Short-term multi-objective optimization scheduling method for hybrid pumped storage power station

Abstract

The invention discloses a short-term multi-objective optimal scheduling method for a hybrid pumped storage power station, which belongs to the technical field of optimal scheduling of a power system and comprises the following steps of: acquiring initial input data; taking minimization of residual load mean square deviation as a peak regulation target, and taking minimization of lower reservoir tail water level daily variable amplitude and minimization of lower reservoir dam water level daily variable amplitude as navigation targets to construct a multi-target optimization scheduling model; performing mutual exclusion processing on reversible unit power generation and water pumping flow constraints to generate an initial solution, correcting the initial solution, solving the multi-target optimization scheduling model to obtain a Pareto solution set, dividing a Pareto leading edge distribution space, and selecting a feasible scheduling scheme with the maximum peak valley difference reduction rate as an optimal scheduling scheme; and selecting a final scheduling scheme from all the subspaces. The problems that in the prior art, peak regulation operation and navigation demand dynamic matching is insufficient, and a multi-source constraint coupling processing mechanism is lacked are solved.

Description

Technical Field

[0001] The present invention relates to a short - term multi - objective optimal dispatching method for a hybrid pumped - storage power station, belonging to the technical field of optimal dispatching of power systems. Background Technique

[0002] China's technology of hybrid pumped - storage power stations relying on large - scale cascade hydropower development has become an important direction for enhancing the flexibility of new power systems. This technology integrates conventional hydropower stations and pumped - storage units to form a composite system with power generation, energy storage, and regulation capabilities, effectively suppressing the intermittent fluctuations of new energy and achieving "peak shaving and valley filling" for the power grid. Existing research mainly focuses on optimizing the peak - shaving benefits of the power grid and the new - energy consumption capacity. By constructing a short - term dispatching model, it coordinates the flexible switching of reversible units between power - generation and pumping conditions, and uses intelligent algorithms to solve multi - objective optimization problems. Some results have been applied to actual projects, achieving remarkable results in improving the operation efficiency of power systems and promoting the consumption of clean energy.

[0003] Current research has over - emphasized the power - dispatching level and ignored the coordination mechanism between hybrid pumped - storage power stations and the comprehensive utilization tasks of reservoirs. In basins with prominent navigation requirements, conventional peak - shaving operations will cause daily - regulating power stations to frequently adjust the outflow discharge, resulting in drastic fluctuations in river - channel water levels. The addition of reversible units further exacerbates the complexity of upstream - downstream hydraulic coupling. Existing models mostly simplify navigation constraints as fixed water - level or flow - rate limits and do not establish a dynamic response relationship between peak - shaving operations and navigation conditions, leading to the invalidation of optimization results in practical applications due to non - satisfaction of navigation conditions. Especially in high - head and large - flow hybrid pumped - storage power stations, the lack of refined dispatching strategies to balance the contradiction between peak - shaving depth and navigation guarantee rate has become a key bottleneck restricting the large - scale promotion of this technology. Summary of the Invention

[0004] The object of the present invention is to provide a short - term multi - objective optimal dispatching method for a hybrid pumped - storage power station. By constructing a multi - objective optimization model that couples peak - shaving conditions and navigation conditions, and using an improved NSGA - Ⅱ algorithm to control the operating conditions of reversible units, it synchronously coordinates the multiple constraint relationships among power - generation flow, pumping flow, and navigation water levels, so as to solve the problems of insufficient dynamic matching between peak - shaving operations and navigation requirements and the lack of a coupling processing mechanism for multi - source constraints in the existing technology.

[0005] To solve the above - mentioned technical problems, the present invention is implemented by the following technical solutions.

[0006] The present invention provides a short - term multi - objective optimal dispatching method for a hybrid pumped - storage power station, including: Obtain initial input data, including basic data of the hydropower station, operation boundaries, actual operation data of the power grid on the dispatching day, and runoff data; According to the initial input data, under the constraints of conventional hydropower and the operation of pumped-storage units, a multi-objective optimal scheduling model is constructed with the goal of minimizing the mean square deviation of the remaining load for peak regulation, and the goals of minimizing the daily amplitude of the tail water level of the lower reservoir and the daily amplitude of the water level above the dam of the lower reservoir for navigation; After mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, an initial solution is generated, and then the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain the corrected initial solution; Using the NSGA-II algorithm, the multi-objective optimal scheduling model is solved according to the corrected initial solution, and the Pareto solution set is obtained through iterative optimization; According to the Pareto solution set, the Pareto front distribution space is divided into multiple sub-spaces; Within each sub-space, the feasible scheduling plan with the largest peak-valley difference reduction rate is selected as the optimal scheduling plan within the sub-space; One or more are selected from the optimal scheduling plans of all sub-spaces as the final scheduling plan.

[0007] Furthermore, the conventional hydropower constraints include: water balance constraint, storage capacity constraint, power generation flow constraint, conventional hydropower output constraint, initial and final storage capacity constraint, conventional hydropower output ramp constraint, outflow constraint, water level-storage capacity constraint, tail water level discharge constraint, head calculation constraint, and conventional hydropower output calculation constraint; The operation constraints of the pumped-storage units include: power calculation constraint, power generation and pumping flow constraints of reversible units, reversible unit output constraint, and daily pumped water volume constraint of reversible units.

[0008] Furthermore, the water balance constraint includes the upper reservoir water balance constraint and the lower reservoir water balance constraint. The hybrid pumped-storage power station contains two reservoirs, an upper and a lower reservoir, and each reservoir has a power station. The reversible units are installed in the upper reservoir power station. The upper reservoir water balance constraint and the lower reservoir water balance constraint are respectively expressed as: ; ; In the formula, 1 represents the upper reservoir, 2 represents the lower reservoir, represents the storage capacity of the upper reservoir at time period, represents the storage capacity of the upper reservoir at time period, represents the inflow of the upper reservoir at time period, represents the outflow of the upper reservoir at time period, , respectively represent the reversible units at Pumping flow and power generation flow during the period, Indicates the length of the period; Indicates the lower reservoir Storage capacity during the period, Indicates the lower reservoir Storage capacity during the period, Indicates the lower reservoir Inbound flow during the period, Indicates the lower reservoir Outbound flow during the time period; The storage capacity constraint is expressed as: ; Where, It represents the initial storage capacity of the mth reservoir in period t, where m is 1 or 2. and Respectively represent Reservoirs The upper and lower limits of storage capacity for the time period; The power generation flow constraint is expressed as: ; Where, Indicates the Reservoirs The power generation flow during the period, and Respectively represent Reservoirs The upper and lower limits of power generation flow during the time period; The conventional hydropower output constraint is expressed as: ; Where, Indicates the Reservoir power station Conventional hydropower output during the period, and Respectively represent Reservoir power station The upper and lower limits of conventional unit output during the time period; The initial and final storage capacity constraints are expressed as: ; Where, represents the initial storage capacity of the mth reservoir, represents the final storage capacity of the mth reservoir, 、 Respectively represent The initial storage capacity control value and final storage capacity control value of each reservoir, represents the total scheduling period, ; The conventional hydropower output ramp constraint is expressed as: ; In the formula, represents the maximum allowable output change amplitude between adjacent time periods of the power station of the th reservoir, and represents the conventional hydropower output of the power station of the th reservoir at the time period; The out - flow discharge constraint is expressed as: ; ; In the formula, represents the out - flow discharge of the th reservoir at the time period, respectively represent the spillage discharge of the th reservoir at the time period, and respectively represent the upper and lower limits of the out - flow discharge of the th reservoir at the time period, , and respectively represent the ecological flow and the minimum navigation flow of the th reservoir at the time period, represents the maximum - value function; The water - level storage - capacity constraint is expressed as: ; In the formula, represents the water level above the dam of the th reservoir at the time period, represents the water - level storage - capacity relationship curve of the th reservoir; The tail - water level discharge constraint is expressed as: ; In the formula, represents the tail - water level of the th reservoir at the time period, represents the tail - water level - discharge relationship curve of the th reservoir; The head - calculation constraint is expressed as: ; In the formula, and respectively represent the The power station of a reservoir The average net head and average head loss during a period Denote the th reservoir The water level above the dam during a period The above-mentioned conventional hydropower output calculation constraint is expressed as: ; In the formula, Denote the density of water Denote the acceleration due to gravity Denote the power generation efficiency of the power station of the th reservoir Denote the total number of power stations, and M takes 2 The above-mentioned power calculation constraint includes a power generation power constraint and a pumping power constraint, and the power generation power constraint and the pumping power constraint are respectively expressed as: , ; , ; In the formula, and respectively denote the power generation power and pumping power of the reversible unit during the period and respectively denote the power generation efficiency and pumping efficiency of the reversible unit and respectively denote the average net head and average head loss for power generation of the reversible unit during the period and respectively denote the average pumping head and average head loss for pumping of the reversible unit during the period and respectively denote the water level above the upper reservoir dam and the water level above the lower reservoir dam during the period The power generation and pumping flow rate constraint of the reversible unit is expressed as: ; ; ; In the formula, and respectively denote the power generation flow rate and pumping flow rate of the reversible unit during the period and respectively denote the upper limit and lower limit of the power generation flow rate of the reversible unit during the period and respectively represent the upper and lower limits of the pumping flow rate during the time period of the reversible unit; The output constraint of the reversible unit is expressed as: ; ; In the formula, and respectively represent the upper and lower limits of the power generation power of the reversible unit during the t time period, and respectively represent the upper and lower limits of the pumping power of the reversible unit during the t time period; The daily pumped water volume constraint of the reversible unit is expressed as: ; In the formula, represents the minimum daily power generation that the hybrid pumped-storage power station needs to meet in the electricity contract transaction.

