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Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

Abstract

Understanding the local needs and challenges is critical for technology adoption in the energy sector. However, it is still a big challenge for most ecosystem stakeholders. Furthermore, technology adoption theories have mainly focused on the technology itself, and the business ecosystem perspective has been neglected. Therefore, this paper proposes an ecosystem-driven business opportunity identification method, a systematic approach for ecosystem stakeholders to conduct business opportunity analysis and evaluation based on the CSTEP ecosystem analysis and evaluation method. This method includes four correlated steps: Step 1: Identify the five CSTEP dimensions of the business ecosystem; Step 2: Identify potential changes in the business ecosystem; Step 3: Identify future ecosystem trends and timeline; Step 4: Select business opportunities; and Step 5: Potential solution identification. A web-based tool called opportunity identifier is developed for implementing the proposed method. A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool. Three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition.

Introduction

Undoubtedly, understanding the local needs and challenges is the first and most crucial stage for the success of any implementation, especially in the energy sector. Business opportunities come from needs and challenges in the existing markets and trends towards the transition to future markets. Companies need to capture opportunities and predict when the market will have the needs. However, although companies realize the importance of the above matters and try to improve the situation, it is still a big challenge for most companies.

In recent years, ecosystem thinking has been popularly used for investigating complex systems from a business perspective. The use of ‘ecosystem’ in business has started since the term ‘business ecosystem’ was introduced in 1993 (Moore 1993) to describe how the economic community works. Without ecosystem thinking, companies mainly focus on developing customer insight, building core competencies, and beating the competition. In the business ecosystem domain, the evolution/co-evolution perspective is rarely discussed, although there are discussions in, e.g., system thinking (Rubenstein-Montano et al. 2001); furthermore, there is no systematic approach for investigating unmet needs and megatrends in a given business ecosystem.

Technology and innovation adoption has been well discussed in the literature, and several popular technology adoption models are proposed, e.g., Rogers’ adoption curve (Rogers 2003). The technology adoption theories try to understand the adoption behaviors toward new technologies, especially behaviors and constructs during the decision process. However, technology adoption theories have mainly focused on the technology itself, the adoption process and influential factors for decision making, and the business ecosystem perspective has been neglected.

Some theories in strategy management, such as ETPS (Economic, technical, political, and social; Aguilar 1967), STEP (Social, technical, economic, political; Brown and Weiner 1984), and STEPE (Social, technical, economic, political, and ecological; Davenport and Prusak 1997), intend to investigate the impact factors in business and strategies. However, the main focus is personal or organizational.

Therefore, this paper proposes a method for identifying business opportunities based on the theories of business ecosystem modelling (Ma 2019), ecosystem architecture design (Ma et al. 2021), and CSTEP-the five business ecosystems dimensions (Ma 2022). Furthermore, the proposed method is implemented as a web-based tool (called ‘business opportunity identifier’) to be applied in research and teaching.

A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is chosen to demonstrate the application of the method with a complex ecosystem impacted by all the five CSTEP business ecosystem dimensions. The electric vehicle home charging energy ecosystem is chosen because there potentials for EVs to provide energy flexibility due to their larger energy consumption compared to other home appliances (Ma et al. 2018a; Howard et al. 2020) and the potential flexibility due to intelligent EV charging algorithms (Billanes et al. 2017, 2018). However, EV home charging usually involve multiple stakeholders from both energy and EV ecosystems which potentially causes high uncertainty (Ma et al. 2015, 2017a).

Background

Business ecosystem theory

The term of ecology was introduced by Haeckel in 1866 as the science of relations between organisms and the surrounding outer world (Haeckel 1866). Accordingly, based on ecology and observations of how biological organisms function, the ecosystem considers nature, society and business as integrated from a system’s perspective (Capra and Luisi 2014). In general, an ecosystem is a system with thousands of organisms that live in a constant relationship with their environment, the members benefit from each other's participation through symbiotic relationships, and relationships also develop among them (Maracine and Scarlat 2008).

