Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era

Journal Pre-proof Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era

Chankook Park, WanGyu Heo PII:

S0959-6526(20)30790-3

DOI:

https://doi.org/10.1016/j.jclepro.2020.120743

Reference:

JCLP 120743

To appear in:

Journal of Cleaner Production

Received Date:

08 July 2019

Accepted Date:

23 February 2020

Please cite this article as: Chankook Park, WanGyu Heo, Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era, Journal of Cleaner Production (2020), https://doi. org/10.1016/j.jclepro.2020.120743

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Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era Authors 1. Chankook Park E-mail: [email protected] Affiliation: Energy Industry Research Group, Korea Energy Economics Institute

2. WanGyu Heo* E-mail: [email protected] Affiliation: College of Business, Korea Advanced Institute of Science and Technology

* Corresponding author. Tel.: +82 52 714 2236 E-mail address: [email protected] Postal address: College of Business, Korea Advanced Institute of Science and Technology, 85, Hoegiro, Dongdaemun-gu, Seoul, 02455, Republic of Korea

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Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era

Abstract

We examine the changing aspects of the electricity industry and requirements for the industry development in the information and communications technology (ICT) convergence era based on the framework of value chain change in terms of the counter-flow, multi-dimensionalization, insertion, and removal of a value chain, away from the existing ‘value chain elements’-centric approach. As a result, we confirmed various value chain changes and directions for the development of the electricity industry. Energy consumers are turning into prosumers and influential stakeholders. In addition, the importance of data has been highlighted and the influence of platforms has been expanded. With the changes come along, various market participants appear, and at the same time businesses that do not adapt to the changes are at risk in terms of their market power. It can be seen that the changes demand efforts to encourage consumer participation into the energy market, rational mechanism of energy data sharing, and setting up of an efficient regulatory system. This study contributes to the understanding of the new changes and the demands resulting from ICT convergence in the electricity sector.

Keywords Industry value chain; ICT convergence; Smart energy; Electricity industry

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Review of the Changing Electricity Industry Value Chain in the ICT Convergence Era 1. Introduction In an environment where the fundamentals of the energy industry competitiveness are transiting from conventional resources to technological capability, the energy industry is expected to increasingly converge with the information and communications technology (ICT) industry. The term smart energy is defined as a series of activities or transitions to increase the efficiency, safety, and eco-friendliness of an energy system by ICT convergence along the energy life cycle of production, transmission, and consumption. As smart energy is pushed ahead, the term 'smart energy' has come into common use as a differentiated term from conventional energy. Recently, this phenomenon of smart energy has become prominent especially in the process of electric power distribution and consumption (Bartels, 2005; Booz & Company, 2008; IEA, 2011; Kisker, 2012; Bullis, 2013; Zhang and Liu, 2015; Colak et al., 2016). ICTs include wired/wireless communication network and services, ICT devices, software, and computer-involved services under the classification of the information and communication industry. The term 'convergence', meanwhile, refers to the phenomenon of reproducing something by a combination of many other elements or functions, or a new kind of element or function (Lee et al., 2008). Therefore, the ICT convergence can be understood as a trend of goods advancement, service innovation, and added value creation by embedding ICT in goods and services belonging to other industries (Joint Relevant Governmental Offices and Agencies, 2012). We observe the shift of the contemporary paradigm in which new values are created by the convergence beyond the technology advancement in the 21st century. Herein ICT operates as a catalyst of convergence to create new goods and services. As the boundary between ICT and non-ICT is broken down, the capability of ICT based convergence is emerging as a key to future competitiveness (Joint Relevant Governmental Offices and Agencies, 2012). The electricity industry is not excluded from ICT convergence. Even though ICT convergence in the electricity industry is driven relatively slowly compared with other industries such as the automotive industry and the media industry because of the heavy governmental regulation, it has been promoted by recent continuous smart grid projects. The ICT convergence in the electricity industry is bringing about great changes in policies for the industry (MIT, 2011; Zarakas, 2015; Attwood et al., 2017). The transition to smart energy includes challenges that need to be examined comprehensibly in socio-economics in terms of changes in the value chain of the energy industry, the increase of consumers' participation in the energy market, and the appearance of many different stakeholders. Therefore, many studies have examined the changes in the power industry due to ICT convergence. Attwood et al. (2017) presented the impact of ICT convergence on business models and market opportunities. WEF (2016) emphasized that the digitization of the electric power industry has enabled asset lifecycle management, power grid optimization, and integrated customer service, and is promoting decarbonization and decentralization at the industry level. IEA (2017) presented challenges such as changes in energy demand and cybersecurity due to ICT convergence. Although these studies have the advantage of the comprehensive review of ICT convergence in the electric power sector, there is a limitation that the analytical framework is not systematic from an academic point of view. Some studies have examined the changes in the electric power industry due to ICT convergence by applying the industry value chain framework. Valocchi et al. (2014) emphasized the importance of information and consumer by comparing the existing aspects of the value chain of the electric power industry with the changed aspects. Annunziata and Bell (2015) assessed the impact of digital

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technology on the power industry value chain. Booth et al. (2016) also explored opportunities by energy value chain for ICT convergence. CGI (2017) predicted that the changes in the energy industry value chain would accelerate as the influence of consumers increases by ICT. SAP (2017) stressed that ICT convergence is changing market rules for power generation, transmission, distribution, and retail. The above studies applying the value chain analysis framework focus on how the ICT convergence is technically progressing and which market opportunities exist for each value chain segment. However, the studies have not explicitly examined how the ICT convergence has changed the value chain of the electric power industry and the impact of the change. This study examines how the value chain changes in the convergence era and reviews the transformation of the electric power industry due to ICT convergence more systematically, instead of looking at ICT convergence in each sector of the power industry value chain, such as power generation, transmission, distribution, and retailing. In other words, we examine the changing aspects of the electric power industry based on the framework of value chain change in the convergence era, away from the existing ‘value chain elements’-centric approach. We refer to energy and electric power in the same sense. ICT convergence is being carried out not only in electricity but also in other energy sectors such as oil and gas. However, when we refer to energy in this study, we discuss it with a focus on the electric power sector. In chapter 2 and chapter 3, we describe the literature review and methodology of this study. Chapter 4 examines how changes in the power industry value chain through the ICT convergence are taking place. Chapter 5 examines the association between changing aspects-related keywords and value chain-related keywords through text mining to complement the content of the literature analysis more objectively. Chapter 6 discusses what challenges are created by ICT convergence in the power industry and how to deal with them. Chapter 7 concludes the paper. 2. Literature Review Generally speaking, convergence in the field of technology refers to the phenomenon of combining different technologies or functions and reproducing a new kind of technology or function. Technological convergence is closely related to knowledge convergence and is defined as the combination of previously distinct technologies into a common product (Gambardlla and Torrisi, 1998; Basole et al., 2015). Technological convergence leads to new value-added services and products (Stieglitz, 2003; Papadakis, 2007). The new converging process with ICT, however, is underway not only between technologies but also between industries and between technology and human life. The ICT-centered industry convergence is now at the center of current industrial structural changes (Han et al., 2009; Park and Kim, 2014; Lee, 2005), and areas outside the realm of information are joining the transition trend called informatization including the conjunction between on-line and off-line spheres (Lee, 2005; Hacklin, 2010). The industry convergence results in a collision of business models and gradual blurring, or redefinition, of market boundaries (Basole, 2015; Hamel and Prahalad, 1994). With the ICT-centered convergence, the value chain structure is also changing. The concept of a value chain was originally proposed to distinguish each activity in the production by a company, but with its extensive use in the business area, the concept has been expanded to represent the entire industrial structure (Joo et al., 2010). The value chain, the concept of which was presented by Porter (1985), refers to a linked series of directly or indirectly involved activities, functions, and processes in providing value for customers. Porter further extended the concept from a business value chain to an industry value chain, which was referred to as a value system. He emphasized the importance of the linkage between the businesses, its suppliers, and customers in the analysis of the industry value chain (Porter, 1985). The ICT-centered industry convergence leads to a reconfiguration of the value chain through the addition or elimination of activities such as merging sub-sectors breaking up the value chains and reuniting the individual value-added stages (Wirtz, 2001; Wang, 2010). However, it is necessary to pay more attention to the changes occurring in the value system rather than the industrial value chain structure to examine the changes in the industry. The currently ongoing