[0009] Furthermore, the peak shaving objective function is expressed as: U; In the formula, represents the minimization function , represents the mean square error of the remaining load, represents the total number of power stations, M is taken as 2, , respectively represent the grid load and the remaining load during the time period, represents the average value of the remaining load within the total scheduling time period represents the th conventional hydropower output of the power station in the reservoir during the and respectively represent the pumping power and the power generation power of the reversible unit during the time period.

[0010] Furthermore, the navigation objective function is expressed as: ; ; In the formula, represents the minimization function , represents the daily amplitude change of the tail water level of the lower reservoir of the hybrid pumped-storage power station, represents the minimization function , Represents the daily amplitude of the water level above the dam of the lower reservoir of a hybrid pumped-storage power station, and respectively represent the maximum value of the water level above the dam and the maximum value of the tail water level of the lower reservoir of the hybrid pumped-storage power station at time period t, and respectively represent the minimum value of the water level above the dam and the minimum value of the tail water level of the lower reservoir of the hybrid pumped-storage power station at time period t, Represents the total dispatching period, Represents the total number of power stations.

[0011] Furthermore, after mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, an initial solution is generated, and then the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain the corrected initial solution, including: Aggregate the power generation and pumping flows of the reversible units into the reversible unit flow, and generate the initial solution using real number coding and random functions; Modify the remaining load and water balance constraints for each time period in the multi-objective optimal dispatching model according to the reversible unit flow; Based on the initial storage capacity control value, the inflow, and the initial solution, and then use the water balance equation to correct the solutions in the initial solution that are not within the final storage capacity constraint range to obtain the corrected initial solution.

[0012] Furthermore, the generation process of the reversible unit flow is expressed as: ; In the formula, Represents a random number generated by a random function, Represents a random function, Represents the reversible unit flow at time period t, When it is negative, it means that the reversible unit is in the power generation mode at time period t, When it is positive, it means that the reversible unit is in the pumping mode at time period t, and respectively represent the upper and lower limits of the power generation flow of the reversible unit at time period, and respectively represent the upper and lower limits of the pumping flow of the reversible unit at time period, Represents the conditional judgment statement "if"; The initial solution is expressed as: ; In the formula, Represents the initial solution, Represents the set composed of the reversible unit flows for each time period generated by using a random function, Represents the union operation of sets, and respectively represent the sets of the upper reservoir discharge flow rates and the lower reservoir discharge flow rates generated by using a random function in each time period. The sets of the upper reservoir discharge flow rates and the lower reservoir discharge flow rates in each time period are expressed as: ; ; In the formula, represents the upper reservoir discharge flow rate at time period t, , respectively represent the lower limit and the upper limit of the upper reservoir discharge flow rate at time period t, represents the lower reservoir discharge flow rate at time period t, , respectively represent the lower limit and the upper limit of the lower reservoir discharge flow rate at time period t, represents the total scheduling time period.

[0013] Furthermore, according to the reversible unit flow rate, the remaining loads and the water balance constraints in each time period in the multi-objective optimal scheduling model are modified. Among them, the remaining load, the storage capacity of the upper reservoir, and the storage capacity of the lower reservoir are respectively expressed as: ; ; ; In the formula, and respectively represent the grid load and the remaining load at time period represents the total number of power stations, represents the average value of the remaining loads within the total scheduling time period, represents the th reservoir power station output at time period , respectively represent the pumping power and the generating power of the reversible unit at time period represents the storage capacity of the upper reservoir at time period .... represents the storage capacity of the upper reservoir ]>at time period represents the inflow of the upper reservoir at time period represents the outflow of the upper reservoir at time period , respectively represent the pumping flow rate and the generating flow rate of the reversible unit at time period represents the time period length; Indicates the lower reservoir The storage capacity at time period Indicates the lower reservoir The storage capacity at time period Indicates the lower reservoir The inflow at time period Indicates the lower reservoir The outflow at time period Indicates the flow rate of the reversible unit at time period t.

[0014] Furthermore, the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain the corrected initial solution, where the corrected initial solution is expressed as: ; In the formula, Indicates the corrected initial solution, And Respectively represent the sets of corrected outflows from the upper reservoir and the lower reservoir; Among them, the corrected outflows include the sets of corrected outflows from the upper reservoir and the lower reservoir And , and the corrected outflow is expressed as: ; In the formula, Represents the th reservoir The corrected outflow at time period And Respectively represent the upper limit and the lower limit of the outflow of the th reservoir At time period Represents "or", Represents the th reservoir's outflow before correction at time period t, including the outflows of the upper reservoir and the lower reservoir before correction at time period t; Among them, the outflows of the upper reservoir and the lower reservoir before correction at time period t are respectively expressed as: ; ; In the formula, Represents the outflow of the upper reservoir before correction at time period t, Represents the outflow of the lower reservoir before correction at time period t, Represents the flow rate of the reversible unit included in the initial solution at time period t, 、 Respectively represent the corrected storage capacity of the upper reservoir and the corrected storage capacity of the lower reservoir at time period t; Among them, the corrected storage capacity includes the corrected storage capacity of the upper reservoir and the corrected storage capacity of the lower reservoir, and the corrected storage capacity is expressed as: ; ; In the formula, represents the corrected storage capacity of the m-th reservoir, including the corrected storage capacity of the upper reservoir and the corrected storage capacity of the lower reservoir, represents the final corrected storage capacity of the m-th reservoir, represents the storage capacity of the m-th reservoir before correction at time t.

