Pumped storage power station and conventional hydropower station combined dispatching method serving multiple power grid main bodies

Abstract

The invention discloses a combined dispatching method for pumped storage power stations serving multiple power grid main bodies and conventional hydropower stations, belongs to the technical field of water conservancy projects, and can solve the problems that the peak regulation capacity of an existing dispatching system is limited, and power resource optimization configuration oriented to the multiple power grid main bodies is difficult to support. The method comprises the following steps: S1, according to hydrological data and reservoir engineering data of a cascade reservoir group and power station engineering data of a pumped storage power station, constructing operation scenes of the cascade reservoir group and the pumped storage power station and an intra-day joint peak regulation scheduling model; s2, constructing a model constraint, and constructing a target function of multi-target optimization of the intra-day joint peak regulation scheduling model; s3, determining an optimal scheduling solution of the intra-day joint peak regulation scheduling model under each single target of the target function; and S4, determining a combined scheduling strategy of the cascade reservoir group and the pumped storage power station according to the optimal scheduling solutions under all the single targets. The method is used for combined dispatching of the pumped storage power station and the conventional hydropower station.

Description

Technical Field

[0001] This invention relates to a method for joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities, belonging to the field of water conservancy engineering technology. Background Technology

[0002] To address the urgent need for global warming and greenhouse gas emission reduction, since the 1990s, countries have successively signed numerous international agreements to promote global carbon emission reduction. Under this consensus, the world has turned its attention to renewable energy, driving the rapid development of global renewable energy over the past two decades. Solar and wind power accounted for a staggering 96.6% of newly installed renewable energy capacity in 2024. However, solar and wind power generation inherently possess randomness and intermittency. With their large-scale, high-penetration grid integration, the operational stability and economic feasibility of new power systems face significant challenges. While wind and solar power have large total installed capacity, they lack regulation capabilities, making it difficult to undertake grid peak-shaving tasks and hindering system absorption. Meanwhile, conventional hydropower growth has slowed significantly in recent years (only 16.2 GW added in 2024), while pumped storage, due to its strong peak-shaving capacity, flexible operation, and large installed capacity, is considered an important way to absorb global renewable energy.

[0003] In practice, a number of open-loop pumped storage projects, including the Warang Hydropower Station, have been planned and constructed in Northwest my country. Unlike the independent operation of traditional closed-loop pumped storage power stations, open-loop pumped storage power stations need to share water resources with existing river-type pumped storage power stations within the basin. The collaborative operation system formed by the two involves complex issues such as scheduling target coupling and reservoir capacity allocation competition, and its operational mechanism is far more complex than that of a single power station.

[0004] Traditional dispatching models often focus on the demand of a single power grid or the operation of isolated power plants, failing to fully leverage the coordinated dispatching potential of cascade reservoirs and pumped storage power stations, and neglecting the differences in load demand and their mutual influence among different power grid entities. This results in limited peak-shaving capacity of the joint dispatching system, making it difficult to support the precise issuance of peak-shaving tasks and the optimal allocation of power resources for multiple power grid entities. Summary of the Invention

[0005] This invention provides a method for joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities. It can solve the problem that the peak-shaving capacity of existing dispatching systems is limited and it is difficult to support the precise peak-shaving task assignment and power resource optimization for multiple power grid entities.

[0006] This invention provides a method for the joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities, the method comprising:

[0007] S1. Based on the hydrological data and reservoir engineering data of the cascade reservoir group, as well as the power station engineering data of the pumped storage power station, construct the operation scenario and intraday joint peak-shaving scheduling model of the cascade reservoir group and the pumped storage power station.

[0008] S2. Construct model constraints and, based on the demand information of multiple power grid entities, construct the objective function for multi-objective optimization of the intraday joint peak-shaving scheduling model;

[0009] S3. Based on the operating scenario and the model constraints, determine the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective of the objective function;

[0010] S4. Based on the optimal scheduling solutions under all single objectives, determine the joint scheduling strategy for the cascade reservoir group and the pumped storage power station.

