DEVELOPING NEW SMART SERVICES IN BUILDINGS FOR DEMAND SIDE MANAGEMENT IN THE MUNICIPALITY OF SION, SWITZERLAND

Objective: Business Model Innovation approaches in service business model context are not adequately studied in the extant literature and there is a clear need for extension of different business model typologies for innovating service business models. Principal aim of this study is to explore how service business model archetypes can be explained as a business model representation such as Business Model Canvas, particularly what the term managed services really mean. Also, it is intended to add clarity to current business model innovation approaches in providing managed services, especially business process services in a service value chain. This will prompt further research to address related implications of networked business model setup and information technology as an enabler in managed services context. Methods/Statistical Analysis: Established framework of Business Model Canvas from extant literature and recent trends in business model representations that cover digitally enabled service business models are compared and contrasted. In line with service dominant logic, approaches for management and optimization of key resources and key activities are used as basis of stacking up different service business model archetypes. Service value chain and network business model implications are emphasized for further differentiating the innovation approaches in managed services business models. Findings: Among various business model design tools and representations, service business models can be better represented as a business model canvas with special emphasis on how key resources and key activities are managed and optimized in a service value chain. Digital enablement with the use of IT and/or cloud infrastructure shall act as accelerators for effective business model innovation in service context. Service business models shall be categorized as a stack of service offerings such as outsourced services, resource outsourcing, professional services, BPO/ITeS, managed services and Business Process As A service (BPaaS). services currently mean to only manage IT services but there is new paradigm of Managed Business Services which is emerging. Applications/Improvements: Suggested findings of this paper and the identification of broad types of service offerings, which clearly differentiate managed services from other service models, can be applied in practice. This approach shall bring in more consensuses about understanding of managed services business models among practitioners and researchers.

• Studies on energy flexibility of residential buildings have been reviewed. • Energy flexibility technologies by types, purposes, and scopes are summarized. • Modeling techniques, tools, and characterization of flexible resources are presented. • Quantification methods and metrics of energy flexibility are analyzed. • Research trends, open questions, and future research opportunities are identified. With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Towards greener production, manufacturing companies face several challenges, for example peak load shaving or flexible production planning as parts of demand-side management (DSM). DSM uses processes that can be shut down, shifted, or controlled. Advances in digitalization in the energy sector and manufacturing systems create transparency which in turn offers new opportunities to commercialize energy flexibility potentials as optimally and automatically as possible. The variety of flexibilities in manufacturing systems and various dependencies of different kinds of complex manufacturing processes complicate the modelling and aggregation of flexibility. To overcome this challenge, we developed a method for the aggregation of energy flexibilities that is based on a generic energy flexibility data model. The method proposes a two-step approach to aggregate flexibilities cost efficiently and considers manufacturing specific limitations. For cost-efficient aggregation, we use in the first step the merit-order model known from the energy industry and in the second step the bin-packing problem originating from combinatorial optimization, adapted according to the generic data model. The two-step approach allows energy flexibilities to be aggregated across industries, facilities, and systems, thus ensuring broad applicability.KeywordsDemand-side managementEnergy flexible manufacturingEnergy flexibility aggregation

This paper presents algorithms and architecture models for a home energy management system. It's based on customers' behavior that is modeled by a decision-making chain, and smart appliances' use for demand side management. The proposed architecture is scalable and extensible to upper levels of smart grid as the development approach used is bottom-up. Once the model is validated for home use, we can go up and apply it for holdings, factories, and micro-grids contexts. Scalability models and strategies are also presented and discussed. Ensuring supply and demand balance at real time is the main problematic of smart grids. The proposed solution meets this objective, because it allows large scale renewable energy resources integration. Hence, it leads to global energy efficiency and demand side management optimization in smart grids.

