基于多粒度柔性负荷特性的多时间尺度混合电价型需求响应

Integrated Electricity Tariff-based Demand Response at Multi-time Scales Based on Multi-granularity Flexible Load Features

  • 摘要: 随着新型电力系统中可再生能源比例的不断提升,单一电价机制已难以满足系统的灵活性需求。为了充分挖掘电力柔性负荷资源,提出一种多时间尺度混合电价机制。该机制融合容需电价、分时电价、尖峰电价等定价策略,通过电价信号在多时间尺度上引导和鼓励用户调整用电行为。首先,通过时域分析提取用户负荷在不同时间尺度上的变化规律、分布情况及响应特性,评估响应潜力。随后引入多粒度建模思想,根据柔性负荷的容量规模和响应精度划分粒度层级并建立多粒度响应模型。最后,构建季节、日前、日内尺度混合电价优化框架,分别建立两部制电价、阶梯分时电价、尖峰电价和直接控制优化模型,开展电力需求响应。仿真结果表明,该机制能够促进用户负荷在多个时间尺度上转移,在改善负荷曲线、降低用电成本、缓解电网压力等方面表现出显著优势,也为电力市场精细化电价设计提供依据。

     

    Abstract: The increasing proportion of renewable energy in new power systems makes a singular electricity tariff mechanism no longer sufficient to meet the flexibility demands of these systems. A multi-time scale integrated electricity tariff mechanism is proposed to fully exploit the potential of flexible load resources. It combines various pricing strategies, including two-part tariff, time-of-use tariff, and critical peak tariff, aiming to guide and encourage consumers to adjust their electricity consumption behavior across multiple time scales through targeted tariff signals. Firstly, the time-domain analysis is utilized to extract the variation regulation, distribution, response characteristics on the demand side at various time scales, thereby assessing the response potential.Subsequently, the concept of multi-granularity modeling is introduced, which involves dividing the granularity level according to the capacity and response accuracy of flexible loads and establishing a multi-granularity response model. Finally, the optimization models of two-part tariff, stepwise time-of-use tariff, critical peak tariff and direct load control are established respectively, aiming to facilitate demand response within an integrated electricity tariff optimization framework at seasonal, day-ahead, and intraday scales. The simulation results indicate that the proposed mechanism effectively promotes the transfer of load resources across different time scales, thereby offering significant advantages in enhancing power load curve, reducing electricity bills, and alleviating grid pressure. The results also provide a theoretical foundation for the refined pricing design of the electricity market.

     

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