From Energy as a Commodity to Energy as a Service—A Morphological Analysis of Smart Energy Services

To better understand the different smart energy services and the corresponding changes in the energy industry, it makes sense to look at the traditional energy value chain. Fig. 1 illustrates the value chain which shows the processes from generation, supply to final consumption (NIST 2014; Richter 2012).

After energy is generated in central bulk power plants, it is fed into the grid, eventually traded in electricity markets and led over transmission networks with high voltage towards distribution networks that supply the end customer with low voltage electricity. The retail part mainly consists of administrative tasks such as the purchase of energy from producers and traders and selling it to end customers as well as metering and billing. The last step of the energy value chain is consumption. With the upcoming smart grid and energy technologies the traditional value chain undergoes several changes. Energy storage systems play an increasingly important role as volatile renewable energy plants constitute a higher amount of energy generation. The fluctuation in the generation also leads to higher significance of the consumption process through demand side management. Moreover, possibilities for trade change, for instance, new markets emerge, where consumers and aggregators buy and sell energy (NIST 2014). Since private households not only consume but also generate, manage and store their own energy (and thus become “prosumers”) there are growing opportunities to provide services for residential customers along generation, supply and consumption processes, for example, with consultation, installation, financing, operation, maintenance, and warranties (Richter 2012). In the future, the smart energy value chain is expected to transform into a smart energy value network due to the increase of bidirectional information flows between the energy resources and actors and growing importance of prosumer resources and decrease of centralized production (International Energy Agency 2015; Shomali and Pinkse 2016).

Kranz et al. (2015) define smart energy “as the use of ICTs [Information and Communication Technologies] in energy generation, storage, transmission, and consumption, aiming at increasing efficiency, encouraging eco-friendly behavior, and decreasing the emission of GHG [Greenhouse Gas]” (p. 8). A smart energy system is a cyber-physical system consisting of various sensor and actuator networks, which are integrated with the physical electricity grid infrastructure (Kranz et al. 2015).

Lund et al. (2012) regard smart energy systems as the broader concept in contrast to smart grid. A smart grid is an ICT-enhanced intelligent electricity grid that is able to integrate renewables and coordinates the unsteady energy production with demand (Goebel et al. 2014). Thus, a smart grid is part of an overall smart energy system that refers to several kinds of energy, not only electricity (Goldbach et al. 2018; Lund et al. 2012). Furthermore, a smart energy system can exist on a business or household level (e.g., by using a photovoltaic (PV) system, storage, and intelligent energy manager), and therefore it does not necessarily need to be connected to the overall power grid (Van Dam et al. 2010; Weiller and Neely 2014).

Smart energy can be subsumed as a topic of the Internet of Things which assumes that every physical object becomes part of the Internet. Thereby, smart energy technologies such as an intelligent photovoltaic system facilitate bidirectional data and information flows between the environment, the consumer, and other objects (Fleisch et al. 2014; NIST 2014). In accordance with Porter and Heppelmann (2014) and Beverungen et al. (2019), smart energy systems are looked at in the form of smart products that consist of physical objects embedding intelligent components, like micro-processors, sensors, controls, software, data storage, and a connectivity component that together enable new monitoring, control, optimization and autonomous capabilities and facilitate an intelligent energy management. Since the focus is on the household level, different smart energy products are used to manage the energy flows intelligently by gathering data from the environment or even interacting and communicating with the environment and other objects (Fleisch et al. 2014). The different smart energy products for the consumers are depicted in Fig. 2. Some are rather production-oriented, whereas others focus on the consumption side or serve to connect the different products.

To analyze the components of a smart product and to assess their potential for new business options such as commercial services, it is useful to apply a layered smart product architecture such as the architecture proposed by Fleisch et al. (2014). Following this, a smart energy system can be structured in distinctive layers representing the respective components deployed and functionalities provided by the system. The layers build up on each other and the lower layers enable the higher layer functionalities (Fleisch et al. 2014). Building on these general architectures, Fig. 3 shows a smart energy product architecture.

On the bottom layers, physical objects such as thermostats are rooted. Equipped with sensors, they gather data and send information to the higher layers. Through a connectivity layer the object can communicate with the internet as well as other objects and can use cloud services for data analytics. Based on the underlying layers, which constitute a smart energy product, smart energy services can be provided to the end user, e.g. in form of smart home or demandresponse applications.