Chapter 5 - Thermal energy storage for enhanced building energy flexibility

The smart city concept has entered the public debate over the last decade as a concept for the development of urban space for the efficiency, improvement and availability of public and private services and sustainability. The Business Models Canvas is most often used in the literature for the creation of business models of smart services. On the basis of the above, we investigated whether the Business Models Canvas is the most used tool for creating business models for public smart services in Slovakia and whether cities and municipalities need to evaluate their models for the provision of public smart services. However, there is no commonly used methodology for evaluating smart city business models to help both practitioners and researchers choose the best option. The goal of the research is to create a tool for evaluating business models of public smart services in smart cities. The base method used was the Delphi method, based on the previous primary (content) analysis process of the Business Model Canvas best practices. In total, 709 towns and villages participated in the primary research. Subsequently, the obtained data were evaluated and used for further research using the Delphi method, in which 28 experts participated. The research was carried out between 2020 and 2023 in Slovakia. Primary research confirmed that the Business Models Canvas is the most used tool for creating business models for public smart services in Slovakia and cities and municipalities need to evaluate their models for the provision of public smart services. Areas and basic building blocks were also identified for the design of the evaluation methodology of business models for public smart services. The proposal of the methodology for evaluating business smodels for public smart services in Slovakia was implemented using the Delphi method with the cooperation of 28 experts. Based on the results of the Delphi method, a methodological procedure for evaluating business models for public smart services was established. The methodology proposed in the paper is a simple, organized, flexible and transparent system that facilitates the work of evaluators of business models of public smart services and marketing.

The current energy system is dealing with an increasing share of renewable energy that, because of its intermittent availability, can affect the effectiveness of the energy supply. To cope with the problem, buildings need to become energy flexible. According to the definition given by IEA EBC Annex 67, energy flexibility is the ability of a building to manage its demand and generation according to local climate conditions, user needs and grid requirements. Users of energy-flexible buildings play a crucial role for an effective implementation, thus user acceptance and proper behaviour are important factors. In order to understand the current level of awareness on the topic and the general acceptance of the users, this paper presents the results of a large-scale survey distributed in the office buildings of the Province of Bolzano (Italy). This study investigates the information, experience, beliefs, and desires of the building users (i.e., office employees) with concepts and technologies dealing with energy flexibility, such as smart grids, smart appliances, and smart meters. This study identifies (i) the main socio-demographic characteristics associated to the information and desires about energy flexibility in office buildings, and (ii) the main conditions of social acceptance of flexible energy usages. Although this work is focused on a specific user type (i.e., office workers in the Province of Bolzano) and the results cannot be generalized, the analysis offers an interesting insight on the user perspectives and acceptance on energy flexibility and can be easily replicated. The results can be used at local level to provide insights for policies and strategies to encourage building users to be more flexible.

The business model of energy as a service (Energy-as-a-service, EaaS) is considered as a direction of development of the concept of 3D (Decarbonization, Decentralization, Digitalization) and the conceptual model of the Internet of energy. At the same time, EaaS is formed in the form of a "package" service model, in which the customer is provided with hardware and software and energy services. EaaS solutions should include consumption management and energy efficiency services, promote the introduction of renewable energy sources (RES) and other decentralized energy sources, and optimize the balance between supply and demand in the electricity market. EaaS is shown to be a broad term for service-driven business models with innovative potential to transform the energy industry To assess the specifics of EaaS application to Microgrid, the construction and operation of Microgrid as a local power system or power supply system, which is a technological complex consisting of generation facilities (energy sources), energy flexibility sources and electricity consumers, which are collected under a single management ensuring the most efficient and consumer-friendly energy supply. It is determined that the technological guarantee of the efficiency of modern Microgrid is the ability to integrate and optimally combine different energy sources and flexibility, as well as the presence of a single control loop that allows the best use of these sources. Smart Grid as a Service (SGaaS) based on Service-Oriented Architecture is presented. The SGaaS hierarchical architecture provides a promising three-tier architecture that includes an intelligent network level for global optimization, such as minimizing global protection or global costs, a level of coordination to maintain reliability and security in the Smart Grid, and a Microgrid level to monitor end-user device status. The implementation of the EaaS and SGaaS mechanisms has stimulated the development of Microgrid as a Service (MaaS) - as a service that offers the deployment of Microgrid, reducing the initial cost of investment and complexity. MaaS has been identified as a new flagship funding mechanism that allows organizations to deploy Microgrid without any prior investment, as a solution that does not require advance capital for energy consumers and focuses on results such as on-site energy. MaaS mechanisms offer customers more control over their energy needs, enabling them to increase the sustainability and reliability of their energy supply, balance energy use, achieve clean energy goals and explore other innovative products and services.

