Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems

• Published in: Energy (Oxford) Vol. 66; pp. 677 - 688
• Main Authors: Kamjoo, Azadeh, Maheri, Alireza, Putrus, Ghanim A.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2014; Elsevier
• Subjects: Applied sciences; Chance constrained programming; Computer simulation; Design engineering; Design under uncertainties; Energy; Equipments, installations and applications; Exact sciences and technology; Hybrid wind–PV–battery; Mathematical models; Monte Carlo methods; Natural energy; Photovoltaic conversion; Programming; Reliability; Renewable energy; Renewable resources; Solar energy; Standalone hybrid systems; Uncertainty; Wind energy; Design under uncertainties; Hybrid wind–PV–battery; Reliability; Chance constrained programming; Standalone hybrid systems; Monte Carlo method; Wind energy; Renewable energy; Reliability of supply; Battery; Photovoltaic array; Constraint logic programming; Numerical simulation; Electric power production; Modeling; Hybrid system
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2014.01.027

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. •Solving chance constrained problems with assumption of normal Gaussian distribution ignores a set of feasible solutions.•Proposed method solves chance constrained problem with no assumption on the type of joint distribution of uncertainties.•Depending on the site location different modelling methods should be used for wind speed and solar irradiance variations.