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Three real-lab trigeneration microgrids are investigated in non-residential environments (educational, office/administrational, companies/production) with a special focus on domain-specific load characteristics. For accurate load forecasting on such a local level, à priori information on scheduled events have been combined with statistical insight from historical load data (capturing information on not explicitly-known consumer behavior). The load forecasts are then used as data input for (predictive) energy management systems that are implemented in the trigeneration microgrids. In real-world applications, these energy management systems must especially be able to carry out a number of safety and maintenance operations on components such as the battery (e.g. gassing) or CHP unit (e.g. regular test runs). Therefore, energy management systems should combine heuristics with advanced predictive optimization methods. Reducing the effort in IT infrastructure the main and safety relevant management process steps are done on site using a Smart & Local Energy Controller (SLEC) assisted by locally measured signals or operator given information as default and external inputs for any advanced optimization. Heuristic aspects for local fine adjustment of energy flows are presented.
The twin concept is increasingly used for optimization tasks in the context of Industry 4.0 and digitization. The twin concept can also help small and medium-sized enterprises (SME) to exploit their energy flexibility potential and to achieve added value by appropriate energy marketing. At the same time, this use of flexibility helps to realize a climate-neutral energy supply with high shares of renewable energies. The digital twin reflects real production, power flows and market influences as a computer model, which makes it possible to simulate and optimize on-site interventions and interactions with the energy market without disturbing the real production processes. This paper describes the development of a generic model library that maps flexibility-relevant components and processes of SME, thus simplifying the creation of a digital twin. The paper also includes the development of an experimental twin consisting of SME hardware components and a PLC-based SCADA system. The experimental twin provides a laboratory environment in which the digital twin can be tested, further developed and demonstrated on a laboratory scale. Concrete implementations of such a digital twin and experimental twin are described as examples.
Der verstärkte Einsatz von Wärmepumpen bei der Realisierung einer klimaneutralen Wärmeversorgung führt zu einer signifikanten Zunahme und Änderung der elektrischen Lasten in den Verteilnetzen. Daher gilt es, Wärmepumpen so zu steuern, dass sie Verteilnetze wenig belasten oder sogar unterstützen.
Inhalt des Projekts „PV²WP - PV Vorhersage für die netzdienliche Steuerung von Wärmepumpen“ (Projektlaufzeit 1.07.2018 – 30.06.2021) war die Demonstration eines neuen Ansatzes zur Steuerung von Heizungssystemen, die auf Wärmepumpen und thermischen Speichern basieren und in Kombination mit einer Photovoltaikanlage betrieben werden. Das übergeordnete Ziel war dabei die Verbesserung der Netzintegration und Smart-Grid-Tauglichkeit entsprechender Heizungssysteme durch eine kostengünstige Technologie bei gleichzeitiger Erhöhung der Wirtschaftlichkeit.
Dabei wurden drei zukunftsweisende Technologien in Kombination genutzt und demonstriert: wolkenkamerabasierte Kurzfristprognosen, prädiktive Steuerung und Regelung sowie machinelearning-basierte Systemmodellierung als Basis für die Optimierung. Als Demonstrationsumgebung diente mit dem Projekthaus Ulm ein real bewohntes Einfamilienhaus.Umweltforschung
Am Institut für Angewandte Forschung wird seit Jahren eine Mikroprozessorfamilie unter dem Kurznamen SIRIUS entwickelt, die inzwischen in verschiedenen Applikationen eingesetzt wird und in hohem Maß nun auch kommerziell interessant wird. Im Mittelpunkt der Arbeiten des letzten Jahrs stand die Ausreifung der Strukturen, wobei zum erstenMal auf Benchmarks zurückgegriffen werden konnte, die einen direkten Vergleich der Leistungsfähigkeit von Prozessoren ermöglicht. Als Benchmark wurde in einer Master-Arbeit von Herrn Roth der Core-Mark Benchmark für unsere SIRIUS-Architektur übersetzt, der einen direkten Vergleich mit sehr leistungsfähigen Boliden wie der ARM-Cortex-Architektur aber auch klassischen kommerziellen Produkten von Renesas wie auch von ATMEL ermöglicht.