In the service science field, the S‑D logic is very popular and becomes even more important with the emergence of smart products. The S‑D logic states that a customer does not only acquire and use a good or service but participates in the value creation which is called co-creation in the service system. Value is co-created by using the product and service or being engaged with it (Beverungen et al. 2019; Vargo and Lusch 2004, 2008).

In contrast to traditional service environments, smart energy services engage the consumers more actively as resource integrators, since their user data is provided to the service provider (Turber et al. 2014; Vargo and Lusch 2004; Yoo et al. 2012). Moreover, not only service providers and consumers act as resource integrators but also smart energy products by networking themselves with other service providers, consumers, or products in the physical environment. The value co-creation by the smart energy product, the consumer and the provider enable new levels of values such as predictive maintenance based on the collected user, product, and context data (Beverungen et al. 2019; Porter and Heppelmann 2014). Smart energy services also facilitate more frequent customer interactions and monetization opportunities, for instance, by sending notifications or by providing new software features to the customer. Further, the tracked data from smart energy products, users and context facilitate new services (Hui 2014; Shomali and Pinkse 2016).

S‑D logic assumes that services underlie all economic exchanges (Vargo and Lusch 2004, 2008). Following this, smart energy products are not offered in terms of selling goods but serve as platforms for services. As a result, smart energy services are an integrated aspect of smart energy products, blurring traditional distinctions between goods and services (Fleisch et al. 2014; Lusch and Nambisan 2015).

In addition, smart energy products are also smart product-service systems since they combine digital services and physical products as an entire solution to the customer (Goedkoop et al. 1999; Valencia et al. 2015). Since smart energy services require smart energy products, which in turn frequently contain supporting services, for example, the financing and installation of a smart PV system, these services are summarized in combination with smart energy products as smart energy service offerings as well (Paukstadt 2019). In Fig. 4 these smart offerings are named as “smart energy services in a broader sense” in contrast to smart services which are pure data-driven, digital services (“smart energy services in a narrow sense”).

The value co-creation in the service system is also important due to the layered architecture of smart products that were introduced in the section before: By decoupling the single layers, smart energy services can be co-created. Different stakeholders can contribute on different layers of the smart product architecture, for instance, by offering sensors, actuators, and network assets as well as software for end users or data analytics services. Hence, the layers serve as sources of value co-creation by various participants in the ecosystem (Mejtoft 2011; Yoo et al. 2010). Beyond the use of a single smart energy product, more sophisticated smart energy services are enabled by the combination of different smart energy products, the data they deliver, and their controlling capabilities; thus increasing the value of the system for the customer (Porter and Heppelmann 2014).

In the following section, the S‑D logic is applied to foster a service-oriented perspective on smart energy products. Moreover, the S‑D logic highlights the importance of information as a central source for the value-creation and supports a better understanding of the new collaborative value creation logic between consumers, service providers and smart energy products.

For this paper, smart energy services are defined as services building on smart energy products (e.g., smart meters, thermostats, PV systems), and thus, making extensive use of data-based digital technologies to offer enhanced and new tailored (energy-related) values for customers. Furthermore, smart energy services can be offered as a pure digital service (e.g., monitoring of energy consumption) but can also be an embedded part of a smart energy product (e.g., a smart light bulb) and include optional (physical respectively human-based) supporting services (e.g., installation by a technician).

Smart energy services that fall under the efficient energy use function mainly involve services that help to reduce energy consumption (e.g., energy monitoring or control options via smart thermostats) (Geelen et al. 2013; Hamwi and Lizarralde 2017). Moreover, specific energy supply tariffs and “energy-as-a-service” offerings—for example, “supply of warmth”, “supply of light” or room specific energy tariffs based on smart meter data—are placed in this category, since an individual’s supply of energy must be consumed (Fox-Penner 2009; Giordano and Fulli 2012), which leads to value-in-use (Vargo and Lusch 2004). Furthermore, consumption can be more efficient due to the detailed energy data and control.

