The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management.
PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.
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