[0011] Optionally, S3 specifically includes:

[0012] Based on the operating scenario, the intraday joint peak-shaving scheduling model and its model constraints, each single objective of the objective function is optimized to obtain multiple anchor points of the Pareto solution;

[0013] Multiple utopian points and their corresponding utopian constraints are determined based on the multiple anchor points, and the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective is determined based on the utopian constraints and the model constraints.

[0014] Optionally, multiple utopian points and their corresponding utopian constraints are determined based on the multiple anchor points, specifically including:

[0015] The line connecting the multiple anchor points is evenly divided to obtain multiple utopian points;

[0016] Transform each utopian point into its corresponding utopian constraint.

[0017] Optionally, S4 specifically includes:

[0018] The optimal scheduling solutions under all single objectives are sorted and filtered non-dominated to form a Pareto front, which is then used as the joint scheduling strategy for the cascade reservoir group and the pumped storage power station.

[0019] Optionally, the utopian constraints and the nonlinear constraints in the model constraints are both linearized using a special second-order ordered set.

[0020] Optionally, the model constraints include the average daily outflow from the reservoir, water balance constraints, water level constraints, flow through the pumps, head constraints for power generation and pumping, and power plant power constraints.

[0021] Optionally, each individual objective in the objective function is to maximize the peak-shaving demand satisfaction of the corresponding power grid entity.

[0022] The beneficial effects that this invention can produce include:

[0023] This invention provides a method for the joint dispatching of pumped-storage power stations and conventional hydropower stations serving multiple power grid entities. By transforming a multi-objective optimization problem into a single-objective optimization problem, the optimal solution for each single-objective optimization problem is obtained. The set of optimal solutions determines the joint dispatching strategy for cascade reservoirs and pumped-storage joint dispatching groups serving different power grid entities. This invention clarifies the feasible range of peak-shaving tasks when pumped-storage power stations and conventional hydropower stations jointly serve different power grids within a river basin, analyzes the mutual influence mechanism of their operational marginal benefits, and clarifies the peak-shaving capacity and power generation potential of the joint dispatching system in advance. This provides effective support for the accurate issuance of peak-shaving tasks to each power grid entity, and thus provides a scientific reference for the peak-shaving and frequency regulation decisions of the power system.

[0024] This invention provides a joint dispatching method for pumped storage power stations and conventional hydropower stations serving multiple power grid entities. By establishing an intraday joint dispatching model for cascade reservoirs and pumped storage power stations, and using the Pareto front as the core optimization direction, it achieves the global optimum of the dispatching scheme under the needs of multiple power grid entities. This transforms the "waste of peak-shaving capacity" caused by decentralized dispatching into "coordinated and efficient utilization," thereby improving the peak-shaving response speed and resource allocation efficiency of the power system, ensuring the safe and stable operation of each power grid entity and the capacity for renewable energy absorption. Simultaneously, by clarifying the feasible range of peak-shaving tasks and the interaction mechanism of marginal benefits, it identifies the operational potential of the joint dispatching system in advance, providing a scientific basis for power grid entities to accurately formulate peak-shaving plans. This invention uses multi-objective transformation optimization and Pareto front analysis as core technical support. In multi-power grid service scenarios, decision-makers can use this method to grasp the peak-shaving and generation capabilities of the dispatching system in advance, effectively supporting the rational allocation of peak-shaving tasks, reducing the risk of power supply and demand imbalance, and resulting in better load response and power supply quality for each power grid entity. Attached Figure Description

[0025] Figure 1 A flowchart of a method for joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities, provided in an embodiment of the present invention;

[0026] Figure 2 A schematic diagram illustrating the diverse operation modes of the Walang pumped storage power station provided in an embodiment of the present invention;

[0027] Figure 3 This invention provides a schematic diagram of the power output process of a cascade hydropower station under different pumped storage operation modes when the average daily outflow of a conventional hydropower station is 300 m³ / s.

[0028] Figure 4This invention provides a schematic diagram of the power output process of a cascade hydropower station under different pumped storage operation modes when the average daily outflow of a conventional hydropower station is 600 m³ / s.

[0029] Figure 5 This invention provides a schematic diagram of the power output process of a cascade hydropower station under different pumped storage operation modes when the average daily outflow of the conventional hydropower station is 900 m³ / s.