Business ecosystems are analogous to biological ecosystems. In 1993, Moore (1993) uses biological metaphors and introduces the business ecosystem concept. Moore describes how the economic community works and highlights the interaction between companies and their business environment. Moore (1996) divides the business ecosystem into four stages for analysis and management (the definition is shown in Table 1). These four stages represent the business ecosystem life cycle.

Table 1 Business ecosystem life cycle by Moore (1996)

Following Moore’s definition, (Iansiti and Levien 2002) describes the business ecosystem as a large number of loosely interconnected participants who depend on each other for mutual effectiveness and survival. Iansiti and Levien (2004) introduces a framework for studying and understanding innovation and operations management in business ecosystems. They define specific indicators of ecosystem structure and develop specific operational implications for different types of ecosystem roles and corresponding strategies as dominator, keystone, and niche firms (the definitions are shown in Table 2; Levien 2004).

Table 2 Three types of roles in business ecosystems

Among many definitions in the literature, there are three key phases in the business ecosystem defined as the community of interdependent organizations, business environment (opportunity space), platform and co-evolution (the definitions are shown in Table 3; Rong and Shi 2015).

Table 3 Three key phases in the business ecosystem (Rong and Shi 2015)

Besides the discussion of the business ecosystem by Moore (1993, 1996); Iansiti and Levien 2002, 2004) and (Power and Jerjian 2001), there are other ecosystem analogies discussed in the literature, e.g., service ecosystem, digital business ecosystem (Peltoniemi and Vuori 2004), IT/Technology ecosystem (Iansiti and Richards 2006; Adomavicius et al. 2006), platform ecosystem (Ceccagnoli et al. 2012; Parker et al. 2016; Gawer and Cusumano 2014), digital ecosystem (Cliff and Grand 1999; Iyawa et al. 2016), innovation ecosystem (Adner 2006; Oh et al. 2016). Some popular definitions are listed in Table 4.

Table 4 Definitions of various ecosystem analogies

Other ecosystem analogies used regularly in academic research and business practice have been discussed as the customer ecosystem that focuses on the customer views of the business ecosystem, e.g., (Ma et al. 2017b;; Manning et al. 2002), the organizational ecosystem that emphasizes the aspect of human organizational structures (Mars et al. 2012), and product ecosystem that denotes “the consideration of multiple related products in a coherent process, compared with the conventional viewpoint of static, isolated products” (Zhou et al. 2011).

Although a large amount of literature has discussed and analyzed the business ecosystem structure, no systematic approach has been proposed. Therefore, (Ma 2019) proposes a framework for business ecosystem modeling based on the combined theories from system engineering, ecology, and business ecosystem. This framework includes three parts of business ecosystem architecture development: factor analysis, ecosystem simulation, and reconfiguration. Based on the work by Ma (2019), a methodology for business ecosystem architecture design with the business ecosystem ontology is introduced by Ma et al. (2021). Several business ecosystem architecture terms are defined in Ma et al. (2021). This methodology has been popularly applied in the energy field. For instance, (Ma et al. 2019a) applies the method to investigate microgrid solutions for reliable power supply in India's power system, and (Hack et al. 2021) investigate the digitalization potentials in the electricity ecosystem in Germany and Denmark.

Business ecosystem dimensions

In the framework for studying and understanding the management of innovation and operations in business ecosystems proposed by Iansiti and Levien (2004), the indicators of the ecosystem structure ‘health’ is defined with three dimensions:

  • Robustness: a business ecosystem’s capability of facing and surviving perturbations and disruptions.

  • Productivity: how effectively does the ecosystem convert raw materials into living organisms.

  • Niche creation: the ecosystem’s capacity to create new valuable niches. It refers to the capacity to increase meaningful diversity over time by creating new valuable functions.

The measures for the three dimensions are also proposed by Iansiti and Levien (2002) as shown in Table 5.