[0015] Furthermore, using the NSGA-II algorithm, the multi-objective optimal scheduling model is solved according to the corrected initial solution, and the Pareto solution set is output through iterative optimization, including: According to the corrected initial solution, a parental population is formed; Repeat the following operations until the preset number of iterations or convergence conditions are reached: Based on the multi-objective optimal scheduling model, calculate the objective function values of each individual in the parental population, including the peak shaving objective function value and the navigation objective function value, and correct the fitness of the individuals that do not meet the conventional hydropower constraints and the pumped-storage unit operation constraints by using the dynamic penalty function method; According to the Pareto dominance relationship, the parental population is hierarchically sorted to obtain the non-dominated levels, where the first non-dominated level includes all non-dominated individuals, and the other non-dominated levels include only the individuals dominated by the previous non-dominated level; Within each non-dominated level, calculate the crowding distance of the individuals in the objective function space; If the non-dominated levels of the feasible scheduling schemes are different, the feasible scheduling schemes with smaller non-dominated levels are subjected to crossover and mutation operations to obtain a number of new feasible scheduling schemes; If the non-dominated levels of the feasible scheduling schemes are the same, the feasible scheduling schemes with larger crowding degrees are subjected to crossover and mutation operations to obtain new feasible scheduling schemes; After mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, the new solution set is corrected by the method of correcting the final storage capacity constraint to obtain the corrected solution set, which is added to the offspring population as offspring; The individuals in the parental population and the offspring population are merged, and non-dominated sorting and crowding degree calculation are performed, and the first P feasible scheduling schemes are selected as the Pareto solution set, where, .

[0016] Compared with the prior art, the beneficial effects achieved by the present invention:

[0017] 1. Through conventional hydropower constraints, pumped-storage unit operation constraints, peak shaving objective function, and navigation objective function, the present invention constructs a multi-objective optimization scheduling framework with strong robustness, and innovatively adopts mutually exclusive constraint handling and final reservoir capacity dynamic correction technology to achieve the global optimization of the short-term scheduling plan for a hybrid pumped-storage power station. The Pareto solution set obtained by the present invention using the NSGA-II algorithm can completely characterize the trade-off relationship between peak shaving benefits and navigation guarantee rate, and through refined optimization by dividing the solution set space into multiple sub-regions, it can automatically identify the optimal scheduling plan with the largest peak-valley difference reduction rate under different navigation demand scenarios. Compared with traditional methods, by quantifying and optimizing the peak-valley difference reduction rate, the present invention not only significantly improves the peak shaving ability of the power grid, but also can dynamically coordinate the impact of the operation of reversible units on the navigation water level, while ensuring the new energy consumption efficiency, greatly improving the navigation rate, and providing intelligent decision-making support with both economy and safety for the cascade power station group scheduling under complex hydraulic connections;

[0018] 2. The mutually exclusive constraint handling technology and final reservoir capacity dynamic correction mechanism adopted by the present invention effectively solve the flow conflict problem generated during the switching of reversible units between power generation and pumping conditions, enabling the scheduling plan to strictly meet physical constraints such as upstream and downstream hydraulic connections and unit operation restrictions, while greatly improving the generation rate of feasible solutions for model solving. Compared with traditional simplified constraint handling methods, by explicitly expressing non-linear constraints such as tail water level discharge relationship and head loss calculation, the present invention significantly reduces the adjustment frequency of the scheduling plan due to constraint violations during actual implementation, providing directly implementable optimization decision-making support for engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic flow chart of a short-term multi-objective optimization scheduling method for a hybrid pumped-storage power station provided by an embodiment of the present invention;

[0020] Figure 2 is a flow chart of iterative optimization to output the Pareto solution set provided by an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of the Pareto front distribution provided by an embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of the selection of a feasible scheduling plan provided by an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of the peak shaving process of Scheme 1 provided by an embodiment of the present invention;

[0024] Figure 6 is a schematic diagram of the lower reservoir water level process of Scheme 1 provided by an embodiment of the present invention;

[0025] Figure 7 It is a schematic diagram of the peak shaving process of Scheme 6 provided by an embodiment of the present invention;

[0026] Figure 8 It is a schematic diagram of the lower reservoir water level process of Scheme 6 provided by an embodiment of the present invention;

[0027] Figure 9 It is a schematic diagram of the peak shaving process of Scheme 12 provided by an embodiment of the present invention;

[0028] Figure 10 It is a schematic diagram of the lower reservoir water level process of Scheme 12 provided by an embodiment of the present invention. Detailed implementation manners

[0029] The technical solution of the present invention will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present invention are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0030] The term "and / or" only describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0031] Embodiment 1

[0032] As Figure 1 shown, this embodiment introduces a short-term multi-objective optimal dispatching method for a hybrid pumped-storage power station, which is characterized by including:

[0033] Step 1: Obtain initial input data, including basic data of the hydropower station, operation boundaries, actual operation data of the power grid on the dispatching day, and runoff data.

[0034] By integrating the basic parameters of the hydropower station, operation boundaries, real-time load demand of the power grid, and runoff prediction data, the present invention constructs the digital twin foundation of the dispatching model, provides complete information input for the multi-objective optimal dispatching model to be constructed, ensures the physical authenticity of the constraint conditions and the objective function, improves the matching degree of the dispatching scheme with the actual situation, reduces the risk of model deviation caused by data missing, and improves the decision-making reliability.

[0035] Step 2: According to the initial input data, under the constraints of conventional hydropower and the operation of pumped-storage units, a multi-objective optimal dispatching model is constructed with minimizing the mean square error of the remaining load as the peak shaving objective, and minimizing the daily amplitude of the lower reservoir tail water level and minimizing the daily amplitude of the upper reservoir water level as the navigation objectives.

[0036] Under the constraints of conventional hydropower operation and pumped-storage units, the present invention establishes a conflict optimization model that simultaneously takes minimizing the mean square deviation of the remaining load as the peak regulation objective and minimizing the daily variation amplitude of the tail water level of the lower reservoir and minimizing the daily variation amplitude of the water level above the dam of the lower reservoir as the navigation objectives, explicitly expresses the coupling relationship of multi-physical processes, and transforms complex engineering problems into computable mathematical optimization problems. It realizes the quantitative coordination of peak regulation and navigation, breaks through the limitations of traditional single-objective scheduling, and at the same time improves the global feasibility of the scheduling scheme through joint multi-constraint modeling.

[0037] Step 3: After mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, an initial solution is generated, and then the solutions in the initial solution that are not within the final reservoir capacity constraint range are corrected to obtain the corrected initial solution.

[0038] The present invention aggregates the power generation flow and the pumping flow into the flow of the reversible unit, avoiding flow conflicts during condition switching. The present invention also corrects the final reservoir capacity, randomly generates the reservoir capacity and forces the final reservoir capacity to be a preset value, and dynamically adjusts the outflow to meet the water level constraint, solving the flow contradiction caused by the condition switching of the unit and ensuring the feasibility of the scheduling scheme under strict water level restrictions. It greatly improves the generation rate of feasible solutions and significantly reduces the need for posterior correction. The present invention also improves the physical fidelity of the model by explicitly processing non-linear constraints such as the tail water level-discharge relationship.

[0039] Step 4: Using the NSGA-II algorithm, solve the multi-objective optimal scheduling model according to the corrected initial solution, and obtain the Pareto solution set through iterative optimization.

[0040] The present invention adopts the non-dominated sorting genetic algorithm NSGA-II, and through iterative selection, crossover, and mutation operations, approaches the Pareto front of the multi-objective problem, generates a series of non-dominated solutions between the peak regulation benefit and the navigation guarantee rate, reveals the trade-off relationship between the two, provides a variety of scheduling schemes, and meets the preference selection of decision-makers for risks and benefits. The computational efficiency of the present invention is improved by 30%-50% compared with traditional methods.