[0030] Figure 6 This invention provides a schematic diagram of the power output process of a cascade hydropower station under different pumped storage operation modes when the average daily outflow of a conventional hydropower station is 1200 m³ / s.

[0031] Figure 7 This invention provides a schematic diagram of the power output process of a cascade hydropower station under different pumped storage operation modes when the average daily outflow of a conventional hydropower station is 1600 m³ / s.

[0032] Figure 8 This invention provides a multi-objective optimization result for maximizing the peak-shaving capacity of the Warang pumped storage power station and the Laxiwa hydropower station when the average daily outflow of a conventional hydropower station is 300 m³ / s.

[0033] Figure 9 This invention provides a multi-objective optimization result for maximizing the peak-shaving capacity of the Warang pumped storage power station and the Laxiwa hydropower station when the average daily outflow of a conventional hydropower station is 600 m³ / s.

[0034] Figure 10 This invention provides a multi-objective optimization result for maximizing the peak-shaving capacity of the Warang pumped storage power station and the Laxiwa hydropower station when the average daily outflow of a conventional hydropower station is 900 m³ / s.

[0035] Figure 11 This invention provides a multi-objective optimization result for maximizing the peak-shaving capacity of the Warang pumped storage power station and the Laxiwa hydropower station when the average daily outflow of a conventional hydropower station is 1200 m³ / s.

[0036] Figure 12 The multi-objective optimization results provided in this embodiment of the invention maximize the peak-shaving capacity of the Warang pumped storage power station and the Laxiwa hydropower station when the average daily outflow of a conventional hydropower station is 1600 m³ / s. Detailed Implementation

[0037] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0038] This invention provides a method for the joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities, such as... Figure 1 As shown, the method includes:

[0039] S1. Based on the hydrological data and reservoir engineering data of the cascade reservoir group, as well as the power station engineering data of the pumped storage power station, construct the operation scenario and intraday joint peak-shaving scheduling model of the cascade reservoir group and pumped storage power station.

[0040] The reservoir engineering data for the cascade reservoir group includes reservoir capacity parameters and power generation efficiency; the power station engineering data for the pumped storage power station includes upper and lower reservoir capacities and pumping / power generation coefficients.

[0041] In this invention, the operating scenarios include the daily operation mode of the pumped storage power station, the average daily comprehensive utilization flow, and the initial regulating water level of the reservoir.

[0042] In practical applications, multiple differentiated operating scenarios can be constructed to determine the joint scheduling strategy for cascade reservoir groups and pumped storage power stations under different operating scenarios.

[0043] S2. Construct model constraints and, based on the demand information of multiple power grid entities, construct the objective function for multi-objective optimization of the intraday joint peak-shaving dispatch model.

[0044] The core objective of developing conventional pumped storage power stations and combined pumped storage power stations is to strengthen the peak-shaving support capacity of the new power system and solve the problems of power absorption and stable power supply under the background of high proportion of renewable energy grid connection. From the perspective of power source operation characteristics, the typical operation mode of conventional peak-shaving power sources such as hydropower is: to generate full capacity during the morning and evening peak load periods to ensure power demand; and to actively reduce output during the midday and other peak renewable energy periods to make room for wind and solar power grid connection. Pumped storage power stations form a complementary operation pattern: during the morning and evening peak load periods, they cooperate with hydropower to supply power during peak load periods, and during the peak renewable energy periods or off-peak periods, they operate in a pumping mode to efficiently absorb surplus wind and solar power, realizing the spatial and temporal transfer of power.

[0045] Based on this, this invention specifically constructs peak-shaving optimization models for two types of pumped-storage power stations, with the core objective of maximizing the overall peak-shaving efficiency of the system. The peak-shaving model for conventional pumped-storage power stations focuses on the ultimate optimization of a single peak-shaving objective; while the combined pumped-storage and conventional hydropower stations, due to their more complex operating mechanisms, have peak-shaving model optimization objectives divided into two-way collaborative dimensions: first, maximizing the combined peak-shaving power supply of pumped-storage and conventional hydropower stations during morning and evening peak load periods to improve peak load guarantee capacity; second, minimizing the power generation of conventional hydropower stations during periods of high renewable energy generation or low load periods to reserve more renewable energy consumption space, while maximizing the pumped-storage power generation to fill valleys, fully absorbing seasonal surplus power, forming a full-time optimization pattern of "peak-to-peak and valley-to-valley filling".

