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Research Platform: Decentralized Energy System for Sector Coupling
Chemical Engineering & Technology
Abstract
Industrial facilities usually need multiple energy subsystems, e.g. for heat, cold and electric power supply. Normally those energy subsystems are controlled locally and independent of each other. Coupling of the different subsystems can open up additional potentials. Fraunhofer IISB developed a demonstration and research platform for investigation of the benefits of such sector coupling. A major precondition is to understand the energy flows in the system as well as establishing an overall and flexible system control to realize the required algorithms for setting up an intelligent decentralized energy system. Major components of the overall system are various storages, which extend the degree of freedom for sector coupling as well as increase the effectiveness of the different subsystems.
... Furthermore, the slopes of the outputs are limited by Δ (negative slope) and Δ (positive slope), given in and described in eq. (11). Δ is the simulation time step. ...
... The following subsections show the development of the energy management strategies for the EMS (general info: see [9] [10] [11]). There are two types of strategies being considered: The rule-based strategies (4.5.1) and the optimisation-based strategies (4.5.2). ...
A Marine Vessel Energy Management System (MVEMS) is a sophisticated technology designed to optimize energy usage and distribution onboard ships, ensuring efficient operation while minimizing fuel consumption and emissions. The main aim of T2.3 described in D2.3 is the development of a flexible energy management system that will be applied to the two demo ships DEMO-1 Gunnerus and DEMO-2 Atatürk. A Python based simulation framework was established in which the on-board electrical system is modelled and combined with various EMS strategies. In the first step, input data for the simulation were acquired, consisting of synthetic load profiles covering typical cases, as well as measurement data from the two DEMO vessels. Subsequently, the components (battery, generator, and onshore charging) were modelled, and the model library was subsequently generalized to fit a wide variety of applications. The EMS strategies were developed, tested, and compared within the simulation environment. A distinction is made between rule-based and optimization-based strategies. Methods such as deterministic finite state machines (FSM), mixed-integer linear programming (MILP), and equivalent consumption minimisation strategy (ECMS) are used. The EMS strategies were applied to the load profiles based on the simulated on-board electrical system and the fuel reduction was evaluated. The DC switchboard serves as a central hub for distributing direct current (DC) power throughout the vessel. It receives power from various sources, such as batteries and/or generators, distribute it to different onboard systems and equipment. This centralized distribution system allows for better control and monitoring of power flow, enabling operators to allocate power where it's needed most efficiently. The Power Management System (PMS) is connected to the EMS by a communication interface and is responsible for monitoring and optimizing the entire vessel's power network. It continuously analyses power demand and availability, considering factors like propulsion requirements, electrical loads from equipment, and energy storage levels. By dynamically adjusting power distribution and consumption based on the setpoints provided by EMS, the PMS ensures that the vessel operates at maximum efficiency (as few losses as possible) while maintaining safety and reliability. It reports all states and measurements to the EMS. Together, the DC switchboard and PMS form the backbone of a Marine Vessel EMS, providing seamless integration of power sources, distribution, and consumption to optimize energy usage and enhance overall vessel performance.
... These forecasts use production planning data and weather forecasts to predict load curves for the electricity, heating and cooling sectors. They can be used to generate an operating strategy for the EGI components in order, for example, to charge or discharge storage facilities in an optimized manner, to run energy plants at the optimal operating point and to reduce load peaks [9,12]. ...
The ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
KeywordsEnergy efficiencyForecastingMachine learning
... The real-world laboratory at Fraunhofer IISB enables measurements in an energy system under nearly industrial conditions [34]. The algorithm described in Section 3.2 was integrated to the energy management system of the institute site (see also Appendix C). ...
In order to reduce power peaks in the electrical grid, battery systems are used for peak shaving applications. Under economical constraints, appropriate dimensioning of the batteries is essential. A dimensioning process is introduced consisting of a simulation environment to determine the behavior of the energy system, a real-time peak shaving control algorithm and an optimization process for detection of battery and algorithm parameters. The dimensioning process is investigated on the basis of four exemplary load profiles and in comparison to a conventional approach. Deviations between -7% and 75% for capacity and up to 43% for discharging power indicate undersized batteries using the conventional approach. The proposed approach relies on 1-min measurement data and does not require prediction data, leading to accurate dimensioning results for a given load profile, as verified in simulation. The practical use and effectiveness of the control algorithm is proven in a real-world laboratory. A battery system of 60 kWh capacity and 65 kW maximum power achieved successful peak load reduction by 50 kW (8%) for an a priori defined limit of 570 kW. The comparison with simulation shows only small deviations below 17 kW (4.1%) for the resulting load profile proving the realistic representation of an energy system in simulation.