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gigantic ICT-centered convergence is creating new industries, new services, and new values, and its characteristics can be summarized as counter-flow, multi-dimensionalization, insertion, and removal of a value chain (Lee, 2005; Hacklin, 2010; Wirtz, 2001; Mathur, 2010). The counter-flow of a value chain means that the starting point of a value chain is not goods but rather customers. The advancement of ICT causes an increase in the amount and the flow of data and enables consumers to have more influence in the market than before (Nambisan, 2002; Pacauskas, 2016). ICT has increased customer value, reducing information asymmetry, and enhancing transparency (Barrutia and Echebarria, 2005; Seshadrinath and Pan, 2018). With the ICT advancement, the value creation of a business starts at the point where it meets the needs of its consumers. While a conventional value chain begins from the production of goods and ultimately ends with the delivery of the goods to customers via a distribution network, the value chain in the era of convergence begins with the demands of customers and ultimately goes back to the satisfaction of customers, forming a chain. The multi-dimensionalization of a value chain refers to the phenomenon that break-ups and new combinations of existing value chains take place, creating a new value chain, and the delivery paths for goods and information in a value chain become more complicated than the traditional paths. Revenuegenerating value exchanges are just a part of a value network. The flow of knowledge value and intangible benefits also has similar importance in a value network (Allee, 2000). In terms of knowledge value exchange, a firm provides personally targeted information offerings based on user preferences and users provide feedback for product development and customer usage data (Allee, 2000). Intangible benefits include a sense of community, customer loyalty, image enhancement, or co-branding opportunities (Allee, 2000; Allee, 2008). The multi-dimensional and complex interactions increase the demand for standardized platforms upon which new value creation is accelerated (Gawer, 2014). The insertion of a value chain means that the emergence of providers and services delivering new value brings about a restructuring of conventional value chains. The new arrangement of stages and participants can create additional value for customers and providers beyond the incremental cost savings (HBR, 2000; Deloitte, 2015). The diminution or removal of a value chain is a process where a portion of an existing value chain, which is unable to provide any more value for customers, disappears. As the convergence accelerates, the pace of this change will become faster, and at the same time, as many rings of a value chain disappear as new rings are inserted. Existing enterprises promote strategic alliances or mergers and acquisitions to adapt to the rapidly changing environment of convergence (Appelgren, 2004).

Fig. 1. Changes of Value Chain in the Era of Convergence

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The value chain of the electric power sector is composed of the path of power generationtransmission- distribution-retail from energy production to the final consumption. The flow of energy and information has been one-way, and almost all consumers have played passive roles. The introduction of ICT, however, is multi-dimensionally changing the direction in which electricity and information are delivered and introducing new market players and business models, thereby intensifying the complexity of the network. Distributed energy sources such as consumer-owned renewable energy, plug-in electric vehicles, and energy storage devices have been extending the value chain to make it possible to maintain asset management more closely with end consumers (Nijhuisa et al., 2015; Zahurul et al., 2016). It means that counter-flow and multi-dimensionalization of a value chain are occurring. As counter-flow and multi-dimensionalization of a value chain are underway, the value proposition of the energy providers will be reconstructed, and the value model in the entire industry will be modified. The value model can be defined as a mix of values offered to consumers and in return, reproduced to the providers by the customers. In the traditional value model of the electric power industry, the consumers who are stably supplied with electricity pay a monthly rate, which is the revenue of the utility company. However, today's consumers demand further values such as ‘environmentally friendly’ and ‘convenient’, beyond the stable power supply from electricity providers (Oracle, 2009; Accenture, 2010; IBM, 2011). The conventional industrial value chain and the one-way flow of energy and information will experience insertion in the value chain of a new information model, a new relationship with customers, and the introduction of distributed energy resources. At the same time, the value chain is extended and becomes more complicated in the future, and there will be many different participants who did not have any direct relations with the industry before. Consumers that have taken electric power products passively will become active participants in the value chain. Furthermore, information and electric power will form various streams, and new business models will be created that will take advantage of data that is increased exponentially in the network. Besides, the distributed energy sources such as distributed power generation, storage devices, and electric vehicles will increasingly play essential roles in the sides of management and value creation. In the long run, they will modify the traditional value chain consisting of power generation, transmission, distribution, and retail (Valocchi et al., 2014). Moreover, the enterprises or services that cannot proactively respond to this trend tend to regress and ultimately will be removed from the value chain. Staying in the conventional framework without reflecting the needs of the customers and the market will cause the enterprises or services to end up losing influence in the value chain (IBM, 2012; Cognizant, 2013). 3. Methodology This study attempts to analyze how the changes in the electric power industry are taking place based on the analysis framework of the value chain change according to convergence. We review the related literature and try to reinterpret the changes in the electric power industry in terms of the counter-flow, multi-dimensionalization, insertion, and diminution or removal of value chain stages. We collected literature from the academic database Sciencedirect, Google scholar, and DBpia with search keywords such as energy prosumer in terms of counter-flow of value chain, new value chain in energy or electricity industry in terms of multi-dimensionalization, new entrants or participants in the industry in terms of insertion of value chain stages, and changing role of existing businesses in the industry in terms of removal of value chain stages. In analyzing the electric power industry, the search keyword energy industry is also included because electric power and energy are often used in a mixed way. After collecting the academic literature, we first excluded the duplicated documents based on the title and abstract and those that lacked direct relevance to the direction of this study. Next, we examined the contents of the full-text and excluded the studies that lacked relevance. We also collected relevant reports from research institutes and consulting companies through Google search. Academic papers have advantages in giving a local basis of arguments, but they have

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limitations in providing practical information. This study aims to examine the changing aspects of the electricity industry based on the framework of value chain change according to the ICT convergence. Therefore, we utilized the reports from research organizations as well to enhance practical insights. We used the same search keywords we used to collect academic papers when we collected relevant reports. However, those reports mix hyperbole sometimes, and we have to be cautious of the information. Therefore, in screening the reports, we have excluded reports that focus on promoting companies' products or services, or reports advertising events such as conferences and forums, in addition to the screening criteria of the academic literature. Also, we tried to provide adequate evidence of arguments that may arouse controversy. The final reports included were mainly helpful in summarizing the use of ICT in the electricity sector, market prospects, corporate strategy cases, and policy cases.

Fig. 2. Four-phase flow diagram for literature review We also referred to news articles to obtain concrete data relevant to the transformation of the electric power industry. However, we used news articles only to collect energy business cases from non-energy firms. We used Google search to collect related news articles and used the search keyword

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of energy business and keywords indicating non-energy industries such as ICT, communications, automotive, electronic appliances, solution, semiconductor, etc. The main content of the news stories collected includes energy business cases of non-energy firms. After analyzing the literature on the changes in the value chain of the power industry, we quantitatively examined the association between keywords related to the electricity industry changes, which were derived from the literature review, and keywords related to the electricity industry value chain. We also examined the association between each keyword and the keyword digital. The reason why we tried text mining is to supplement objectively the result of qualitative literature analysis. For text mining, we put energy industry-related keywords into Sciencedirect and collected journal abstracts. Search keywords include energy industry, energy market, and energy business as related keywords that can represent the energy industry inclusively, and include energy production, energy trading, energy delivery, energy storage, and energy sale, which represent the value chain of the energy industry. The collection period was set for the last 10 years from 2010 to 2019. As a result of collecting journal abstracts, we collected a total of 57,359. There are actually more abstracts, but Sciencedirect restricts to 6,000 collectable documents per year. And then, only the abstracts of energy journals have been recaptured to focus on energy issues. To do this, we used a list of 750 energy-related journals provided by Scopus. As a result, 28,990 abstracts were reselected. The abstracts were then divided into two periods, which are 1st period (2010-2014; 12,690 abstracts) and 2nd period (2015-2019; 16,300 abstracts). The keywords of interest in this study are those derived through qualitative literature analysis such as consumer, big data, platform, startup, and utility, and energy industry value chain words such as production, trading, delivery, storage, and consumption. These keywords include similar words as shown in Table 1. Table 1. Keyword grouping for text mining Category keywords related to the electricity industry changes

Group name consumer

consumer, prosumer, household, customer

platform

platform

big data

big data, analytics, artificial intelligence, AI, machine learning, deep learning

startup

startup, new business, new company, new pioneer, new firm, new corporation, new entity, new entrant, new player

utility

utility, power company, electricity company, power corporation

production keywords related to the electricity industry value chain

production, generation

trading

trading, trade, transaction

delivery

delivery, transmission, distribution

storage

storage, ESS, battery

sale consumption

keyword digital

Keywords included1

digital

sale, retail consumption, demand digital, ICT, intelligence, smart

Plural words are also included in the similar word integration process. Only singular keywords are shown in the table. 1

Furthermore, we tried to compose the contents in a balanced manner by suggesting the additional issues to be responded to according to ICT convergence beyond showing changes in the electric power industry. The additional issues were extracted based on the content of academic papers and reports collected through the literature collection process presented above. The issues were mainly derived

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from changes in the value chain and consisted of challenges to be addressed to promote ICT convergence in the electric power industry.