Table 5 The measures of robustness, productivity, and niche creation proposed by Iansiti and Levien (2002)

However, the three dimensions proposed by Iansiti and Levien (2002) only focus on the business aspect of a business ecosystem and do not cover all aspects. For instance, in an energy business ecosystem, the climate is an important dimension that impacts the energy production (e.g., wind energy or solar power), and all segments in the energy supply chain, e.g., the lighting, heating, or cooling at the consumption side. Therefore, (Ma 2022) proposes five critical business ecosystem dimensions called CSTEP for systematically understanding a targeted business ecosystem (as shown in Table 6). Furthermore, each dimension consists of several sub-dimension and macro and micro levels (as shown in Table 7). Various energy ecosystem cases have applied the CSTEP, e.g., microgrids (Ma et al. 2018b) and distribution tariffs (Christensen et al. 2021).

Table 6 Definitions of CSTEP five dimensions (Ma 2022)
Table 7 Macro and micro levels of the five CSTEP dimensions

Technology adoption theories and models

The innovation adoption theory is firstly introduced by Rogers in 1960, in his publication called “Diffusion of Innovation Theory” (Rogers 1962). This theory's essential elements are the S-shaped (logistic function) shown in Fig. 1 and the adoption rate curve shown in Fig. 2.

Fig. 1
figure 1

Rogers’ S-shaped adoption curve (Rogers 2003)

Fig. 2
figure 2

Rogers’ adoption rate curve with adopter categorization (Rogers 2003)

Additional technology adoption theories and models have been addressed for many years. The theory tries to describe the adoption behavior toward new technology. Understanding and knowing such behavior can help develop business models aiming to achieve a fast and/or high adoption. In total, 30 technology adoption theories are identified from the literature (Gangwar et al. 2014; Taherdoost 2018; Sharma and Mishra 2014; Lai 2017; Oliveira and Martins 2011; Maryam Salahshour et al. 2018; Molinillo and Japutra 2017; Qayyum and Ali 2012) and shown in Table 8. Among the 30 theories, the main focuses of the most popular discussed technology adoption theories are summarized as shown in Table 9 based on the discussion in Taherdoost (2018) (Sharma and Mishra 2014; Lai 2017).

Table 8 Technology adoption theories in the literature
Table 9 The main focuses of the most popular technology adoption theories

Furthermore, many constructs in the technology adoption theories have been identified and discussed in the literature, as shown in Table 10. The application of these constructs in the technology adoption decision processes can be divided into “before adoption,” “adoption decision,” and “after the decision,” as shown in Table 11.

Table 10 Definition of the identified constructs in the technology adoption theories
Table 11 Constructs applied in the technology adoption theories and technology adoption decision process

Technology adoption has been applied in the energy domain with several focuses. For instance, Ma et al. (2018c) identifies influential factors for Industrial consumers to adopt smart grid concept. Ma et al. (2019b) conducts a survey to investigate demand response control preferences, stakeholder engagement, and cross-national differences for retail stores’ demand response adoption. Furthermore, technology evaluation and adoption of energy related solutions has been conducted with modeling and simulations, and applied for both energy efficiency (Christensen et al. 2020a, 2019) energy flexibility (Værbak et al. 2019; Christensen et al. 2020b), and CO2 emission reduction (Christensen et al. 2020c).

Methodology

To identify business opportunities in a business ecosystem, it is essential to clarify two terms unmet needs and megatrends that trigger the potential changes in a business ecosystem:

  • Unmet needs usually indicate needs, demands, or challenges that have not yet been met or solved in the current business ecosystem. The unmet needs are usually related to climate (climate changes) or economic challenges in the energy ecosystems, e.g., electricity supply for the inhabited islands in Indonesia.

  • Megatrends usually indicate how a business ecosystem evolves and how the future of the targeted business ecosystem will look. Megatrends are usually due to political goals (e.g., climate neutrality in 2050 in Denmark), advanced technologies (e.g., digitalization), or society's willingness in industrial ecosystems. Megatrends can help to understand what future ecosystems look like. There might be several or many megatrends in an ecosystem. The application of CSTEP can facilitate the evaluation of these future trends and the selection of the most potential ones for development.

Four steps in the ecosystem-driven business opportunity identification method are designed for the investigation of business opportunities in the targeted business ecosystem, and each step includes several sub-steps (as shown in Fig. 3):

Fig. 3
figure 3

CSTEP-driven business opportunity identification method process

Step 1: Identify the CSTEP dimensions of the current business ecosystem.