... Recent examples for combined simulations of heat and electrical networks were presented for example in [13,14] for domestic areas and in [15] for an industrial site. While addressing similar scenarios like InSekt, these works stay on the level of mathematical modelling, while the energy agent approach is much closer to the required technical implementation. ...
A sustainable and climate-friendly energy supply needs flexible and efficient distribution systems. Key factors to implement this kind of systems are intelligent coordination (smart grid approaches) and the integration of different energy sectors. This article introduces the unified energy agent as an agent-based approach for a comprehensive modelling and control of energy conversion systems. This approach enables both the simulation and optimization of coupled energy networks, and then in a next step, the development of corresponding smart grid solutions to be applied in the field. Its applicability for the simulation of coupled networks is presented by a real-world use-case of an innovative combined heat and electrical network, which was implemented for the city of Lemgo, Germany. Preliminary results from the project are discussed and an outlook on future work is given.
The presentation discussed the use of simulations for optimising energy systems, focusing on the challenges and strategies for efficient operational of energy systems, involved plants and storages and the application on heating grids. Several realisation types for operational strategies where presented followed by a concrete example consisting of a combined heat and power plant, a thermal energy storage and a battery from the real-world laboratory for decentral intelligent energy systems at Fraunhofer IISB. Simulations are the backbone of the optimisation process, as they enable the non-inversive investigation of the system. They are used for planning and optimisation, for load prognosis, grid analysis and management and energy efficiency measures. Additionally, the presentation covered the optimization of heat networks through multi-stage simulation-based operational enhancements based on the services in leveraging flexibility for the energy transition of VK Energie. Overall, the presentation provided insights into the complexities of energy system optimisation and the practical applications of simulation tools in this context.
The presentation covered the project ProEnergie - Bayern and the energy system at Eirenschmalz. In ProEnergie, strategies and software tools were developed that support companies in increasing the efficiency and flexibility of their energetic building infrastructure. Industrial companies from various branches (automotive engineering, metal, lightweight construction, technical textiles, plastics, ceramics) as well as the two Fraunhofer institutes IISB and IPA worked on the project.
Subsequently, the company Eirenschmalz presented their energy concept and the expanded energy system at the Schwabsoien site, which showed a real example of applications of the software tools from ProEnergie.
Intelligent energy management is the basis for economical and low-emission operation of decentral energy systems. This presentation gives a brief overview on the basics of an intelligent energy management system (iEMS) and explains some of its aspects (e.g., operational strategies, modeling and simulation) using concrete examples. An industrial-scale real-world laboratory was set up at Fraunhofer IISB, where, among other things, many aspects of an iEMS were implemented and demonstrated. In the framework ToSyCo an approach for the control of energy systems was developed. Two levels have been defined, which divide the operational strategy into a global level (storage schedules, load forecasts, etc.) and a local level (for real-time control, security, etc.).
Peak loads cause high and unpredictable loads on the power grids and increased transmission losses in the distribution networks. The compensation of short-term high electrical energy demand also requires inefficient and expensive peak load power plants as well as oversized grid components. In order to achieve an electrical energy demand by larger consumers that is as even as possible, the grid operators set financial incentives. These include a demand rate, which depends on the maximum power value during the billing period. A reduction of the peak loads thus opens up high savings potentials for industrial and commercial companies.
The methodology and application of peak shaving across different energy sectors as well as the influence on the energy system are investigated in the present thesis. In addition to electrical energy storage systems, thermal plants are also used for peak shaving to increase the reduction potential, which is different from existing literature. This includes heat and cold water supply systems, which are often part of industrial energy systems. The thesis focuses on a combined heat and power plant with heat energy storage and a cooling plant with cold thermal energy storage. However, the approach can be transferred to similar components. The energy storages provide the flexibility, which is needed for the additional usage of the plants for peak shaving. Data-based models are used to represent the behavior of the components (plants, energy storages and peripherals) in simulations, which enable a non-invasive investigation of the cross-sectorial peak shaving.