Renewable energy is starting to play a serious role in the electricity world, gradually displacing the reliable (though polluting and resource-finite) conventional electricity generation technology that has served us over the last century. However, renewables offer much less control over the production of electricity, and thereby ask for new sources of flexibility. Storage is expected to become one of the key ingredients for the further development of the energy transition, as it can bridge the gap between supply and demand in time. As a lot of renewable generation is added at the lower tiers of the grid, storage can also help to keep energy local, and thereby reduce costly grid investments and transport losses, bridging the gap between supply and demand in space. Although physical energy storage (e.g. in batteries) is generally expensive, demand side management (DSM) promises to provide a different form of "storage" at (almost) no additional cost by exploiting the intrinsic flexibility within electricity consuming and producing devices. The energy transition introduces many new devices that have some flexibility in their electricity consumption or production, such as electric vehicles (EVs), heat pumps or combined heat and power (CHP) systems. What remains is to control this sea of flexibility and let the devices play their part in the smart grid. However, the control of devices in DSM turns out to be a hard problem, because the flexibility in devices is restricted, scattered, and there are costs associated with the use of the flexibility. To decide which devices are used (turned on or off) to reach some given goal, coordination is used to exploit the diversity of devices (in space). Furthermore, the control decisions impact the situation in the near future. To account for this, planning approaches may be used to exploit the flexibility of the devices over time. Together, this leads to a problem that is coupled in space and time, which is in general too large to be optimized directly, and should therefore be addressed in practice with heuristics or approximate methods. In this thesis, we address this DSM coordination/optimization problem. In this context, earlier work at the University of Twente led to TRIANA as a scalable optimization and control approach for DSM in smart grids. TRIANA partitions the optimization problem according to the hierarchical structure of the electricity grid, and splits up the DSM control problem in three phases: forecasting, planning, and real-time control. Although the approach is scalable and conceptually elegant, it simplifies the problem to such an extent that the solutions are sometimes far from being optimal. Therefore, the phases of TRIANA should be considered as dependent problems: for example, the result of real-time control depends on the forecasting and planning phases, and the planning phase should already account for this. We introduce more sophisticated planning methods (column generation and profile steering) to improve the planning results, and place these methods in a general model. To evaluate the methods, we took part in the development of an extensive simulation scenario called Flex Street. For this scenario we determine a lower bound on the cost to manage this scenario. Both of the developed planning methods bring the plan closer to the optimum than the original planning method from TRIANA (within 1-2% of the lower bound of the Flex Street scenario in a deterministic setting). A key strategy to keep the developed approaches scalable is a local optimization that already takes the needs of the nodes higher up in the hierarchical structure into account. Flexible devices are in general a major source of uncertainty themselves, since their operation depends on human behaviour, which makes the forecasting of available flexibility for specific devices difficult. Dynamic dispatch approaches address this uncertainty by exploiting the interchangeability of devices, meaning that we decide just-in-time which specific devices are going to be used, e.g. with a flexibility auction. Although this dynamic dispatching makes the approach more robust against disturbances of individual devices, it also makes the reasoning about the behaviour of the system more difficult for the planning. We propose a method to plan such a system based on the simulation of the dispatch process, where the planning result determines the configuration of a controller. We evaluate the method with a subset of Flex Street, and find that the method achieves results within 2-10% of the lower bound, depending on the considered configuration. This approach gives robust results even with large forecast errors and a small number of devices. To bring DSM methodologies to practice, there are still some barriers at a household level. One of these barriers is a limited standardization of the interface to flexible devices, leading to high software development and maintenance costs. A challenge in this standardization is that control methods differ in their perspective on flexibility. The energy flexibility interface (EFI) reacts on this challenge by proposing to communicate the structure of energy flexibility instead of a specific perspective on flexibility. We develop a comprehensive TRIANA energy application prototype that implements the EFI. The prototype supports the decentralized planning and control of real devices on low cost embedded hardware, and demonstrates that the concepts developed in this thesis are applicable in an externally given framework. It also shows that EFI maps to multiple perspectives on energy flexibility in addition to only just-in-time auction based methods. Concluding, this work lays a foundation for the further development of a flexible, effective and efficient coordination approach for flexibility in smart grids, bringing the dream of DSM - and thereby the cost effective implementation of the energy transition - a bit closer to reality.

