Decentralized operation strategies for an integrated building energy system using a memetic algorithm
The emerging technology in net-zero building and smart grids drives research moving from centralized operation decisions on a single building to decentralized decisions on a group of buildings, termed a building cluster which shares energy resources locally and globally. However, current research has focused on developing an accurate simulation of single building energy usage which limits its application to building clusters as scenarios such as energy sharing and competition cannot be modeled and studied. We hypothesize that the study of energy usage for a group of buildings instead of one single building will result in a cost effective building system which in turn will be resilient to power disruption. To this end, this paper develops a decision model based on a building cluster simulator with each building modeled by energy consumption, storage and generation sub modules. Assuming each building is interested in minimizing its energy cost, a bi-level operation decision framework based on a memetic algorithm is proposed to study the tradeoff in energy usage among the group of buildings. Two additional metrics, measuring the comfort level and the degree of dependencies on the power grid are introduced for the analysis. The experimental result demonstrates that the proposed framework is capable of deriving the Pareto solutions for the building cluster in a decentralized manner. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Rong, Aiying & Hakonen, Henri & Lahdelma, Risto, 2008. "A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems," European Journal of Operational Research, Elsevier, vol. 190(3), pages 741-755, November.
- Arroyo, Jose Elias Claudio & Armentano, Vinicius Amaral, 2005. "Genetic local search for multi-objective flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 167(3), pages 717-738, December.
- Hamalainen, Raimo P. & Mantysaari, Juha, 2002. "Dynamic multi-objective heating optimization," European Journal of Operational Research, Elsevier, vol. 142(1), pages 1-15, October.
- Rong, Aiying & Lahdelma, Risto & Luh, Peter B., 2008. "Lagrangian relaxation based algorithm for trigeneration planning with storages," European Journal of Operational Research, Elsevier, vol. 188(1), pages 240-257, July.
- Loukil, Taicir & Teghem, Jacques & Fortemps, Philippe, 2007. "A multi-objective production scheduling case study solved by simulated annealing," European Journal of Operational Research, Elsevier, vol. 179(3), pages 709-722, June.
- Lee, Wen-Shing & Chen, Yi -Ting & Wu, Ting-Hau, 2009. "Optimization for ice-storage air-conditioning system using particle swarm algorithm," Applied Energy, Elsevier, vol. 86(9), pages 1589-1595, September.
- Garcia-Gonzalez, Javier & Parrilla, Ernesto & Mateo, Alicia, 2007. "Risk-averse profit-based optimal scheduling of a hydro-chain in the day-ahead electricity market," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1354-1369, September.
- Rong, Aiying & Lahdelma, Risto, 2007. "An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning," European Journal of Operational Research, Elsevier, vol. 183(1), pages 412-431, November.
When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:185-197. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.