4. Transformation of the Electricity Industry in the wake of ICT Convergence 4.1. Counter-flow of Value Chain: Increase of Energy Prosumers As previously mentioned, with respect to the counter-flow of a value chain, the value delivery direction has been shifting from one-way to multi-way. Energy consumers do not remain just consumers but are becoming prosumers involved in energy production (Miller and Senadeera, 2017). The prosumer refers to a customer who is the consumer and producer of energy at the same time (Zafar, 2018). The prosumer participates in the energy market and promotes the changes in the value chain mentioned above. The influence of prosumers is growing in the energy market. The prosumer household is usually equipped with smart appliances, devices such as a smart meter, a sensor, a home gateway, and solutions like an energy management system (EMS) that are connected to a central server. Among them, the EMS performs the role of monitoring, controlling, and optimizing the production and consumption of energy. An energy prosumer can offset power demand in peak times through the EMS by producing or storing electricity, helping to ensure the flexibility on both sides of supply and demand. The prosumer can also provide the power grid with negawatt that is saved energy at a particular time for load shaving (Zpryme, 2014). The realization of the prosumer energy market is attributed to the intelligent communication equipment that can be connected to other devices through a variety of protocols such as Bluetooth, WiFi, 4G, and 3G. Currently, the communication channels between utility companies and consumers are increasing with the introduction of services such as customer service websites, online chatting, e-mail, and text messages. Many customers use their smartphones to check data, deliver complaints, and ask questions or give answers through mobile social media (Kelsven, 2016). New smart devices such as Nest Learning Thermostat of Google not only enable distributed decision making but also empower a prosumer to have the right to make a decision. The intelligent devices and the engaged algorithms will play the role of the agent of the prosumer (Kelsven, 2016). Along with exploiting smart devices, power companies are taking advantage of social media in helping outage management, and consumers are demanding more different services through this kind of platform. It has stimulated the so-called "MoSoLoCo,", a new approach to customer involvement. MoSoLoCo, which takes the first two letters of mobile, social, local, and commerce, describes the feature that these elements are combined to change the way in which consumers interact, search, and trade. This format has already been applied in bank transactions or e-commerce (Zpryme, 2014). Smart grid infrastructure, as well as intelligent devices, encourages the prosumers to participate in the energy market. Demand response (DR) is a good example. With DR, the prosumers change their power consumption patterns in response to the fluctuation of electricity rates, and in turn, receive the reward from the effects. The DR contributes to the adjustment of the utility demand by reducing the consumer's use of the resource instead of enlarging the supply to meet the power demand (MorenoMunoz et al., 2016). A demand response program is classified into price-based and incentive-based programs. In a price-based demand response program, consumers receive information about the price of electricity in a specific time slot. Based on the price information, the consumers shift their power use or change their behavior. The incentive-based demand response program helps maintain the stability of the power grid in the event of an emergency that affects the supply and demand balance. In the incentive-based program, consumers receive an incentive as a reward for reducing their electricity consumption at a peak time and moving to a non-peak time (Paterakis, 2017; Alasseri et al., 2017; Kim et al., 2012). Wang et al. (2018) showed that the total electricity consumption and related costs could be reduced by 7% and 34% after simulating the various types of energy demands in a multi-occupant household through a price-based demand response scheme.

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Another example is consumer-owned distributed power generation. The distributed generation refers to small module power generating technology (typically 3~10,000kW)1 that can be connected to load management and energy storage systems to improve the quality and reliability of power supply (Brown and Ciliberti-Ayresm, 2012). The Global distributed generation market is expected to grow at a CAGR of about 15% during 2018-2025 (Energias Market Research, 2018). Unlike the traditional centralized system, this kind of power generation system is located on the spot of energy consumption or nearby. A concept of distributed energy resources that is larger than that of distributed generation encompasses wind turbines, photovoltaic systems, fuel cells, geothermal systems, V2G (vehicle to grid), waste to energy, micro-turbines, micro-grids, and other technologies. Since these technologies can be used for many purposes such as for the base load, the maximum load, auxiliary services, high quality, and the satisfaction of heating and cooling demand, they are increasingly taking an essential place in the national energy plan of each country (Zpryme, 2014). 4.2. Multi-dimensionalization of Value Chain 4.2.1. Enlargement of the Platform Influence A platform is defined as a common architecture that provides the standard base in the transaction between two or more parties and the rules; the architecture includes the products, services, and infrastructure design to activate the interaction between users, and the rules are protocols, rights, pricing conditions and the like (Valocchi et al., 2014). Generally speaking, the platform provides a means to enable the interaction between suppliers and purchasers of products and services, creating value that cannot be generated elsewhere. The platform lowers the service providing cost by leading to a certain degree of standardization of transaction and reducing redundancy (Bhadoria, 2014). In this respect, the power network can be regarded as one of the early technology platforms. The electricity network provides a way for electric power generators to deliver the electricity to buyers and for buyers to accept delivery of the electricity, and standardized technological specification such as 120V/ 60Hz (U.S.) and 230V/50Hz (Europe) standards around which thousands of applications such as heating, cooling, mechanical power, etc. would be built over the years. There are various types of platforms in consumer and business information technology, and the Internet platform and social networking sites are typical examples (Valocchi et al., 2014). Many platforms are single-sided where the goods are usually delivered from a seller to a buyer with an intermediary (a distributor). The conventional power network has been operated on this onesided platform. The electricity companies have sold the electricity directly to customers without any intermediaries by owning the entire value chain exclusively (Valocchi et al., 2014). However, a new value has been produced in the network through the spread and interchange of the existing value, and multi-sided platforms and new businesses are being developed. In these platforms, there are various types of vendors and customers, and occasionally one party will perform both roles of the seller and the purchaser (Hagiu and Wright, 2013). Smart grids that transmit energy and information to many destinations will support the interactions between all the participants in the future and accelerate the development of multi-sided platforms in the power industry. The multi-sided platforms will interlink energy suppliers, service providers, equipment manufacturers, application developers, and end-users. So far, the energy industry has not had sufficient cause to devise multi-sided platforms since the delivery of its product has been an entirely physical process. The flow of energy and information has

1

The size of the distributed generation capacity is variously set by country or institution. In New Zealand, generating units of capacity less than 5 MW are generally considered distributed generation (Grand View Research, 2018). Swedish legislation treats generating units under 1.5MW differently from units of higher than that capacity. In Australia, generating units under 30 MW are considered as a distributed generation (Ackerman et al., 2001). The Electric Power Research Institute (EPRI) considers small generation units from a few kW up to 50 MW and / or energy storage devices located near customer loads or distribution and sub-transmission substations as distributed energy resources (Balmat and Dicaprio, 2000).

