兼顾经济效益与寿命损失的含储能配电网多时间尺度协调调度

Multi-Time Scale Optimal Dispatching Method of Active Distribution Network Considering the Influence of Cycle Life for Energy Storage System

作为一种优质的快速调节资源,电储能在系统调峰调频等关键场景中发挥重要作用的同时,依然面临着老化速率较快、规模化应用成本较高、经济效益难以保障等困境。对此,在深入挖掘储能系统经济-寿命影响特性基础上,提出了一种考虑储能系统循环寿命影响的主动配电网多时间尺度优化调度方法。首先,评估储能系统循环寿命与充放电深度间关联关系,并据此建立一种均衡考虑深度充放电场景下寿命损失与经济收益的储能系统精细化运行模型,以充分挖掘储能系统灵活调节潜力。其次,结合模型预测控制理论,提出一种考虑储能运行效益与寿命损失的主动配电网多时间尺度协调调度框架,以实现调峰等关键场景下系统储能容量的高效利用。该架构在日内阶段,建立考虑储能系统深度充放电影响的运行策略,并协调长短时间尺度下的快、慢响应特性资源;在实时阶段,基于超短期预测信息建立反馈校正调节模型,对储能等快速响应资源进行实时修正以跟踪新能源波动。最后,通过算例分析验证了所提方法可显著提升储能在调峰等关键场景下的利用效率,并有效改善含储能配电网的运行经济性与可靠性。

As a high-quality,fast-regulating resource,electric energy storage plays a critical role in key scenarios such as system peak shaving and frequency regulation.However,it still faces challenges such as high degradation rates,high costs for large-scale applications,and uncertain economic benefits.To address these issues,this study explores the economic-life characteristics of energy storage systems and proposes a multi-timescale optimal dispatch method for active distribution networks,considering the impact of energy storage system cycle life.First,the correlation between the cycle life of energy storage systems and the depth of charge/discharge is evaluated,and a refined operation model is developed to balance life loss and economic benefits under deep charge/discharge scenarios,aiming to optimize the flexible regulation potential of energy storage systems.Second,by integrating model predictive control,a multi-timescale optimal dispatch framework is proposed for active distribution networks.This framework considers operational benefits and life loss,ensuring efficient use of energy storage capacity in critical scenarios like peak shaving.This framework establishes an operational strategy considering the impact of deep charge/discharge of energy storage systems during the intraday stage and coordinates heterogeneous response characteristics across different timescales,and during real-time operations,feedback corrections are made based on ultra-short-term forecasting information.This enables real-time adjustments of rapidly responsive resources,such as energy storage systems,to track fluctuations in renewable energy generation effectively.A case study verified that the proposed method significantly enhances the efficiency of energy storage utilization in peak shaving and improves the economic efficiency and reliability of distribution systems with energy storage.