Step 2: Identify potential changes in the business ecosystem.

Step 3: Identify future ecosystem trends and timeline.

Step 4: Select business opportunities.

Step 5: Potential solution identification.

Step one: Identify CSTEP dimensions for the current business ecosystem

To identify related CSTEP dimensions in a business ecosystem, firstly, it is necessary to investigate CSTEP dimensions to the related actors and objects in the defined business ecosystem, as shown in Table 12. The relevant value chain segments, actors and objects can be identified and listed during the business ecosystem architecture development introduced by Rogers (2003). However, not all actors and objects are relevant to the evolution of the ecosystem.

Table 12 Investigation of related CSTEP dimensions for business ecosystem elements

For instance, in the EV home charging energy ecosystem (presented in the case study section), there is an actor called electricity supplier. The electricity supplier buys electricity from the electricity markets and is obliged to supply all household customers with electricity with a payment. However, this is not relevant to the evolution of the EV home charging energy ecosystem. Therefore, to reduce the analysis workload, this step should focus on the critical actors and objects relevant to the ecosystem's evolution.

Based on the result of Table 12, the current business ecosystem condition can be further described in detail (as shown in Table 13). Meanwhile, it is important to analyze the ecosystem conditions with references. The investigation of the regulations at the P-dimension can help to understand the current ecosystem condition, and the policies will later be used for understanding the future business ecosystem. The main difference between Tables 12 and 13 is: Table 12 is from the individual ecosystem elements’ perspective, and Table 13 is from the relevance of the ecosystem perspective.

Table 13 Identification of the current ecosystem conditions

Step two: Identify potential changes in the business ecosystem

To identify potential changes in the business ecosystem, step two is divided into two sub-steps:

  1. 1.

    Identify political or business statements critical to the business ecosystem

  2. 2.

    Portray future ecosystem condition

    • Sub-step 1: Identity political or business statements critical to the business ecosystem

      Although some policies related to the identified actors and objects are investigated in Step one, the policies related to the future ecosystem conditions are not completed. Therefore, it is necessary to investigate and identify political or business statements critical to the business ecosystem.

      The transformation of a business ecosystem is usually strongly influenced by the ecosystem dominators, e.g., governmental authorities or leading companies. For instance, energy-related business ecosystems are driven by political agendas, such as 70% CO2 reduction in 2030 and climate neutrality in 2050 in Denmark; High-tech related business ecosystems, are usually driven by leading giant companies. For instance, in the social media business ecosystem, the announcement of Facebook to be in the metaverse business indicates a social media ecosystem trend.

      Although the initiatives created by leading giant companies, such as Google glasses, can provide inspiration or highly possibly become megatrends in the related business ecosystems, the future (e.g., when and in what way) is unclear because megatrends are usually formed with strong collective effort. Therefore, investigating unmet needs or megatrends in a given business ecosystem is recommended to focus on the political goals. The ecosystem boundary can indicate related political or business statements, since the boundary is defined by the supply chains, market or systems in a certain geographical/cultural boundary.

      There are two approaches to identifying relevant policies: domain expert recommendations for those familiar with the related areas; Policy or trend investigation for each actor/object and their roles in the ecosystem. Especially, the information in governmental white papers and reports provides a detailed description of the focus areas, and is often supported by numbers and data.

    • Substep 2: Portray future ecosystem conditions

      The critical political or business statements usually provide a direction where the current business ecosystem might evolve (the future business ecosystem). Therefore, the potential changes can be identified by the gap analysis. To do so, it is necessary to ask the following questions about each current ecosystem condition with each identified critical political or business statements:

    • Whether the current ecosystem condition can fulfil this identified policy or trend?

    • If not, what future ecosystem conditions should look like to fulfil the identified policy or trend?

Sub-step 2 strongly requires expert input, and at some dimensions, there might not be any significant difference between current and future ecosystems. Therefore, it doesn’t need to be included. The summarized guideline and result of Step two: Identify potential changes in the business ecosystem is shown in Table 14.

