A stochastic multi-objective optimization model for renewable energy structure adjustment management – A case study for the city of Dalian, China

Abstract

Due to the depletion of traditional resources and the deterioration of environmental quality, Dalian has always encouraged the explorations and utilizations of renewable energy sources. The research objective of this study is to develop a multi-objective stochastic chance constrained programming (MOSCCP) model for assisting local government to design and execute rational energy exploration and management strategies. The main advantage of this model is that it effectively broadens the decision maker's choice space and provides a more balanced optimization plan. A variety of renewable energy exploration schemes were identified under the reciprocal influence of different weight combinations and constraints-satisfaction levels. The calculation of power generation considering the variation in the meteorological factors was incorporated into the proposed optimization model, which effectively avoid the potential imbalance between electricity supply and demand under climate change. The obtained results verified that the abundant renewable energy sources in Dalian play an important complementary role to traditional energy, where the wind and solar energy always occupy a dominant position in the renewable energy utilization process with an optimal mix of around 83.6% of wind, 10.5% of solar, 5.8% of hydropower and 0.1% of biomass energy generation. In addition, the climate change scenario certainly altered the electricity demand and provision magnitudes, but did not markedly change the core role of wind and solar energy.