Hybrid pumped-storage optimal scheduling method and system for balancing peak shaving and risk aversion

Abstract

The present invention relates to the technical field of multi-objective optimal operation of a water-wind-solar complementary system with a hybrid pumped storage. A hybrid pumped storage optimal dispatching method and system that take into account peak shaving and risk aversion are disclosed; historical data of cascade basin hydropower, wind power, solar power stations and grid loads are obtained, initial dispatching data are generated through prediction, and dispatching scenario data capable of characterizing the uncertainty of new energy are generated based on the initial dispatching data and prediction errors; an optimization model is constructed, the optimization model is solved, and based on the solution of the optimization model, dispatching instructions for cascade hydropower stations and hybrid pumped storage power stations in the basin are generated; through the optimization model, the collaborative optimization of minimizing the risk of abandoned power and load loss on the hydropower side and meeting the peak shaving requirements on the grid side is realized, the flexible regulation advantages of the hybrid pumped storage are fully utilized, and the stable operation and reliability of the system are promoted; through linearization processing, the speed and accuracy of the solution are facilitated, the efficiency of real-time calculation of the system is guaranteed, and the formulation of dispatching decisions is made more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-objective optimization operation of a water-wind-solar complementary system including a hybrid pumped storage, and specifically to a hybrid pumped storage optimization scheduling method and system that takes into account both peak load regulation and risk avoidance. Background Art

[0002] As the proportion of new energy in my country's energy structure increases year by year, the challenges of its consumption brought about by seasonal, random and intermittent fluctuations are becoming increasingly prominent. Relying on the existing large-scale cascade hydropower bases, building a water, wind and solar multi-energy complementary dispatching system provides an effective way to solve the problem of new energy consumption. However, constrained by factors such as water supply, shipping, and flood control, the flexible adjustment capacity of conventional hydropower is limited. With the continuous increase in the penetration rate of new energy, the phenomenon of wind and solar abandonment is difficult to avoid. The formation of a water, wind and solar multi-energy complementary system with energy storage has higher flexibility and helps to alleviate the phenomenon of wind and solar abandonment. Pumped storage, as a mature form of energy storage, can play a flexible adjustment role in both electricity and hydraulic power, and is suitable for water, wind and solar multi-energy complementary systems.

[0003] Pumped storage power stations can be divided into pure pumped storage power stations and hybrid pumped storage power stations according to their types. As a relatively mature technology at present, pure pumped storage power stations have been widely used in the dispatching of complementary systems. However, due to the limitations of geological and head conditions, pure pumped storage power stations are difficult to site and build, and it is difficult to meet the demand for energy storage scale for high-proportion access to new energy. The integration and transformation of conventional cascade hydropower into hybrid pumped storage power stations can shorten the construction period, reduce the difficulty of site selection, and effectively promote the development of pumped storage. Therefore, cascade hydropower with hybrid pumped storage will be a common form of cascade hydropower in the future basin.

[0004] In the day-ahead dispatch, it is necessary to formulate a suitable power generation plan for the water-wind-solar complementary system with hybrid pumped storage to ensure that hydropower has sufficient regulation capacity to compensate for the fluctuation of renewable energy. Affected by the forecast error of renewable energy, there may be a deviation between the power generation plan and the actual output, which is easy to cause the risk of power abandonment and load loss, and bring potential risks to the complementary operation of the hydropower side. Therefore, in the complementary system with hybrid pumped storage, the risk of power abandonment and load loss should be reduced. In addition, in the actual dispatch, in order to enable the receiving power grid to adopt the dispatch plan submitted by the complementary system, the power generation plan should also meet the peak load regulation needs of the power grid as much as possible. Cascade hydropower with hybrid pumped storage has the dual characteristics of source and load, and has better peak load regulation performance. However, due to the anti-peak load regulation characteristics of renewable energy, in the peak load regulation operation mode, the power generation plan of the complementary system is often similar to the load trend, but contrary to the renewable energy power generation trend. When the system regulation capacity is insufficient, achieving the peak load regulation demand may increase the risk of power abandonment and load loss of the complementary system. Therefore, how to balance the risk of power abandonment and load loss and the peak load regulation demand during the operation of the complementary system needs to be studied urgently.

