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The last 20 years has seen rapid expansion of sustainable energy deployment in the European Union (EU) and the United States (U.S.) that is driving the demand for trained professionals. An engineering degree with coursework in sustainable energy systems is a desirable initial qualification. However, engineering students should also appreciate the societal and environmental impacts of the sustainable energy transition. Furthermore, since the sustainable energy transition is a global endeavor, an international perspective is needed. The sustainable energy engineering course described in this paper taught students the scientific and engineering principles underlying the major types of emerging sustainable energy technologies from a chemical engineering perspective. The technical content served as context for comparing renewable energy deployment in the EU country of Austria with the U.S. The broader impacts (societal and environmental) of renewable energy deployment were then illustrated through student presentations. Survey results showed that students gained understanding of the engineering fundamentals underlying these renewable energy systems and challenges of their deployment in Austria and the U.S. Therefore, a unique outcome of this course was that students gained an international perspective on the expansion of sustainable energy systems needed to secure a low-carbon energy future. • New approach for developing and teaching a sustainable energy engineering course. • Topics sequenced through Technology-Resources-Deployment-Broader Impact subtopics. • Renewable energy technologies taught from a chemical engineering perspective. • International perspectives by comparing US and Austria renewable energy deployment. • Broader (societal, environmental) impact discussions through student presentations. [ABSTRACT FROM AUTHOR]

The operation of energy systems has changed significantly with the increase of intermittent renewable energy sources. New market players that produce, consume, and store electricity- prosumages, along with the different global factors, influence price spreads in the electricity market. This paper gives a comprehensive analysis of the economic viability of two of the currently most cost-effective electricity storage technologies: pumped hydro storage (PHS) and lithium-ion (Li-ion) when used for price arbitrage. The analysis is conducted using a price-taker approach for electricity market prices from 2011.-2019. in which the market has changed. The mixed-integer optimization model is applied with the subtraction of the total storage costs. Arbitrage opportunities for two different energy systems- one with a high generation of renewables and one with high shares of fossil generation are analyzed. Results indicate that pumped hydro storage with a total cost of 0.032 €/kWh is economically justified contrary to Li-ion batteries with a total cost of 0.217 €/kWh. The average yearly profit of PHS, for a study case with 83% of electricity generation from renewables, is 65% lower compared to the case highly dependent on fossil generation. Comparison between study cases indicates that profits of price arbitrage are dependent on full load hours, electricity price spreads, and flexibility options available, rather than on installed shares of renewables. • Storage economic viability depends on the full load hours, electricity market prices and total costs of the technology. • Average yearly arbitraging profit of PHS in Austria is 65% lower compared to the Bosnia and Herzegovina case. • Total costs for 2000 full load hours are: Li-ion 0.217 €/kWh and PHS 0.032 €/kWh. • Energy arbitrage of Li-ion storage is unprofitable. • Increase of energy storage profits isn't proportional to higher shares of renewable generation. [ABSTRACT FROM AUTHOR]

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). • A MILP model for renewable based hydrogen production, seasonal storage and mobility. • A real case study is analyzed by extending a microgrid lab facility in Austria. • Multiple investment optimization scenarios relative to two distinct reference cases. • CO 2 reduction by at least 66% and at most 99% relative to the diesel reference case. • Introducing carbon prices to bridge the cost gap between hydrogen and diesel fuel. [ABSTRACT FROM AUTHOR]

Wooden biomass is the main source for energy based on biomass in Austria. Only a part of the wooden biomass for energy directly originates in forests. Other major sources include post-consumer wood and by-products of the Austrian forest-based industries. Consumption of wooden biomass has been growing much more than domestic production, forest-based industries are building up capacities in neighbouring countries, leaving less raw materials for exports to Austria than in the decades before. The authors have assessed the possible effects of a wood raw material shortage with a System-Dynamics simulation model of the Austrian forest-based sector (FOHOW). The model covers the interactions between the general economy and the forest-based sector, including wood-based energy. The simulation period ranged from 2006 to 2025. Beside a business as usual scenario, scenarios with a sawlog import reduction, a sawmill capacity reduction as well as a paper and panel capacity reduction were simulated. Probably the most notable result of the analysis is the strong impact of the sawmill industry on the fuelwood prices and availability. Despite increased fuelwood supply from forests, reduction of sawmill capacity will lead to the inability of fully meeting renewable energy policy objectives due to a shortage of sawmill residues. With exemption of the panel & paper capacity reduction scenario all other scenarios project a slight reduction of growing stock until 2025. But after decades of harvest below the increment such a development is not per se unsustainable. It can be expected that a shift in the Austrian forests towards younger stands will slightly increase the average increment. [ABSTRACT FROM AUTHOR]