Table 14 Identification of future ecosystem conditions

Based on Table 14, the future ecosystem conditions can be identified at each CSTEP dimension. In most cases, the policy and regulation dimension will be blank, since governmental authorities make decisions. Meanwhile, there might be overlaps among the identified future ecosystem conditions across the CSTEP dimensions. Therefore, it is necessary to conduct merging and reorganizing, and present the future ecosystem conditions precisely and comprehensively.

Step three: Identify future business ecosystem trends and timeline

Although the future ecosystem conditions are identified at Step two. The realization timeline is not clear. The realization timeline relates to when (in short-, medium-, or long terms) and what (which part of the ecosystem) will change. A CSTEP five-dimensional three-scale evaluation method for ecosystem trends (shown in Table 15) is introduced to answer this question.

Table 15 CSTEP five-dimensional three-scale evaluation

This evaluation method might have different weights among the five CSTEP dimensions. The presentation of the weight differences can be qualitative (indirect and descriptive) or quantitative (direct and quantified). In different cases, it might apply different prioritization based on the purpose of the evaluation, e.g., research gap identification.

As Table 15 shows, the higher score at a dimension, the higher likelihood that a trend would happen. It is based on the principle that the evolution of a business ecosystem is always towards the direction that can benefit the ecosystem the most.

To identify future ecosystem trends and timeline, this step is divided into 3 substeps:

  1. 1.

    Modify the evaluation criteria from the CSTEP five-dimensional three-scale evaluation (shown in Table 12) if necessary.

  2. 2.

    Evaluate the future ecosystem conditions, and score how likely the future ecosystem conditions will happen in the near future based on the CSTEP three-scale ecosystem trend evaluation method with scores (shown in Table 16).

  3. 3.

    Rank the future ecosystem conditions based on the total scores

Table 16 Evaluation of ecosystem potential change

Step four: Select business opportunities

The ranking of the total scores from Step three represents the realization timeline of the identified trends. The ecosystem roadmap and the transition stages can be identified based on this ranking. According to Ma et al. (2021):

  • Ecosystem roadmap: is a critical path with sequenced ecosystem transition stages for achieving the planned/future ecosystem.

  • Transition stage: One Minimum Variable Ecosystem (MVE) or expanded/shifted ecosystem is designed at one transition stage. The sequence of the transition stages can be either horizontal (boundary scale) dependent on the boundary coverage or vertical (time scale) dependent on the realization terms (short, medium, and long terms).

Therefore, each of the top-ranked ecosystem trends will be at one transition stage. Based on the ranking, the sequence of the transition stages can be identified. Sometimes, there are sub-transition stages at one transition stage because the ecosystem trends can happen simultaneously or the ecosystem trend happens with certain conditions. Therefore, it is important to ensure the sequence of the (sub)transition stages according to the realization conditions. However, there might be different results due to different stakeholders’ focuses.

It is necessary to match identified policies with the identified trends. It not only can clarify the goals of the identified trends, but also can confirm whether the identified trends are the megatrends or unmet needs in the targeted ecosystem. The related policies can be stated as shown in Table 17.

Table 17 Identified transition stages and related policies

To portray the future ecosystem, it is necessary to map the transition stages to the identified relevant actors and objects with value chain segments of the ecosystem (at Step one) as shown in Table 18. Therefore, the future ecosystem can be described according to the summary in Table 18. Furthermore, the value proposition for each actor can be proposed as shown in Table 19.

Table 18 The future ecosystem description
Table 19 The proposed value propositions

Step five: Potential solution identification

With the identified value propositions, potential solutions can be investigated, evaluated and identified. To do so, two sub-steps should be conducted:

  • State-of-the-art (SoA) solution investigation

  • SoA solution evaluation

  • State-of-the-art (SoA) solution investigation

The SoA investigation includes market research and literature (sometimes, patent search is also conducted to avoid any infringement issue). The purpose of the market research is to investigate whether there are any existing products in the targeted ecosystem that provide similar value. If yes, this value proposition is not considered for further because there is no opportunity for the ecosystem stakeholders unless the existing product can not fully fulfil the value proposition.

The literature research aims to investigate whether there is any solution that (1) can provide the identified value; (2) has not been implemented in the current ecosystem, and (3) uses the most modern or advanced techniques or methods.