[0041] Step 5: According to the Pareto solution set, divide the Pareto front distribution space into multiple subspaces.

[0042] The present invention divides the Pareto front into several sub-regions according to the distribution of objective function values such as the peak regulation depth and the navigation guarantee rate, reduces the analysis complexity of the high-dimensional solution set, facilitates the screening of local optimal solutions, improves the interpretability of the solution set, supports decision-makers to select preference solutions as needed, and provides a structured framework for multi-scenario adaptive scheduling.

[0043] Step 6: In each subspace, select the feasible scheduling scheme with the largest peak-valley difference reduction rate as the optimal scheduling scheme within the subspace.

[0044] By taking the reduction rate of the peak-valley difference as an index within each subspace, the present invention selects the feasible solution with the best peak shaving effect, ensuring the maximization of the power grid peak shaving capacity under different navigation demand scenarios. The reduction rate of the peak-valley difference is increased by 10%-15% compared with the traditional method, enhancing the new energy consumption capacity. Also, by dynamically matching the navigation water level requirements, the risks of water abandonment or navigation interruption are avoided.

[0045] Step 7: Select one or more from the optimal scheduling schemes of all subspaces as the final scheduling scheme.

[0046] By comprehensively comparing the optimal schemes of each subspace and combining dynamic information such as real-time navigation demand and power grid load prediction, the present invention selects the final scheduling scheme, achieving global optimization decision-making under multiple objectives and multiple scenarios. In the practical application of the scheduling scheme, the constraint violation rate is reduced to less than 2%, significantly improving the engineering feasibility. The present invention also provides a balance paradigm of economy and safety for cascade power station groups by quantifying the synergy benefits of peak shaving and navigation.

[0047] Embodiment 2

[0048] Based on the same inventive concept as Embodiment 1, taking the Goupitan-Silin pumped-storage power station as the background, including the already built Goupitan and Silin hydropower stations and the planned 1800MW reversible units, to verify the effectiveness of the present invention. The completion of the Goupitan and Silin hydropower stations has fully canalized the upstream river channel, improving the navigation capacity of the Wujiang River. At the same time, the planned construction of the conceived pumped-storage power station will also greatly relieve the peak shaving and valley filling pressures of the Guizhou power grid. However, there is a water use contradiction between conventional hydropower peak shaving and navigation, and the complex hydraulic connection of the pumped-storage power station makes this contradiction more complex. The present invention selects a typical day to calculate according to the above model method, and the data involved in the calculation, such as power grid load, interval runoff, initial and final reservoir storage, etc., all adopt the actual values of the typical day. This embodiment introduces an implementation method of a short-term multi-objective optimal scheduling method for a pumped-storage power station, including:

[0049] Step 1: Obtain initial input data, including hydropower station basic data, operation boundaries, actual operation data of the power grid on the scheduling day, and runoff data.

[0050] Step 2: According to the initial input data, under the constraints of conventional hydropower and the operation constraints of pumped-storage units, with minimizing the mean square error of the remaining load as the peak shaving objective and minimizing the daily amplitude variation of the tail water level of the lower reservoir and the daily amplitude variation of the water level above the dam of the lower reservoir as the navigation objective, construct a multi-objective optimal scheduling model.

[0051] In some embodiments, the conventional hydropower constraints include: water volume balance constraint, reservoir capacity constraint, power generation flow constraint, conventional hydropower output constraint, initial and final reservoir capacity constraints, conventional hydropower output ramp constraint, discharge flow constraint, water level - reservoir capacity constraint, tail - water discharge constraint, head calculation constraint, and conventional hydropower output calculation constraint;

[0052] In some embodiments, the pumped - storage unit operation constraints include: power calculation constraint, reversible unit power generation and pumping flow constraint, reversible unit output constraint, and reversible unit daily pumped - water volume constraint.

[0053] In this embodiment, the water volume balance constraint includes the upper - reservoir water volume balance constraint and the lower - reservoir water volume balance constraint. The hybrid pumped - storage power station contains two reservoirs, an upper reservoir and a lower reservoir, with a power station in each reservoir. The reversible units are installed in the upper - reservoir power station. The upper - reservoir water volume balance constraint and the lower - reservoir water volume balance constraint are respectively expressed as:

[0054] ;

[0055] ;

[0056] In the formula, 1 represents the upper reservoir, 2 represents the lower reservoir, represents the reservoir capacity of the upper reservoir at time period ; represents the reservoir capacity of the upper reservoir at time period ; represents the inflow of the upper reservoir at time period ; represents the outflow of the upper reservoir at time period ; , respectively represent the pumping flow and power - generation flow of the reversible unit at time period ; represents the time - period length; represents the reservoir capacity of the lower reservoir at time period ; represents the reservoir capacity of the lower reservoir at time period ; represents the inflow of the lower reservoir at time period ; represents the outflow of the lower reservoir at time period ;

[0057] In this embodiment, the reservoir capacity constraint is expressed as:

[0058] ;

[0059] In the formula, represents the initial reservoir capacity of the m - th reservoir at time period t, where m takes the value of 1 or 2, and respectively represent the upper and lower limits of the storage capacity of the th reservoir during the

[0060] In this embodiment, the power generation flow constraint is expressed as:

[0061] ;

[0062] In the formula, represents the power generation flow of the th reservoir during the and respectively represent the upper and lower limits of the power generation flow of the th reservoir during the

[0063] In this embodiment, the conventional hydropower output constraint is expressed as:

[0064] ;

[0065] In the formula, represents the conventional hydropower output of the power station of the th reservoir during the and respectively represent the upper and lower limits of the output of the conventional units of the power station of the th reservoir during the

[0066] In this embodiment, the initial and final storage capacity constraints are expressed as:

[0067] ;

[0068] In the formula, represents the initial storage capacity of the mth reservoir, represents the final storage capacity of the mth reservoir, represents the initial storage capacity of the mth reservoir, represents the final storage capacity of the mth reservoir, , respectively represent the initial storage capacity control value and the final storage capacity control value of the th reservoir, represents the total scheduling period, ;

[0069] In this embodiment, the conventional hydropower output ramp constraint is expressed as:

[0070] ;

[0071] In the formula, Indicates the Reservoir power station The maximum output variation allowed in adjacent time periods. Indicates the Reservoir power station Conventional hydropower output during the time period;

[0072] In this embodiment, the outbound flow constraint is expressed as:

[0073] ;

[0074]

[0075] Where, Indicates the Reservoirs Outbound flow during the period, Respectively represent Reservoirs Discarded water flow rate during the period;

[0076] In this embodiment, the water level storage capacity constraint is expressed as:

[0077] ;

[0078] Where, Indicates the Reservoirs The water level on the dam during the period, Indicates the The water level-storage capacity relationship curve of each reservoir;

[0079] In this embodiment, the tailwater level discharge constraint is expressed as:

[0080] ;

[0081] Where, Indicates the Reservoirs The tail water level of the period, Indicates the The tailwater level-discharge relationship curve of each reservoir;

[0082] In this embodiment, the water head calculation constraint is expressed as:

[0083] ;

[0084] Where, and Respectively represent Reservoir power station Average net head and average head loss for the period, Indicates the reservoir water level above the dam during the

[0085] In this embodiment, the conventional hydropower output calculation constraint is expressed as:

[0086] ;

[0087] In the formula, represents the density of water, represents the acceleration due to gravity, represents the power generation efficiency of the reservoir power station, and M is taken as 2;

[0088] In this embodiment, the power calculation constraint includes a power generation power constraint and a pumping power constraint, and the power generation power constraint and the pumping power constraint are respectively expressed as:

[0089] , ;

[0090] , ;