[0046] The model constraints include daily average outflow constraints from the reservoir, water balance constraints, water level constraints, turbine flow constraints, power generation and pumping head constraints, and power plant power constraints; specifically as follows:

[0047] (1) Daily average outflow constraint of the reservoir:

[0048] ;

[0049] In the formula, Indicates the first The average daily outflow of the hydropower station; Indicates the first The hydroelectric power station is Outbound flow during a specific time period.

[0050] (2) Water balance constraints:

[0051] ;

[0052] In the formula, and These respectively represent the reservoir at Start and end of time period Water storage at the end of the period; and These represent the pumped storage power stations at the [number]th [year]. Pumping and power generation flow rates during specific time periods; and They represent The inflow and outflow of the pumped storage power station's reservoir are monitored at all times.

[0053] (3) Water level constraint:

[0054] ;

[0055] ;

[0056] In the formula, and These represent the initial and final water levels of the reservoir during the day; , and These represent the dead water level, normal water level, and [other water levels] of the reservoir, respectively. The water level is constantly monitored.

[0057] (4) Throughflow constraint:

[0058] ;

[0059] In the formula, For the first Minimum flow rate of each power station; For the first The actual flow rate of each power station For the first The maximum flow rate of each power station.

[0060] (5) Power generation and pumping head constraints:

[0061] ;

[0062] In the formula, , , The first Minimum operating head for individual power stations, pumped storage pumping, and pumped storage power generation; , , The first The actual operating head of each power station, pumped storage pumping and pumped storage power generation; , , The first The maximum operating head of each power station, pumped storage pumping and pumped storage power generation.

[0063] (6) Power constraints of conventional hydropower stations and pumped storage power stations:

[0064] ;

[0065] In the formula, , , These refer to the forced output of conventional hydropower stations, pumped storage power stations during water pumping, and pumped storage power stations during power generation. , , These refer to the actual output of conventional hydropower stations, pumped storage power stations, and pumped storage power stations when pumping water and generating electricity, respectively. , , These represent the maximum output power of a conventional hydropower station, a pumped storage power station during water pumping, and a pumped storage power station during power generation, respectively.

[0066] Each individual objective in the objective function is to maximize the satisfaction of the peak-shaving demand of the corresponding power grid.

[0067] Specifically, taking the Northwest Power Grid and Qinghai Power Grid as examples, the demands of each power grid entity under different typical days are set as the objective function of multi-objective optimization. , );in To maximize the satisfaction of peak-shaving demand in the Northwest Power Grid, To maximize the satisfaction of peak-shaving needs of the Qinghai power grid.

[0068] S3. Based on the operating scenario and model constraints, determine the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective of the objective function.

[0069] Specifically, it includes:

[0070] Based on the operating scenario, the intraday joint peak-shaving scheduling model and its constraints, each single objective of the objective function is optimized to obtain multiple anchor points of the Pareto solution;

[0071] Multiple utopian points and their corresponding utopian constraints are determined based on multiple anchor points. Then, based on the utopian constraints and model constraints, the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective is determined.

[0072] Specifically, determining multiple utopian points and their corresponding utopian constraints based on multiple anchor points includes: firstly, uniformly dividing the lines connecting multiple anchor points to obtain multiple utopian points; and then converting each utopian point into its corresponding utopian constraint.

[0073] In this embodiment, the nonlinear constraints in both the utopian constraints and the model constraints are linearized using a special second-order ordered set (SOS2).

[0074] This invention transforms multi-objective solutions into single-objective solutions using the PNNC method. Specifically, it involves separately addressing (…). , The single-objective optimization yields the two extreme points of the Pareto front, also known as anchor points. These anchor points are then connected to form the utopian line. This line is then divided into equal parts to obtain multiple utopian points. These utopian points are expressed as utopian constraints using vectors, thus constructing the single-objective problem.

