2019; CEN-CENELEC-ETSI Smart Grid Coordination Group 2012). The definition of each dimension and the description of all zones, domains, and operability layers are shown in Table 1.

Table 1 Terms and definition in the SGAM framework (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012)

In the SGAM framework, use cases are identified and mapped with the required information of: Name, scope, and objective; case diagram; actor names, types; preconditions, assumptions, postconditions; use case steps; information which is exchanged among actors. (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012) uses the term “use case” for solutions in the smart grid, for example, the use case “control reactive power of Distributed Energy Resources (DER) unit”. This paper maps the use cases found in the literature into the SGAM framework based on the domains, zones, and interoperability layers. This approach is used to identify missing entities in the literature from a smart grid architecture point of view.

Five steps of the literature analysis are conducted based on the SGAM framework:

  • The first step is to map all physical components from the use cases to the component layer. Examples of components are controllers, computers, the grid, EVs, and transformers. These components are derived from actors’ information, as actors can be of type, devices, applications, persons, and organizations.

  • The next step is to map the business layer. The “domains” and “zones” covered by the components compose an area. The business objectives, economic, and regulatory constraints included in this area influence the mapped use case. These objectives and constraints are located in the business layer and need to be taken into account as non-functional requirements for implementations. In the third step, the needed functions from different components are mapped into the function layer, e.g. DER control for a controller and SCADA for SCADA system components.

  • The fourth step is to add information flows into the information layer. The layer is divided into a business context and a canonical layer. The business context layer describes the information flow, such as sending voltage measurement. Whereas, the canonical information layer is describing the standards such as CIM standard (IEC 61968–4) which is appropriate for exchanging information objects in the “enterprise” and “operation” zones (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012).

  • At the last step, the communication standards are mapped into the communication layer, e.g. IEC 61850 which is the state-of-the-art communication protocol in power system automation (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012). Meanwhile, it should be clear which roles are responsible for which functions when realizing the business process. In case that an entity cannot be assigned any role the responsibility for that element is unclear and calls for further investigation before realization.

Literature search results

This section presents the scoping review search results. Table 2 shows the used search strings for the different databases including the literature results after removing all duplicates. The removal of all duplicates is done after all searches have been performed and citations are downloaded to the reference management software tool, which in this case is EndNote X9 (Zhu et al. 2016). A literature search is done before the one seen in Table 2 for practicing search strings in different databases. The search criteria are overall based on smart energy solutions and business models within that field. The search is based only on title results to reduce the number of results. Synonyms and notations for the different searching keywords are used to make sure relevant literature is not excluded. For example, the keyword ‘energy’ is also representing electricity and heating.

Table 2 Literature search

From the searching results shown in Table 2 and the initial practicing search, a total of 558 results are found. The found results’ citations are downloaded to EndNote. In EndNote a keyword search on the keywords of ‘solution’, ‘business model’, and ‘solution AND (business model)’ is used to remove non-relevant literature. Table 3 shows that a total number of 240 references are related to the scope.

Table 3 Relevant search results

The last step of the quantitative scoping review search process is to identify literature for the qualitative scoping review analysis. To do so, the 240 references’ titles and abstracts are evaluated and assessed into different categories. The categories are physical components/technologies, software, service, smart house/building, and EV oriented. EV-oriented smart energy solutions are chosen as an example of how to use the SGAM framework in practice. Ten articles are found to be EV-oriented and are selected for the qualitative scoping review analysis. Table 4 shows the 10 articles with a description of their key aspects.

Table 4 Selected references with focuses on electric vehicle-oriented solutions

This paper analyzes the use cases in each article in detail and matches the use cases with the three dimensions of the domain, zone, and interoperability in the SGAM framework. Seventeen features have been identified through a thorough analysis process (shown in Table 5). Meanwhile, Table 11 in the APPENDIX explains how the 17 identified features are identified based on the literature analysis with the three dimensions of interoperability layer, domain, and zone of the SGAM framework.