[0091] In the formula, and respectively represent the power generation power and pumping power of the reversible unit during the and respectively represent the power generation efficiency and pumping efficiency of the reversible unit; and respectively represent the average net head and power generation average head loss of the reversible unit during the and respectively represent the average pumping head and pumping average head loss of the reversible unit during the and respectively represent the water level above the dam of the upper reservoir and the water level above the dam of the lower reservoir during the

[0092] In this embodiment, the power generation and pumping flow rate constraint of the reversible unit is expressed as:

[0093] ;

[0094] ;

[0095] ;

[0096] In the formula, and Respectively represent reversible units The power generation flow and pumping flow of the period, and Respectively represent reversible units The upper and lower limits of power generation flow during the time period, and Respectively represent reversible units The upper and lower limits of pumping flow for the time period;

[0097] In this embodiment, the output constraint of the reversible unit is expressed as:

[0098] ;

[0099] ;

[0100] Where, and They represent the upper and lower limits of the power generation of the reversible unit during period t, and They represent the upper and lower limits of the pumping power of the reversible unit during period t;

[0101] In this embodiment, the daily pumping power constraint of the reversible unit is expressed as:

[0102] ;

[0103] Where, It represents the minimum daily power generation that the hybrid pumped storage power station needs to meet in the power contract transaction.

[0104] In this embodiment, the peak shaving objective function is expressed as:

[0105] ;

[0106] Where, Represents the minimization function , represents the residual load mean square error, Indicates the total number of power stations, M is 2, 、 Respectively The grid load and residual load of the time period, Indicates the total scheduling period The average value of the remaining load within Indicates the Reservoir power station Conventional hydropower output during the period, and Respectively represent reversible units Pumping power and power generation power during the period.

[0107] In this embodiment, the navigation objective function is expressed as:

[0108] ;

[0109] ;

[0110] Where, Represents the minimization function , It represents the daily fluctuation of the tailwater level of the lower reservoir of the hybrid pumped storage power station. Represents the minimization function , It represents the daily fluctuation of the water level above the dam of the hybrid pumped storage power station. and They represent the maximum water level above the dam and the maximum tailwater level of the hybrid pumped storage power station during period t. and They represent the minimum water level above the dam and the minimum tailwater level of the hybrid pumped storage power station during period t. represents the total scheduling period, Indicates the total number of power stations.

[0111] Step 3: After mutually exclusive processing of the power generation and pumping flow constraints of the reversible unit, an initial solution is generated. Then, the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain the corrected initial solution.

[0112] In some embodiments, after mutually exclusive processing of the power generation and pumping flow constraints of the reversible unit is performed, an initial solution is generated, and then the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain a corrected initial solution, including:

[0113] Step 3.1: Aggregate the power generation and pumping flow of the reversible unit into the reversible unit flow, and generate the initial solution using real number coding and random function.

[0114] Step 3.2: Modify the residual load and water balance constraints for each period in the multi-objective optimization scheduling model based on the reversible unit flow.

[0115] Step 3.3: Based on the initial storage capacity control value, the inflow flow, and the initial solution, the water balance equation is used to correct the initial solution that is not within the final storage capacity constraint range to obtain the corrected initial solution.

[0116] In this embodiment, the generation process of the reversible unit flow rate is expressed as follows:

[0117] ;

[0118] In the formula, represents a random number generated by a random function, represents a random function, represents the flow rate of the reversible unit in the t period, When it is negative, it means that the reversible unit is in the power generation mode in the t period, When it is positive, it means that the reversible unit is in the pumping mode in the t period, and respectively represent the upper and lower limits of the power generation flow rate of the reversible unit in the period, and respectively represent the upper and lower limits of the pumping flow rate of the reversible unit in the period, represents the conditional judgment statement "if";

[0119] The initial solution is expressed as:

[0120] ;

[0121] In the formula, represents the initial solution, represents a set composed of the flow rates of the reversible units in each period generated by a random function, represents the union operation of sets, and respectively represent the set of the upper reservoir outflows and the set of the lower reservoir outflows in each period generated by a random function. The sets of the upper reservoir outflows and the lower reservoir outflows in each period are expressed as:

[0122] ;

[0123] ;

[0124] In the formula, represents the upper reservoir outflow in the t period, , respectively represent the lower limit and the upper limit of the upper reservoir outflow in the t period, represents the lower reservoir outflow in the t period, , respectively represent the lower limit and the upper limit of the lower reservoir outflow in the t period, represents the total scheduling period.

[0125] In this embodiment, according to the flow rate of the reversible unit, the remaining load and the water balance constraint in each period in the multi-objective optimal scheduling model are modified, where the remaining load, the storage capacity of the upper reservoir and the storage capacity of the lower reservoir are respectively expressed as:

[0126] ;

[0127] ;

[0128] ;

[0129] wherein, and respectively represent the grid load and the remaining load during the time period, represents the total number of power stations, represents the average value of the remaining load during the total dispatching time period, represents the th output of the power station in the reservoir during the time period, , respectively represent the pumping power and generating power of the reversible unit during the time period, represents the storage capacity of the upper reservoir during the time period, represents the storage capacity of the upper reservoir during the time period, represents the inflow of the upper reservoir during the time period, represents the outflow of the upper reservoir during the time period, , respectively represent the pumping flow rate and generating flow rate of the reversible unit during the time period, represents the time period length; represents the storage capacity of the lower reservoir during the time period, represents the storage capacity of the lower reservoir during the time period, represents the inflow of the lower reservoir during the time period, represents the outflow of the lower reservoir during the time period, represents the flow rate of the reversible unit at time t.

[0130] In this embodiment, the solutions in the initial solution that are not within the final storage capacity constraint range are corrected to obtain the corrected initial solution, where the corrected initial solution is expressed as:

[0131]

[0132] wherein, represents the corrected initial solution, and respectively represent the sets of corrected outflow of the upper reservoir and the lower reservoir;

[0133] Among them, the corrected outflow includes the set of corrected outflows from the upper reservoir and the lower reservoir. and , the corrected outflow is expressed as:

[0134] ;

[0135] In the formula, represents the corrected outflow of the th reservoir during the and respectively represent the upper limit and the lower limit of the outflow of the th reservoir during the represents "or", represents the outflow of the th reservoir before correction during the t period, including the outflows of the upper reservoir and the lower reservoir before correction during the t period;

[0136] Among them, the outflows of the upper reservoir and the lower reservoir before correction during the t period are respectively expressed as:

[0137] ;

[0138] ;

[0139] In the formula, represents the outflow of the upper reservoir before correction during the t period, represents the outflow of the lower reservoir before correction during the t period, represents the flow rate of the reversible unit included in the initial solution during the t period, , respectively represent the corrected reservoir capacity of the upper reservoir and the corrected reservoir capacity of the lower reservoir during the t period;

[0140] Among them, the corrected reservoir capacity includes the corrected reservoir capacity of the upper reservoir and the corrected reservoir capacity of the lower reservoir, and the corrected reservoir capacity is expressed as:

[0141] ;

[0142] ;

[0143] In the formula, represents the corrected reservoir capacity of the mth reservoir, including the corrected reservoir capacity of the upper reservoir and the corrected reservoir capacity of the lower reservoir, represents the final corrected reservoir capacity of the mth reservoir, represents the reservoir capacity of the mth reservoir before correction during the t period.

[0144] Step 4: Use the NSGA-II algorithm to solve the multi-objective optimal scheduling model according to the corrected initial solution, and output the Pareto solution set through iterative optimization.

[0145] As Figure 2 shown, in some embodiments, using the NSGA-II algorithm to solve the multi-objective optimal scheduling model according to the corrected initial solution, and output the Pareto solution set through iterative optimization, including:

[0146] Step 4.1: Construct a parental population according to the corrected initial solution.

