A Two-Stage Dispatching Method for Wind-Hydropower-Pumped Storage Integrated Power Systems

With the fossil energy crisis and environmental pollution, wind energy and other renewable energy have been booming. However, the strong intermittence and volatility of wind power make difficult of its integration into grid. To solve this problem, this study proposes a complementary power generation model of wind-hydropower-pumped storage systems, which uses hydropower and pumped storage to adjust the fluctuation of wind power. How to consider the uncertainty and unpredictability of wind power output and make more reliable hydropower generation plan and pumped storage generation plan is the key problem to be solved in the grid with the high proportion of renewable energy. The martingale model of forecast evolution is used to describe the uncertainty evolution of wind power in different regions. According to the flexible load in the region, the flexibility index is used to quantify flexibility, and the transaction price is set to be proportional to flexibility. The two-stage framework of day-ahead and real-time dispatching model is then developed. In the day-ahead stage, different regions trade with each other. If the power after trading is imbalanced, it will be supplemented by hydropower and the grid to meet the power demand. In the real-time stage, the pumped storage is added to quickly balance the deviation of wind power and load between the real-time and day-ahead stages. Finally, considering the positive effect of hydropower on wind power consumption in the grid, a benefit allocation method based on improved Shapley value method is proposed. Test cases are simulated to verify the rationality of the proposed dispatching model and the benefit allocation method. After the cooperation of hydropower and pumped storage, the average revenue growth is 3.02%. The improved benefit allocation scheme makes more benefit of hydropower and pumped storage and promotes the cooperation of multi-participants.

Substrate traits govern the assembly and spatial organization of microbial community engaged in metabolic division of labor

Metabolic division of labor (MDOL) is widespread in nature, whereby a complex metabolic pathway is shared between different strains within a community for mutual benefit. However, little is known about how communities engaged in MDOL assemble and spatially organize. We hypothesized that when degradation of an organic compound is carried out via MDOL, substrate concentration and its toxicity modulate the benefit allocation between the two microbial populations, thus governing the assembly of this community. We tested this hypothesis by combining individual-based simulations with pattern formation assays using a synthetic microbial community. We found that while the frequency of the first population increases with an increase in substrate concentration, this increase is capped with an upper bound determined by the biotoxicity of the substrate. In addition, our model showed that substrate concentration and its toxicity affect levels of intermixing between strains. These predictions were quantitatively verified using an engineered system composed of two strains degrading salicylate through MDOL. Our results demonstrate that the structure of the microbial communities can be quantitatively predicted from simple environmental factors, such as substrate concentration and its toxicity, which provides novel perspectives on understanding the assembly of natural communities, as well as insights into how to manage artificial microbial systems.

Substrate traits govern the assembly and spatial organization of microbial community engaged in metabolic division of labor

Metabolic division of labor (MDOL) is widespread in nature, whereby a complex metabolic pathway is shared between different strains within a community for mutual benefit. However, little is known about how communities engaged in MDOL assemble and spatially organize. We hypothesized that when degradation of an organic compound is carried out via MDOL, substrate concentration and its toxicity modulate the benefit allocation between the two microbial populations, thus governing the assembly of this community. We tested this hypothesis by combining individual-based simulations with pattern formation assays using a synthetic microbial community. We found that while the frequency of the first population increases with an increase in substrate concentration, this increase is capped with an upper bound determined by the biotoxicity of the substrate. In addition, our model showed that substrate concentration and its toxicity affect levels of intermixing between strains. These predictions were quantitatively verified using an engineered system composed of two strains degrading salicylate through MDOL. Our results demonstrate that the structure of the microbial communities can be quantitatively predicted from simple environmental factors, such as substrate concentration and its toxicity, which provides novel perspectives on understanding the assembly of natural communities, as well as insights into how to manage artificial microbial systems.