Table 5 Identified 17 features

For instance, Feature 1 represents the EV in the component interoperability layer and energy storage through V2G in the function layer. Feature 2 represents in the component layer a charging point having a communication technology. The charging points both charge EVs and communicate with the smart grid. Hence, it locates between the zones of Process and Field, and between the domains of Customer premises and DER. Feature 2 further includes a public key encryption system and identity-based cryptography in the communication layer. In the function layer, feature 2 represents sending user identity to a control unit. Feature 10 represents identity-based cryptography in the communication layer and EV identity in the information layer.

(Nicanfar et al. 2013) has features of 1, 2, 10, 11, 12, and its use case may include some implicit entities, however, during the analysis process, this paper only uses the descriptions stated and included in each selected article. Therefore, to realize the (Nicanfar et al. 2013)‘s use case in the smart grid, some entities are needed as a minimum, e.g. communication and information standards and charging functions. All papers implicitly indicate that the grid (including transformers) is presented in the component layer with the link between all domains in the process zone. Hence, the grid is not in Table.

Result and analysis

This paper further matches the identified 17 features in the use cases in the literature (EVs’ roles: solutions and business models) into each layer of the SGAM framework (shown in Tables 6, 7, and 8).

Table 6 Domains allocated in features and selected articles
Table 7 Zones allocated in features and selected articles
Table 8 Interoperability layers allocated in features and selected articles

Table 6 shows how the identified features are allocated in the Domains. The Domains are presented in the table as the Generation (G), Transmission (T), Distribution (D), Distributed Energy Resources (DER), Customer Premises (C) in the SGAM framework. As shown in Table 6, the Distributed Energy Resources (DER) and Customer Premises (C) are the most frequent Domains presented in the selected articles and the features which have been discussed 48 and 56 times, respectively.

For instance, in Feature 15, use cases in (Santos et al. 2015; Rezania and Prüggler 2012) include distribution (D), Distributed Energy Resources (DER), and customer premises (C) Domains. For (Santos et al. 2015), the communication standards, such as ISO 15118, and information flow (e.g. EV’s State-of-Charge) are between the charging point located in Feature 2 and the energy scheduler in Feature 14. Thus, feature 15 is a link between these features, thereby covering the three Domains (distribution, DER, and customer premises). For (Rezania and Prüggler 2012), the information flow of the EV’s driving pattern is the link between the charging point (feature 2) and the aggregator (feature 14). Feature 6 is only in the generation (G) Domain as the feature includes thermal power plants in reference (Li et al. 2012; McGee et al. n.d.), and power plant cost minimization for (Ahmad and Sivasubramani 2015).

Table 7 shows the 17 features allocate at the Zones. The Zones are presented in the table as the Process (P), Field (F), Station (S), Operation (O), Enterprise (E), Market (M) in the SGAM framework. As shown in Table 6, the Process (P) and Field (F) are the most frequent Zones presented in the selected articles and the features which have been discussed 62 and 23 times, respectively.

For instance, in Feature 4, use cases in the 3 articles (Hilshey et al. 2012; Budde Christensen et al. 2012; Rezania and Prüggler 2012) include the process (P), field (F), and station (S) Zones. For (Hilshey et al. 2012)‘s use case, the process Zone is the transformation of the voltage level from medium to low voltage. Both (Budde Christensen et al. 2012; Rezania and Prüggler 2012)‘s use cases involve charging stations (including battery switching stations for (Budde Christensen et al. 2012)), hence the process of charging the EVs is present in this feature. The transformer in (Hilshey et al. 2012)‘s use case includes protection equipment and can be part of a substation placing the use case in the field and station Zone. The charging/battery switching station in (Budde Christensen et al. 2012)‘s use case contains a smart meter and control device managing the charging of the EVs placing the use case in both field and station Zone. For (Rezania and Prüggler 2012)‘s use case, the aggregation of charging EVs including field equipment (see Table 1) places the use case in the field and station zone.