  • SoA solution evaluation

Not all the investigated SoA solutions are feasible to be applied in the targeted ecosystem. Therefore, a feasibility assessment needs to be conducted and identify the most feasible solutions that potentially can be applied in the targeted ecosystem. One method can be applied with modification is the feasibility assessment method applied (Christensen et al. 2021).

Software architecture

The software architecture of the CSTEP tool aims to capture and describe the fundamental building blocks and what they consist of. The tool is built on a classic client/server approach, which relies on considering the separation of concern on the client-side and server-side. The architecture consists of the three following tiers:

  • Frontend

  • Backend

  • Database

On the client-side, the frontend, acting as a presentation tier, provides the user interface and allows for sending requests to the server side. The communication for these requests are established through the API (Application Programming Interface) exposed by the server-side, consisting of the backend and the database. The backend act as a business tier, responsible for handling the incoming requests from the user and replying with a response. All data required for the functionality to function is stored in the database, acting as a data tier. Together, these components constitute the foundation for a web application offering the functionality required by the proposed method.

With the basics in place, a more detailed description of the architecture, what components the tiers hold and how they associate is now introduced. Figure 4 depicts the three tiers, including their respective components. The structure and content of each tier are highly affected by the different technologies applied in the project. The goal is to include technologies that help ensure the ability to provide the required functionalities in terms of following the procedure of the proposed method and promote core software qualities appropriate for the application supporting these functionalities. The focus was to create a lightweight, easily maintainable and flexible application for the users.

Fig. 4
figure 4

System Architecture

Frontend

The nature of Vue.js and Nuxt.js highly impacts the architecture of the frontend. These frameworks allow developers to build user interfaces on a component-based programming model that allow for easy structuring and encourages flexibility. Together, these frameworks offer features that ease and improve the development experience through easy routing, modularity and reusability, virtual DOM rendering, reactive data binding and more. Communication from the frontend to the backend is established through a tool called Axios, which is an HTTP client for JavaScript, providing the ability to make HTTP requests from the application running in the user’s browser.

Backend

The backend and API are built using Node.js as it provides a great runtime environment for backend services, where fast and easy development in JavaScript, simple file structure, support for many open-source libraries and performance are in focus. The architecture of Node.js ensures asynchronous handling of requests from the user, allowing more efficient processing and the ability to serve multiple clients on one thread without having to create a thread for every request. This makes Node.js suitable for this project as the potentially many concurrent users and the nature of the features in the application result in I/O-intensive activity.

Regarding the API, the backend uses a tool called Express to expose the endpoints accessible from the frontend. Each endpoint calls a method from a controller related to the object related to the requested functionality. These methods that set the boundary and actions of an event are defined in the controllers' folder. The exposed endpoints are specified in the app.js file, which also holds information on how the connection to the database is established. This connection is made possible through the Mongoose library. This library is applied in the backend and not only allows the backend to manipulate data in the database but also to help define data models or schemes for the documents stored in the database.

Database

For storing data about the users and the projects they create in the application, MongoDB is used. MongoDB is a document-based NoSQL database offering flexibility and scalability. Data on users and projects are stored in separate collections, analogous to tables in relational databases, that each holds a set of individual documents, one for each user or project. These documents are similar to rows in a relational database and are structured as specified by the backend model, which looks similar to a JSON object when stored.

Case study

An example of the EV home charging energy ecosystem is used to explain the implementation of the proposed method. The ecosystem map generator investigates the business ecosystem architecture of the case (ecosystemmapgenerator.sdu.dk). Meanwhile, the critical actors and objects relevant to this case study are exported to the tool-CSTEP business opportunity identifier, as shown in Table 20.

Table 20 The critical actors and objects related to the EV home charging energy ecosystem

CSTEP dimension identification for the targeted business ecosystem

According to Table 12 (Investigation of related CSTEP dimensions for business ecosystem elements) in the methodology section, the CSTEP dimensions related to each actor and object can be added as shown in Table 21. Furthermore, the current ecosystem conditions can be summarized and presented based on CSTEP.