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been one-way, and the end consumers have not needed to communicate with energy providers except for to deal with a few issues such as service provision, pricing, and problem-solving. However, now that many different kinds of industrial demands, as mentioned above, are appearing, the types of the platform will be diversified (Valocchi et al., 2014). The multi-sided platform allows the flexible utilization of the distributed energy resources provided by prosumers in a way that maintains grid reliability (Garnier and Madlener, 2016; Specht and Madlener, 2019) 4.2.2. Rapid Increase of Data and Appreciation in Analytic Value As ICT has converged into the energy sector, the value of data has been highlighted. Especially with the building of smart grids, the energy-ICT converged infrastructure produces a vast amount of data. The ability to analyze the data thus is related to the capability of the business (Valocchi et al., 2014; Schuelke-Leech et al., 2015; Zhou et al., 2016). Smart grid analytics market would be expected to increase from $1.15 billion in 2018 to $2.31 billion in 2025, with an annual average growth rate of 10.4% (Frost and Sullivan, 2019). Assuming that a smart meter acquiring data every 15 minutes produces about 400 Megabytes of data per year, a power company, which distributes a smart meter to each of its 100 million customers, will have to deal with 400 Terabytes of data annually to extract useful information (MaRS, 2014; Park, 2013). Bloomberg New Energy Finance (BNEF) (2017a) predicted global smart meter penetration would increase from 37.9% in 2017 to 81.2% in 2030. The smart meter deployment is not the only factor to increase the amount of data. The growing use of geographic information systems (GIS) dramatically contributes to the rise in the amount of data. In a distribution automation project of which the purpose is to boost outage management and asset utilization, thousands of GIS-related monitors, switches, and remote terminal units will be installed. A power company of the United States, Oklahoma Gas and Electric, estimates that around two million event messages a day will be sent out due to the notice function of its meter's alarm and distribution management system. The data storage and processing capacity required in the GIS system will be further expanded as the GIS system is integrated with individual meters (Park, 2013). A smart grid application such as a synchro-phasor unit (SPU) produces a large quantity of data (Moon, 2009). The SPU unit is a device installed in power plants and substations to measure the voltage and current precisely in the form of a phasor. Using the satellite signal (GPS), collected data are displayed by the unit of a microsecond. The data taken at the spot of the SPU are sent to the central monitoring analysis system 60 times per second for thorough monitoring of the state of power distribution network operation, observation of the dynamic characteristics of the distribution grid, and analysis of the stability. Thus, the SPU has a function to give an early warning of the risk of a massive power outage (Moon, 2009). The bottom line is how to utilize that vast amount of data (Wilcox et al., 2019). The analysis of energy data can be a foundation for the electricity company to increase its operational efficiency. The firm will be able to improve efficiency through optimized asset management and electric power grid management. For example, theft of electricity can be detected by the analysis of smart meter data. The potential theft can be grasped by comparing the smart meter data with the data from the sensor attached to a transformer or a feeder, or any power loss by comparing the technical loss with the total quantity. If the total power consumption measured from the meter is less than the total power consumption from the feeder or the transformer, the cause of the power loss will be checked by dispatching a technician to determine whether it is a bypass connection of a power line, meter tampering or a technical problem. Xiao and Ai (2018) illustrated through simulation case studies that the accuracy of identifying fraudulent customers could be over 90%. The smart meter data may also increase the accuracy of the load prediction by providing detailed data at the point of consumption. Checking the load in the electric flow at a particular part of the power distribution infrastructure can be effectively applied to the demand response or dynamic pricing. The precise estimate of the load will

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be helpful for many parts of electricity business processes, including power generation, power trading, capacity planning, and demand management (Kim and Park, 2012). As the Internet of Things (IoT) becomes more utilized in the energy sector, we will be able to acquire vital field data and use it for predictive maintenance, performance modeling, and analytics. IoT contains sensors that record data, semiconductor chips, network, software platform, cloud storage, and analytics software (The Faktory, 2014; BNEF, 2017b). According to a case study of BNEF (2018a), asset performance management (APM), which is a main application form many IoT platforms, shows its benefits in the electricity industry as Table 2 when using sensor and machine data in conjunction with analytical software to optimize plant reliability, increase availability, and decrease operation costs. Table 2. Summary of operational benefits delivered by APM 1. Typical benefits of APM

Range

Increase in capacity factor

3-7%

Improvement in technical availability Reduction in maintenance costs

1-20% 5-45%

Typical benefits of APM Improvement in labor utilization Reduction in insurance premiums Reduction in equipment failures

Range 5-30% 5-10% 5-50%

BNEF collected the benefits summarized in Table 2, through a series of research calls with APM providers and users (BNEF, 2018a). 1

IEA (2017) estimated that cumulative savings from the widespread use of digital data and analytics in power plants and electricity networks could average around USD 80 billion per year or 5% of total annual power generation costs by reducing operations and maintenance (O&M) costs, improving power plant and network efficiency, reducing unplanned outages and downtime, and extending the operational lifetime of assets. Data analytics will be utilized effectively for renewable energy facilities expanding rapidly. For example, it is known that incorporating data analytics during wind turbine siting can reduce development costs by 10%. Data analytics and predictive maintenance software can also lead to 10% reduction of operation and management costs in wind turbines (BNEF, 2018a; GE, 2018). Companies such as E.ON, Vestas Wind Systems, and Iberdrola all utilize artificial intelligence (AI) to reduce cost in the O&M of wind plants and improve the performance of their assets in real-time. AI called machine learning allows operators to optimize the lifetime of components with the function of predictive maintenance. Enel reduced the cost of O&M of onshore wind plants by 24% between 2009 and 2017 by digitalizing its assets (BNEF, 2018b) In addition to IoT and AI as high-impact digital technologies in the energy sector, new technologies such as edge processing and blockchain are emerging as promising technologies. Edge processing indicates data analysis that is done locally, using computing power on the device itself. It is important for autonomous devices without network connections, improved security by keeping data on-site, individual devices become smarter. Blockchain technology is a secure and digital ledger useful for tracking information. It tracks data between connected devices for security or record-keeping, micropayments to prosumers, enabling community microgrids (BNEF, 2017c). The marginal effect of improving efficiency through data analytics can be reduced over time. However, when we compare the case of creating and effectively using big data and the case of not doing so, the difference in effect between the two cases becomes clear. 4.3. Insertion of Value Chain Stages: New Business Entry in the Electricity Industry

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The increase of various services and products due to the ICT convergence in the energy industry has been examined in the convergence trends in terms of the energy sources and the life cycle (Choi and Lee, 2015; MOTIE, 2017). This increment of services and products means that there are enterprises providing them. It suggests that not only energy companies but also many different businesses of communications, software, terminal devices, security, construction, and automobiles will participate in energy-related commerce and that the degree of the participation will grow. In the case of a smart grid, various businesses compose the smart grid system, which can be sorted into an electric power system, a communication system, an application system (Park and Yong, 2009). In the electric power system, power generators and transmission and distribution operators can be called the mainstream. In the communication system, the center is the providers of the communication network such as a local area network (LAN), wide area network (WAN), field area network (FAN), and home area network (HAN). In the application system, many different companies are taking advantage of the platform of a smart grid including electric vehicle charging, power storage from distributed generation, electric network optimization, demand response, and meter data management (Park and Yong, 2009). Notably, the entry of ICT companies into the energy industry is occurring in many other aspects. The cases of Apple and Google, representative ICT operators, are as follows: On June 2, 2014, Apple announced the strategy for a Connected Home with the launch of HomeKit which is a home automation platform. Although security and lighting are the main utilization areas of HomeKit, home energy management and other tools are also included in the scope of applications (BNEF, 2014). Apple’s Home Automation strategy will have a positive influence on lighting apparatus suppliers who try to accelerate the introduction of controllable LEDs to every household. As lighting accounts for 12% of residential energy consumption, accelerating the dissemination of high-efficiency LED will also contribute to a reduction of electric power demand (Park, 2009). Google is expanding its investment in the energy business, too. Google acquired Nest, an intelligent thermostat vendor, for $3.2 billion in January 2014, and restarted the home energy data analysis and management business that had been inactivated with the cessation of the PowerMeter project (Sioshansi, 2014). The thermostat of Nest facilitates ubiquitous access to the Internet through smartphones and control of the home temperature and living patterns according to a user's preferences. It also provides consumers an intuitive understanding of the results of monitoring and analysis. There is a case in which an e-commerce operator takes part in the electricity retail business. Rakuten Inc. is well-known as Japan’s largest e-commerce company with web operations stretching from Internet banking to electronic books. Initially, in 2012, Rakuten Solar was launched as a department and as a service where the company sold photovoltaic systems bundled with engineeringprocurement-construction (EPC) services online through its e-commerce platform. In 2013, it changed the department name from Rakuten Solar to Rakuten Energy and started an electricity retail service first to the high-voltage sector. In 2016, it launched a second electricity retail service to the low-voltage sector after the full deregulation of the retail market. In December 2017, it launched the Rakuten Energy Trading System, a trading platform using blockchain technology to facilitate the exchange of J-Credits, which are carbon offset credits certified by the Japan’s government (BNEF, 2018c). The participation of ICT companies in peer to peer (P2P) electricity trading business is increasing. For example, Piclo, a UK-based P2P electricity trading platform provider, provides a P2P electricity service by collaborating Good Energy, a renewable energy company. Piclo has developed software that connects the electricity providers and consumers whose preferences match and is responsible for attracting customers. Piclo matches electricity consumers and providers every 30 minutes using a meter data, power generation cost, and consumer preference information. Electricity suppliers and consumers utilize Piclo's online services on their computers or smartphones (Park and Yong, 2017). In addition to Piclo, dozens of companies are currently utilizing blockchain technology for the electricity P2P business (SolarPlaza, 2017). Sia-partners (2018) simulated the Brussel area and found that P2P electricity trading

