Allocation Model of Non-hydro Renewable Energy Power Quota among Provinces Based on the Bi-level Programming Approach
The advancement of renewable energy power is a vital impetus for China's energy structure reform,and the development of a scientific non-hydro renewable energy power quota allocation scheme is an important guarantee for achieving the 2030 carbon peak and 2060 carbon neutrality goals.In June 2020,the National Development and Reform Commission and the National Energy Administration jointly introduced the 2020 renew-able energy power consumption responsibility weights for each provincial-level administrative region.In practice,the effectiveness of these policies has been suboptimal.Thus,devising a rational allocation plan for non-hydro renewable energy power quotas is a significant issue that demands concentrated research.In recent years,numerous scholars have conducted research on the implementation issues of the renewable portfolio standard policy.From a methodological perspective,existing research primarily employs optimization models,which overlook the heterogeneity of government interests in the allocation process of non-hydro renewable energy power quotas.In reality,the central and local governments,as the formulators and implementers of the renewable portfolio standard,have different objectives and demands.This issue represents a typical leader-follower bi-level optimization problem.From a research perspective,the existing literature mainly focuses on minimizing the total system cost,without considering the impact of subsidy costs and the environmental improve-ments brought by carbon dioxide emission reductions.In light of this,this paper is based on China's current industry management system and thoroughly considers the diverse interests of the central and local governments.By designating the central government as the upper-level decision-maker and local governments as the lower-level decision-makers,it integrates subsidy costs into the central government's quota allocation objectives.A provincial non-hydro renewable energy power quota allocation model based on bi-level multi-objective nonlinear programming is developed.Utilizing relevant data from 30 provinces in China and employing a genetic algorithm to solve the model,an optimal allocation scheme that balances cost,environment,and equity is achieved.This allocation scheme's superiority is validated by comparing it with the current government allocation scheme.Furthermore,this study computes the proportions and execution rates of non-hydro renewable energy power quotas for the 30 renewable energy-generating provinces under various central government objective preference scenarios.The allocation schemes under different scenarios are analyzed and discussed.This will help the government more scientifically optimize the setting of non-hydro renewable energy power quota allocation schemes.The research findings reveal that:(1)Some local governments(e.g.,Jilin,Henan,Yunnan)exhibit limited willingness to adhere to the central government's quota scheme,while others(e.g.,Liaoning,Xinjiang,Gansu)display notable enthusiasm,even surpassing their targets.(2)In comparison to the government alloca-tion scheme,the bi-level optimization approach leads to lower subsidy costs,reduced emission reduction costs,and decreased energy substitution costs,while also promoting better equity.(3)Although there are variations in Gini coefficients calculated using different indicators,all Gini coefficients for the bi-level optimization scheme are below 0.2,indicating a high level of fairness.(4)Despite some differences in non-hydro renewable energy power quota allocation schemes under various scenarios,the trends align with real-world conditions.These results suggest that the model exhibits robust internal consistency and can offer valuable insights to the govern-ment for policy-making under different circumstances.Future research will further explore topics such as cross-regional power trading to enhance the alignment of renewable portfolio standard studies with practical realities.
non-hydro renewable energy powerquota allocationbi-level multi-objective programming
万方数据
维普
