Preißinger, Markus
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Keywords
- Demand side management (4)
- Desalination (3)
- Bubble column humidifier (2)
- Demand response (2)
- Energy storage (2)
- Heat pump (2)
- Humidification-dehumidification (2)
- Minkowski sum (2)
- Organic Rankine Cycle (2)
- Power tracking (2)
Industrial demand side management has shown significant potential to increase the efficiency of industrial energy systems via flexibility management by model-driven optimization methods. We propose a grey-box model of an industrial food processing plant. The model relies on physical and process knowledge and mass and energy balances. The model parameters are estimated using a predictive error method. Optimization methods are applied to separately reduce the total energy consumption, total energy costs and the peak electricity demand of the plant. A viable potential for demand side management in the plant is identified by increasing the energy efficiency, shifting cooling power to low price periods or by peak load reduction.
In the regime of incentive-based autonomous demand response, time dependent prices are typically used to serve as signals from a system operator to consumers. However, this approach has been shown to be problematic from various perspectives. We clarify these shortcomings in a geometric way and thereby motivate the use of power signals instead of price signals. The main contribution of this paper consists of demonstrating in a standard setting that power tracking signals can control flexibilities more efficiently than real-time price signals. For comparison by simulation, German renewable energy production and German standard load profiles are used for daily production and demand profiles, respectively. As for flexibility, an energy storage system with realistic efficiencies is considered. Most critically, the new approach is able to induce consumptions on the demand side that real-time pricing is unable to induce. Moreover, the pricing approach is outperformed with regards to imbalance energy, peak consumption, storage variation, and storage losses without the need for additional communication or computation efforts. It is further shown that the advantages of the optimal power tracking approach compared to the pricing approach increase with the extent of the flexibility. The results indicate that autonomous flexibility control by optimal power tracking is able to integrate renewable energy production efficiently, has additional benefits, and the potential for enhancements. The latter include data uncertainties, systems of flexibilities, and economic implementation.
The humidification-dehumidification process (HDH) for desalination is a promising technology to address water scarcity issues in rural regions. However, a low humidifier efficiency is a weakness of the process. Bubble column humidifiers (BCH) are promising for HDH, as they provide enhanced heat and mass transfer and have low maintenance requirements. Previous studies of HDH-systems with BCHs draw different conclusions regarding the impact of superficial air velocity and liquid height on the humidification. Furthermore, the impact of flow characteristics has never been investigated systematically at all. In this study, an optimized BCH test setup that allows for optical analysis of the humidifier is used and evaluated. Our test setup is validated, since the influence of water temperature on the humidification, which is exponential, is reproduced. Measurements with seawater show that the normalised system productivity is increased by about 56 % with an increase in superficial air velocity from 0.5 to 5 cm/s. Furthermore, the system productivity is increased by around 29 % with an increase in liquid height from 60 to 378 mm. While the impact of superficial air velocity can be traced back to temperature changes at the humidifier and dehumidifier outlets, the impact of liquid height is shown to be caused by a smaller heat loss surface in the humidifier with an increase in liquid height. For the impact of sieve plate orifice diameter, a clear influence on the humidification is not apparent, this parameter needs to be investigated further. Finally, our new test setup allows for analysing the humidification of air (1) in a systematic way, (2) in relevant measurement ranges and (3) in comparison with optical analyses of the flow characteristics.
In contrast to fossil energy sources, the supply by renewable energy sources likewind and photovoltaics can not be controlled. Therefore, flexibilities on the demandside of the electric power grid, like electro-chemical energy storage systems, are usedincreasingly to match electric supply and demand at all times. To control those flex-ibilities, we consider two algorithms that both lead to linear programming problems.These are solved autonomously on the demand side, i.e., by household computers.In the classic approach, an energy price signal is sent by the electric utility to thehouseholds, which, in turn, optimize the cost of consumption within their constraints.Instead of an energy price signal, we claim that an appropriate power signal that istracked in L1-norm as close as possible by the household has favorable character-istics. We argue that an interior point of the household’s feasibility region is neveran optimal price-based point but can result in a L1-norm optimal point. Thus, pricesignals can not parametrize the complete feasibility region which may not lead to anoptimal allocation of consumption.We compare the price and power tracking algorithms over a year on the base ofone-day optimizations regarding different information settings and using a large dataset of daily household load profiles. The computational task constitutes an embarrassingly parallel problem. To this end, the performance of the two parallel computation frameworks DEF [1] and Ray [2] are investigated. The Ray framework is used to run the Python applications locally on several cores. With the DEF frameworkwe execute our Python routines parallelly in a cloud. All in all, the results providean understanding of when which computation framework and autonomous algorithmwill outperform the other.
Verbraucherseitige Laststeuerung (Demand Side Management – DSM) wird als ein möglicher Ansatz betrachtet, um die Auswirkungen des Ausbaus von fluktuierenden Erneuerbaren im Stromnetz auszugleichen. Sollen viele verteilte Energiesysteme damit angesprochen werden, stellen zentralistische Ansätze dabei hohe Anforderungen an die Kommunikationsinfrastruktur. Als Alternative wird vielfach eine autonome Laststeuerung (ADSM) mit anreizbasierter Optimierung direkt auf dem Verbrauchergerät betrachtet. Dabei kann die Anreizfunktion mittels unidirektionaler Kommunikation übertragen werden.
Am Forschungszentrum Energie der Fachhochschule Vorarlberg wurden in den letzten Jahren Algorithmen und Prototypen für den Einsatz von ADSM auf verschiedensten verteilten Energiespeichern im elektrischen Stromnetz entwickelt. Dabei werden sowohl thermische Energiespeicher (z. B. Haushalts-Warmwasserspeicher) als auch elektrochemische Speicher (z. B. Batteriespeichersysteme oder Elektroautos) betrachtet. Außerdem werden die Auswirkungen solcher Systeme auf das elektrische Verteilnetz untersucht. Dieser Artikel gibt einen Überblick über die entwickelten Methoden und Ergebnisse aus diesem Forschungsfeld mit dem Ziel, ein weitreichendes Verständnis für die Chancen und Grenzen des ADSM zu schaffen.
The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots.