Aggregating Energy Flexibility for Demand-Side Management in Manufacturing Companies – A Two-Step Method

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.

Data modelling patterns provide a good guideline for creating a data model of a software or service. However, there is a lack of such a pattern respecting data protection requirements that an online service should satisfy. Especially for social media services, this is a relevant topic as end-users' data are collected and processed. In this work, we introduce conceptual data model in different abstraction layer: a generic and an application specific layer. The generic conceptual data model for a social media service can be refined to present the conceptual data model of a specific social media service. The primary target audience of this work are academics, who are interested in the research on social media service analysis with respect to privacy and data protection. However, the generic conceptual data model of social media services may support social media service designers in obtaining an overview of data that a service collects and processes for service provisioning. By using our descriptive conceptual data model, service providers are supported to gain valuable information for addressing data protection regulations and to document collected and processed data in a transparent and understandable way. For example, the service providers can use the data model (created by refining the generic conceptual data model) for generating more transparent and usable privacy policies for their end-users. Furthermore, the created data model supports communication of different stakeholders, i.e. service developers, service providers and other involved stakeholders. We illustrate application of our generic conceptual data model with refining it for the online dating application Tinder. We consider our generic conceptual model for capturing data model of social media services as a pattern candidate.

The constant irruption of new sensors is a challenge for software systems that do not rely on generic data models able to manage change or innovation. Several data modeling standards exist. Some of these address the problem from a generic perspective but are far too complex for the kind of applications targeted by this work, while others focus strictly on specific kinds of sensors. These approaches pose a problem for the maintainability of software systems dealing with sensor data. This work presents ASTROLABE, a generic and extensible data model specifically devised for trajectory determination systems working with sensors whose error distributions may be fully modeled using means and covariance matrices. A data model relying on four fundamental entities (observation, state, instrument, mathematical model) and related metadata is described; two compliant specifications (for file storage and network communications) are presented; a portable C++ library implementing these specifications is also briefly introduced. STROLABE, integrated in CTTC’s trajectory determination system NAVEGA, has been extensively used since 2009 in research and production (real-life) projects, coping successfully with a significant variety of sensors. Such experience helped to improve the data model and validate its suitability for the target problem. The authors are considering putting ASTROLABE in the public domain.

On-site batteries, low-pressure biogas storage, and wastewater storage could position wastewater resource recovery facilities as a widespread source of industrial energy demand flexibility. This work introduces a digital twin method that simulates the coordinated operation of current and future energy flexibility resources. We combine process models and statistical learning on 15 min resolution sensor data to construct a facility's energy and water flows. We then value energy flexibility interventions and use an iterative search algorithm to optimize energy flexibility upgrades. Results from a California facility with anaerobic sludge digestion and biogas cogeneration predict a 17% reduction in electricity bills and an annualized 3% return on investment. A national analysis suggests substantial benefit from using existing flexibility resources, such as wet-weather storage, to reduce electricity bills but finds that new energy flexibility investments are much less profitable in electricity markets without time-of-use incentives and plants without existing cogeneration facilities. Profitability of a range of energy flexibility interventions may increase as a larger number of utilities place a premium on energy flexibility, and cogeneration is more widely adopted. Our findings suggest that policies are needed to incentivize the sector's energy flexibility and provide subsidized lending to finance it.

Towards energy flexible and energy self-sufficient manufacturing systems

Chapter 5 - Thermal energy storage for enhanced building energy flexibility

Developing High Quality Data Models

The data model of machine tools plays a key role to share and manage machine tools information among modern manufacturing systems and has been considered as a fundamental basis for achieving various manufacturing activities such as process planning, machining simulation and production scheduling. Although research into modelling machine tools has been done, these modelling approaches are either machine vendor specific, or limited and incomplete in their scope to represent machine tools. Therefore, a comprehensive and generic machine tools data model (CGMTDM) is proposed in this paper. The proposed data model is constructed based on the oriented-object EXPRESS language, which not only includes the information of mechanical elements, but the information of the electro-mechanical and electronic elements. The case study is used to demonstrate the proposed data model at the end. It has been concluded that CGMTDM can consistently, completely and flexibly represent the information of machine tools. [Received 14 April 2016; Revised 4 September 2016; Accepted 8 September 2016]