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helped consumers save about 11% of their electricity bills. Long et al. (2018) analyzed that P2P electricity trading could reduce consumers' electricity rates by about 12.4%. Besides cases mention above, various industrial businesses have engaged in energy projects, including communication service operators such as Comcast (Greentechmedia, 2014; UtilityDive, 2014), Telstra (Van Hout, 2016), Verizon (Verizon, 2018), and KT (GSM Association, 2017); automotive companies such as Toyota (Merchant, 2017), Tesla (Tesla, 2016), and GM (GM, 2017); consumer electronic appliances vendors such as LG Electronics (ForbesCustom, 2016) and Whirlpool (Whirlpool, 2017); solutions providers such as POSCO-ICT (Seo, 2017) and LG CNS (LG CNS, 2018); and semiconductor producers such as Freescale (Freescale, 2013). 4.4. Diminution or Removal of Value Chain Stages: Changing Role of Conventional Energy Businesses The changing environment of the energy industry requires changes in the traditional business model of energy enterprises, particularly in the electricity sector, which is undergoing a rapid transformation. The cost for power network operation is growing with the spread of renewable energy, which has low carbon emissions but intermittent power production, and the revenue base is shrinking with consumers being equipped with their own distributed power generation systems (EPRI, 2014; Castaneda et al., 2017). Challenges across the value chain are as Table 3. Table 3. Challenges that electricity companies face Generation

Transmission & Distribution

∙ Aging fossil fuel fleet, Increasing failure, worker safety ∙ Environmental protection ∙ Falling power demand Peakier load profiles and greater ramping needs ∙ Need to cut costs ∙ Lower annual run-hours

∙ Renewables / Distributed resources integration ∙ Congestion on existing grid ∙ Resiliency and reliability ∙ Co-ordination among aggregators, DSO, TSO, and consumers ∙ Aging asset base ∙ Need to cut costs

Energy management

Retail

∙ Mismatch between supply and demand ∙ New technologies to do demand response and aggregation ∙ Keen competition with new players ∙ demand-side flexibility

∙ More competition and higher customer churn in liberalized markets ∙ Customers demanding real-time information, greener electricity and ways to save energy ∙ Consumers who produce and trade electricity directly

Source: IEA (2017); BNEF(2018d); Liboni et al. (2018); IRENA (2019). While the ICT convergence environment in the energy industry contributes to the overall enlargement of the energy market by inviting other industrial businesses to get involved, it also puts energy companies and newly participating enterprises in a competitive relationship for the same market. In addition, smart grid technologies including AMI provide ground to help energy consumers choose the energy supplier based on market price signals. The energy storage technology and microturbines create an environment in which prosumers are independent of the central power grid, deviating from the past perception that customers would always rely on the electricity network of a power vendor company (Parag and Sovacool, 2016; Lavrijssen and Parra, 2017). The electric power industry has enjoyed a stable business model from the early 20th century with a vertically integrated structure of power generation, transmission, distribution, and sales. However, it is facing increased demand for innovative change resulting from the growth of distributed energy, the development of clean technologies, the expansion of energy efficiency programs, ICT-accustomed customers' growing needs of energy management, and competition with third energy suppliers (Defeuilley, 2019). Businesses that do not meet customers’ demands and do not adapt to the new environment are bound to fall behind the competition. The electric power industry will respond to the trend of the energy transition by actively utilizing ICTs to overcome the current challenges and adapt to changing technological environment. For example, Tepco, Tokyo’s 66-year-old big electric power

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company, has a new strategy for its long-term survival: reinventing itself as a cutting-edge innovator using blockchain to manage power flows and hunting for startups and other investments with the potential to revolutionize the power sector (BNEF, 2018e). At present, the removal of the value chain stage is not clearly seen since ICT adoption is promoted to optimize asset management and improve operational efficiency. However, if more innovative technologies such as blockchain are spread in the long term, changes in the value chain structure can become more visible. For example, energy retailers may face many changes depending on the degree of application of the blockchain. In the future, retail business can be transformed into a single application if the retail environment is to be automated through smart contracts in an environment where distributed generation, storage, and consumer devices interoperate (Buchmann, 2018; Govindan et al., 2018; Andoni et al., 2019). For the meter operation, there is no need to collect and record data separately on the blockchain. It is because all consumption and transaction data are automatically and accurately recorded by the blockchain technology (PWC, 2016). In terms of the diminution or removal of the value chain stage, it is necessary to further examine the impact of future technological innovations from a longer-term perspective. 5. Association between Keywords related to the Electricity Industry Changes and Value Chain In Fig. 3 below, the previous five figures show the association between keywords related to power industry change and those related to power industry value chain. The final sixth figure shows the assoication between keyword digital and keywords related to power industry change and value chain. The X-axis in the figure represents the overall association between keywords, which is the mean of confidence values in the first and second half of the 2010s. Confidence is one of the representative indices of association rules and is the conditional probability that word B comes with word A when a word A emerges from a document (Park and Heo, 2020). The Y-axis is a rate of increase that shows how much confidence increased in the 2nd half compared to the first half of the 2010s. Table 4 below provides numerical information for viewing the figures in more detail.

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Fig. 3. Association between keywords in graphs1 1 On platform (the 2nd graph) and startup (the 4th graph), the increase rate in association with sale was infinite so sale (blue square) was placed at the top of the graph. In the first figure in Fig. 3, all of the value chain-related sectors, except production, in association with consumer, showed a positive growth rate. In particular, consumer showed a high increase rate of association with storage and trading. This suggests that consumers have more room to use energy storage than ever before, and consumers' influence is also increasing in energy trading. Platform's association with trading and sale showed a high growth rate. Although the mean values of confidence were weaker than other words, they increased significantly in the late 2010s. This suggests that academic interest in the platform related to trading and sales is increasing. Big data showed a positive growth rate for all other value chain words except storage. In particular, the association between big data and consumption showed a higher average and higher growth rate compared to the association between big data and other value chain keywords. This shows that there is an increasing interest in big data analysis in the energy consumption sector. Startup is highly related in trading, storage and delivery. The sale did not appear to be relevant to startup at all in the first half of the 2010s, but in the second half of the 2010s, new connections were made to show potential developments. At utility, the association with all value chain sectors showed a positive growth rate. Among them, delivery showed the highest growth rate. As the power industry is changing rapidly, interest in utilities, key stakeholders in the power industry, has increased throughout the value chain.

