Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems
Alireza Maheri and
Ghanim A. Putrus
Energy, 2014, vol. 66, issue C, 677-688
Performance of a HRES (hybrid renewable energy system) is highly affected by changes in renewable resources and therefore interruptions of electricity supply may happen in such systems. In this paper, a method to determine the optimal size of HRES components is proposed, considering uncertainties in renewable resources. The method is based on CCP (chance-constrained programming) to handle the uncertainties in power produced by renewable resources. The design variables are wind turbine rotor swept area, PV (photovoltaic) panel area and number of batteries. The common approach in solving problems with CCP is based on assuming the uncertainties to follow Gaussian distribution. The analysis presented in this paper shows that this assumption may result in a conservative solution rather than an optimum. The analysis is based on comparing the results of the common approach with those obtained by using the proposed method. The performance of the proposed method in design of HRES is validated by using the Monte Carlo simulation approach. To obtain accurate results in Monte Carlo simulation, the wind speed and solar irradiance variations are modelled with known distributions as well as using time series analysis; and the best fit models are selected as the random generators in Monte Carlo simulation.
Keywords: Standalone hybrid systems; Hybrid wind–PV–battery; Reliability; Chance constrained programming; Design under uncertainties (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:66:y:2014:i:c:p:677-688
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Series data maintained by Dana Niculescu ().