Der Leitfaden „Energiegemeinschaften im Tourismus“ zeigt, welche Möglichkeiten Energiegemeinschaften für Tourismusbetriebe, ihre Beschäftigten und Menschen, die in Tourismusregionen leben, bieten können und wie eine Energiegemeinschaft ins Leben gerufen werden
kann.

Mikro-Netze (Microgrids), ein Unterbereich der Intelligenten Strom/Energie-Netze (Smartgrids),
die sich durch eine enge räumliche Bindung von Energieerzeugungseinheiten und Verbraucher
auszeichnen wird international ein sehr starkes Wachstum zugeschrieben. Microgrids sind kleine,
lokale Energienetze für Strom, Wärme und Kälte, die Haushalte, Betriebe und Gemeinden mit
Energie versorgen. Diese lokalen und regionalen Konzepte der Energieversorgung können in
Zukunft einen wesentlichen Beitrag in Richtung Energieunabhängigkeit und effizientere
Integration von Erneuerbaren in das Energiesystem leisten. Sie können ihren Energiebedarf
selbstständig aus erneuerbaren Energien oder anderen Energieformen decken, etwa Biomasse,
Wärmepumpen, PV, Windräder oder Kraftwärmekopplungen. Diese können nach den
individuellen Zielen der Gemeinden, Haushalte oder der Betriebe gesteuert werden, um
Kostenreduktionen, CO2 Einsparungen oder eine Erhöhung des Unabhängigkeitsgrades zu
realisieren. Sie berechnen den aktuellen und zukünftigen Verbrauch und können Energie im
Bedarfsfall dorthin verlagern, wo sie gerade benötigt wird, oder sie reduzieren den
Energieverbrauch direkt.

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).

Winter tourism is an energy-intensive branch of industry. The aim of the FFG funding project Clean Energy for Tourism is to support Salzburg’s skiing areas on the way to decarbonization by developing technologies and business models. In this article, the developed ICT infrastructure, the optimization algorithms and the business models are presented.

Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality because of several limitation factors. Challenges are already encountered during the planning phase since a large number of decision variables have to be considered depending on the number and type of community participants and distributed technologies. This paper overcomes these challenges by establishing a mixed-integer linear programming based optimal planning approach for renewable energy communities. A real case study is analyzed by creating an energy community testbed with a leading energy service provider in Austria. The case study considers nine energy community members of a municipality in Austria, distributed photovoltaic systems, energy storage systems, different electricity tariff scenarios and market signals including feed-in tariffs. The key results indicate that renewable energy communities can significantly reduce the total energy costs by 15% and total carbon dioxide emissions by 34% through an optimal selection and operation of the energy technologies. In all the optimization scenarios considered, each community participant can benefit both economically and ecologically.

OptEnGrid is a cross-sectoral multi-energy system optimization tool for the optimal planning and dispatch of the Distributed Energy Resource (DER) technologies in smart- and microgrids. The methodology of OptEnGrid considers an optimization model which is based on Mixed-Integer Linear Programming (MILP) framework. The following sub-sections provide more details about the energy flow and system optimization inside OptEnGrid and the choice of the optimization over simulation

The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions. Piecewise affine functions are used to approximate the non-linear cost versus capacity behavior. The developed methods are applied to the optimal planning of a case study in Austria. The results of the models are compared based on the accuracy and real-time performance together with the impact of piecewise affine cost functions versus non-piecewise affine fixed cost functions. The results show that the investment decisions of both models are in good agreement with each other while the computational time for the 8760-h based model is significantly greater than the model having three representative periods. The models with piecewise affine cost functions show larger capacities of technologies than non-piecewise affine fixed cost function based models.