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Looking at the association between digital and the rest of the keywords in the last figure, we can see that digital was more related to consumption sector keywords such as consumer and consumption than typical supply sector keywords such as production and delivery. Digital is also showing higher growth rates with value chain keywords such as trading, sale, and storage. It shows that digital technologies are being applied across the value chain beyond energy production and delivery, and relevant academic interest is also on the rise. Table 4. Association between keywords in numbers LHS

consumer

platform

big data

startup

utility

digital

RHS production trading delivery storage sale consumption production trading delivery storage sale consumption production trading delivery storage sale consumption production trading delivery storage sale consumption production trading delivery storage sale consumption production trading delivery storage sale consumption consumer utility platform startup big data

1st half 0.436 0.065 0.151 0.098 0.037 0.600 0.600 0.011 0.116 0.200 0.000 0.347 0.294 0.059 0.059 0.235 0.000 0.412 0.628 0.032 0.096 0.181 0.000 0.394 0.467 0.055 0.166 0.152 0.031 0.496 0.482 0.027 0.250 0.256 0.012 0.616 0.281 0.079 0.018 0.018 0.012

2nd half 0.423 0.095 0.184 0.159 0.043 0.691 0.529 0.133 0.143 0.190 0.024 0.514 0.408 0.078 0.087 0.146 0.000 0.631 0.507 0.081 0.135 0.284 0.041 0.378 0.489 0.068 0.226 0.194 0.033 0.588 0.448 0.071 0.210 0.307 0.023 0.710 0.329 0.110 0.024 0.019 0.038

IR(%) -3.150 47.638 22.432 63.577 17.693 15.229 -11.905 1166.667 23.377 -4.762 Inf 48.052 38.641 32.039 48.544 -38.107 NaN 53.259 -19.262 154.054 41.141 56.916 Inf -3.871 5.619 23.469 36.208 27.211 6.900 18.575 -6.949 157.464 -16.200 19.854 89.769 15.305 17.248 38.293 33.171 6.537 209.622

6. Requirements for the Electricity Industry Development in the era of ICT Convergence

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We have looked at the changes in the value chain of the power industry so far. However, there are significant challenges for each of these changes. Regarding the counter-flow of the value chain, it was confirmed that the influence of consumers in the deployment of renewable energy and maintenance of system stability is increasing, and consumers are becoming main stakeholders in the industry. However, the effects differ depending on how much consumers are embracing smart energy systems. Therefore, it is emphasized that enhancing consumer acceptance of the smart energy system is an important task (SGCC, 2010; IEA, 2011; Park et al., 2014; Chawla and Kowalska-Pyzalska, 2019). Regarding the multi-dimensionalization of the value chain, the proliferation of digital platforms and the increase in data value were emphasized. Basically, when access rights to the data are sufficiently secured, the digital platform will be effectively used, and various services using the data will be provided (MaRS, 2014; Lycklama et al., 2019). In terms of the insertion of value chains, new services and companies are increasing. However, there are conflicts of interest among companies as various industries that have not been in the energy business take part in the energy industry. Also, maintaining interoperability has emerged as an essential task due to ICT convergence (Muto, 2017; de Wildt et al., 2019). Regarding the diminution or elimination of the value chain, much emphasis has been placed on existing utilities to provide them with opportunities to compete fairly with the new market conditions (Cambini et al., 2016). On the other hand, it is strongly argued that efforts to prevent exposure of personal information and counter cybersecurity threats should be strengthened with the digitization of the power industry (EC, 2017; El Mrabet et al., 2018; Kimani et al., 2019). 6.1. Securing Consumer Acceptance For smart energy to be sustainable, it is required that the end consumers should accept smart energy technologies (SGCC, 2010; IEA, 2011; EPRI, 2012; GridWise Alliance, 2013). This is in the same context of the phenomenon that the influence of energy consumers is growing more significant than before along with the increase of prosumers. To enlarge the acceptability of smart energy, it is necessary to heighten the awareness of the consumer for smart energy, and it is also essential to provide consumer education and unexaggerated information using precise terminologies. The understanding of the smart grid improves the recognition of the ease of use and the usefulness of the smart grid, which in turn grows the intent to use the smart grid (Park et al., 2014). Furthermore, consumer education and the public relations on smart energy should be tailored to consumers. It should also be taken into account that the use of simple and clear terms and educational skill contributes to improving consumers' perception of smart energy. At the same time, it is necessary to reduce irrational concerns about the risks related to smart energy. To lower the awareness of smart energy risks, it is required to define the ownership of consumer data, the authorization of access to and use of them institutionally. And it is also essential to designate the entity to ensure the security and privacy of consumer data and to classify the consumer data according to the degree of security risk to differentiate the business use and the way of sharing them. Even so, the delivery of the watered-down information on smart energy risks could raise consumer's expectations about smart energy too high, which in turn would deepen the mismatch between the expectation and satisfaction from smart energy, resulting in a negative impact on the intent to use. Therefore, it is necessary to increase positive awareness of smart energy by heightening the perception of the smart energy and highlighting the advantages of the smart energy through education and public relations. However, there should be no exaggeration about the information (Park et al., 2017). There have been various empirical studies on consumer acceptance of smart energy or smart grids (Ellabban and Abu-Rub, 2016). For example, Park et al. (2014) surveyed adults in large cities in Korea and identified smart grid acceptance factors. The perceived risk, as well as the perceived usefulness and perceived ease of use in the study, were significant factors for smart grid acceptance. Perri and Corvello (2015) conducted an empirical analysis of smart grid acceptance factors for adults in Cosenza

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and Rende, Italy, and found that attitude, subjective norm, and perceived behavioral control positively, which affected the adoption intention. Park et al. (2018) examined the factors affecting the risk perception of IoT-based home energy management services for Korean energy consumers. The specific perceived risks contain perceived financial risk, perceived performance risk, perceived security/privacy risk, and perceived electromagnetic radiation risk in the study. Research on consumer acceptance needs to be carried out steadily as technologies applied in the energy sector continue to develop. To date, research on consumer acceptance has focused on the acceptance of smart meters, home energy management systems, and IoT application systems. However, due to the proliferation of smart energy systems using new technologies such as AI, edge processing, and blockchain, research is needed to analyze consumers' perceptions of more advanced and intelligent technologies in the energy sector. In addition, the analysis of the usefulness and risk perception of technology is conducted together with consumer acceptance studies, but few studies have been conducted to analyze the relationship between the perceived usefulness and the perceived risk. Park et al. (2014), examining the relationship between usefulness perception and risk perception, pointed out that the arguments in the existing literature are mixed, and there is a lack of in-depth review of the relationship. Therefore, we need to analyze what kind of usefulness perceptions and what kind of risk perceptions are correlated. Furthermore, in order to increase public acceptance of smart energy systems, it is necessary to identify the cause of the difference between experts’ perception and public perception of smart energy systems. 6.2. Enlargement of Energy Data Sharing and the Opportunity to Participate in the Market The surge of energy data will cause an increase in not only the data storage and management but also activities to take advantage of the data appropriately. As the energy data are increasingly actively utilized, the value of the energy data market will grow (Zhou et al., 2016; Pfenninger et al., 2017). The conventional participants who have not taken full advantage of the energy data will request data sharing, and at the same time, more third parties will want to join the growing market. In order for ICT to be seamlessly converged into the energy industry to produce positive effects, there should be data sharing and fair opportunities to participate in the market. The environment of smart energy generates a massive amount of data, and big data-analyzing technologies are being developed, making the data more valuable. Besides, new services in the energy field are expanding with the data analysis. In this environment, data sharing will play a key role in vitalizing the smart energy market. To avoid conflict between data sharing and privacy protection, it is essential to seek a means of sharing operators' consumer data rationally while protecting private information (MaRS, 2014; Douris, 2017). Therefore, research on an efficient data sharing platform to promote data sharing needs to be carried out continuously, and research on the institutional basis that can support the development of the data sharing market while minimizing the problem of personal information infringement should be also carried out. In particular, research to examine the effectiveness and improvement measures of various policies and institutional support measures to promote new energy businesses through data sharing is also important. Furthermore, as long as a market participant does not have any specific disqualifications, it should not be deprived of the opportunity to participate in the smart energy market in advance, so that the competition among market players can be fair. Of course, this does not mean an unconditional openness but emphasizes building a competitive system that can improve efficiency without compromising the fairness and reliability of the energy supply (Lycklama et al., 2019). Creating a fair and open market is an important task for many to sympathize with in energy transitions (Eurelectric, 2016; IEA, 2017). Therefore, it is necessary to discuss the competition system that can maintain the neutrality of technology and provide better services to consumers. At the same time, there is a need for research on alternatives that can cope with the challenges of price stability,