[0147] Step 4.2: Repeat the following operations until the preset number of iterations or convergence conditions are reached:

[0148] Step 4.2.1: Based on the multi-objective optimal scheduling model, calculate the objective function values of each individual in the parental population, including the peak shaving objective function value and the navigation objective function value, and correct the fitness of the individuals that do not meet the conventional hydropower constraints and the pumped-storage unit operation constraints by using the dynamic penalty function method.

[0149] Step 4.2.2: Stratify and sort the parental population according to the Pareto dominance relationship to obtain the non-dominated levels, where the first non-dominated level includes all non-dominated individuals, and other non-dominated levels include only the individuals dominated by the previous non-dominated level.

[0150] Step 4.2.3: Calculate the crowding distance of each individual in the objective function space within each non-dominated level.

[0151] Step 4.2.4: If the non-dominated levels of the feasible scheduling schemes are different, perform crossover and mutation operations on the feasible scheduling schemes with smaller non-dominated levels to obtain new feasible scheduling schemes.

[0152] Step 4.2.5: If the non-dominated levels of the feasible scheduling schemes are the same, perform crossover and mutation operations on the feasible scheduling schemes with larger crowding degrees to obtain new feasible scheduling schemes.

[0153] Step 4.2.6: Based on the method of correcting the final reservoir capacity constraint after mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, correct the new solution set to obtain the corrected solution set, which is added to the offspring population as offspring.

[0154] Step 4.2.7: Combine the individuals in the parental population and the offspring population, perform non-dominated sorting and crowding degree calculation, and select the top P feasible scheduling schemes as the Pareto solution set, where, .

[0155] In this embodiment, the Pareto solution set output through iterative optimization is asFigure 3 The Pareto front distribution shown. From Figure 3 it can be seen that there is an obvious contradiction between the peak shaving target and the navigation target, and the incorporation of reversible units makes this contradiction more complex. It shows a trend that both the daily amplitude change of the water level above the dam and the daily amplitude change of the tail water level increase with the peak shaving effect, the randomness of the Pareto front distribution increases, and the decision-making difficulty increases. It is impossible to simply achieve the purpose of controlling the water level above the dam of the lower reservoir and the daily amplitude change of the tail water level by controlling the discharge flow of the lower reservoir.

[0156] Step 5: According to the Pareto solution set, divide the Pareto front distribution space into multiple sub-spaces.

[0157] Step 6: In each sub-space, select the feasible scheduling scheme with the largest peak-valley difference reduction rate as the optimal scheduling scheme within the sub-space.

[0158] Step 7: Select one or more from the optimal scheduling schemes of all sub-spaces as the final scheduling scheme.

[0159] In this embodiment, according to Figure 3 the range of the mean square deviation distribution of the remaining load, take it as an interval, such as Figure 4 shown, according to the Pareto solution set, divide the Pareto front distribution space into multiple sub-spaces, and select the scheme with the largest peak-valley difference reduction rate within the sub-space as the alternative scheme, and the results are shown in Table 1.

[0160]

[0161] At the same time, within the last peak shaving effect interval, additionally select the scheme with the smallest peak-valley difference reduction rate, that is, the scheme with the worst peak shaving effect, and compare and analyze it with the scheme with the best peak shaving effect, as shown in Scheme 1 and Scheme 12 in Table 1.

[0162] As can be seen from Table 1, among the 12 alternative schemes, the hourly change rate of the water level above the dam is relatively small. Secondly, there is a positive correlation between the hourly change rate of the tail water level and the daily amplitude change of the tail water level. Therefore, when selecting the optimal decision-making scheme, the range of the mean square deviation distribution, the peak-valley difference reduction rate, the daily amplitude change of the water level above the dam, and the daily amplitude change of the tail water level can be mainly concerned. The present invention selects three typical schemes from Table 1 for further comparison and analysis of the daily operation, namely Scheme 1, Scheme 6 and Scheme 12, and the results are as Figures 5 - 10 shown.

[0163] As can be provided by this embodiment Figures 5 - 10 it can be known that:

[0164] (1) From the perspective of peak shaving and navigation effect, for Scheme 1, the remaining load curve fluctuates very little, and the peak-valley difference is only 2764 MW. The reduction rate of the peak-valley difference reaches 71.95%, and the peak shaving effect is very significant. However, the daily amplitude of the water level above the dam of the lower reservoir and the daily amplitude of the tail water level of the lower reservoir reach 0.91 m and 4.33 m respectively, seriously deteriorating the navigation environment in the lower reservoir and downstream; for Scheme 12, the daily amplitude of the tail water level of the lower reservoir is 0.00 m, and the daily amplitude of the water level above the dam of the lower reservoir is only 0.18 m, which is the difference between the actual start and end control water levels on a typical day. However, the remaining load curve fluctuates very greatly, and the peak-valley difference of the remaining load is as high as 9464 MW, and the reduction rate of the peak-valley difference is only 3.95%. At this time, the hybrid pumped-storage power station sacrifices almost all peak shaving effects to improve the navigation environment in the lower reservoir and downstream; Scheme 6 is a multi-objective coordination solution for peak shaving and navigation. At this time, the remaining load curve fluctuates less, and the peak-valley difference of the remaining load is 6301 MW, and the reduction rate of the peak-valley difference reaches 36.05%. The daily amplitude of the water level above the dam of the lower reservoir and the daily amplitude of the tail water level of the lower reservoir are only 0.56 m and 1.60 m respectively, and the maximum hourly change amplitude of the tail water level is only 0.97 m, with relatively gentle changes, taking into account the requirements of peak shaving and navigation.

[0165] (2) From the perspective of the operating state of the reversible units, when focusing on peak shaving benefits, the reversible units operate in a mode of one pumping and two generating, with the last pumping period connected to the low load period of the next day. It can be considered that the pumping operation is only once a day, as Figure 5 shown. At this time, the reversible units mainly cooperate with the power grid to pump and fill valleys during the valley period and cooperate with the conventional hydro-generating units to peak generate during the peak period; when focusing on navigation benefits, the reversible units operate in a mode of two pumping and two generating, as Figure 7 and Figure 9 shown. At this time, the reversible units add a pumping operation during the middle period between the early peak and the late peak of the load, and reduce the power generation during the peak period. The main reason is that the peak generation flow of the upper reservoir is large during the peak period, and the anti-regulation storage capacity of the lower reservoir is limited. Constrained by the navigation conditions in the lower reservoir, appropriately increasing the pumping operation during the middle period between the early peak and the late peak can reduce the daily amplitude of the water level above the dam of the lower reservoir.

[0166] In summary, the present invention can provide decision-making references for the dispatching operation of hybrid pumped-storage power stations with navigation requirements. Dispatchers can flexibly select decision-making schemes according to different emphases on navigation benefits and peak shaving benefits, and has certain practical value.

[0167] Example 3

[0168] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of the method in the above-mentioned Embodiment 1 or 2 are implemented.

[0169] Example 4

[0170] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method in Embodiment 1 or 2 above.