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
The PHOTOPUR project aims to develop a photocatalytic process as a type of AOPs (Advanced Oxidation Processes) for the elimination of plant protection products (PPP) of the cleaning water used to wash sprayers. At INES a PV based energy supply for the photocatalytic cleaning system was developed within the framework of two bachelor theses and assembled as a demonstration unit. Then the system was step by step extended with further process automation features and pushed to a remote operating device. The final system is now available as a mobile unit mounted on a lab table. The latest step was the photocatalytic reactor module which completed the first PHOTOPUR prototype. The system is actually undergoing an intensive testing phase with performance checks at the consortium partners. First results give an overview about the successful operation.
An energy oriented design concept was developed within the research project PHOTOPUR which has the development of a PV powered water cleaning system as main focus. During a wine season Plant Protection Products (PPP) are several times sprayed on plants to protect them of undesired insects and herbs or avoid hazardous fungus
types. A work package of the project partner INES in Offenburg led to a design introducing energy profiling already in the early beginning of a product design. The concept is based on three pillars respecting first the
requirements of the core process making up filtering and cleaning and secondary aspects which run, support, maintain and monitor the system to secure availability and product reliability.
The presented paper shows that the results of the design tools guided the developers to assemble a functional model of the water decontamination unit which was manually tested with its concatenated steps of the water cleaning process.
Die Digitalisierung kann der Türöffner sein, um effizient die mittelständische Industrie und den Energiemarkt zu verbinden. Das Projekt GaIN hat das Ziel, mit hochaufgelösten Produktions- und Messdaten von zehn mittelständischen Industriebetrieben neuartige Tarife und angepasste Marktplattformen zu entwickeln, die Prognosegüte für Energiebedarf, Nachfrage und Flexibilitätsverfügbarkeit zu erhöhen, die Interaktion vieler flexibler Unternehmen im Verteilnetz und in dem Bilanzkreis zu bewerten und die Auswirkung einer Nutzung der Daten auf die Energiewende anhand einer Systemanalyse zu beurteilen.
In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.
The conversion of space heating for private households to climate-neutral energy sources is an essential component of the energy transition, as this sector as of 2018 was responsible for 9.4 % of Germany’s carbon dioxide emissions. In addition to reducing demand through better insulation, the use of heat pumps fed with electricity from renewable energy sources, such as on-site photovoltaics (PV) systems, is an important solution approach.
Advanced energy management and control can help to make optimal use of such heating systems. Optimal here can e.g. refer to maximizing self-consumption of self-generated PV power, extended component lifetime or a grid-friendly behavior that avoids load peaks. A powerful method for this is model predictive control (MPC), which calculates optimal schedules for the controllable influence variables based on models of the system dynamics, current measurements of system states and predictions of future external influence parameters.
In this paper, we will discuss three different use cases that show how artificial intelligence can contribute to the realization of such an MPC-based energy management and control system. This will be done using the example of a real inhabited single family home that has provided the necessary data for this purpose and where the methods are implemented and tested. The heating system consists of an air-water heat pump with direct condensation, a thermal stratified storage tank, a pellet burner and a heating rod and provides both heating and hot water. The house generates a significant portion of its electricity needs through a rooftop PV system.
One of the major challenges impeding the energy transition is the intermittency of solar and wind electricity generation due to their dependency on weather changes. The demand-side energy flexibility contributes considerably to mitigate the energy supply/demand imbalances resulting from external influences such as the weather. As one of the largest electricity consumers, the industrial enterprises present a high demand-side flexibility potential from their production processes and on-site energy assets. In this direction, methods are needed with a focus on enabling the energy flexibility and ensure an active participation of such enterprises in the electricity markets especially with variable prices of electricity. This paper presents a generic model library for an industrial enterprise implemented with optimal control for energy flexibility purposes. The components in the model library represent the typical technical units of an industrial enterprise on material, media, and energy flow levels with their operative constraints. A case study of a plastic manufacturing plant using the generic model library is also presented, in which the results of two simulation with different electricity prices are compared and the behavior of the model can be assessed. The results show that the model provides an optimal scheduling of the manufacturing system according to the variations in the electricity prices, and ensures an optimal control for utilities and energy systems needed for the production.