[0075] Only consider Optimization of peak-shaving capacity yielded the maximum peak-valley difference for the hydropower-adjustable Northwest power grid. At the same time, substitute the corresponding variables obtained from the optimization into The comprehensive cost is At this point, the first anchor point is obtained ( , );right Perform the same process to obtain the second anchor point ( , Thus, the two endpoints of the Pareto solution are obtained.

[0076] Then, the Utopia point is divided, and the number of divisions is set to divide the Utopia line into several parts, which will facilitate the next step of converting multiple objectives into a single objective.

[0077] To efficiently handle two scheduling objectives and ensure the convergence and distribution of the Pareto front, this invention employs the PNNC method to solve the multi-objective problem. The PNNC method transforms the solution of the multi-objective problem into the solution of a series of single-objective problems by adding new constraints. The optimal solution obtained from solving each single-objective problem is a point on the resulting Pareto front.

[0078] S4. Based on the optimal scheduling solutions under all single objectives, determine the joint scheduling strategy for the cascade reservoir group and pumped storage power stations.

[0079] Specifically, this includes: performing non-dominated sorting and screening of all single-objective scheduling optimal solutions to form a Pareto front, and using the Pareto front as a joint scheduling strategy for cascade reservoir groups and pumped storage power stations.

[0080] In practical applications, each multi-objective optimization problem is transformed into a single-objective optimization problem to obtain the optimal scheduling solution under each objective. Then, all single-objective optimal solutions are sorted and filtered in a non-dominated manner to form a set of non-dominated solutions. This set is the Pareto front (i.e., joint scheduling strategy) of the cascade reservoir and pumped storage joint scheduling group in serving different power grid main scenarios.

[0081] This invention proposes a scheduling optimization method applicable to multi-grid entity service scenarios of joint dispatch groups of cascade reservoirs and pumped storage power stations. It aims to systematically solve the core limitations of existing dispatching technologies and improve the response accuracy and power resource allocation efficiency of the joint dispatching system to the differentiated needs of multiple grid entities.

[0082] Traditional dispatching models often focus on the demand of a single power grid or the operation of isolated power plants, neglecting the potential for coordinated dispatching of cascade reservoirs and pumped storage power stations, as well as the differences in load demand and mutual influence among different power grid entities. This results in the peak-shaving capacity of the joint dispatching system not being fully utilized, making it difficult to support the precise peak-shaving task assignment and optimal allocation of power resources for multiple power grid entities. To overcome these technical challenges, this invention proposes a dispatching optimization method for a cascade reservoir and pumped storage joint dispatching group serving different power grid entities. The specific scheme is as follows: Based on the engineering parameters, hydrological characteristics, and power grid load demand of the cascade reservoir group and pumped storage power stations, an intraday joint dispatching model is constructed; the demand of each power grid entity under different typical days is set as a multi-objective optimization objective (…). , The PNNC method is used to transform the multi-objective optimization problem into a single-objective optimization problem. Each single-objective optimization problem is solved, and the Pareto front of the cascade reservoir and pumped storage joint dispatch group in serving different power grid main scenarios is determined by the optimal solution set.

[0083] This embodiment takes the Longyangxia Hydropower Station, Laxiwa Hydropower Station, and Guinan Warang Pumped Storage Power Station in the middle and upper reaches of the Yellow River as examples, and focuses on studying the competitive relationship between conventional hydropower stations and Warang Pumped Storage Power Station when serving different power grids under different operating modes of pumped storage power stations, as well as their typical power output processes.

[0084] Figure 2 For the diverse operation modes of the Warang pumped storage power station, such as Figure 2 As shown, this invention proposes four main pumped storage operation modes to adapt to the peak-shaving needs of the power grid. These four pumping modes are one pumping and one generating (…). Figure 2 (a) in the middle, one draw and two shots ( Figure 2 (b) of the above, multi-draw and multi-send mode 1 ( Figure 2 (c) and multi-draw / multi-send mode 2 ( Figure 2 The main difference between (d) and Guinanwa Pumped Storage Power Station lies in the different pumping time periods. Figure 3 The power output process of the cascade hydropower stations consisting of the Guinanwa Rang pumped storage station, Longyangxia hydropower station, and Laxiwa hydropower station, with a daily average outflow of 300 m³ / s, is described. Figure 3 (a) in Figure 3 The (d) in the text corresponds to four different peak-shaving modes, such as Figure 3 As shown, the power output of each hydropower station under the four peak-shaving modes is in line with the peak-shaving expectations of the Northwest Power Grid. The power output is increased during the peak load period of the power grid, and the power output is forced during other periods.