[0171] In summary of the above embodiments, the present invention constructs a multi-objective optimization scheduling framework with strong robustness through conventional hydropower constraints, pumped-storage unit operation constraints, peak regulation objective function, and navigation objective function, and innovatively adopts mutually exclusive constraint handling and final reservoir capacity dynamic correction technology to achieve the global optimization of the short-term scheduling plan for a hybrid pumped-storage power station. The Pareto solution set obtained by the present invention using the NSGA-II algorithm can completely characterize the trade-off relationship between peak regulation benefits and navigation guarantee rate, and by dividing the solution set space into multiple sub-regions for refined optimization, it can automatically identify the optimal scheduling plan with the largest peak-valley difference reduction rate under different navigation demand scenarios. Compared with traditional methods, by quantifying and optimizing the peak-valley difference reduction rate, the present invention not only significantly improves the peak regulation ability of the power grid, but also dynamically coordinates the impact of the operation of reversible units on the navigation water level, greatly improving the navigation rate while ensuring the new energy consumption efficiency, providing intelligent decision-making support with both economy and safety for the scheduling of hybrid pumped-storage power stations under complex hydraulic connections;

[0172] The mutually exclusive constraint handling technology and final reservoir capacity dynamic correction mechanism adopted by the present invention effectively solve the flow conflict problem generated during the switching of reversible units between power generation and pumping conditions, enabling the scheduling plan to strictly meet physical constraints such as upstream and downstream hydraulic connections and unit operation restrictions while significantly increasing the generation rate of feasible solutions for model solving. Compared with traditional simplified constraint handling methods, by explicitly expressing non-linear constraints such as tail water level discharge relationship and head loss calculation, the present invention significantly reduces the adjustment frequency of the scheduling plan due to constraint violations during actual implementation, providing directly implementable optimization decision-making support for engineering applications. Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can be in the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0174] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that several operating steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0176] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the present invention and the claims. These all fall within the protection scope of the present invention.

Claims

1. A short-term multi-objective optimal scheduling method for a hybrid pumped-storage power station, characterized in that Including: Obtain initial input data, including basic data of the hydropower station, operating boundaries, actual operating data of the power grid on the dispatching day, and runoff data; According to the initial input data, under the constraints of conventional hydropower and the operation of pumped-storage units, with minimizing the mean square deviation of the remaining load as the peak regulation objective, and minimizing the daily amplitude change of the tail water level of the lower reservoir and minimizing the daily amplitude change of the water level above the dam of the lower reservoir as the navigation objectives, construct a multi-objective optimal dispatching model; After mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, generate an initial solution, and then correct the solutions in the initial solution that are not within the final storage capacity constraint range to obtain a corrected initial solution; Use the NSGA-II algorithm to solve the multi-objective optimal dispatching model according to the corrected initial solution, and obtain the Pareto solution set through iterative optimization; According to the Pareto solution set, divide the Pareto front distribution space into multiple sub-spaces; In each sub-space, select the feasible dispatching plan with the largest reduction rate of the peak-valley difference as the optimal dispatching plan within the sub-space; Select one or more from the optimal dispatching plans of all sub-spaces as the final dispatching plan.

2. The short-term multi-objective optimal dispatching method for a hybrid pumped-storage power station according to claim 1, characterized in that The conventional hydropower constraints include: water balance constraint, storage capacity constraint, power generation flow constraint, conventional hydropower output constraint, initial and final storage capacity constraint, conventional hydropower output ramp constraint, out-flow constraint, water level-storage capacity constraint, tail water level discharge constraint, head calculation constraint, and conventional hydropower output calculation constraint; The operation constraints of the pumped-storage units include: power calculation constraint, power generation and pumping flow constraints of the reversible units, reversible unit output constraint, and daily pumped water volume constraint of the reversible units.

3. The short-term multi-objective optimal dispatching method for the hybrid pumped storage power station according to claim 2, wherein The water balance constraint includes the water balance constraint of the upper reservoir and the water balance constraint of the lower reservoir. The hybrid pumped-storage power station has two reservoirs, an upper and a lower reservoir, and each reservoir has a power station. The reversible units are installed in the upper reservoir power station. The water balance constraint of the upper reservoir and the water balance constraint of the lower reservoir are respectively expressed as: ； ； In the formula, 1 represents the upper reservoir, and 2 represents the lower reservoir. represents the storage capacity of the upper reservoir at time period represents the storage capacity of the upper reservoir at time period represents the inflow of the upper reservoir at time period represents the outflow of the upper reservoir at time period and respectively represent the pumping flow rate and the power generation flow rate of the reversible unit at time period represents the time period length; represents the storage capacity of the lower reservoir at time period represents the storage capacity of the lower reservoir at time period represents the inflow of the lower reservoir at time period represents the outflow of the lower reservoir at time period; The storage capacity constraint is expressed as: ； In the formula, represents the initial storage capacity of the m-th reservoir at the beginning of the t-th period, where m takes the value of 1 or 2, and respectively represent the upper and lower limits of the storage capacity of the th reservoir during the t-th period; The power generation flow constraint is expressed as: ； In the formula, represents the th reservoir 's power generation flow rate during the th period, and represent the upper and lower limits of the power generation flow rate of the th reservoir during the th period respectively; The conventional hydropower output constraint is expressed as: ； In the formula, represents the conventional hydropower output of the power station of the th reservoir during the period, and represent the upper and lower limits of the conventional unit output of the power station of the th reservoir during the The initial and final storage capacity constraint is expressed as: ； In the formula, represents the initial storage capacity of the m-th reservoir, represents the final storage capacity of the m-th reservoir, , respectively represent the initial storage capacity control value and the final storage capacity control value of the -th reservoir, represents the total scheduling period, ; The conventional hydropower output ramp constraint is expressed as: ； In the formula, represents the maximum allowable variation in output between adjacent time periods for the power station of the th reservoir, represents the conventional hydropower output of the power station of the th reservoir during the The out-flow constraint is expressed as: ； ； Wherein, represents the outflow discharge of the th reservoir during the th period; and respectively represent the wastewater discharge of the th reservoir during the th period; , and respectively represent the th reservoir ecological flow and minimum navigation flow during the th period; represents the maximum value function; The water level-storage capacity constraint is expressed as: ； In the formula, represents the water level above the dam of the th reservoir during the th period, and represents the water level-storage capacity relationship curve of the The tail water level discharge constraint is expressed as: ； In the formula, represents the tail water level of the nth reservoir during the nth tail water level - discharge relationship curve of the reservoir; The head calculation constraint is expressed as: ； In the formula, and respectively represent the average net head and the average head loss of the power station in the th reservoir during the th period, represents the water level above the dam of the th reservoir during the th period; The conventional hydropower output calculation constraint is expressed as: ； In the formula, represents the density of water, represents the acceleration due to gravity, represents the power generation efficiency of the power station of the th reservoir, and the total number of power stations is represented by M, where M takes 2; The power calculation constraint includes a power generation power constraint and a pumping power constraint. The power generation power constraint and the pumping power constraint are respectively expressed as: ， ； ， ； Wherein, and respectively represent the power generation power and pumping power of the reversible unit during the time period, and respectively represent the power generation efficiency and pumping efficiency of the reversible unit; and respectively represent the average net head and average head loss of power generation of the reversible unit during the time period, and respectively represent the average pumping head and average head loss of pumping of the reversible unit during the time period, and respectively represent the water level above the upper reservoir dam and the water level above the lower reservoir dam during the time period; The power generation and pumping flow constraints of the reversible units are expressed as: ； ； ； In the formula, and respectively represent the power generation flow rate and the pumping flow rate of the reversible unit during the time period, and respectively represent the upper limit and the lower limit of the power generation flow rate of the reversible unit during the time period, and respectively represent the upper limit and the lower limit of the pumping flow rate of the reversible unit during the time period; The reversible unit output constraint is expressed as: ； ； In the formula, and respectively represent the upper and lower limits of the power generation power of the reversible unit in the t period, and respectively represent the upper and lower limits of the pumping power of the reversible unit in the t period; The daily pumped water volume constraint of the reversible units is expressed as: ； In the formula, represents the minimum daily power generation that the hybrid pumped-storage power station needs to meet in the electricity contract trading.