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system safety, privacy, and universal access to electricity, which are concerned with the market opening. 6.3. Coordination of Conflicts between Stakeholders and Securing of Interoperability The energy-ICT convergence involves different stakeholders from many industries such as energy, communications, software, consumer electronic appliances, automotive, construction, etc. The successful establishment and utilization of smart energy demand a systematic balance of various elements, thus requiring cooperation among the market participants. The interests of the participants in the smart energy business can be the same, but there is also a possibility of conflict. Therefore, at the regulator position, the role of adjusting the different interests as well as understanding the complexity in coordinating the interests of smart energy stakeholders is becoming more critical (de Wildt et al., 2019). A mechanism is required as a control tower to mediate any interest conflicts among different stakeholders, including operators, consumers, and third parties (Díaz et al., 2017). In addition, it is necessary to emphasize the securing of interoperability. Interoperability is an essential element to link a variety of systems and ensure their smooth operation (NIST, 2012). A standard is thus required to ensure interoperability. The standard is a means to form an open and fair smart energy architecture for a number of suppliers so that they can interoperate in the environment, and it accelerates the development of smart energy and encourages the involved businesses to invest therein (Goirdano and Fulli, 2012). However, the scope of smart energy is so broad and complex that the selection of the standard is difficult (Lopes et al., 2011). As a result, this also requires the preparation of policy and institutions to mediate the different interests and promote collaboration among various stakeholders. Because of various stakeholders, different interests, and the number and complexity of available standards, a governance structure is needed to carry a formal and national standardization. The governance must maintain participation, openness, accountability, and transparency (Muto, 2017). Furthermore, the interoperability of the inter-system must be more intensively discussed and defined rather than that of the intra-system (Park, 2009). Coordination of interests needs to be supported through research into the role of each stakeholder. An in-depth study of the roles of specific stakeholders may be important, but an overview of the roles of different stakeholders is also important. It is also important to look at the relationship between the roles of stakeholders rather than presenting the roles of each stakeholder. In other words, we need to discuss how one stakeholder's role affects the role of another. 6.4. Efficient Regulation to Stimulate Investment As the energy industry is changing rapidly with ICT convergence, the traditional energy companies that do not fit in these circumstances will eventually see their market power reduced. This phenomenon is especially remarkable in the electric power sector. In the power utility industry, where the energy-ICT combination is rapidly taking place, a change in the regulatory framework should be accompanied so that the traditional electricity firms can cope with the changing environment more voluntarily and actively (Mah et al., 2017). Different new technologies should be applied to the modernization of the power transmission and distribution network, the increase of demand response, the expansion of renewable energy sources, the dissemination of electric vehicles, and the utilization of distributed energy. Furthermore, a risky new investment is also inevitable. Under the traditional regulation scheme with a fair rate of return, power operators can make a new investment without contemplating the investment risk heavily. However, with the information on new technologies lacking and the lifespan being generally short, the risk of cost recovery increases. As a result, power companies tend to avoid risks and hesitate to make the necessary investment. Overly conservative investment decisions by the utility operators and regulators

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may cause a delay of reasonable investments in new technologies (Yoo et al., 2011; Agrell et al., 2013; Mah et al., 2017). It is also necessary to note that the general pricing plan follows the method that the rate is determined based on power consumption and fixed-rate billing system. With this pricing scheme, it is difficult to efficiently reflect the changing cost of the power system depending on time-slots. Further, this type of pricing makes the electricity companies reluctant to expand energy-saving programs or distributed energy sources and preferably leads them to seek a business goal to increase electricity sales. Even in distributed power generation, a similar distortion can be found. In countries applying net metering systems for distributed generation, the households applying the net metering are enjoying some benefits without paying sufficient cost for the power transmission and distribution network. The zero net energy building program is also a similar case. Under the mechanism in which the cost for power transmission and distribution is recovered according to the power consumption, the consumers participating in this program enjoy the full benefits that can be obtained from the connection to the power grid, but seldom pay the cost arising from their impacts on the physical network and system. Furthermore, when the purchase of electricity is reduced due to independent power generation or investments in efficiency improvement, most of the resultant distribution cost is passed on to the customers who do not have a private power generating system, thereby causing an inequity (Yoo et al., 2011). This inequity can be a horizontal inequity that occurs within a similar customer group and a vertical inequity that is passed on to low-income customers (Yoo et al., 2011). Therefore, it is critical to study and prepare measures to share risks and recover the investment costs in order to increase the investments in smart energy infrastructure and enable both power consumers and suppliers to enjoy the benefits from energy savings. It is also necessary to have a regulatory approach to help power businesses voluntarily try to save the investment costs instead of passing all of the costs to customers and actively respond to the improvement of energy efficiency and the adoption of new technologies. A reasonable pricing system should be studied and provided that makes it possible to appropriately recover the investments in the modernization of the power transmission and distribution network and does not cause any distortion or inequity among consumers in the process of price charging (Costello, 2019). As for the utilities, some incentives need to be developed so that the power providers will not have to rely on the electricity sales for their revenue and will voluntarily make investments in energy-saving and new technologies (MIT, 2011; Agrell et al., 2013; Cambini et al., 2016). Utilities and regulators alike recognize the necessity of systematic change to address current market forces, including low growth, the expansion of distributed generation, the anticipation of new services, and increasing customer engagement. However, utilities are a profit-seeking company and cannot change the regulatory model. Ultimately, regulators and legislators must make efforts to provide a mechanism to change the existing structures (Scott, 2016). 6.5. Responding to New Risks With ICT convergence in the energy sector, risks that were not highlighted as much as before in the conventional energy industry have emerged as problems. The major risks of smart energy are the threat to cybersecurity and the leakage of personal information (Campbell, 2011; DOE, 2011; ISGAN, 2012; Haidar et al., 2015). As distributed energy resources, including wind power and photovoltaic system, are linked to the power grid in a smart energy environment, the connection points to the electricity network are increasing, and the participation of various stakeholders causes an increment of real-time, two-way information exchange. And unlike the traditional grid, a smart energy system with an open architecture ensuring inter-system interoperability is threatened by the issue of cybersecurity. The security threats caused by the construction of a smart grid can be listed as follows: First, the security threat by the use of two-way communication technology can disturb the power system operation through attacks of illegal data forgery and financial damage by manipulating the rate charging information. Second, the use of hardware and software whose system information and