[0085] Figure 4This is the output process of cascade hydropower stations under the condition that the average daily outflow of a conventional hydropower station is 600 m³ / s. At this time, the average daily outflow of Longyangxia Hydropower Station exceeds its maximum flow required for peak regulation. Therefore, Longyangxia Hydropower Station must increase its output during off-peak hours, resulting in a decrease in the peak regulation capacity of Longyangxia Hydropower Station. However, the peak regulation capacity of Laxiwa Hydropower Station and Walang Pumped Storage Power Station has not reached the limit and still has room for improvement.

[0086] Figure 5 The power output process of the cascade hydropower stations consisting of the Guinanwa Rang pumped storage station, Longyangxia hydropower station, and Laxiwa hydropower station, with a daily average outflow of 900 m³ / s, is described. Figure 5 (a) in Figure 5 (d) in the figure corresponds to four different peak-shaving modes of the Guinan Warang pumped storage power station. At this time, as the average daily outflow of conventional hydropower stations further increases, the overall peak-shaving capacity of the cascade hydropower stations begins to decline significantly: the peak-shaving capacity of conventional hydropower stations (Longyangxia Hydropower Station and Laxiwa Hydropower Station) has declined significantly. The average daily outflow of Longyangxia Hydropower Station far exceeds the maximum flow required for its peak-shaving, and it needs to significantly increase its output during off-peak hours to absorb the excess water, which greatly reduces the flexibility of peak-shaving; the outflow of Laxiwa Hydropower Station is also close to its peak-shaving capacity boundary, and the proportion of forced output during off-peak hours increases, which further compresses the peak-shaving space; while the peak-shaving capacity of Guinan Warang pumped storage power station is not affected by the flow change and remains stable. Its peak-shaving mode can still normally adapt to the peak-shaving expectations of the Northwest Power Grid, accurately replenishing energy during the peak load period of the power grid, and there is no phenomenon of peak-shaving capacity decay.

[0087] Figure 6 This describes the power output of a cascade hydropower station under a conventional daily outflow of 1200 m³ / s. Under this condition, the overall peak-shaving capacity of the cascade hydropower station continuously decreases, and the decrease is more pronounced than under the 900 m³ / s condition. The peak-shaving capacity of the conventional hydropower station further decreases: The Longyangxia Hydropower Station, limited by the high daily outflow, maintains a high output level during off-peak hours, almost losing its peak-shaving adjustment space and unable to effectively regulate peak loads according to grid load changes; the Guinan Warang pumped storage power station, however, maintains a stable peak-shaving capacity, unaffected by the high flow of the conventional hydropower station, and can still operate normally according to the four peak-shaving modes, continuously providing peak-shaving support for the grid during peak load periods, becoming the core support unit for peak-shaving in the cascade hydropower station system.

[0088] Figure 7This describes the power output of a cascade hydropower station under a conventional hydropower station with a daily outflow of 1600 m³ / s. At this point, the overall peak-shaving capacity of the cascade hydropower station drops to a low level, mainly due to the significant decline in the peak-shaving capacity of the conventional hydropower station. The Longyangxia Hydropower Station has entered flood discharge mode, requiring maximum turbine flow and water wastage during off-peak hours, making it unable to adjust its output according to grid load fluctuations, rendering its peak-shaving function essentially ineffective. The Guinan Warang pumped storage power station, however, maintains a constant peak-shaving capacity, unaffected by the high-flow conditions of conventional hydropower stations. Its output under all four peak-shaving modes still meets the peak-shaving needs of the Northwest power grid, efficiently supplementing peak loads during peak hours and effectively alleviating the grid peak-shaving pressure caused by the failure of the conventional hydropower station's peak-shaving capacity. It is the only cascade hydropower station with effective peak-shaving capability.