In einem zellularen Energiesystem wird die physikalische Balance zwischen Energieangebot und -nachfrage so weit als möglich bereits auf regionaler, lokaler Ebene hergestellt. Der zentrale Baustein dabei ist die Energiezelle. Sie kann Energie in Form von Wärme, Elektrizität oder Gas aufnehmen und/oder Elektrizität und Wärme (z. B. aus erneuerbaren Energien) selbst erzeugen, um so den eigenen Wärme- und Elektrizitätsbedarf zu decken. Energieüberschüsse können (elektrisch und/oder thermisch) gespeichert oder anderen Zellen im Nahbereich oder einem Energieversorger zur Verfügung gestellt werden. Ein Energiezellenmanagement kann in Koordination mit Nachbarzellen den Ausgleich von Erzeugung und Verbrauch über alle vorhandenen Energieformen organisieren.
Die Planung und der Betrieb zellularer Energiesysteme ist eine komplexe Aufgabe, da eine Vielzahl von dezentralen Energietechnologien, verschiedenste Ziele und auch Entscheidungsträger berück-sichtigt werden müssen.
Der vorliegende VDE Impuls beschreibt als ersten Schritt die Planung einer Energiezelle, welche mit Energieversorgern interagieren kann. Er ist der Auftakt einer Reihe weiterer Veröffentlichungen zur detaillierten Planung von Energiezellen und zellularen Energiesystemen.

The intra-hour intermittency of solar energy and demand introduce significant design challenges for microgrids. To avoid costly energy shortfalls and mitigate outage probability, islanded microgrids must be designed with sufficient distributed energy resources (DER) to meet demand and fulfill the energy and power balance. To avoid excessive runtime, current design tools typically only utilize hourly data. As such, the variable nature of solar and demand is often overlooked. Thus, DER designed based on hourly data may result in significant energy shortfalls when deployed in real-world conditions. This research introduces a new, fast method for optimizing DER investments and performing dispatch planning to consider intra-hour variability. A novel set of constraints which operate on intra-hour data are implemented in a mixed-integer-linear-program microgrid investment optimization. Variability is represented by the single worst-case intra-hour fluctuation. This allows for fast optimization times compared to other approaches tested. Applied to a residential microgrid case study with 5-minute intra-hour resolution, this new method is shown to maintain optimality within 2% and reduce runtime by 98.2% compared to full-scale-optimizations which consider every time-step explicitly. Applicable to a variety of technologies and demand types, this method provides a general framework for incorporating intra-hour variability into microgrid design.

Microgrids are local energy grids that (partly) cover their own energy demand. Decentralized renewable energy sources reduce energy costs and CO2 emissions in a microgrid. Various storage systems and strategies like load shift are employed to balance the volatile energy flows. Intelligent controllers improve the energy management of the micro and smart grids. BEST GmbH is the industry leader when it comes to biomass control systems in Austria. Thus, BEST GmbH is already combining this knowledge within the “OptEnGrid” (FFG 858815) and “Grundlagenforschung Smart- und Microgrid“ (K3-F-755/001-2017) research projects, which are based on the leading microgrid optimization tool DER-CAM from Lawrence Berkeley National Laboratory at the University of California. These two BEST GmbH basic research projects form the basis for new innovative microgrid controller concepts which will be implemented and tested in the presented Microgrid Research Lab in Wieselburg (project Microgrid Lab 100%). The Microgrid Research Lab will include the Technology- und Reseach Centre (tfz) Wieselburg-Land and the new firefighting department next to the tfz.

Microgrids, a research topic within the smart grids area, build on close relationships between demand and supply and will create a 170 Mrd. € market potential in 2020[1]. These individual markets are characterized by different technologies in use. For example, biogas will play a key role in microgrids in Asia compared to Photovoltaics, Combined heat and Power (CHP), as well as storage technologies in North America. All these different technologies need to be coordinated and controlled. BIOENERGY2020+ GmbH is the industry leader when it comes to biomass control systems in Austria. Thus, BIOENERGY2020+ GmbH is already combining this knowledge within the OptEnGrid and “Grundlagenforschung Smart- und Microgrid“ (K3-F-755/001-2017) research projects, which are based on the leading microgrid optimization tool DER-CAM from Lawrence Berkeley National Laboratory at the University of California in Berkeley. These two BIOENERGY2020+ GmbH basic research projects constitute the basis for new innovative microgrid controller concepts and these new microgrid controller will be implemented and tested in the suggested Microgrid Research Lab in Wieselburg. The Microgrid Research Lab will include the Technology- und Reseach Centre (tfz) Wieselburg-Land and the new firefighting department next to the tfz.

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings.

Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization framework is introduced, specifically designed for resilience to macrogrid outages. The framework solves the same optimization problem twice, where the second solution using full data is informed by the first solution using representative data to size and select DER. This framework includes a novel constraint on the state of charge for storage devices, which allows the representation of multiple repeated days of grid outage, despite a single 24-h profile being optimized in the representative model. Multiple approaches to the hybrid optimization are compared in terms of their computation time, optimality, and robustness against infeasibilities. Through a case study on three real Microgrid designs, we show that allowing optimizing the DER sizing in both stages of the hybrid design, dubbed minimum investment optimization (MIO), provides the greatest degree of optimality, guarantees robustness, and provides significant time savings over the benchmark optimization.

A disconnect between real world financing and technical modeling remains one of the largest barriers to widespread adoption of microgrid technologies. Simultaneously, the optimal design of a microgrid is influenced by financial as well as technical considerations. This paper articulates the interplay between financial and technical assumptions for the optimal design of microgrids and introduces a design approach in which two financing structures drive an efficient design process. This approach is demonstrated on a descriptive test case, using well accepted financial indicators to convey project success. The major outcome of this paper is to provide a framework which can be adopted by the industry to relieve one of the largest hurdles to widespread adoption, while introducing multiple debt financing models to the literature on microgrid design and optimization. An equally important outcome from the test case, we provide several points of intuition on the impact of varying financing terms on the optimal solution.

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice.

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice.

Kernthema dieses Beitrags ist die ganzheitliche Konzeption von Mikronetze, die sich auf die Reduzierung von Kosten und CO2-Emissionen konzentriert. Mikronetze, oder auch Microgrids, ermöglichen die koordinierte Energieerzeugung von dezentralen Energieressourcen, die Speicherungen der produzierten Energie und ein Lastmanagement zum Ausgleich von Wärme-, Kälte- und Elektrizitätsdienstleistungen. Mikronetze können vom breiteren Versorgungsnetz getrennt werden, können diverse Dienstleistungen erbringen und/oder selbst Energie erzeugen sowie in Überschusszeiten speichern und bei Bedarf wieder Kosten- oder Stabilitäts-orientiert freigeben.
Die mathematische Optimierung dient als unvoreingenommene Alternative für eine gesamtheitliche Planung von dezentralen Energietechnologien. Dieses Kriterium wird bei einer Kosten- oder CO2-Reduktion vor allem dann essentiell, wenn vielfältigen Kombinationen von Technologien und Kapazitäten möglich sind. Modernste Ansätze betrachten jedoch einen quasistatischen Aufbau unter Verwendung linearisierte Modelle und Mixed Integer Linear Optimization (MILP), wobei dynamische Effekte vernachlässigt werden. Unter Berücksichtigung von Lasten, geografischen, ökonomisch-ökologischen und tariflichen Daten sind mathematische Optimierungsalgorithmen in der Lage, verschiedene Anwendungsfälle zu beurteilen, wobei Effekte wie Vorwärmung, Sollwertänderungen oder kurzfristige Sonnenschwankungen unberücksichtigt bleiben. Dies bedeutet, dass die in quasistatischen Ansätzen verwendete Wärme- und Strombilanzen ungenau sein können (eventuell können physikalische Randbedingungen sogar verletzt werden, was zu suboptimalen Ergebnissen bei der Planung führen würde).
Die Notwendigkeit besteht quasistatische Optimierung mit einer weiteren Modellierungsart zu vergleichen und die Auswirkungen auf traditionelle quasistatische Ansätze, wie sie in DER-CAM oder ReOpt eingesetzt werden, aufzudecken. Um Abweichungen - bestehend aus dynamischen oder sogar Rebound Effekten - zu erkennen, werden mit TRNSYS Gebäude- und Anlagensimulationen für eine geplante Siedlungsanlage erstellt und ein Energiekonzept mit dem mathematischen Optimierungsprogramm OptEnGrid berechnet. Der Ansatz wird für vier Doppelhäuser und ein Mehrfamilienhaus getestet. Die Gebäude werden in TRNSYS simuliert und bieten thermische Lastdaten für den Referenzfall. Auch die Stromerzeugung mit PV-Modellen und der elektrische Verbrauch mit synthetischen Lastprofilen sind sowohl in der Optimierung als auch in der Simulation beteiligt. In der elektrischen Stromerzeugung zeigt die mathematische Optimierung bereits eine Abweichung von mehr als 5% auf Jahresbasis zur TRNSYS-Simulation. Ergebnisse im thermischen Energiebereich folgen nach weiterer Auswertung.

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.