Hybrid green energy system, consisting of photovoltaic (PV), fuel cell and battery, receives wide attention because of its autonomy, flexibility and promising potential in accelerating the development of carbon neutrality in the field of power generation. However, the efficient power dispatching of the hybrid energy system is challenging due to the inevitable uncertainties of the solar energy. To this end, stochastic dynamic programming (SDP) is used in this paper to find the optimal solution to minimize the total fuel consumption during a 72-hour operating cycle, with constraints on the magnitude and rate of the fuel cell and battery operation, in which the stochastic characteristics of the solar power are described by Markov chain. The influence of sampling time and number of states on the solar prediction accuracy is discussed. For comparison, the traditional rule-based algorithm and dynamic programming (DP) algorithms are also utilized to describe and solve the power distribution problem, corresponding to different decision results in terms of how to distribute the energy flow for each time period. Numerical optimization results within the 72-hour period demonstrate that, the SDP has a 20.61% economy and 66.34% battery SOC improvement than that of rule-based algorithm, and in most uncertain cases, the SDP produces superior economic performance than those of both the rule-based algorithm and DP, benefited from the inclusion of the solar power probabilities into the optimization framework. The results of this paper lay a solid foundation for the efficient energy management of the hydrogen and solar hybrid energy system.

由光伏（PV）、燃料电池和电池组成的混合绿色能源系统因其自主性、灵活性和在加速发电领域碳中和发展的潜力而受到广泛关注。然而，由于太阳能不可避免的不确定性，混合能源系统的高效电力调度具有挑战性。为此，本文使用随机动态规划 (SDP) 来寻找最优解，以在 72 小时运行周期内最小化总燃料消耗，同时限制燃料电池和电池运行的幅度和速率，在用马尔可夫链描述太阳能的随机特性. 讨论了采样时间和状态数对太阳预测精度的影响。为了比较，传统的基于规则的算法和动态规划（DP）算法也被用来描述和解决功率分配问题，对应于不同的决策结果如何分配每个时间段的能量流。72小时内的数值优化结果表明，SDP比基于规则的算法具有20.61%的经济性和66.34%的电池SOC提升，并且在大多数不确定情况下，SDP产生的经济性能优于两者。基于规则的算法和 DP，得益于将太阳能概率纳入优化框架。