Optimal design of multi-energy complementary power generation system considering fossil energy scarcity coefficient under uncertainty

摘要

At present, most island energy supply is highly dependent on long-distance transportation of fossil energy, which give rise to high cost and risk of energy supply system. Therefore, establishment a multi-energy complementary power generation system (MECP) is an urgent need to realize a safe and efficient energy supply model in that region. In this study, a copula-based interval full-infinite programming (CIFP) method was developed for optimal design MECP. CIFP method not only can be used to represent a variety of uncertainties with interval values, functional intervals and probability distributions, but also can reflect the uncertainty between random variables with different probability distributions of unknown correlations. Then, based on CIFP method, a CIFP-MECP model formulated for optimal designing MECP in an island of South China Sea. In this model, considering eight fossil energy scarcity coefficients for nine constraint-violation levels. Results reveal that uncertainties existed in system components have significant effects on outputs of decision variables and system total cost, and variation of system cost reached [49.48%, 56.25%]. Results also disclose that the proportion of renewable energy generation increased gradually with the increase of fossil energy scarcity coefficient, and when Scarcity = 30, the proportion reached the maximum, accounting for [98.54%, 99.10%]. Through this study of island MECP, the use of renewable energy is increased and the proportion of fossil energy in energy structure is greatly reduced, so as to reduce the generation of pollutants and provide a feasible basis for the island to use clean energy. This study can help decision maker identify the dynamic characteristics of regional power generation system, determine the required decision-making schemes and analyze the effects of interactions among multiple uncertainties on system outputs. (C) 2020 Elsevier Ltd. All rights reserved.