Peak shaving with the plants and storages requires algorithms and operational strategies for the detection of relevant peak loads, calculation of setpoints as well as for the operation in normal and peak modes. In comparison to the state of the art, relevant characteristics of the components (e.g. startup procedures) are fully taken into account. The methods can be transferred to fields, which were not considered in the thesis, e.g., compressed air and air reservoir. The overall objective is to comply with a power limit, which can be varied over time to apply for individual network fees like atypical network usage.
The models and operational strategies are merged in an expandable and flexible simulation environment. This is used to present and investigate various scenarios as well as to optimize components and parameters. The components are linked dynamically within the program via a netlist, which greatly simplifies extensions and thus enables extensive investigations. The simulations show that a battery is necessary to observe a predefined load limit, as it can be operated very dynamically and provides a continuously variable output power. In the first scenario, a battery system is considered. A peak shaving potential of about 10 % is determined for the parameters of a reference system, which shows load peaks in the range of one Megawatt. This leads to a payback time of less than five years. With the additional consideration of a combined heat and power plant with thermal energy storage this value increases to approx. 18 %. A combination with a cold thermal energy storage shows a potential of 21 %. This leads to an annual saving of 21 thousand euros assuming a demand rate of 100 euros per kilowatt. A second annual data set from the reference system confirms a similar impact of the measures on total savings. If the normal operation of the CHP is also taken into account, the savings are in the range of 139 thousand euros per year, which results in a payback period of less than three years. As the absolute results are strongly dependent on the plant dimensions, a method for the calculation of the reduction potential with variation of nominal power and capacity of the plants and storages is shown and applied for numerous parameter sets.
The algorithms and operational strategies were implemented into a reference system. It provides measurements, which are used for validation of the simulations. Compared to the measurements, only minor differences with a mean absolute error of four kilowatts occur for the resulting transformer power. The present thesis thus provides an approach for planning and realization of the successful cross-sectorial peak shaving. The thesis also illustrates the exemplary application of component dimensioning, optimization of algorithm parameters and implementation of operation strategies in a real system.
The rising cost of energy in Australia is causing industry to look for energy sources other than the conventional grid. Industry has been slow to adopt non-dispatchable renewable sources partly because of their inherent variability, however their inclusion can reduce greenhouse gas emissions and energy costs. By using an alternative fuel production facility as a case study an installation of a hybrid renewable energy system is explored. This paper studies the effects of varying the robustness of the design with respect to load and renewable variability separately. Lithium ion battery packs are also included to act as a buffer in the system. The installation is modelled using a robust Mixed Integer Linear Programming (MILP) framework and the potential cost savings are quantified at various levels of robustness. Broad sizing guidelines are drawn from the results for the case study that should result in a robust energy supply.
Nowadays, the urgent need to reduce emissions has led to the development of Renewable Energy Sources (RES). Wind energy via Wind Generators is a very common RES, which, however, is characterized by great intermittency. Consequently, wind energy production may vary from zero to excessive (way above the load demand), which makes wind power plants’ penetration very difficult, making the need for energy storage essential. On the other hand, Electric Vehicles (EVs) offer a new, eco-friendly and more effective means of transportation. The energy stored in their batteries is used to move the EVs. Nevertheless, the penetration of EVs will increase the energy demand from Thermal Plants, and will therefore harm the environment unless it is combined with an increased RES integration. The advantages of combined use of RES and EVs are evident. EVs can augment Wind Power Plants penetration through their storage capabilities and use some of their excessive wind energy output for environmental-friendly mobility. In this paper, the combination of EVs, RES (Wind Generators) and Thermal Plants of an island is examined as an alternative for the future grids. Two scenarios are being studied with focus on peak shaving. Taking into account the Medium Voltage (MV) line load demand, wind power and thermal units output hourly data in a yearly basis, a special algorithm is developed so as to study the case of absorbing the excess energy via off peak charging and reintroducing it into the Microgrid scheme in peak demand periods. The studies are conducted for two cases: a) low penetration and b) high penetration of EVs. All findings are thoroughly discussed in conclusions section.