Table 8 shows the interoperability layers that the selected article and features include. The interoperability layers are presented in the table as the component (C), function (F), business (B), information (I), and communication (Cc) that are defined in the SGAM framework. As shown in.

Table 8, the interoperability layers of component (C) and function (F) are the most frequent layers presented in the selected articles and the features which have been discussed 31 and 24 times, respectively.

For instance, in feature 2, use cases in 4 articles include the component (C) layer. For use cases of (Hilshey et al. 2012; Rezania and Prüggler 2012), the component layer of feature 2 is the smart meter (Advanced Metering Infrastructure), and for use cases of (Nicanfar et al. 2013; Santos et al. 2015), the component layer of feature 2 is the charging points with communication technology. The function (F) layer for feature 6 represents a bulk and emergency generation in (Li et al. 2012) and baseload power in (McGee et al. n.d.). The business (B) layer for feature 1 represents the reduction of charge for electricity and promotes load balance in (Santos et al. 2015), subscription agreement in (Budde Christensen et al. 2012), and financial leasing from the government to public transport companies in (Zhong and You 2012).

Furthermore, the information (I) layer for feature 15 represents CANbus protocol sending a basic set of information such as State-of-Charge, State-of-Health, vehicle position, available energy, charging time, etc. in (Santos et al. 2015). In the use case of (Rezania and Prüggler 2012), the information layer represents the driving pattern send from EV owner to aggregator and the tariffs and charging schedule send back to the owner from the aggregator. The communication (Cc) layer for feature 10 represents SAE J1772 charging standard and negative-side signaling using CANbus protocol in (McGee et al. n.d.). Meanwhile, the use case of (McGee et al. n.d.) includes analog communication with non-intelligent EVs and digital communication enabling V2G with intelligent EVs, and the use case of (Nicanfar et al. 2013) has an identity-based cryptography communication in the communication layer.

Discussion

Technical details for identified features

This paper finds that each feature has identical focuses on the three dimensions in the SGAM framework, based on the feature focuses, the 17 identified features can be categorized as in Table 9. Table 9 shows the current focuses of EV solutions in the literature. Therefore, if a simulation only focuses on the EV charging based on electricity production by DERs (Feature 7), it is easy to identify the technical details of this feature as shown in the corresponded row in Table 11 (in APPENDIX).

Table 9 Category of 17 identified features

Meanwhile, features in the same category do not have the same technical details if the purposes are different. For instance, Table 10 shows that the four features in the communication categories have significant differences at the interoperability layers of the SGAM framework. These technical details and differences can support simulations to simulate the features with certain accuracy.

Table 10 Communication technical details

Gap identification in the related literature

This paper maps all use cases of EV solutions and business models in the literature into the SGAM framework not only to provide an overview of three dimensions of domains, zones, and interoperability layers but also to identify the gap in the research field.

The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs’ potentials to work as energy storage (Li et al. 2012; McGee et al. n.d.; Nicanfar et al. 2013; Ahmad and Sivasubramani 2015; Santos et al. 2015; Rezania and Prüggler 2012). The V2G concept requires communication between EVs and the grid operators or balance responsible parties who often locate in the operation zone. In the SGAM framework, the communication layer includes communication standards which are only considered in (Santos et al. 2015). In general, all use cases in the references do not include standards in the information and communication layers. Furthermore, references, such as (Zhong and You 2012), do not consider any aspect of the smart grid architecture. Instead, (Zhong and You 2012) investigates a financial leasing business model for increasing the penetration of EVs.

As the V2G concept is not yet commercialized in the current electricity grids. Therefore, a completed mapping of the V2G use cases in the SGAM framework is recommended to investigate solutions and business models that can match the technical, market, and business requirements of a given energy ecosystem. Meanwhile, the technical details for the focused EV solutions in the literature are not precise enough to be directly implemented in simulations, maybe due to the focuses in each literature that are not for simulations.