In flexibility management, flexibility is defined as the modification of supply or demand in response to an external signal (price signal or activation) with the aim of providing a service in an energy system (Bundesnetzagentur 2019). As highlighted in Sect. 4, customers can contribute to provide different forms of flexibility, for example, through demand response. Electric vehicle batteries can also provide vehicle-to-grid services (Weiller and Neely 2014). The review of the real-life examples also indicated that companies like Caterva let customers participate financially by providing their battery storage as a flexibility resource. Some home energy storage or EV chargers can also make use of price difference due to time-of-use tariffs. Some smart storage devices buy stored energy when it costs less, (e.g., during the daytime) and sell it in the evening when energy prices are high (Rodríguez-Molina et al. 2016) (e.g., Sunverge One stores energy when it is inexpensive).

The aforementioned examples provide flexibility to the macro-grid which helps to balance the overall grid. The household-level balancing of supply and demand with, for example, an energy management system, PV system or energy devices, is classified in the morphological box as efficient energy use and self-production & storage. Energy communities are similarly classified as self-production & storage and efficient energy use, since they balance supply and demand within a community.

Smart energy services can also control and manage energy production from, for instance, local PV systems, so that the energy is feed into the grid when the prices are high (Geelen et al. 2013). In general, EV charging is regarded as the co-creation of self-production & storage, since energy consumption and storage are time separated. Smart energy services for community management can facilitate energy production, since they facilitate the local production of electricity by sharing and aggregating resources (Hamwi and Lizarralde 2017; Hyytinen and Toivonen 2015).

Energy trading is the purchase and sale of energy produced by customers to other customers or energy providers, and it serves as another form of co-creation. In this situation, the signing of an energy contract would not be regarded as energy trading, but as efficient energy use. The feed-in of customers-produced energy is one simple form of energy trading. Another, more sophisticated form of energy trading is P2P energy marketplaces (Geelen et al. 2013; Löbbe and Hackbarth 2017).

Individual resources involve smart meter reading and billing (Apajalahti et al. 2015) as well as vehicle-to-home services (Weiller and Neely 2014).

If one offering indicated the use of aggregated resources as part of its core offering (e.g., OhmConnect) or even only as a small extra service (e.g., Caterva’s offering), it was classified as an aggregation, otherwise it was classified as individual resources. Hence this dimension is mutually exclusive.

Financial benefits can be efficiency & cost savings and additional income. Cost savings are often a result of energy savings. For instance, energy monitoring, controlling and automation services enhance energy efficiency, and thus lead to energy and cost savings (Byun et al. 2011; Richter and Pollitt 2018). Furthermore, flexible tariffs can help private households lower electricity bills (Hamwi and Lizarralde 2017; Niesten and Alkemade 2016). To gain an extra income, customers can co-create by providing energy and flexibility resources to a service provider. The extra income in exchange for energy and flexibility can be achieved, for instance, through incentives in direct load control programs and vehicle-to-grid services or by selling own produced energy (Geelen et al. 2013; Weiller and Neely 2014).

Other services enable autarky & access to energy resources. Some customers aim to be self-sufficient and independent of large energy providers by producing their own energy (Koirala et al. 2016; Löbbe and Hackbarth 2017). However, an individual does not need to possess microgeneration technology to use decentralized energy. For instance, in energy communities, microgeneration units and storage are shared within the community, and thus lower financial barriers offer more households the option to participate in the renewable energy system (Hamwi and Lizarralde 2017; Zhang 2016).

Smart energy products provide consumers with more information on their energy usage, which can lead to higher perceived awareness through transparency and control over their energy consumption. Perceived control can be further achieved by the remote control of devices (Niesten and Alkemade 2016).

As the focus of this research is on energy-related values, the morphological box does not consider security surveillance in smart homes, but it does consider comfort, as this factor is often an important aspect of energy saving measures or demand response programs. Through the use of controlling and automation services, the energy processes can be managed without user action, and thus can enhance comfort (Helms 2016; Niesten and Alkemade 2016).

Beyond a pure digital service, the offering could further comprise physical respectively human-based service components. For instance, the digital component could be a predictive maintenance service for a microgeneration unit and the physical service could comprise a technician who is notified of repair tasks by the system. The full bundle of smart energy products, digital and optional physical services is considered a smart product-service system (Mittag et al. 2018).

Due to the complexity of smart energy products, technical setup and support services are often considered necessary (Richter and Pollitt 2018). Kahma and Matschoss (2017) have suggested bundling smart energy services and products in packages and offering financing options for the technologies as well as regulation guarantees, such as free access to an electricity network and a specific price for feed-in electricity; these elements can be further complemented by reliable advice. Financing can help potential customers overcome high upfront costs, whereas guarantees and advice can support building users’ trust and the credibility of real energy saving potential. Advice can be given in person or via chatbots or other digital forms. It can further consist of legal advice, advice regarding funding as well as presales product consulting (e.g., ecobee). Sometimes no additional service is required or offered.

