PV-FC-BS混合系统全状态模型预测能量管理优化策略

Full-state model predictive energy management optimization for PV-fuel cell-battery hybrid system

光伏-燃料电池-蓄电池(Photovoltaic-Fuel Cell-Battery System,PV-FC-BS)混合系统因能有效解决光伏输出功率间歇性、波动性问题而得到广泛关注,克服各构成单元利用局限性同时实现各单元间的能量高效管理成为PV-FC-BS应用推广的关键。针对传统能量管理策略分别在BS开、关模式下建立不同预测模型,工作模式转换使系统在预测模型切换过程中稳定性难以保证的问题,提出新型BS充放电约束条件,构建全状态多输入多输出动态功率交换预测模型。通过将能量管理问题转化为控制优化问题,减小算法复杂度且避免模式切换影响,设计动态预测模型滚动寻优方法,实现PV-FC-BS混合系统能量最优分配。同时考虑BS使用寿命和充放电效率,提出BS参数自适应估计算法,提高能量调度准确性。在Matlab环境下对PV-FC-BS混合系统全状态模型预测能量管理优化策略进行仿真验证,结果表明:所提出的策略优化计算量小,能量分配准确,不同应用情形适应性强。

It is widely acknowledged that PV-fuel Cell-battery System （PV-FC-BS） hybrid energy storage system can effectively solve the intermittent and volatility of PV output power. And it is significant for the application and promotion of PV-FC-BS to overcome the limitations of each component utilization and realize efficient power dispatching. Traditional energy management strategy often creates separated prediction models in different switch modes. It is difficult to guarantee the stability in mode switching process. New BS charging-discharging constraints are proposed and full-state multiple-input-multiple-output dynamic power exchange model is built. Therefore such energy management problem is transformed into an optimization problem, reducing the complexity of algorithm and avoiding the impact of mode switching. PV-FC-BS hybrid system energy optimal allocation is realized by designing dynamic prediction model and quick rolling optimization methods. Taking charging or discharging efficiency coefficient of batteries into account, an adaptive estimation algorithm of battery constant charging-discharging parameter is proposed to achieve an accurate scheduling of energy. In the Matlab environment, PV-FC-BS hybrid system simulation results show that the proposed strategy has small optional calculation as well as accurate energy assignment and it is adaptable to different application conditions. This work is supported by National Natural Science Foundation of China （No. 51407114）, Natural Science Foundation of Shanghai （No. 15ZR1418200 and No. 15ZR1418000）, Shanghai Engineering Research Center of Green Energy Grid-connected Technology （No. 13DZ2251900）.