Capacity allocation of hybrid solar-wind energy system based on discrete probabilistic method

Complementary renewable energies like wind and solar power may be more sufficient to satisfy reliability requirements. This paper proposes a quantitative capacity allocation method of a hybrid wind and solar energy system. First, discrete probability distributions are established to model the random factors including the volatility of power outputs and the failure of components. Then a multiobjective optimization model is formulated with objectives of minimization of the total investment, the nodal voltages violating limits probability, and power supply inadequacy probability. For the purpose of fast probability computing with a satisfactory precision degree, an innovative probabilistic load flow algorithm is introduced, which deals with means and increments of random variables separately and uses cumulants as well as Gram--Charlier series to obtain probabilistic distributions of state variables. A modified parallel elitist nondominated sorting genetic algorithm II is used to search the Pareto optimal configuration solutions.

Anahtar Kelimeler:

Wind turbine, photovoltaic, capacity allocation, discrete probability distribution, multiobjective optimization, probabilistic load flow, NSGA-II

Capacity allocation of hybrid solar-wind energy system based on discrete probabilistic method

Keywords:

Wind turbine, photovoltaic, capacity allocation, discrete probability distribution, multiobjective optimization, probabilistic load flow, NSGA-II,

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Conclusions and outlook
In this paper, a quantitative configuration model of a hybrid wind and solar system is proposed. The premise of the model is that all random variables of the system must be independent of each other, but in terms of the actual system, this is sometimes too idealistic. In future research, the quantitative capacity configuration of a hybrid system involving correlated random variables will be discussed.
The simulation platform in this paper is a PC equipped with a pair of Intel Xeon 2.33 GHz quad-core CPU and 8 GB DDR3 memory. The speedup factor [20] of the NSGA-II with the population size of 50 and maximum evolution generation of 50 run parallelly on 8 processors reached 12.32. To further enhance optimization speed, we plan to use computer clusters to replace the current single machine with multicore mode.
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