Renewable distributed energy generation (DEG) system plays an important role in future power developments and is one of the options to reduce energy consumption. It is envisaged that energy efficiency of DEG systems can be improved via load shifting (LS). This study proposed a heuristic-based numerical approach to perform LS analysis on renewable stand-alone DEG systems. The technique is an extension from a method known as the Electric System Cascade Analysis (ESCA). The new technique, which focuses on efficient electricity utilisation is able to determine the optimal: (i) load profiles, (ii) capacity of power generator, (iii) capacity and power of energy storage (ES) and (iv) charging/discharging schedule of ES. The stage-wise technique allows user to compare and determine the optimal design in a flexible way while having a better understanding of the selection of options. The application of ESCA-LS on a case study revealed that after incorporation of direct LS (load manipulation) in addition to LS by ES (supply manipulation), the power generators and ES capacity can be further reduced. While reduction of 3.1 % for solar-PV installation area and 3.9 % for biomass power generator is recorded, ES power-related capacity and energy-related capacity managed a higher reduction of up to 19.0 and 13.2 % for the main case study
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
Im Kontext der Energiewende sind Energiespeicher ein zentrales technisches, wirtschaftliches und energiepolitisches Thema.Die Autoren dieses kompakten Werkes geben einen umfassenden Uberblick Uber die verschiedenen Aspekte der Energiespeicherung. Sie beschreiben zunachst die Bedeutung von Energiespeichern in der Energieversorgung und definieren ihre Rolle darin. Dann gehen sie auf den Speicherbedarf in der Strom-, Warme- und Kraftstoffversorgung im Kontext der Energiewende ein. Im Hauptteil werden die verschiedenen Speichertechnologien ausfUhrlich vorgestellt sowie ihre Vor- und Nachteile diskutiert. Praktische Anwendungsbeispiele und die Integration von Speichern Uber alle Energiesektoren hinweg runden das Buch ab. Zahlreiche Grafiken und Beispiele veranschaulichen das gesamte Feld der Energiespeicher und sind als Erganzung samt Animationen online in Farbe verfUgbar.Die ZielgruppenDas Lehr- und Fachbuch wendet sich an Ingenieure, Wissenschaftler, Energieplaner und Energiewirtschaftler in Forschung und Industrie sowie an Studierende an Hochschulen und Universitaten in den Bereichen Maschinenbau, Verfahrenstechnik, Elektrotechnik und Energietechnik.Die AutorenProf. Dr.-Ing. Michael Sterner erforscht und lehrt an der Technischen Hochschule Regensburg in der Fakultat Elektro- und Informationstechnik die Bereiche Energiespeicher, Energiewirtschaft und Integration erneuerbarer Energien. Zuvor war er am Fraunhofer IWES in leitender Funktion verantwortlich fUr die Bereiche Systemanalyse und Energiewirtschaft und hat mit Kollegen die Speichertechnologie Power-to-Gas entwickelt. Der Ingenieur arbeitet ehrenamtlich in der Energietechnischen Gesellschaft des VDE, dem bayerischen Wirtschaftsministerium und dem Weltklimarat (IPCC), leitet Speicherkonferenzen von VDI und OTTI, ist beratend fUr die Bundesregierung tatig und im wissenschaftlichen Beirat der International Renewable Energy Storage Conference der Eurosolar sowie der Energy Storage Düsseldorf.
Prof. Dr.-Ing. habil. Ingo Stadler forscht und lehrt an der Fachhochschule Köln und ist dort für die Erneuerbaren Energien und Energiewirtschaft verantwortlich. Er habilitierte an der Universität Kassel. Seine Arbeiten umfassen die Netzintegration Erneuerbarer Energien und Energiesysteme mit hohen Anteilen an Erneuerbaren Energien und konzentriert sich auf die über die Elektrizität hinausgehenden, nichtelektrischen Speicher und das Lastmanagement. Er ist Mitglied des Beirats der International Renewable Energy Storage Conference sowie des International Centre for Sustainable Development of Energy, Water and Environment Systems. Über ein Jahrzehnt arbeitete er im Photovoltaik-Programm der Internationalen Energieagentur IEA.
Accelerated development of eco-friendly technologies such as renewable energy, smart grids, and electric transportation will shape the future of electric power generation and supply. Accordingly, the power consumption characteristics of modern power systems are designed to be more flexible, which impact the system sizing. However, integrating these considerations into the design stage can be complex. Under these terms, this paper presents a novel model based on mixed integer linear programming for the optimization of a hybrid renewable energy system with a battery energy storage system in residential microgrids in which the demand response of available controllable appliances is coherently considered in the proposed optimization problem with reduced calculation burdens. The model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models are considered. This paper investigates the effect of load flexibility on the component sizing of the system for a residential microgrid in Okinawa. Also under consideration are different operation scenarios emulating technical limitations and several uncertainty levels.