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch flexible assets in microgrids. However, the major bottleneck to achieve maximum benefits with these methods is their predictive accuracy. This paper addresses this bottleneck by developing a novel multi-step forecasting method for a Model Predictive Control framework. The presented methods are applied to a real test-bed of a renewable energy community in Austria, where its operational costs and CO 2 emissions are benchmarked with those of a rule-based control strategy for Flat, Time-of-Use, Demand Charge and variable energy price tariffs. In addition, the impact of forecast errors and electric battery capacity on energy community operational savings are examined. The key results indicate that the proposed controller can outperform a rule-based dispatch strategy by 24.7% in operational costs and by 8.4% in CO 2 emissions through optimal operation of flexibilities if it has perfect foresight. However, if the controller is deployed in a realistic environment, where forecasts for electrical load and PV generation are required, the same savings are reduced to 3.3% for cost and 7.3% for CO 2 , respectively. In such environments, the proposed controller performs best in highly dynamic tariffs such as Time-of-Use and Real-time pricing rates, achieving real cost savings of up to 6.3%. These results show that the profitability of optimization-based control of microgrids is threatened by forecast errors. This motivates future research on control strategies that compensate for forecast errors in real-world operation and more accurate forecasting methods. [Display omitted] • Advanced microgrid controller framework based on Model Predictive Control. • Novel method for multi-step-ahead time series forecasting. • A real Austrian test-bed with multiple operational optimization scenarios. • Comprehensive economic analysis considering forecast errors and multiple battery sizes. • Reduction of operational costs by 6% and carbon emissions by 7% [ABSTRACT FROM AUTHOR]

Redispatch measures are a key instrument of the Central European electricity market design to prevent congestion of several transmission lines after the market clearing. In addition to generation rescheduling, demand-side flexibility can reduce this redispatch need. This paper mainly aims to analyse the flexibility demand application of electric vehicle fleets in the redispatch market. The developed European electricity market model minimises dispatch- and subsequently redispatch costs whilst using electric vehicle fleets as flexible demand. A novel modelling approach allows the integration of this flexibility into large-scale linear programming models without losing their essential parameters. This case study is examined in Austria with a remarkably high share of electricity from renewable energy sources and different market penetrations of electromobility. Results show that integrating this system as a redispatch measure leads to a reduced curtailment of renewable energies (up to 25%), whilst less additional thermal power plant usage is needed. Furthermore, redispatch cost and the associated CO 2 emissions are reduced by 3.3% to 13.9%. By contrast, using this flexibility as a market-based charging strategy raises CO 2 emissions and redispatch costs drastically by 186% if 2 million electric vehicles are considered. Especially with the high electrification rate of the transport sector, the provided flexibility potential significantly impacts the electricity market. Further efforts could address the influence of demand-side flexibility not only on redispatch within one control area but also on cross-border redispatch and counter-trading. • Implementation of electric vehicle fleets as demand-side flexibility. • Integration of flexibility in the dispatch and redispatch of the electricity market. • Flexibility as redispatch measure reduces redispatch costs and CO 2 emissions. • Flexibility as market-based charging strategy leads to an increase in congestion. [ABSTRACT FROM AUTHOR]