Mit dem Klimaschutzgesetz 2021 wurden von der Bundesregierung die Klimaschutzvorgaben verschärft und die Treibhausgasneutralität bis 2045 als Ziel verankert. Zur Erreichung dieses ambitionierten Ziels ist es notwendig, im Bereich der Mobilität weitgehend von Verbrennungsmotoren mit fossilen Kraftstoffen auf Elektromobilität mit regenerativ erzeugtem Strom umzusteigen. Dabei ist die zügige Bereitstellung einer ausreichenden Ladeinfrastruktur für die Elektrofahrzeuge eine große Herausforderung. Neben der Installation einer ausreichend großen Zahl von Ladepunkten selbst besteht die Herausforderung darin, diese in das bestehende Verteilungsnetz zu integrieren bzw. das Verteilungsnetz so auszubauen, dass weiter ein sicherer Netzbetrieb gewährleistet werden kann. Dabei sind insbesondere Lösungen gefragt, bei denen der Ausbau der Ladeinfrastruktur und der Netzbetriebsmittel durch intelligentes Management des Ladens so gering wie möglich gehalten wird, indem vorhandene oder neu zu installierender Hardware möglichst effizient genutzt wird.
Hier setzte das Projekt „Intelligente Ladeinfrastruktur für Elektrofahrzeuge auf dem Parkplatz der Hochschule Offenburg (INTLOG)“ (Projektlaufzeit 15.11.2020 – 30.09.2022) an. Inhalt des Projekts war es, einen Ladepark für den Parkplatz der Hochschule Offenburg mit 20 Ladepunkten à 11 kW und somit einer Gesamtladeleistung von 220 kW an einen vorhandenen Ortsnetztransformator mit 200 kW Nennleistung anzuschließen, der aber bereits von anderen Verbrauchern genutzt wurde. Das übergeordnete Ziel war es also, eine Ladeinfrastruktur von maßgeblichem Umfang in die bestehende Netzinfrastruktur ohne zusätzlichen Ausbau zu integrieren.
Dabei wurden zukunftsweisende Technologien genutzt und weiterentwickelt sowie teilweise in Praxis, im Labor und in der Computersimulation demonstriert.
A balcony photovoltaic (PV) system, also known as a micro-PV system, is a small PV system consisting of one or two solar modules with an output of 100–600 Wp and a corresponding inverter that uses standard plugs to feed the renewable energy into the house grid. In the present study we demonstrate the integration of a commercial lithium-ion battery into a commercial micro-PV system. We firstly show simulations over one year with one second time resolution which we use to assess the influence of battery and PV size on self-consumption, self-sufficiency and the annual cost savings. We then develop and operate experimental setups using two different architectures for integrating the battery into the micro-PV system. In the passive hybrid architecture, the battery is in parallel electrical connection to the PV module. In the active hybrid architecture, an additional DC-DC converter is used. Both architectures include measures to avoid maximum power point tracking of the battery by the module inverter. Resulting PV/battery/inverter systems with 300 Wp PV and 555 Wh battery were tested in continuous operation over three days under real solar irradiance conditions. Both architectures were able to maintain stable operation and demonstrate the shift of PV energy from the day into the night. System efficiencies were observed comparable to a reference system without battery. This study therefore demonstrates the feasibility of both active and passive coupling architectures.
This paper focuses on appropriately measuring the accuracy of forecasts of load behavior and renewable generation in micro-grid operation. Common accuracy measures like the root mean square of the error are often difficult to interpret for system design, as they describe the mean accuracy of the forecast. Micro-grid systems, however, have to be designed to handle also worst case situations. This paper therefore suggests two error measures that are based on the maximum function and that better allow understanding worst case requirements with respect to balancing power and balancing energy supply.
Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.
In this paper we report on further success of our work to develop a multi-method energy optimization which works with a digital twin concept. The twin concept serves to replicate production processes of different kinds of production companies, including complex energy systems and test market interactions to then use them for model predictive optimizing. The presented work finally reports about the performed flexibility assessment leading to a flexibility audit with a list of measures and the impact of energy optimizations made related to interactions with the local power grid i.e., the exchange node of the low voltage distribution grid. The analysis and continuous exploration of flexibilities as well as the exchange with energy markets require a “guide” leading to continuous optimization with a further tool like the Flexibility Survey and Control Panel helping decision-making processes on the day-ahead horizon for real production plants or the investment planning to improve machinery, staff schedules and production
infrastructure.