Kältespeicher werden überall dort benötigt, wo maschinell oder auf natürlichem Wege erzeugte Kälte zu einer bestimmten Zeit an einem bestimmten Ort für eine bestimmte Anwendung im industriellen oder technischen Maßstab zuverlässig zur Verfügung stehen muss. Das vorliegende Buch führt ausführlich in die Grundlagen der Kältebereitstellung ein und widmet sich im zweiten Teil speziellen Speichertechniken, die maßgeblich von den eingesetzten Stoffen und der zu realisierenden Anwendung abhängen. Umfassende Fallstudien zur Modellierung, Simulation und Implementierung von individuellen Kältespeichern und Kältespeicheranlagen runden das Buch ab.
This paper presents an analysis of a price-based control system in conjunction with energy storage using phase change materials for two applications: space heating in buildings and domestic freezers. The freezer used for this experimental study was provided with energy storage trays containing a eutectic solution of ammonium chloride (melting point of −15 °C). In the building application, DuPont wallboards were used to provide thermal storage. Experimental results showed that using thermal storage material in conjunction with the proposed price-based control method can improve performance of these systems and lead to a successful peak load shifting. Depending on electricity price trends, cost savings using the proposed strategy can vary. Savings of up to 16.5% and 62.64% per day were achieved for the freezer and building applications respectively, based on New Zealand electricity rates.
A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools. New coverage of forecasting in the design of feedback and feedforward control schemes. A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes. Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series. A review of the maximum likelihood estimation for ARMA models with missing values. Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
Numerical models for air- and water-cooled centrifugal, reciprocating and screw chillers are presented. The models were established by curve-fitting manufacturers' chiller performance data. They relate the electricity demand of a chiller to its cooling output and to the temperature of the condenser inlet cooling medium. Including the cooling medium temperature as an independent variable in the model allows its effect on chiller performance to be accounted for explicitly. This contrasts with relying on an assumed relationship between this temperature and the chiller load (e.g. according to the ARI Standard rating conditions). Using chiller models based on such an assumption can lead to erroneous energy use predictions for chiller plants comprising multiple chillers. Predictions of the chiller models agreed reasonably well with chiller performance data extracted from reocrds of plant operation. The models have been incorporated into an air-conditioning system simulation program BECON as its built-in chiller model. A simulation study aimed at comparing the performance of alternative chiller number-size combinations for a building is described. This shows that such studies should be based on realistic rated COP values for chillers, as they will strongly influence the predicted electricity consumption and thus the conclusion on which combination is to be adopted.
The current study presents an intelligent decision support model using rule sets based on a typical building energy management system. In addition, the model's impact on the energy consumption and indoor quality of a typical office building in Greece is presented. The model can control how the building operational data deviates from the settings as well as carry out diagnosis of internal conditions and optimize building's energy operation. In this context, the integrated “decision support model” can contribute to the management of the daily energy operations of a typical building, related to the energy consumption, by incorporating the following requirements in the best possible way: (a) the guarantee of the desirable levels of living quality in all building's rooms and (b) the necessity for energy savings.
The present paper presents a methodology to perform the optimal sizing of an autonomous hybrid PV/wind system. The methodology aims at finding the configuration, among a set of systems components, which meets the desired system reliability requirements, with the lowest value of levelized cost of energy.Modelling a hybrid PV/wind system is considered as the first step in the optimal sizing procedure. In this paper, more accurate mathematical models for characterizing PV module, wind generator and battery are proposed. The second step consists to optimize the sizing of a system according to the loss of power supply probability (LPSP) and the levelized cost of energy (LCE) concepts.Considering various types and capacities of system devices, the configurations, which can meet the desired system reliability, are obtained by changing the type and size of the devices systems. The configuration with the lowest LCE gives the optimal choice.Applying this method to an assumed PV/wind hybrid system to be installed at Corsica Island, the simulation results show that the optimal configuration, which meet the desired system reliability requirements (LPSP=0) with the lowest LCE, is obtained for a system comprising a 125 W photovoltaic module, one wind generator (600 W) and storage batteries (using 253 Ah). On the other hand, the device system choice plays an important role in cost reduction as well as in energy production.
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Erneuerbare Energien in Deutschland -Daten zur Entwicklung im Jahr
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