Feasibility of district heating networks for areas with low heat demand of passive and low energy houses implies the development of innovative concepts for the production, storage, distribution and supply of thermal energy. Low supply temperatures enables the use of heat from renewable and alternative sources, currently been neglected due to the usually high supply temperatures used in conventional district heating systems. Further on, low network temperatures are supporting the reduction of operational and investment costs. The paper describes the development of economically and ecologically optimized concepts for low temperature district heating networks using four representative case studies in Austria: Aktivpark Güssing, Seestadt Aspern, Winklweg Siedlung and Hummel Kaserne. The four case studies offered the possibility to investigate different supply and demand connection schemes and consequently deriving optimized scenarios taking into account local framework conditions, such as consumption and production settings and the related control strategies. The scenarios analysis is performed considering both economic and ecological issues. The results of the study show that the availability and economic conditions of low temperature heat sources is a key factor for facilitating LTDH networks. In rural areas, lower heat losses due to lower network temperatures are beneficiary for the LTDH network performance. [ABSTRACT FROM AUTHOR]

Performance simulation applications require reliable information regarding the intensity of solar irradiance on arbitrarily oriented building surfaces in order to properly predict buildings' energy use or to configure building-integrated solar energy systems. Since measured irradiance databases typically include only horizontal irradiance values, solar radiation intensity on inclined surfaces must be computationally derived. In this context, the present paper compares six options to derive, from horizontal irradiance data, solar radiation intensity levels on inclined surfaces. To evaluate these options, simulated downward vertical irradiance on four orientations were compared with measurements obtained in Austria. Two options that use both global and diffuse horizontal irradiance values for sky radiance generation delivered slightly better results than the others, which require only global horizontal irradiance. However, the range of errors was rather high for all options. Even for the best-ranked option, no more than 64% of the results had a relative error of less than ±20%. [ABSTRACT FROM AUTHOR]

Abstract: A forced use of renewable energy sources (RES) is necessary to reduce greenhouse gas emissions significantly. Among RES biomass-based resources play a specific role regarding their CO2-reduction potentials, their energetic potentials and their overall costs for different derived energy carriers. From various categories of biomass resources – forestry, agricultural crops, short rotation coppices or waste products – different alternative energy carriers (AEC) like biofuels 1st or 2nd generation, electricity or hydrogen can be produced. In this paper we analyse possible biomass-based energy chains for different AEC in Austria. We investigate their overall potential by 2050, corresponding CO2-reduction potentials and resulting CO2 saving costs. The core results of this analysis are: (i) the overall potential by 2050 is approximately 130 PJ compared to 30 PJ in 2010; and (ii) the corresponding CO2-reduction potential is about 7 million tons CO2equ. This is roughly two-third reduction compared to the use of conventional fuels. The major conclusion is that only if a tuned portfolio of actions – CO2-tax, ecological monitoring system, a focussed R&D programme for second generation biofuels and fuel cells – is implemented the potential of new biomass-based AEC can be exploited up to 2050 in an optimal way for society. [Copyright &y& Elsevier]

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. • Novel optimization strategies for renewable energy communities in microgrids. • Advanced mixed-integer linear programming model with 8760-hour time horizon. • A real Austrian case study with nine community participants and six scenarios. • Renewable energy transfer with different tariff scenarios and market signals. • Reduction of community costs by 15% and community carbon emissions by 34%. [ABSTRACT FROM AUTHOR]

The integration of renewable electricity generation poses challenges for today's energy system. Since renewable potentials are geographically unevenly distributed, solutions to overcome bottlenecks such as grid congestion are necessary to raise as much renewable potentials as possible. In specific, a part of Austria is characterised with high photovoltaic installation inquiries and low electricity demand. Electricity grids are designed to cope with today's loads, therefore the integration of inquired photovoltaic would overstrain the built infrastructure. In this paper we investigate how power-to-gas can ease this photovoltaic related grid strain. The feed-in of hydrogen into natural gas transmission pipelines and the usage of hydrogen and biogas from local fermentation plants for methanation feeding synthetic natural gas (SNG) into local distribution grid is assessed in various scenarios. Based on the technical assessment results, an economic evaluation of each scenario is performed to determine generation costs of hydrogen or SNG. Depending on the electrolysers location in the electricity grid, the installable photovoltaic power can be increased significantly without causing electricity grid congestion. Costs for hydrogen and SNG vary across each scenario, mainly influenced by CapEx for electrolyser and methanation as well as electricity purchasing costs. • Increasing installed renewable energies power through power-to-gas. • Implementing renewable energy sources. • Power-to-gas as grid relief measure. • Hydrogen and SNG generation costs. [ABSTRACT FROM AUTHOR]