TY - CONF
T1 - Energy management of automatic dairy farms with integration in regional grids
AU - Bernhardt, H.
AU - Graeff, A.
AU - Woerz, S.
AU - Hoehendinger, M.
AU - Hoeld, M.
AU - Stumpenhausen, J.
N1 - Funding Information:
The project is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme.
PY - 2017
Y1 - 2017
N2 - The socially desired withdrawal from nuclear power in Germany is an opportunity for the agricultural sector to gain new markets by the marketing of energy. Dairy farms can produce more energy than consumed by itself through photovoltaic on the roof, wind turbines, geothermal energy and the use of manure in biogas plants. By automated dairy cow stables with milking (AMS), feeding (AFS) and cleaning robots the energy consumption of the stable has become steady and is much more flexible as in classical systems. The target of the designed energy management system is to steer production and consumption in a manner which guarantees that the energy consumption of the dairy farm is secured and also energy marketing is optimized. The welfare of the dairy cows has also to be considered to assure a high milk production. Therefore the energy management system has to be regulated by the animal parameters. By recording of animal parameters (milk production, feed intake, motion profile⋯), stable data (workload, charging cycles, operating specifications⋯) and climate data (temperature, solar irradiation, wind⋯) a short-, medium- and long-term energy profile can be calculated for the future. Within the scope of these profiles consumers in the barn can be switched on and off at short notice in order to be able to provide electricity for the public grid which is subsequently offset by capacity from the public grid. The dairy farms acts as regional grid compensation. The first practical results of the project and the basic structure of the planning algorithm for the energy profiles will be explained.
AB - The socially desired withdrawal from nuclear power in Germany is an opportunity for the agricultural sector to gain new markets by the marketing of energy. Dairy farms can produce more energy than consumed by itself through photovoltaic on the roof, wind turbines, geothermal energy and the use of manure in biogas plants. By automated dairy cow stables with milking (AMS), feeding (AFS) and cleaning robots the energy consumption of the stable has become steady and is much more flexible as in classical systems. The target of the designed energy management system is to steer production and consumption in a manner which guarantees that the energy consumption of the dairy farm is secured and also energy marketing is optimized. The welfare of the dairy cows has also to be considered to assure a high milk production. Therefore the energy management system has to be regulated by the animal parameters. By recording of animal parameters (milk production, feed intake, motion profile⋯), stable data (workload, charging cycles, operating specifications⋯) and climate data (temperature, solar irradiation, wind⋯) a short-, medium- and long-term energy profile can be calculated for the future. Within the scope of these profiles consumers in the barn can be switched on and off at short notice in order to be able to provide electricity for the public grid which is subsequently offset by capacity from the public grid. The dairy farms acts as regional grid compensation. The first practical results of the project and the basic structure of the planning algorithm for the energy profiles will be explained.
KW - Automatic control
KW - Cows
KW - Dairy farms
KW - Energy requirements
KW - Regional grid
KW - Renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85035357374&partnerID=8YFLogxK
U2 - 10.13031/aim.201700262
DO - 10.13031/aim.201700262
M3 - Paper
AN - SCOPUS:85035357374
T2 - 2017 ASABE Annual International Meeting
Y2 - 16 July 2017 through 19 July 2017
ER -