Table 21 Investigation result of related CSTEP dimensions for business ecosystem elements

Potential change identification in the business ecosystem

Related political or business statements can be defined based on the boundary of the EV home charging energy ecosystem:

  1. 1.

    Danish climate goals (Energy and Agency 2022)

    • 70% CO2 reduction by 2030

    • Climate-neutral by 2050

  1. 2.

    Governmental agreement of tax relief on green vehicles for securing 775,000 green vehicles by 2030 (The Danish Ministry of Taxation 2020)

    • Reliefs on electricity used for charging and registration tax

    • An expected reduction of greenhouse gasses of 2 million tons

    • Ambitions of 1 million EVs in 2030 – consideration of further initiatives in 2025 to reach the ambition

  1. 3.

    Sector roadmap for the energy- and supply sector’s contribution to the 70% goal (Regeringens klimapartnerskaber - Energi- og forsyningssektoren. I mål med den grønne omstilling 2030)

    • Modernized pricing

    • Flexibility in households

    • Freeing supply data

    • Local flexibility markets

    • Innovation

  2. 4.

    The Sustainable and Smart Mobility Strategy (European Comission: Mobility Strategy 2020)

    • Reduce transport-related greenhouse gas emissions by 90% by 2050

    • Increasing the uptake of zero-emission vehicles

    • Supporting digitalization and automation

  3. 5.

    European Green Digital Coalition (European Comission 2022)

    • Investing in the development and deployment of green digital solutions with significant energy and material efficiency that achieve a net positive impact in a wide range of sectors

    • Developing methods and tools to measure the net impact of green digital technologies on the environment and climate by joining forces with NGOs and relevant expert organizations

    • Co-creating, with representatives of other sectors, recommendations and guidelines for the green digital transformation of these sectors that benefits the environment, society, and economy

Future ecosystem conditions

Based on Step 2 and Table 14 Identification of future ecosystem conditions, the potential future ecosystem conditions can be identified as shown in Table 22.

Table 22 Investigation results of the potential future ecosystem conditions

Future business ecosystem trend identification

Based on Step three: Identify future business ecosystem trends in the methodology section, the results of ecosystem potential change evaluation for this case study can be shown in Table 23.

Table 23 Results of ecosystem potential change evaluation

Business opportunity selection

Based on Table 23 (Results of ecosystem potential change evaluation), four transition stages are defined as shown in Table 24:

Table 24 The identified transition stages of the EV home charging energy ecosystem with future ecosystem conditions

The transition stages represent the realization potentials. The future ecosystem description and the proposed value propositions for each transition stage can be described as shown in Table 25.

Table 25 The future ecosystem description and the proposed value propositions for each transition stage

Potential solution identification

The value proposition related to the Transition stage 1 (1.1 and 1.2) and 2 (2.1 and 2.2) is considered for the investigation of the potential solutions. Based on the market research, the EV charging algorithms in the Danish market are either the traditional charging that EV users charge EVs immediately when they arrive home or electricity price signal based charging. However, none of these two consider the dynamic distribution tariffs or CO2 emission, and the second charging strategy needs to be manually configured.

Therefore, a literature review is conducted to investigate State-of-the-art (SoA) EV charging strategies. According to Christensen et al. 2020d, the EV charging strategies can be categorized as centralized and decentralized, and the decentralized charging strategies are usually used by the EV users. Furthermore, based on evaluation with the modified feasibility assessment method (Christensen et al. 2021), Real Time Pricing (Nimalsiri et al. 2019), Time-of-Use Pricing (Chunlin et al. 2017), and Timed charging (Huachun et al. 2012) are the most feasible decentralized EV charging strategies in Demark.

Discussion

The case study of the EV home charging energy ecosystem shows that the proposed methodology can facilitate the business opportunity identification process. However, although there are only four steps in the method, it is difficult to follow the steps in practice due to the complex logic behind each step and across steps. Therefore, the web-based tool- CSTEP business opportunity identifier (https://opportunityidentifier.sdu.dk/) solves this challenge.

