A Stackelberg-based biomass power trading game framework in hybrid-wind/solar/biomass system: From technological, economic, environmental and social perspectives

JOURNAL OF CLEANER PRODUCTION(2023)

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摘要
Developing hybrid renewable energy systems (HRES) has been recognized as the best sustainable method for responding to global energy shortages. Introducing biomass power as backup into the HRES enables the improvement of the reliability of HRES powered by 100% renewable energy. But, the motivation behind this integration has been ignored and has inspired this study. In this study, a Stackelberg-based biomass power trading framework was designed to express resource integration and business collaboration between solar, wind, and biomass power operators for uninterrupted power feed-in. Subsequently, a bi-level multi -objective dynamics optimization model was developed to simulate the interplay between stakeholders in a hybrid 100% renewable energy system for the equilibrium between system reliability, profit requirements, environmental benefits, and social value. Finally, the biomass power trading framework and optimization method were successfully simulated in solar-wind-biomass HRES in the Qianjiang area, Chongqing City. The results demonstrated the effectiveness of the proposed methodology. The hybrid wind-solar-biomass renewable energy system could feed in power to the main grid around of 526 million kWh over the year, among which wind power contributes above 57%, and biomass power stations supply a quarter of the electricity. With 2.92% of unmet load rate, operators of HRES and biomass stations can improve their earnings by 0.02 and 0.06 CNY/per kWh compared to their actual operations, respectively. After the sensitivity analyses, valuable conclusions and suggestions about natural resource changes, power delivery strategies selection, and so on were drawn for reference by business operators and local authorities for the multi-dimensional sustainable development of HRES.
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关键词
Biomass power trading,Bio-waste treatment,Bi-level multi-objective optimization,Solar energy,Wind power
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