Smart household appliances enable controlling functions such as remotely switching appliances on or off or using rules. Through the use of rules and other configuration options, controlling enables the personalization of smart energy products (Ford et al. 2017; Porter and Heppelmann 2014). Demand response services can also leverage the control functions of household appliances when generating plans to shift energy consumption to optimal times (Hyytinen and Toivonen 2015; Niesten and Alkemade 2016).

Optimization uses product monitoring and environmental data to perform analyses and execute actions. Smart energy products can also act completely autonomously. For example, a community service could use microgeneration units and the storage of several households to autonomously optimize supply and demand by considering several conditions (e.g., electricity prices, weather and energy forecasts) (Byun et al. 2011; Kahma and Matschoss 2017; Porter and Heppelmann 2014). In this case, one or more smart energy products perform actions without human support, and they not only provide suggestions for decision making but also perform the actions on their own and eventually can coordinate with other connected smart products (Porter and Heppelmann 2014).

However, smart meters are also required to monitor energy production; thus, they can also be incorporated into PV systems in which they are regarded as production-related products. Furthermore, production-related products include PV systems, miniature wind turbines and combined heat and power, whereas storage-related products are EV and household batteries.

Integration-related products are hubs that connect several different application areas. Furthermore, an offering could not include any smart product (e.g., Innogy’s smart home smartphone add-on).

Renting devices for energy visualization from the focal company would be rather use-oriented, particularly if the rent were to depend on the actual usage (i.e., pay per use) (Hamwi et al. 2016).

For some smart energy services, the corresponding smart energy product ownership is not relevant because the product is already available at the customer site. For instance, the customer may buy a digital service for an existing smart home, such as Innogy’s smart home smartphone add-on.

Services may also not include a payment mode (i.e., none), for example, due to non-monetary value for the service provider, such as the use of secondary data, tailored pricing models or higher levels of efficiency (Beverungen et al. 2019). Regarding smart energy, there are digital services that are provided for free, such as smart home apps and flexibility-related services (e.g., the OhmConnect app).

Additionally, the user can pay with energy resources, for instance, with their own produced energy or flexibility (e.g., sonnenCommunity and OhmConnect). Energy resources as well as digital resources & attention are regarded as another form of currency besides money.

The classification of empirical objects not only validated the applicability of the morphological box with real-cases but also provided insight into the frequency of specific characteristics covered by the commercial offerings, as is depicted in Fig. 10. The analysis of occurrences revealed the following main observations:

For the dimension co-creation in energy management, most of the services focus on efficient energy use (71%) and self-production and storage (53%).

Furthermore, 76% of the services use individual energy resources. Only 24% make use of aggregated energy resources, for example, through flexibility provision or energy communities.

The key energy-related customer values advertised on the company websites concentrate on efficiency & cost savings (76%). Only 18% of the offerings explicitly list sustainable environment as a value. Comfort is an important factor for 43% of the smart energy services.

The type of offering is predominantly product-based: 69% of the offerings are smart-product-service systems consisting of a smart product, digital service and eventually supporting services. Digital services in combination with human-based supporting services are not covered at all by the classified examples.

If additional supporting services are offered, they are mainly technical setup & support services (43%). Of all the offerings, 47% do not offer supporting services.

The main customer group addressed is consumers (61%), whereas community offerings are quite rare (10%).

If the ownership of the provided smart product is relevant, in 57% of the cases the product belongs to the customer, whereas in 20% the focal company is the owner.

The capabilities of the smart products are quite balanced between information and monitoring (25%), controlling (31%), optimization (35%). However, only 8% of the offerings act autonomously based on artificial intelligence and are highly interconnected with other products.

Most of the smart energy products included in the offering are consumption-related (37%), particularly smart energy monitoring and smart home devices. There are also pure digital offerings, with no smart product included (27%). One specific classification is two cloud storage offerings that are regarded as pure digital offerings with a storage-related smart energy product included, even though the smart energy storage is owned by the focal company.

