Peak-shaving is a very efficient and practical strategy for a day-ahead hydropower scheduling in power systems, usually aiming to appropriately schedule hourly (or in less time interval) power generations of individual plants so as to smooth the load curve while enforcing the energy production target of each plant. Nowadays, the power marketization and booming development of renewable energy resources are complicating the constraints and diversifying the objectives, bringing challenges for the peak-shaving method to be more flexible and efficient. Without a pre-set or fixed peak-shaving order of plants, this paper formulates a new peak-shaving model based on the mixed integer linear programming (MILP) to solve the scheduling problem in an optimization way. Compared with the traditional peak-shaving methods that need to determine the order of plants to peak-shave the load curve one by one, the present model has better flexibility as it can handle the plant-based operating zones and prioritize the constraints and objectives more easily. With application to six cascaded hydropower reservoirs on the Lancang River in China, the model is tested efficient and practical in engineering perspective.
This paper presents a multireservoir flood control model that incorporates operating rules to alleviate the risk that results from traditional flood control models. The model is accurately reformulated into one that can be solved with the mixed integer linear programming (MILP) and approximated with a two‐stage linear programming (TSLP) to speed up the solution by excluding all the binary variables. A scroll decision‐making strategy is proposed by assuming only a few days of future inflows being predicted with certainty. With 10 historical and 30 designed floods in different magnitudes, the models and methods are applied to the Yangtze River basin, which includes 11 cascade reservoirs. The results on average show the MILP reduces the rule violation by 17%, while the TSLP is more than 150 times faster in CPU time. Excluding one of the reservoirs from the simulation reveals that the Three Gorges (TG) contributes the most in flood detention, accounting for 76% on average for once‐in‐a‐century floods. Interestingly, with the scroll decision‐making strategy implemented, the change from 3 to 7 days of certain forecasting has the most significant reduction in both the rule violation and flood detention, indicating that the forecasting accuracy of more recent inflows is more valuable for flood control.
Accounting information often accounts for more than 70% of an enterprise’s financial report information. Accounting information is an important reference for an enterprise to make major decisions, and it is also the fundamental guarantee for an enterprise to remain invincible under the increasingly fierce business competition. With the vigorous development of enterprise informatization, traditional accounting information processing methods can no longer meet the needs of the information age. Therefore, an excellent enterprise must have a complete set of intelligent accounting information systems. How to extract the information we want from the dazzling accounting information data is a hot topic in the current financial industry. On the basis of analyzing the significance of establishing an information system, this paper creates an intelligent recognition model, which solves the shortcomings of traditional methods such as large calculation errors, time-consuming, and labor-intensive. The research results of the article show that (1) the standardized coefficients of the four influencing factors of CSR, ROE, CEO, and SCALE are relatively large, indicating that these four influencing factors have a significant impact on the development of corporate accounting and you can pay attention to these four aspects. (2) To test the performance of the article model, the experiments are compared with other models. The results show that the model proposed in this paper has the highest running success rate on the two test sets, with a success rate of more than 98%, indicating that the model in this paper has certain advantages in accounting information processing. (3) In the page response time experiment, the financial module has the shortest response time, the number of tests is 60 times, the average response time is 0.5 s, and the success rate can reach 100%. It can reach 0.8 s, and the success rate can be kept above 98%, indicating that the system can work normally. In the system operation stability test, the number of test cases designed for the financial module is 70, the number of executed test cases is 70, and the execution rate can reach 100%. This means that the system can work properly and will not fail during operation.
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