Production companies face the challenge of reducing energy costs and carbon emissions while achieving the logistical objectives at the same time. Active management of electricity demand, also known as Demand Side Management (DSM) or Energy Flexibility (EF), has been recognized as an effective approach to minimize energy procurement costs for example by reducing peak loads. Additionally, it helps to integrate (self-generated, volatile) renewable energies to reduce carbon emissions and has the ability to stabilize the power grid, if the incentives are set appropriately. Although production companies possess great potential for EF, implementation is not yet common. Approaches to practical implementation for integrating energy flexibility into production planning and control (PPC) to dynamically adapt the consumption to the electricity supply are scarce to non-existent due to the high complexity of such approaches. Therefore, this paper presents an approach to integrate EF into PPC. Based on the energy-oriented PPC, the approach identifies and models EF of processes in a generic energy flexibility data model (EFDM) which is subsequently integrated in the energy-oriented production plan and further optimised on the market side. An application-oriented use case in the chemical industry is presented to evaluate the approach. The implementation of the approach shows that EF can have a variety of characteristics in production systems and a clear, structured, and applicable method can help companies to an automated EF. Finally, based on the results of the use case, it is recommended to introduce EF in production companies stepwise by extending existing planning and scheduling systems with the presented approach to achieve a realization of flexibility measures and a reduction of energy costs.

Many firms redesign their business models to be service-oriented in light of the increasingly central role that services play in their businesses and strategies. Two fundamental questions should be addressed in designing service-oriented business models: “how is value created for and with the customers by the service provider?” and, “how is the value captured by the service provider?” The first question deals with “value creation” while the second addresses “value capture” – both of which are important facets of any business model. Thus, we suggest that a service-oriented business model that addresses these two questions can sustain the viability and competitiveness of the firm as a service provider. The extant research mainly focuses on the service design from the value creation perspective. Thereby, service providers’ value capture and its trade off with value created for and with service customers have been inadequately addressed. In this paper, adopting a holistic perspective, we introduce a modeling framework that can assist in understanding, analysis and design of value (i.e. value creation and capture and their interplay) in service-oriented business models. Our modeling framework consists of a set of conceptualizations and a graphical representation. The conceptualizations are derived from insights of the extant theories, constructs and frameworks on value creation and capture in business and service-oriented business models. We illustrate the applicability of our framework by conducting a descriptive case study of the value creation and capture in Amazon’s business model in the period between 1997 and 2001.

Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

Applying decentralized renewable energy in the built environment is a good approach to reduce the CO2 emissions. However this is not without restrictions towards the stability of the energy grid. Using the flexibility within energy generation, distribution infrastructure, renewable energy sources and the built environment is the ultimate sustainable strategy within the built environment. However, at the moment this flexibility on building level is still to be defined. The IEA Annex 67 defines this specific flexibility. Our research is aimed at developing, implementing and evaluating process new control strategies for improving the energy interaction within the building, its environment and the energy infrastructure by effectively incorporating the occupants’ needs for health (ventilation) and comfort (heating/cooling). A bottom-up approach, starting from the user up to the smart grid, offers new possibilities for using buildings’ energy flexibility to stabilize the electrical grid. New intelligent process control concepts are necessary which make use of the dynamic possibilities offered by multi-agent systems in combination with building energy management systems. Increasing demand for electrical energy use in buildings and the corresponding carbon emissions has further emphasized the need for the implementation of strategies that improve the energy performance of buildings. Demand-side management (DSM) strategies, which aim to actively manage user behaviour and how appliances consume energy, is a rapidly growing concept with the potential to contribute worthwhile improvement in building energy performance. A coordinated distributed demand-side management strategy framework for cooling in combination with a battery electrical storage system is presented and implemented in an office building in order to test the concept. The results showed that DSM strategies can be applied while maintaining thermal comfort.

Study on thermo-electric-hydrogen conversion mechanisms and synergistic operation on hydrogen fuel cell and electrochemical battery in energy flexible buildings