One-time payments, for example, the basic sale of smart energy products, are widely applied by 55% of the offerings. Only some of the offerings employ use-based & performance-based payments (12%). Eight offerings (16%) are offered at no cost to the customers because the provider earns money through third-party payments or internal optimization.

Although the customer is generally the direct revenue stream, in some cases indirect revenue is also generated via funding (14%) or via arbitrage & third-party payments (22%). However, most of the services (59%) do not appear to have indirect revenue streams.

In this subsection, the applicability of the morphological box is demonstrated by analyzing four empirical cases: E.ON SolarCloud, which is a cloud storage offering;; a demand response program based on smart thermostats by Southern California Edison (SCE) and an intelligent energy manager by GridX (Table 1).

In highlighting the unique characteristics of smart energy services, the paper provides a more nuanced picture on the nature of smart energy services and their potential in terms of new consumer and business values. Since research has not yet provided theories and explanations on the design of smart energy services for new business and customer values, this study is exploratory and provides an analysis and classification of the new developments in the consumer-oriented smart energy service market which can serve as a fundament for quantitative and qualitative studies in the future.

By employing a smart service science lens (Beverungen et al. 2019; Vargo and Lusch 2004) and the smart product concept from Porter and Heppelmann (2014) this study provides a new perspective on smart energy services. Furthermore, the morphological analysis strengthens the conceptual foundation by exploring important elements that describe smart energy services. Particularly, the use of the S‑D logic (Vargo and Lusch 2004, 2008) facilitates a better understanding of the strong interconnection between smart energy products and services and their underlying co-creation logic. Further, the S‑D logic highlights the increasing customer participation in the value creation, for example, in the form of energy resources and data. In particular, the notion of smart energy products (Porter and Heppelmann 2014) facilitates a better explanation of how digitalization alters the provision of services.

Finally, the morphological box can be used to describe and analyze smart energy offerings as shown in Sect. 6. As there is a tendency towards individual and tailored solutions, ranging from supportive services to comprehensive full-service solutions, the proposed classification can support the configuration of complex service design options.

The classification can stimulate new methods of offering smart energy technology to customers. This is of particular importance in the private household sector, since energy customers often view energy as a commodity with which people have limited involvement (Kahma and Matschoss 2017). Depending on the design, a smart product can be presented as a completely different smart energy service offering. For instance, the company Nest offers its smart learning thermostat as a simple, one-time purchase, but the Nest Thermostat is also part of a utility partner program in which the thermostat is provided for free or at a discount. The thermostat is thus the central component for a residential demand response program.

Another contribution of the morphological analysis is that the services in the literature usually only provide insight into previously existing configurations of smart energy services. The findings of this work can expose potential new service configurations, so that the resulting morphological box can also be regarded as a service innovation tool (Peters et al. 2015).

Several topics were identified that would be beneficial to explore in practice and in future research. Although there are characteristics that are not covered by the classified examples (e.g., digital service with human-based supporting service in the dimension type of offering or attention as a currency), the characteristics are regarded as relevant to the morphological box, as there may be examples in the future which could be classified using this characteristic. Hence, the morphological box is future-oriented and research could explore these characteristics.

Since most of the analyzed market offerings concentrate on the traditional sale paradigm, practitioners could concentrate on more advertising- and data-related monetization options. In particular, the decoupling of purchasing a physical product and offering additional digital services in terms of micropayments like “in-app purchases” for smartphone apps could generate additional revenue.

Furthermore, service providers could experiment with more performance- and usage-based payment models combined with service-oriented offerings. For example, a smart energy product could be supplied to the customer without any upfront costs and could be paid for based on its usage or performance; these factors could easily be monitored using smart technologies. Similar structures have already been implemented in other industries, such as the HP Instant Ink³ service, which is an automatic ink refilling service. The service monitors a printer’s toner level and page consumption. More toner is automatically reordered every time the filling level reaches a critical level. The service is paid via a monthly scale price based on the number of printed pages.

Moreover, people could pay a flat rate for smart energy equipment. For instance, they could pay a subscription fee to use an unlimited number of smart home devices. The usage of the smart home devices would be monitored to ensure proper use, and people could be rewarded for their energy saving goals. Rewards for energy savings would be even more interesting if the provider were the energy supplier and were to offer the consumer energy at a flat rate (which itself could be calculated based on historic smart meter energy-usage patterns). Such service innovations would also decrease the high upfront costs that often accompany smart energy products.