For instance, the first two steps (CSTEP dimension identification for the targeted business ecosystem and potential change identification in the business ecosystem) can be presented on one webpage (a screenshot is shown in Fig. 5). Furthermore, the calculation error increases when evaluating the ecosystem's potential changes including many evaluation subjects. The tool can automatically calculate and rank the total score, making the process much easier (as shown in Fig. 6). Moreover, the analysis result can be downloaded as an Excel file for further work.

Fig. 5
figure 5

Screenshot for the first two steps’ partly results in the web-based tool- CSTEP business opportunity identifier

Fig. 6
figure 6

Screenshot for the final evaluation result

Furthermore, the tool allows a collaborative environment that multiple users can share and edit the same project. In this way, relevant stakeholders can be involved to ensure a clear interpretation shared among the stakeholders, and stakeholders’ opinions/feedback, e.g., on the derived value propositions, can be captured during the whole process.

Conclusion

This paper proposes an ecosystem-driven business opportunity identification method. This method includes four correlated steps, and the proposed method is implemented as a web-based tool. A case study of the EV home charging energy ecosystem is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool.

The results show that the potential changes can be identified, and the future business ecosystem conditions can be portrayed. Furthermore, the business opportunities can be selected, and correlated value chain segments can be placed at the actor and object level. For instance, three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition. This result also illustrates the importance of digitalization in the energy transition, especially for energy efficiency, energy flexibility, and CO2 emission reduction. Moreover, the web-based tool- CSTEP business opportunity identifier proves the ability to facilitate and ease the whole analysis process.

The proposed ecosystem-driven business opportunity identification method addresses gaps and contributes to three research domains: business ecosystem, technology adoption, and strategy management. The proposed method is a systematic approach that allows ecosystem stakeholders to conduct collaborative business opportunity analysis and evaluation. Furthermore, the application of the CSTEP dimensions and ecosystem architecture design ensures all aspects and elements related to the targeted ecosystem can be covered and investigated. Meanwhile, the user-friendly web-based tool, business opportunity identifier, can facilitate teaching in class for students to quickly understand the needs and value of the technical solutions in the energy sector.

The web-based tool, business opportunity identifier, will be available via opportunityidentifier.sdu.dk. The tool is developed and passed the initial verification and validation testing. Later this year, the tool will be further tested in the course of “Ecosystem driven technology development and adoption” for the Master programs of energy system and technology and welfare technology.

Availability of data and materials

Not applicable.

Abbreviations

CSTEP:

Climate, environmental and geographic situation; Societal culture, demographic environment; Technology readiness; Economy and finance; Policies and regulation

TRL:

Technology Readiness Levels

MVE:

Minimum Variable Ecosystem

EV:

Electric Vehicle

DSO:

Distribution System Operator

DDT:

Dynamic Distribution Tariff

ETPS:

Economic, technical, political, and social

SoA:

State-of-the-art

STEP:

Social, technical, economic, political

STEPE:

Social, technical, economic, political, and ecological

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About this supplement

This article has been published as part of Energy Informatics Volume 5 Supplement 4, 2022: Proceedings of the Energy Informatics.Academy Conference 2022 (EI.A 2022). The full contents of the supplement are available online at https://energyinformatics.springeropen.com/articles/supplements/volume-5-supplement-4.

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This paper is part of the national project- Flexible Energy Denmark FED funded by Innovation Fund Denmark (Case no.8090-00069B) and part of the national project- Digital Energy Hub funded by the Danish Industry Foundation.

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ZM developed the methodology, and designed the software specification, and the main contributor to the manuscript writing. KC contributed to the literature analysis of technology adoption theories and models and case study analysis, and is the main contributor to the sections of technology adoption theories and models, and case study. TFR realized the software development and the main contributor to the software architecture section writing. BNJ contributed to the discussion of the methodology, the software specification and software development. All authors read and approved the final manuscript.

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Correspondence to Zheng Ma.

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Ma, Z., Christensen, K., Rasmussen, T.F. et al. Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark. Energy Inform 5 (Suppl 4), 54 (2022). https://doi.org/10.1186/s42162-022-00238-z

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Keywords

  • Energy ecosystem
  • CSTEP ecosystem impact factors
  • Business opportunity
  • Electric vehicle
  • Future trend
  • Ecosystem stakeholder