The data model of machine tools plays a key role to share and manage machine tools information among modern manufacturing systems and has been considered as a fundamental basis for achieving various manufacturing activities such as process planning, machining simulation and production scheduling. Although research into modelling machine tools has been done, these modelling approaches are either machine vendor specific, or limited and incomplete in their scope to represent machine tools. Therefore, a comprehensive and generic machine tools data model (CGMTDM) is proposed in this paper. The proposed data model is constructed based on the oriented-object EXPRESS language, which not only includes the information of mechanical elements, but the information of the electro-mechanical and electronic elements. The case study is used to demonstrate the proposed data model at the end. It has been concluded that CGMTDM can consistently, completely and flexibly represent the information of machine tools. [Received 14 April 2016; Revised 4 September 2016; Accepted 8 September 2016]

Energy flexibility of manufacturing systems helps to meet sustainable manufacturing requirements and is getting significant importance with rising energy prices and noticeable climate changes. Considering the need to proactively enable energy flexibility in modern manufacturing systems, this work presents a component-based design approach that aims to embed energy flexibility in the design of cyber-physical production systems. To this end, energy management using Industry 4.0 technologies (e.g., Internet of Things and Cyber-physical Systems) is compared to the literature on energy flexibility in order to evaluate to what extent the energy flexibility practice takes advantage of Industry 4.0 technologies. Another dimension is the coverage of the life cycle of the manufacturing system which guarantees its sustainable design and the ability to achieve energy flexibility by configuring the energy consumption behaviour. A data-based design approach of the energy-flexible components is proposed in the spirit of the Reference Architectural Model Industrie 4.0 (RAMI 4.0), and then it is exemplified using an electric drive (as a component) in order to show the practical applicability of the approach. The energy consumption behaviour of the component is modelled using machine learning techniques. The digital twin of the studied component is developed using Visual Components virtual engineering environment, then its energy consumption behaviour is included in the model allowing the system integrator/decision-maker to configure the component based on the energy availability/price. Finally, external services in terms of an optimisation module and a deep learning module are connected to the digital twin.

PurposeSince the data model plays an important role in designing manufacturing execution system (MES) database, this paper seeks to provide a generic and adaptable data model for MES, which can facilitate and improve the MES development.Design/methodology/approachExtended entity‐relationship (EER) model technique is adopted to build the data model of MES.FindingsBased on MES functions and database requirement analysis, four subject's structures of MES database is abstracted from a system integration point of view. These four structures are independent relatively but also have close interrelation. Each structure is modeled with EER model.Research limitations/implicationsThe presented data model mainly focuses on discrete manufacturing MES, which perhaps limits its usefulness elsewhere, and the data model still need further testing in manufacturing enterprises with different scales.Practical implicationsA prototype MES system is developed and implemented, the results show that the proposed EER modeling approach can establish and make clear complex relationships among entities existed in a manufacturing system, which lays the foundation for adaptable and modular MES software development and implementation.Originality/valueThis paper fulfils a fundamental need of designing data model for MES and provides a main framework for developing MES data model, which provides reference for researches both in academia and industry to build specific relational data models for specific needs.

Industrial energy flexibility enables companies to optimize their energy-associated production costs and support the energy transition towards renewable energy sources. The first step towards achieving energy flexible operation in a production facility is to identify and characterize the energy flexibility measures available in the industrial systems that comprise it. These industrial systems are both the manufacturing systems that directly execute the production tasks and the systems performing supporting tasks or tasks necessary for the operation of these manufacturing systems. Energy flexibility measures are conscious and quantifiable actions to carry out a defined change of operative state in an industrial system. This work proposes a methodology to identify and characterize the available energy flexibility measures in industrial systems regardless of the task they perform in the facility. This methodology is the basis of energy flexibility-oriented industrial energy audits, in juxtaposition with the current industrial energy audits that focus on energy efficiency. This audit will provide industrial enterprises with a qualitative and quantitative understanding of the capabilities of their industrial systems, and hence their production facilities, for energy flexible operation. The audit results facilitate a company’s decision making towards the implementation, evaluation and management of these capabilities.

Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing, utility and energy systems can be assessed and quantified. This paper presents a generic model library equipped with optimal control for energy flexibility purposes. The components in the model library represent the different technical units of an industrial enterprise on material, media, and energy flow levels with their process constraints. The paper also presents a case study simulation of a steel-powder manufacturing plant using the model library. Its energy flexibility was assessed when the plant procured its electrical energy at fixed and variable electricity prices. In the simulated case study, flexibility use at dynamic prices resulted in a 6% cost reduction compared to a fixed-price scenario, with battery storage and the manufacturing system making the largest contributions to flexibility.

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.

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.