- Title
- Model predictive control of distributed air-conditioning loads for mitigation of solar variability
- Creator
- Mahdavi, Nariman; Braslavsky, Julio H.; Seron, Maria M.
- Relation
- 2016 Australian Control Conference (AuCC) . 2016 Australian Control Conference (Newcastle, N.S.W. 3-4 November, 2016) p. 162-167
- Publisher Link
- http://dx.doi.org/10.1109/AUCC.2016.7868181
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- The growth of distributed solar photovoltaic (PV) generation in low-voltage grids in recent years has raised concerns about power quality disruptions introduced by solar variability. In response, some electricity utilities have introduced restrictions to new PV installations, such as imposing limits to power ramp rates and exports to the grid. These restrictions effectively require PV installations to locally compensate rapid variations in power output, such as those arising from passing clouds, by using batteries, for example. The direct control of responsive loads to follow variations in solar capacity is a cost-effective alternative to batteries to mitigate impacts to the grid. This paper explores the application of model-based predictive control (MPC) of a population of air conditioners (ACs) to achieve optimal tracking of PV generation to compensate solar variability at a substation or distribution transformer aggregation level. The proposed approach exploits an existing model for aggregate AC demand to design an MPC scheme to modulate the demand of clusters of ACs to track fluctuations in PV capacity. The effectivity of the proposed MPC scheme to achieve AC load following and regulation at different levels of PV penetration is analysed in a numerical simulation study, which shows that solar variability can be reduced by 80 to 95% at PV capacities between 25 and 10% of the peak AC demand with minimal impact to end-user comfort levels.
- Subject
- sociology; statistics; aggregates; temperature control; load modeling; thermostats; predictive models
- Identifier
- http://hdl.handle.net/1959.13/1400007
- Identifier
- uon:34715
- Identifier
- ISBN:9781922107909
- Language
- eng
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