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vulnerability are exposed to the external system increases the security threat, compared to the conventional power grid. Third, as millions of smart meters and electric cars are connected to the grid, the access points to the power system at the level of consumers significantly proliferate. Fourth, the increased interconnection with peripheral smart grid devices to provide intelligent service makes risk management more difficult than in the traditional vertical communication structure. Fifth, the dispersed smart grid equipment in a wide area such as smart meters and power distribution sensors also makes risk management and security control difficult (Kim and Park, 2012). There is also growing concern about the leakage of personal information (Kong et al., 2019). Personal information refers to the information with which it is possible to identify an individual such as the person’s name, contact information, identity information, past trade performance, and history of travel and activities. Such personal information may be taken from the information that is contained in an individual's profile or other files of a person, his/her family, friends, or colleagues. The personal information involved in smart energy is the consumer information associated with energy consumption (Kim and Park, 2012). As for a smart grid, the electricity metering happens nearly in real-time with a smart meter, unlike the conventional metering that is taken once a month. Even though the power consumption is not recorded every minute or appliance by appliance, the information about the number of residents, the current state of residence, and the bedtime and wake-up time can be identified by monitoring power consumption. As the personal information about the hobbies, behavior, and lifestyle at home of an individual can be exposed and abused for purposes other than power service, concerns about the infringement of privacy are voiced (Kim and Park, 2012; Véliz and Grunewald, 2018). Regulation may play an important role in protecting privacy, but regulation may not be enough (Véliz and Grunewald, 2018). It is also important for energy service providers to protect their customers' privacy and ensure their trust (Véliz and Grunewald, 2018). We need to share how companies have built trust while protecting their customers' privacy. In addition, research is needed on how trust affects customer behavior and business performance (Martin et al., 2017). While smart energy provides benefits including a response to climate change, improvement of energy efficiency, and value creation through new services, it can be damaged on its network or control system by a cyberattack, possibly resulting in a national security threat such as a nationwide blackout. Concerning personal information, frequent personal information leakage will lower the acceptability of smart energy, making it difficult to obtain positive effects of smart energy. Therefore, these risk factors should be thoroughly addressed beforehand in the process of ICT convergence in the energy industry. In particular, the detailed steps to institutionally strengthen cybersecurity in smart energy must be upgraded continually. To increase the cybersecurity of the grid, we should check and respond to the security vulnerability of the power grid through simulated training against cyberattack. Also, it is significant to obtain technologies and professional human resources to cope with security threats. In the case of smart meters and network systems, it is also necessary to seek measures against physical attacks. Smart meters and other AMI related devices require countermeasures such as encryption of devices that can block access by third parties. However, it is very tough to prevent the attacks in advance because of the diversity of hackers' penetration routes. Therefore, it is necessary to classify the types of risks according to the importance of security considering the damages of the electricity system. It is also necessary to strengthen the security by focusing on the areas where the damage is extensive or the importance is high. For example, servers that contain personal energy usage information are very critical and need to be focused (You et al., 2014; Hawk and Kaushiva, 2014; EC, 2017). Furthermore, as AI applications increase, there is a possibility of expanding existing threats or introducing new ones. AI can augment existing threats by increasing the number of actors performing attacks. In addition, attackers can target loopholes in energy systems that are unknown to humans but only AI and they can produce unexpected results by tampering with the data that energy system operators insert to take advantage of AI (Brundage et al., 2018). Therefore, in order to utilize AI more

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safely and effectively, it is important to study procedures and systems that can continuously review and share AI defects and verify AI safety. Also, there is a need to review information sharing models that promote the safe application of AI, and to study ethical statements and standards on the use of AI (Brundage et al., 2018). 7. Conclusion This study comprehensively examined the impacts and requirements of ICT convergence in the electricity industry, which have been discussed separately, focusing on the changes in the value chain. Counter-flow, multi-dimensionalization, insertion, and removal of the value chain, which emerge with ICT convergence, also become remarkable in the process of ICT convergence into the electricity industry. Energy consumers are turning into prosumers and influential stakeholders. In addition, the importance of data has been highlighted, and the influence of platforms has been expanding. With these phenomena, various market participants appear, and at the same time, businesses that do not adapt to these changes are at risk in terms of their market power. The counter-flow of the value chain implies an increase in consumer influence, which means that the role of consumers in the energy transition is growing. Consumers' participation in expanding renewable energy and using eco-friendly technologies could accelerate the energy transition. Factors that promote multi-dimensionalization of the value chain include massive data and digital platforms, which can facilitate the use of distributed resources and increase the operational efficiency of energy infrastructure. In terms of the insertion, diminution or removal of the value chain, we have found that companies should adapt to digitization to survive, which means that the electric power industry structure will evolve in the direction of increasing economics, eco-environment, and reliability by utilizing digital technologies. As a result of text mining analysis, the value chain areas that are rapidly increasing in association with digital technologies are energy trading and energy sale. While the use of digital technologies has been emphasized in the energy production, delivery and consumption sectors, the proliferation of technologies such as AI and blockchain have led to a sharp increase in the relationship between energy trading and energy sale and digital technologies. In particular, energy trading showed a high rate of increase in association with consumers and startups, and it was confirmed that academic interest in consumers and startups' participation in the energy market through distributed resources and its effects is increasing. Those changes demand other requirements than the traditional needs in the electricity sector. With consumer influence being emphasized, consumer engagement becomes a key driver of smart energy fostering. Efforts to encourage consumer participation are thus needed. As the market value of energy data rises with the expansion of their amount and utilization, the demand for data sharing is increasing, and so is the need for the entry of third parties to create new services. These require rational energy data sharing and the establishment of a fair competition system for enterprises. Also, as the participation of various operators may heighten the possibility of conflicts among businesses, a mechanism to mediate many types of conflicts is also required. It is also necessary to pave a road for the existing energy companies to adapt to the changing environment and make innovation. An efficient regulatory system should allow both existing market players and new participants equipped with sufficient capacity and competitiveness for the new environment. There is another aspect that should not be overlooked. The response to the risks accompanying smart energy should be prepared. ICT-based risks due to ICT convergence also may emerge in the energy industry. Preparing for cybersecurity threats and personal information leakage is critical. The increment of connection points to the energy network and information exchange in a smart energy environment is bound to enlarge the channel for attacks on cybersecurity. As AI utilization expands in the energy sector, it is necessary to respond to new threats related to AI. Furthermore, the utilization of energy data is likely to increase the risk of personal information exposure. Measures thus should be continuously sought to prevent or minimize these risks.

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Smart energy should be regarded as a megatrend that is leading the development of ICTs and constant changes in the energy sector, rather than a mere short-term trend. Currently, many projects involving ICTs are being actively promoted in the power industry. The ICT convergence has spread throughout the energy industry, serving as a solution to various aspects of the industry. This study will contribute to the understanding of the new changes and the demands resulting from ICT convergence in the electricity sector so that smart energy will be invigorated and play a decisive role socially and economically. However, since this study comprehensively examines the structural changes of the electric power industry in terms of changes in the value chain, it has not shown in-depth discussions on specific themes. In the future, in-depth discussions on each topic should be continued. In particular, it is necessary to focus on how the role of electricity market participants will change in the future according to ICT convergence. It is also necessary to pursue a meta-analysis of the benefits and costs of various technologies to enhance the persuasiveness of the digitalization of the electricity industry. References Accenture, 2010. Understanding Consumer Preferences in Energy Efficiency. Accenture: Dublin, Ireland. Ackerman, T., Anderson, G., Soder, L., 2011. Distributed generation: a definition. Electric Power System Research: California, U.S., 195-204. Agrell, P., Bogetoft, P., Mikkers, M., 2013. Smart-grid investments, regulation and organization. Energy Policy. 52, 656-666. Alasseri, R., Tripathi, A., Joji, R.T., Sreekanth, K.J., 2017. A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs. Renewable and Sustainable Energy Review. 77, 617-635. Allee, V., 2000. Reconfiguring the Value Network. Journal of Business Strategy. 21(4), 36-39. Allee, V., 2008. Value Network Analysis and value conversion of tangible and intangible assets. Journal of Intellectual Capital. 8(1), 5-24. Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., ... & Peacock, A., 2019. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143-174. Annunziata, M., Bell, G., 2015. Powering the future. GE: New York, U.S. Appelgren, E., 2004. Convergence and Divergence in Media: Different Perspectives. 8th ICCC International Conference on Electronic Publishing, Brasilia-DF, Brazil. Attwood, J., Curry, C., Wilshire, M., 2017. Digitalization of Energy Systems. Bloomberg New Energy Finance: New York, U.S. Balmat, B.M., Dicaprio, A.M., 2000. Electricity market regulations and their impact on distributed generation. in Proc. Conf. on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2000), London. Barrutia, J.M., Echebarria, C., 2005. The Internet and consumer power: The case of Spanish retail banking. Journal of Retailing and Consumer Services. 12(4), 255-271. Bartels, H.A., 2005. IT for Energy: What's Coming Next, Technologies for Utilities' Near-term and Long-Term Needs. In: The 7th Global Congress on Information and Communication Technology in Energy, Busan, Korea. Basole, R., Park, H., Barnett, B.C., 2015. Coopetition and convergence in the ICT ecosystem. Telecommunications Policy. 39, 537-552.

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Declaration of interests  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

No conflicts of interests.

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Highlights

• Energy consumers are turning into prosumers and influential stakeholders. • The influence of data and platforms has been expanded. • Various market participants appear and businesses not adapting to the changes are at risk. • The changes demand consumer engagement, data sharing, and an efficient regulation.