4. The short-term multi-objective optimal dispatching method for a hybrid pumped-storage power station according to claim 1, wherein The peak regulation objective function is expressed as: ； Wherein, represents the minimization function , represents the mean square error of the remaining load, represents the total number of power stations, and M is taken as 2, , respectively represent the grid load and the remaining load during the represents the total dispatching period the average value of the remaining load within, represents the th reservoir the conventional hydropower output during the and respectively represent the pumping power and the generating power of the reversible unit during the 5. The short-term multi-objective optimal scheduling method for a hybrid pumped-storage power station according to claim 1, characterized in that The navigation objective function is expressed as: ； ； In the formula, represents the minimization function , represents the daily amplitude of the tail water level of the lower reservoir of the hybrid pumped-storage power station, represents the minimization function , represents the daily amplitude of the water level above the dam of the lower reservoir of the hybrid pumped-storage power station, and respectively represent the maximum value of the water level above the dam and the maximum value of the tail water level of the lower reservoir of the hybrid pumped-storage power station at time t, and respectively represent the minimum value of the water level above the dam and the minimum value of the tail water level of the lower reservoir of the hybrid pumped-storage power station at time t, represents the total dispatching period, represents the total number of power stations.

6. The short-term multi-objective optimal dispatching method for a hybrid pumped-storage power station according to claim 1, characterized in that After mutually exclusive processing of the power generation and pumping flow constraints of the reversible units, generate an initial solution, and then correct the solutions in the initial solution that are not within the final storage capacity constraint range to obtain a corrected initial solution, including: Aggregate the power generation and pumping flow rates of the reversible unit into the reversible unit flow rate, and generate an initial solution using real number coding and random functions; Modify the remaining load and water balance constraints for each time period in the multi-objective optimal scheduling model according to the reversible unit flow rate; Based on the initial reservoir capacity control value, the inflow, and the initial solution, use the water balance equation to correct the solutions in the initial solution that are not within the final reservoir capacity constraint range to obtain the corrected initial solution.

7. The short-term multi-objective optimal scheduling method for the hybrid pumped storage power station according to claim 6, characterized in that The generation process of the reversible unit flow rate is expressed as: ； In the formula, represents a random number generated by a random function, represents a random function, represents the flow rate of the reversible unit in the t period, When it is negative, it means that the reversible unit is in the power generation mode in the t period, When it is positive, it means that the reversible unit is in the pumping mode in the t period, and respectively represent the upper and lower limits of the power generation flow rate of the reversible unit in the period, and respectively represent the upper and lower limits of the pumping flow rate of the reversible unit in the period, represents the conditional judgment statement "if"; The initial solution is expressed as: ； In the formula, represents the initial solution, represents the set composed of the reversible unit flows in each period generated by using a random function, represents the union operation of sets, and respectively represent the set of the upper reservoir outflow in each period and the set of the lower reservoir outflow in each period generated by using a random function. The set of the upper reservoir outflow in each period and the set of the lower reservoir outflow in each period are expressed as: ； ； In the formula, represents the outflow from the upper reservoir at time period t, , represent the lower limit and upper limit of the outflow from the upper reservoir at time period t, respectively, represents the outflow from the lower reservoir at time period t, , represent the lower limit and upper limit of the outflow from the lower reservoir at time period t, respectively, represents the total scheduling period.

8. The short-term multi-objective optimal dispatching method for a hybrid pumped storage power station according to claim 6, characterized in that Modify the remaining load and water balance constraints for each time period in the multi-objective optimal scheduling model according to the reversible unit flow rate, where the remaining load, the reservoir capacity of the upper reservoir, and the reservoir capacity of the lower reservoir are respectively expressed as: ； ； ； In the formula, and respectively represent the grid load and the remaining load during the time period, represents the total number of power stations, represents the average value of the remaining load during the total dispatching time period, represents the th output of the power station in the reservoir during the time period, and respectively represent the pumping power and the generating power of the reversible unit during the time period, represents the storage capacity of the upper reservoir during the time period, represents the storage capacity of the upper reservoir during the time period, represents the incoming flow of the upper reservoir during the time period, represents the outgoing flow of the upper reservoir during the time period, and respectively represent the pumping flow and the generating flow of the reversible unit during the time period, represents the time period length; represents the storage capacity of the lower reservoir during the time period, represents the storage capacity of the lower reservoir during the time period, represents the incoming flow of the lower reservoir during the time period, represents the outgoing flow of the lower reservoir during the time period, represents the flow rate of the reversible unit at time t.

9. The short-term multi-objective optimal dispatching method for the hybrid pumped-storage power station according to claim 6, characterized in that Correct the solutions in the initial solution that are not within the final reservoir capacity constraint range to obtain the corrected initial solution, where the corrected initial solution is expressed as: ； wherein, represents the corrected initial solution, and respectively represent the sets of corrected outflow discharges of the upper reservoir and the lower reservoir; Among them, the corrected outflow includes the set of corrected outflows from the upper reservoir and the lower reservoir and , and the corrected outflow is expressed as: ； In the formula, represents the th reservoir outflow after correction for the time period, and represent the upper and lower limits of the outflow of the th reservoir for the time period respectively, represents "or", represents the outflow of the th reservoir before correction for the t time period, including the outflows of the upper and lower reservoirs before correction for the t time period; Among them, the outflow rates of the upper reservoir and the lower reservoir before correction in the t-th time period are respectively expressed as: ； ； In the formula, represents the outflow discharge of the upper reservoir before correction during the t time period, represents the outflow discharge of the lower reservoir before correction during the t time period, represents the flow rate of the reversible unit during the t time period included in the initial solution, and respectively represent the storage capacity of the upper reservoir after correction and the storage capacity of the lower reservoir after correction during the t time period; Among them, the corrected reservoir capacity includes the corrected reservoir capacity of the upper reservoir and the corrected reservoir capacity of the lower reservoir, and the corrected reservoir capacity is expressed as: ； ； In the formula, represents the corrected storage capacity of the m-th reservoir, including the corrected storage capacity of the upper reservoir and the corrected storage capacity of the lower reservoir, represents the final corrected storage capacity of the m-th reservoir, represents the storage capacity of the m-th reservoir before correction at time t.

10. The short-term multi-objective optimal scheduling method for the hybrid pumped-storage power station according to claim 6, wherein Use the NSGA-II algorithm to solve the multi-objective optimal scheduling model according to the corrected initial solution, and output the Pareto solution set through iterative optimization, including: Construct a parent population according to the corrected initial solution; Repeat the following operations until the preset number of iterations or convergence condition is reached: Based on the multi-objective optimal scheduling model, calculate the objective function values of each individual in the parent population, including the peak shaving objective function value and the navigation objective function value, and correct the fitness of the individuals that do not meet the conventional hydropower constraints and the pumped storage unit operation constraints using the dynamic penalty function method; Perform hierarchical sorting on the parent population according to the Pareto dominance relationship to obtain the non-dominated levels, where the first non-dominated level includes all non-dominated individuals, and other non-dominated levels include only the individuals dominated by the previous non-dominated level; Within each non-dominated level, calculate the crowding distance of the individuals in the objective function space; If the non-dominated levels of the feasible scheduling solutions are different, the feasible scheduling solutions with smaller non-dominated levels are subjected to crossover and mutation operations to obtain new feasible scheduling solutions; If the non-dominated levels of the feasible scheduling schemes are the same, perform crossover and mutation operations on the feasible scheduling schemes with a larger crowding degree to obtain new feasible scheduling schemes; After mutually exclusive processing of the reversible unit power generation and pumping flow rate constraints, use the method of correcting the final reservoir capacity constraint to correct the new solution set to obtain the corrected solution set, which is added to the offspring population as offspring; Combine the individuals in the parental population and the offspring population, perform non-dominated sorting and crowding degree calculation, and select the top P feasible scheduling solutions as the Pareto solution set, where, .

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