“…Theoretically, it can be calculated by the formulation of (7), where the loss of water head h loss indicates the energy loss in conversion due to viscous force and external resistances on water. Forebay water level depends on water storage, as formulated in (5), where z(•) is the specific function for the reservoir under concern. Tailwater level is variable based on hydraulic laws, which is relative to the speed of release.…”
Section: B the Forward Scheduling Modelmentioning
confidence: 99%
“…However, in a competitive context, the operational information of other reservoirs is usually inaccessible since a river basin generally consists of multiple stakeholders and all the stakeholders are individually rational and self-interested [4]. Cooperative behavior is believed can promote systemic benefits maximization [5]- [7]. But these behaviors generally involve information sharing, interest transferring, and even breaking regulatory requirements so that the situation is rarely achieved in practice [8], [9].…”
Section: Introductionmentioning
confidence: 99%
“…The literature contains numerous works concerning coordinated scheduling/bidding of hydropower cascades in the single-stakeholder perspective [10]- [13] and many examples concerning cooperative operations in a cooperative multistakeholder perspective [5], [14]- [17], but less attention has been paid to promoting cooperation-oriented operation in a competitive perspective.…”
“…Theoretically, it can be calculated by the formulation of (7), where the loss of water head h loss indicates the energy loss in conversion due to viscous force and external resistances on water. Forebay water level depends on water storage, as formulated in (5), where z(•) is the specific function for the reservoir under concern. Tailwater level is variable based on hydraulic laws, which is relative to the speed of release.…”
Section: B the Forward Scheduling Modelmentioning
confidence: 99%
“…However, in a competitive context, the operational information of other reservoirs is usually inaccessible since a river basin generally consists of multiple stakeholders and all the stakeholders are individually rational and self-interested [4]. Cooperative behavior is believed can promote systemic benefits maximization [5]- [7]. But these behaviors generally involve information sharing, interest transferring, and even breaking regulatory requirements so that the situation is rarely achieved in practice [8], [9].…”
Section: Introductionmentioning
confidence: 99%
“…The literature contains numerous works concerning coordinated scheduling/bidding of hydropower cascades in the single-stakeholder perspective [10]- [13] and many examples concerning cooperative operations in a cooperative multistakeholder perspective [5], [14]- [17], but less attention has been paid to promoting cooperation-oriented operation in a competitive perspective.…”
“…In terms of benefit allocation between different units, some studies have been conducted. Shen et al (Shen et al, 2018) allocated the appropriate benefit of multiple-reservoir cascaded hydropower plants by the game-theoretic Shapley method. Tan et al (Tan et al, 2013) used the Shapley method to study the benefit allocation of wind power and thermal power; the result showed that the method realized the equitable allocation among the units fully.…”
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.
“…Many scholars have studied short-term scheduling and produced many innovative research results, such as peak-shaving scheduling [1]- [5], maximizing the total benefit [6]- [8], and minimizing the total fuel cost [9], [10]. However, due to the large number of HPs, useful forecasting information is limited, the forecasting accuracy is low, large and complex constraint sets are needed, and dimensionality issues can occur [11]- [13]; as a result, the current theoretical approach faces considerable challenges in terms of practicality, universality, and computational efficiency.…”
The short-term scheduling schemes of cascaded hydropower plants are based on day-ahead hydrological forecasting information. Affected by the accuracy of prediction, real-term hydrological information can considerably vary from previously forecasted values, especially for the power load and local inflows. As a result, short-term scheduling schemes are difficult to apply directly in real-time scheduling. To solve this problem, a rolling optimal hourly operation model for interbasin cascaded hydropower plants is proposed to rapidly balance the load demand. The model is deemed an ultrashort-term model, and it has a short-cycle schedule between short-term and real-time schemes. The basic strategy of solving the model is as follows. First, the day-ahead power load and local inflow information are corrected using a statistical method based on the most recent real-time information. Second, the water delay time, defined as the time between when water is released from upstream reservoirs and arrives at downstream reservoirs, is updated based on the most recent real-time information. Third, a heuristic method is proposed to dynamically update the short-term scheduling scheme over the next few hours. The objective is to minimize the maximum relative deviation between the actual water level of a reservoir at the end of 24 hours and the day-ahead forecasted value. This approach can keep the reservoir water level at the end of 24 hours close to the forecasted value and improve the accuracy of estimating the initial water level the next day. The developed method is applied to solve an ultrashort-term scheduling problem involving the cascaded hydropower plants located in Yunnan Province, China. The results indicate that the proposed model can achieve seamless coupling between shortterm scheduling and real-time scheduling. The proposed method can provide a scientific basis for the realtime dispatching of large-scale hydropower plants and improve the practicality of short-term dispatching schemes. INDEX TERMS Ultrashort term, water delay time, cascaded hydropower plants, real-time scheduling, optimal scheduling. NOMENCLATURE The notations used in the mathematical model of hydropower operation are given as follows. A. SETS M Set of reservoirs or hydropower plants (HPs) M 2 Set of HPs that can increase power generation M 3 Set of HPs that can reduce their power generation The associate editor coordinating the review of this manuscript and approving it for publication was Zhiyi Li .
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