Operation management of residential energy-supplying networks based on optimization approaches. (1st December 2016)
- Record Type:
- Journal Article
- Title:
- Operation management of residential energy-supplying networks based on optimization approaches. (1st December 2016)
- Main Title:
- Operation management of residential energy-supplying networks based on optimization approaches
- Authors:
- Wakui, Tetsuya
Kawayoshi, Hiroki
Yokoyama, Ryohei
Aki, Hirohisa - Abstract:
- Highlights: Operation management system for residential energy supply networks is developed. Energy demand prediction, operation planning, and operation control is integrated. Energy demands are predicted by using support vector regression. Energy demand prediction and MILP-based operation planning is updated in the day. Developed system saves energy consumption in residential energy supply network. Abstract: An operation management system for residential energy-supplying networks using multiple cogeneration units was developed by hierarchically integrating energy demand prediction, operational planning, and operational control, using optimization approaches. The energy demand for multiple dwellings was predicted by support vector regression with information on occupant behavior as well as forecasted weather and energy demand history. Mixed-integer linear programming was employed for the operational planning of the cogeneration units to the predicted energy demand. The energy demand prediction and operational planning were updated using a variable frequency receding horizon approach. This was done to limit the unnecessary shutdown and start-up of the cogeneration units and to reduce the influences of prediction errors for energy demand. Regarding the operational control, the actual on–off schedule of the cogeneration units conformed to the operational planning result. Additionally, the power and heat outputs of the cogeneration units and the heat supply from the storageHighlights: Operation management system for residential energy supply networks is developed. Energy demand prediction, operation planning, and operation control is integrated. Energy demands are predicted by using support vector regression. Energy demand prediction and MILP-based operation planning is updated in the day. Developed system saves energy consumption in residential energy supply network. Abstract: An operation management system for residential energy-supplying networks using multiple cogeneration units was developed by hierarchically integrating energy demand prediction, operational planning, and operational control, using optimization approaches. The energy demand for multiple dwellings was predicted by support vector regression with information on occupant behavior as well as forecasted weather and energy demand history. Mixed-integer linear programming was employed for the operational planning of the cogeneration units to the predicted energy demand. The energy demand prediction and operational planning were updated using a variable frequency receding horizon approach. This was done to limit the unnecessary shutdown and start-up of the cogeneration units and to reduce the influences of prediction errors for energy demand. Regarding the operational control, the actual on–off schedule of the cogeneration units conformed to the operational planning result. Additionally, the power and heat outputs of the cogeneration units and the heat supply from the storage tanks were modulated in response to the actual energy demand, based on predefined rules. The developed operation management system was applied to annual operation simulation of a residential energy-supplying network consisting of four cogeneration units using fuel cells in a housing complex. For comparative analysis, history-based approaches for energy demand prediction and separate operation of each cogeneration unit were also considered. The results revealed the effectiveness of the developed operation management system as well as the high energy-saving performance of the residential energy-supplying network. … (more)
- Is Part Of:
- Applied energy. Volume 183(2016)
- Journal:
- Applied energy
- Issue:
- Volume 183(2016)
- Issue Display:
- Volume 183, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 183
- Issue:
- 2016
- Issue Sort Value:
- 2016-0183-2016-0000
- Page Start:
- 340
- Page End:
- 357
- Publication Date:
- 2016-12-01
- Subjects:
- Energy management -- Microgrid -- Cogeneration -- Model predictive control -- Support vector regression -- Mixed-integer linear programming
Power (Mechanics) -- Periodicals
Energy conservation -- Periodicals
Energy conversion -- Periodicals
621.042 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03062619 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apenergy.2016.08.171 ↗
- Languages:
- English
- ISSNs:
- 0306-2619
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1572.300000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7485.xml