Another service configuration would be to combine smart home and EV battery monitoring and controlling services. The use of the EV battery and its location could be monitored, so that charging can be scheduled according to the location and movement patterns. Moreover, a timer for switching on smart home devices like washing machine could be set depending on the time the EV battery can be used. The smart home system could detect if the user did not switch off the lights while leaving the smart home, so it could inform the home owner.

The above-mentioned service ideas were stimulated by our analysis in Sect. 6.1. The ideas could be further extended to a complete offering by using the morphological analysis.

With respect to data-related dimensions, the identified services were assessed based on their different levels of smart (i.e., data-driven) capabilities (e.g., monitoring, controlling, optimization or autonomy) (Porter and Heppelmann 2014). To create additional value, these smart capabilities could also be extended by considering the specific forms of data and analytics required for the services, since this consideration creates significant variations in the functionality and capability of the services. However, to gain deeper insight, this extension would require other research methods, for example, expert interviews, field trials or case studies.

During the market analysis, it further appeared to be important that companies advertise the smart capabilities with regard to their concrete values for the customers. For instance, if an energy manager is able to enhance the level of autarky, it should be communicated with concrete numbers to the customer.

In the future, value-added services, such as security and health services based on smart energy data, could become attractive. Such services would extend beyond the present form of the morphological box. For instance, due to the nature of energy data, one can monitor and analyze living patterns of inhabitants. Thus, assisted living monitoring service could be provided which detects the patterns of inhabitant’s behavior and in case of anomalies, a customer service for assisted living can be informed to look for the inhabitant. Energy data can be also used to enable home security monitoring service by informing the home owner in case of anomalies in the own home. There might be broader application areas for smart energy services with more general platform approaches, such as the consumer-oriented Internet of Things platform geeny⁴.

Another research option is to adopt known (smart) service pattern from other industries to the energy sector, for instance, reorder subscription services similar to Amazon’s Dash Buttons. They could be based on energy usage of specific devices (e.g., dishwasher), hence the need for refillment can be detected and items (e.g., dish washer tabs) can be reordered automatically⁵. Another service pattern which could be adapted from Amazon are cross-selling recommendations. By detecting new electronic purchases and monitoring of soon to be broken electronics, cross-selling recommendations in a smart meter app could be made and would instantiate the currency attention in the morphological analysis.

This paper is subject to some limitations. Since a service classification can never be of truth value, there might be alternative viewpoints and classifications (see, for example, Salah et al. 2017). Moreover, the smart energy services field is still emergent, which mandates regular updates to the morphological box. Another limitation is the number of services that were surveyed and the assessment of these services, which relied on publicly available information on websites. Moreover, it has been difficult to identify if authors in the literature are referring to “smart” energy services, since the service descriptions are often not sufficiently informative.

The real-life examples were only categorized by two researchers. Thus, the classification might be biased by subjective interpretation. We tried to reduce the bias by regularly discussing the classified empirical objects. Moreover, the information on the websites did not always reveal if the companies exploit the data provided from the smart energy products for, for example, internal process optimizations or sale to third parties. Due to the sample size, the analysis of the occurrences cannot be regarded as representative; instead it provides initial in-depth insight into the characteristics of existing smart energy services.

Another limitation is that the analysis of the empirical objects indicated certain dependencies between the characteristics, e.g. between the dimension supporting services and smart energy product included (Ritchey 2011; Zwicky 1967). However, the identification of all dependencies would have extended beyond the scope of this paper.

We only considered money for the payment schedule (e.g., usage- and performance-based, one-time payment, subscription). However, for the future we could think that data as a currency could become more important so that customers could decide what kind of data and how often (i.e. payment mode) they would be willing to offer their data.

Moreover, the morphological box only considers energy-related values that are relevant to smart energy technologies created for residential customers. However, considering non-energy-related benefits and values would have threatened the classification’s usefulness and increased its complexity. Research could be conducted to extend the classification and to identify and describe possible value-added services based on energy data and eventually other data sources from different domains (e.g., health, mobility, security or retail).

Finally, the energy sector is a highly regulated sector, and progress towards a smart grid varies from country to country. For instance, in Germany the smart meter rollout has yet to start, so demand response and smart meter-based tariffs are not available. When designing concrete services, one must consider national regulations.