Abstract: 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 renewable 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 improvements 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 allocation 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 government 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.

Key words: non-hydro renewable energy power, quota allocation, bi-level multi-objective programming

摘要： 立足中国当前的行业管理体制,充分考虑中央政府和地方政府利益诉求的异质性,构建了基于二层多目标非线性规划的省际非水可再生能源电力配额分配模型,基于中国30个省区的相关数据资料,得到了一个兼顾成本、环境与公平的最优分配方案。结果显示,由于各省区能源替代成本和减排成本的不同,各省区在执行中央政府分配的指标任务时的实施意愿存在较为明显的差异。比较分析结果表明:与政府分配方案相比,二层优化方案节省了42.22亿元的补贴成本,降低了2595.12亿元的能源替代成本,并减少了791.39亿元的减排成本。同时,求解得到的二层优化方案的基尼系数小于0.2,属于绝对公平的范畴。该模型可以真实反映中国可再生能源电力配额分配博弈过程的复杂动态,为政府制定非水可再生能源电力配额方案提供更为有效的决策工具和参考。

关键词: 非水可再生能源电力, 配额分配, 二层多目标规划