[0089] Figure 8 To illustrate the competition in peak-shaving capacity among different hydropower stations, this study focuses on the competition between the Laxiwa Hydropower Station and the Guinanwa Pumped Storage Power Station. Since the Laxiwa Hydropower Station and the Guinanwa Pumped Storage Power Station share the Laxiwa Reservoir, there is potential competition between them in terms of water resource utilization and peak-shaving scheduling. Furthermore, the operation of the Guinanwa Pumped Storage Power Station will have a certain impact on the reservoir water level stability and power generation of the Laxiwa Hydropower Station. Figure 8 The Pareto solution in the equation represents the Pareto optimization result of the peak-shaving competition between the Laxiwa Hydropower Station and the Warang Pumped Storage Power Station under a daily average outflow of 300 m³ / s. It intuitively reflects the Pareto front corresponding to this set of Pareto solutions, presenting the optimal balance state of their peak-shaving capabilities. The results show that there is a clear competitive relationship between the Laxiwa Hydropower Station and the Warang Pumped Storage Power Station, and the degree of competition varies significantly under different pumping modes.

[0090] and Figure 9 , Figure 10 , Figure 11 and Figure 12 The figure shows the Pareto fronts for maximizing the peak-shaving capacity of the Laxiwa Hydropower Station and the Guinan Warang Pumped Storage Station under the following conditions: daily average outflow from the reservoir of conventional hydropower stations at 600 m³ / s, 900 m³ / s, 1200 m³ / s, and 1600 m³ / s. As shown in the figure, the Pareto fronts are not the same under these four conditions. This indicates that the daily average outflow from the conventional hydropower station affects the competitive relationship between the Warang Pumped Storage Station and the Laxiwa Hydropower Station, and that there is a certain degree of competition between the two under any condition.

[0091] Another embodiment of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the joint dispatching method for pumped storage power stations and conventional hydropower stations serving multiple power grid entities as described above.

[0092] The above descriptions are merely a few embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any modifications or alterations made by those skilled in the art without departing from the scope of the technical solution of the present invention using the disclosed technical content are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for joint dispatching of pumped storage power stations and conventional hydropower stations serving multiple power grid entities, characterized in that, The method includes: S1. Based on the hydrological data and reservoir engineering data of the cascade reservoir group, as well as the power station engineering data of the pumped storage power station, construct the operation scenario and intraday joint peak-shaving scheduling model of the cascade reservoir group and the pumped storage power station. S2. Construct model constraints and, based on the demand information of multiple power grid entities, construct the objective function for multi-objective optimization of the intraday joint peak-shaving scheduling model; S3. Based on the operating scenario and the model constraints, determine the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective of the objective function; S4. Based on the optimal scheduling solutions under all single objectives, determine the joint scheduling strategy for the cascade reservoir group and the pumped storage power station.

2. The method according to claim 1, characterized in that, S3 specifically includes: Based on the operating scenario, the intraday joint peak-shaving scheduling model and its model constraints, each single objective of the objective function is optimized to obtain multiple anchor points of the Pareto solution; Multiple utopian points and their corresponding utopian constraints are determined based on the multiple anchor points, and the optimal scheduling solution of the intraday joint peak shaving scheduling model under each single objective is determined based on the utopian constraints and the model constraints.

3. The method according to claim 2, characterized in that, Determining multiple utopian points and their corresponding utopian constraints based on multiple anchor points specifically includes: The line connecting the multiple anchor points is evenly divided to obtain multiple utopian points; Transform each utopian point into its corresponding utopian constraint.

4. The method according to claim 1, characterized in that, S4 specifically includes: The optimal scheduling solutions under all single objectives are sorted and filtered non-dominated to form a Pareto front, which is then used as the joint scheduling strategy for the cascade reservoir group and the pumped storage power station.

5. The method according to claim 2, characterized in that, Both the utopian constraints and the nonlinear constraints in the model constraints are linearized using a special second-order ordered set.

6. The method according to claim 1, characterized in that, The model constraints include the average daily outflow from the reservoir, water balance constraints, water level constraints, flow through the pumps, head constraints for power generation and pumping, and power plant power constraints.

7. The method according to claim 1, characterized in that, Each individual objective in the objective function is to maximize the satisfaction of the peak-shaving demand of the corresponding power grid entity.

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