Conclusion

This paper presents a methodology for identifying technical details of EV solutions in the energy system and identifying research gaps in the smart grid context. The proposed methodology includes two parts: A scoping review for literature search and collection; a SGAM framework driven literature analysis, that EV solutions and their business models found in the literature are mapped into the SGAM framework with three dimensions (Domains, Zones, and interoperability layers).

Two types (quantitative and qualitative) of scoping review are applied in the paper to ensure the selected literature matches the paper’s scope. The quantitative scoping review results in a total number of 240 references and 10 references match the criteria based on the qualitative scoping review. The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs’ potentials to work as energy storage.

Seventeen features are identified by mapping the EV use cases (solutions and business models) into the three dimensions (domain, zone, and interoperability layers) of the SGAM framework. The process at the Zone layer is the most popularly covered (mentioned 64 times), and enterprise at the Zone layer and communication at the interoperability layer are the least covered (mentioned 4 times each).

Contributions

Technical details are critical inputs to ensure the simulation quality for smart energy solutions and business models. However, no systematic method serves this purpose in the literature, and the majority of the literature is case-based. The developed methodology fills this gap with an investigation of the EV solutions in the energy systems. Meanwhile, the SGAM framework provides an architecture that can facilitate this process. The SGAM framework can be utilized by researchers and companies working with solutions in the smart grid to identify needed standards for the realization of the solution.

This proposed method provides a systematic approach for identifying research gaps in the smart grid. Meanwhile, the analysis of EVs use cases by using the proposed method, provides an overview of the literature on EVs in smart grid with the aspects of smart energy solutions and business models which was not yet fully discovered in the literature. The results can be the input as alternatives and multiple options for simulations to investigate the what-if scenarios.

Limitation and future works

The paper conducts the literature search in scientific databases to investigate the technical details, and other references, e.g., patents or technical reports are not included due to the main focus of the paper is to introduce the proposed methodology. Therefore, a more comprehensive and broad search is recommended especially for the technical details that have not been covered by the scientific literature. Meanwhile, to prove the proposed methodology can contribute to the mentioned simulations, agent-based simulation for EV solutions and business models are recommended with various scenarios, e.g., EV smart charging and participation in various electricity markets.

Furthermore, although business and market are included in the SGAM model, the aspects of policies, regulations, social and financial aspects are missing in the literature due to its technical focus. Although this paper uses the term ‘technical details’, details from other perspectives are recommended to be included for simulating smart energy solutions and business models in which many stakeholders and aspects are involved.

Abbreviations

CO2:

Carbon dioxide

DER:

Distributed Energy Resources

EU:

European Union

EV:

Electric Vehicle

G2V:

Grid-to-Vehicle

PHEV:

Plug-in Hybrid Electric Vehicle

SCADA:

Supervisory Control And Data Acquisition

SGAM:

Smart Grid Architecture Model

V2G:

Vehicle-to-Grid

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About this supplement

This article has been published as part of Energy Informatics Volume 4, Supplement 2 2021: Proceedings of the Energy Informatics.Academy Conference Asia 2021. The full contents of the supplement are available at <https://energyinformatics.springeropen.com/articles/supplements/volume-4-supplement-2>.

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This work is part of the national project- Flexible Energy Denmark FED funded by Innovation Fund Denmark.

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K.C. developed the first draft of this paper along with additional information from the corresponding work and contribution of the co-authors in the early and late stages of the project. All co-authors read, commented, and approved the final manuscript.

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Correspondence to Kristoffer Christensen.

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Appendix

Appendix

Table 11 Identified 17 features in the SGAM framework’s three dimensions matching use cases in the articles

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Christensen, K., Ma, Z., Demazeau, Y. et al. Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system. Energy Inform 4, 38 (2021). https://doi.org/10.1186/s42162-021-00160-w

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Keywords

  • Electric vehicle
  • Smart energy solution
  • Business model
  • Smart grid
  • Methodology