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Marcus Venzke

Foto von Marcus Venzke
Dr. Marcus Venzke
Raum 4.086, Gebäude E
Am Schwarzenberg-Campus 3
21073 Hamburg
Telefon040 42878 - 3378
Fax040 427 - 3 - 10456
E-Mail

Ich bin seit 1997 im Institut für Telematik, ab 2004 als Oberingenieur. Meine private Homepage finden Sie unter www.MarcusVenzke.de.

Lehre

Projekte

Publikationen

Tobias Lübkert, Marcus Venzke und Volker Turau. Calculating retail prices from demand response target schedules to operate domestic electric water heaters. In Energy Informatics 2018, Oktober 2018. Oldenburg, Germany.
[BibTeX] [Abstract] [DOI]
@InProceedings{Telematik_EI_2018, author = {Tobias Lübkert and Marcus Venzke and Volker Turau}, title = {Calculating retail prices from demand response target schedules to operate domestic electric water heaters}, booktitle = {Energy Informatics 2018}, pages = , day = {11-12}, month = oct, year = 2018, location = {Oldenburg, Germany}, }
Abstract: The paper proposes a demand response scheme controlling many domestic electric water heaters (DEWHs) with a price function to consume electric power according to a target schedule. It discusses at length the design of an algorithm to calculate the price function from a target schedule. The price function is used by the control of each DEWH to automatically and optimally minimize its local heating costs. It is demonstrated that the resulting total power consumption approximates the target schedule. The algorithm was successfully validated by simulation with a realistic set of 50 DEWHs assuming perfect knowledge of parameters and water consumption. It is shown that the algorithm is also applicable to clusters of large numbers of DEWHs with statistical knowledge only. However, this leads to a slightly higher deviation from the target schedule.
Tobias Lübkert, Marcus Venzke, Nhat-Vinh Vo und Volker Turau. Understanding Price Functions to Control Domestic Electric Water Heaters for Demand Response. Computer Science - Research and Development, 81–92, Februar 2018.
[BibTeX] [Abstract] [DOI]
@Article{Telematik_Demand_Response_DEWH_2017, author = {Tobias Lübkert and Marcus Venzke and Nhat-Vinh Vo and Volker Turau}, title = {Understanding Price Functions to Control Domestic Electric Water Heaters for Demand Response}, pages = {81-92}, journal = {Computer Science - Research and Development}, volume = {}, month = feb, year = 2018, }
Abstract: A well-known mechanism for demand response is sending price signals to customers a day ahead. Customers then postpone or advance their usage of electricity to minimize cost. Setting up price functions that adapt the customers' load to availability is a big challenge. This paper investigates the feasibility of finding day-ahead price functions to induce a desired load profile of Domestic Electric Water Heaters (DEWHs) minimizing their electricity cost for demand response. Bilevel optimization is applied for a single DEWH using a simplified linear model and full knowledge. This leads to a solvable bilevel problem and allows understanding optimality of price functions and resulting heating profiles. It is shown that with the resulting price functions the DEWH may select many significantly different heating profiles leading to the same cost. Thus the price does not uniquely induce the desired heating profile. The acquired knowledge forms the basis for a procedure to create price functions for controlling the load profile of many DEWHs.
Tobias Lübkert, Marcus Venzke und Volker Turau. Appliance Commitment for Household Load Scheduling Algorithm: A Critical Review. In 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Oktober 2017. Dresden, Germany.
[BibTeX] [Abstract] [DOI]
@InProceedings{Telematik_SGC_2017, author = {Tobias Lübkert and Marcus Venzke and Volker Turau}, title = {Appliance Commitment for Household Load Scheduling Algorithm: A Critical Review}, booktitle = {2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)}, pages = , day = {23-26}, month = oct, year = 2017, location = {Dresden, Germany}, }
Abstract: The paper analyzes the behavior of two demand response algorithms, both claiming to minimize the energy cost regarding time-varying prices in an optimal way by iteratively scheduling heating phases of water heaters considering hot water consumption. Four issues of the well known algorithm by Du and Lu are identified, which lead to suboptimal behavior. Proposed enhancements lead to an algorithm similar to the second, recently published, method of Shah et al. The effect of each enhancement and its combinations are analysed simulatively reducing the costs.

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