[0005] Under the background of high - proportion access of new energy, how to design a hybrid pumped - storage optimal scheduling method and system that takes into account peak shaving and risk aversion, so as to give full play to the advantages of hybrid pumped - storage, and achieve the coordinated optimization of minimizing the risk of abandoned electricity and load loss on the hydropower side and meeting the peak - shaving demand of the grid side has become an important issue. Summary of the Invention

[0006] Aiming at the above - mentioned problems, the purpose of the present invention is to provide a hybrid pumped - storage optimal scheduling method and system that takes into account peak shaving and risk aversion, which overcomes the problems of difficult peak shaving on the grid side, high risk of abandoned electricity, load loss, much water abandonment, and insufficient flexibility in upward and downward adjustment under the fluctuations of high - uncertainty new energy (wind power and photovoltaic) in the combined cascade hydropower of hybrid pumped - storage power stations. The technical solutions are as follows:

[0007] A hybrid pumped - storage optimal scheduling method that takes into account peak shaving and risk aversion includes the following steps:

[0008] Step 1: Data processing: Obtain the historical data of cascade - basin wind, light, and hydropower stations and grid loads, generate initial scheduling data through prediction, and generate scheduling scenario data based on the initial scheduling data;

[0009] Step 2: Construct a hybrid pumped - storage optimal model that takes into account peak shaving and risk aversion: Determine the first optimization objective with the minimum peak - valley difference of the remaining load to meet the peak - shaving demand of the grid side; determine the second optimization objective with the minimum sum of abandoned electricity and load loss to meet the requirements of reducing complementary operation risks and suppressing new - energy fluctuations, and set corresponding constraint conditions;

[0010] Step 3: Model solution and generation of scheduling instructions: Perform linearization processing on the hybrid pumped - storage optimal model that takes into account peak shaving and risk aversion, convert the original complex mixed - integer nonlinear programming (MINLP) model with high dimensions, multiple constraints, and multiple variables into a mixed - integer linear programming (MILP) model. After importing the scheduling scenario data, call the solver to solve and generate the Pareto solution set by combining the constraint method, and determine the solution of the optimization model in the solution set based on the fuzzy entropy - weight method to generate the scheduling instructions for cascade - basin hydropower stations and hybrid pumped - storage power stations.

[0011] A hybrid pumped - storage optimal scheduling system that takes into account peak shaving and risk aversion includes:

[0012] A data acquisition module: used to obtain the historical data of the output of wind power and photovoltaic power in the cascade basin, the historical data of the flow of reservoirs of each cascade hydropower station in the cascade basin, and the historical data of the load of the receiving - end grid;

[0013] Data processing module: used for data prediction, and based on the predicted data, splitting, scenario generation, reduction and combination are carried out to obtain scheduling scenario data;

[0014] Model construction module: used to establish a hybrid pumped-storage optimization model that takes into account peak shaving and risk aversion, and perform linearization processing to convert the original mixed-integer nonlinear programming model into a mixed-integer linear programming model;

[0015] Solution module: generate a Pareto solution set by solving with a solver in combination with the constraint method, and determine the solution of the optimization model in the solution set based on the fuzzy entropy weight method;

[0016] Control scheduling module: based on the optimal solutions of the cascade hydropower station parameter status and the hybrid pumped-storage power station parameter status obtained by the solution module, generate scheduling instructions for the cascade hydropower stations and the hybrid pumped-storage power stations in the basin.

[0017] The beneficial effects of the present invention are:

[0018] The hybrid pumped-storage optimization scheduling method and system provided by the present invention that take into account peak shaving and risk aversion effectively improve the regulation ability of the hybrid pumped-storage power station, enabling it to cooperate more efficiently with cascade hydropower to suppress the fluctuations of new energy, effectively reducing the system's power abandonment and load shedding phenomena, and improving the consumption rate of new energy. At the same time, it promotes the cascade water-wind-solar complementary system containing hybrid pumped-storage to smooth load fluctuations, reduce the peak-valley difference of the system's remaining load, enhance the stability of the power grid, and reduce the water abandonment phenomenon. In addition, the present invention reduces the water abandonment volume and improves the water energy utilization rate by optimizing the switching of the pumping and generating conditions of the hybrid pumped-storage power station. Description of the drawings

[0019] Figure 1 It is the overall flowchart of the hybrid pumped-storage optimization scheduling method provided by the present invention that takes into account peak shaving and risk aversion.

[0020] Figure 2 It is the structural schematic diagram of the hybrid pumped-storage optimization scheduling system provided by the present invention that takes into account peak shaving and risk aversion.

[0021] Figure 3(a) is the diagram of the daily-ahead scheduling result of the present invention in spring.

[0022] Figure 3(b) is the diagram of the daily-ahead scheduling result of the present invention in summer.

[0023] Figure 3(c) is the diagram of the daily-ahead scheduling result of the present invention in autumn.

[0024] Figure 3(d) is the diagram of the daily-ahead scheduling result of the present invention in winter.

[0025] The meanings of the reference numerals in the figure are as follows: Hybrid pumped-storage optimal scheduling system - 10, data acquisition module - 101, data processing module - 102, data prediction unit - 1021, scenario generation unit - 1022, model construction module - 103, model generation unit - 1031, linearization processing unit - 1032, solution module - 104, model solution unit - 1041, optimal solution selection unit - 1042, control and scheduling module - 105. Detailed implementation manners

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a hybrid pumped-storage optimal scheduling method that takes into account peak shaving and risk aversion, including:

[0028] S1: Data processing: Obtain the historical data of cascade basin hydropower, wind and photovoltaic power stations and grid load, generate initial scheduling data through prediction, and generate scheduling scenario data based on the initial scheduling data.

[0029] S1.1: Obtain the historical data of cascade basin hydropower, wind and photovoltaic power stations and grid load, including: historical data of wind power and photovoltaic power output in the cascade basin, historical data of flow rates of reservoirs of each cascade hydropower station in the cascade basin, and historical data of the load of the receiving-end grid.

[0030] S1.2: Generate initial scheduling data: Predict the day-ahead prediction data of wind power and photovoltaic power output in the cascade basin, the day-ahead prediction data of flow rates of reservoirs of each cascade hydropower station in the cascade basin, and the day-ahead prediction data of the load of the receiving-end grid as the initial scheduling data.

[0031] S1.3: Based on the wind power and photovoltaic power output prediction data in the obtained initial scheduling data, assume that the wind power and photovoltaic power output prediction errors follow a normal distribution and are independent. By and respectively represent the prediction error distributions of wind power and photovoltaic power output in the th period; where, indicates that the variables in the formula follow a normal distribution; is the wind power output prediction error in the th period, is the photovoltaic power output prediction error in the th period; furthermore, the combined prediction error distribution of wind power and photovoltaic power in the th period can be expressed as Subsequently, the Latin hypercube sampling (LHS) stratified random sampling method is adopted to combine with its prediction error for simulation to obtain a set of scenarios with a specific number of wind power and photovoltaic output prediction data. The iterative self-organizing data analysis techniques algorithm (ISODATA) is used to cluster the generated set of scenarios of wind power and photovoltaic output prediction data, generating a representative set of scenarios of wind power and photovoltaic output prediction data characterizing the uncertainty of new energy and the probability of each scenario; and it is combined with the daily predicted flow data of each cascade hydropower station reservoir in the cascade basin and the daily predicted load data of the receiving-end power grid in the initial dispatching data to generate dispatching scenario data.

[0032] S1.3.1: Generate dispatching scenario data, including: a set of scenarios and scenario probabilities of wind power and photovoltaic output prediction data in the cascade basin, receiving-end power grid load prediction data, and predicted flow data of cascade hydropower station reservoirs in the cascade basin.

[0033] S2: Construct a hybrid pumped storage optimization model that takes into account peak shaving and risk aversion: Set the first optimization objective to meet the peak shaving demand on the power grid side; set the second optimization objective to meet the demand for reducing complementary operation risks and suppressing new energy fluctuations, and set the corresponding constraint conditions.

[0034] S2.1: Set the minimum peak-valley difference of the remaining load as the first optimization objective, which is expressed by the formula:

[0035] (1);

[0036] (2);

[0037] In the formula: represents the peak-valley difference of the remaining load, represents a certain time period within the dispatching period, represents the total time of the entire dispatching period, , represents the remaining load in the time period, represents the original load of the power grid, represents the power generation plan in the

[0038] S2.2: Set the minimum sum of the curtailed power and the load shedding amount as the second optimization objective, which is expressed by the formula:

[0039] (3);

[0040] (4);

[0041] (5);

[0042] Where: represents a certain cascade hydropower station in the basin, represents the set of cascade hydropower stations in the basin, ; represents the total sum of abandoned electricity and lost load, represents the scenario, represents the set of scenarios for each season respectively, , represents the scenario corresponding probability, represents the scenario at the time period, the abandoned electricity volume, represents the scenario at the time period, the lost load volume, represents the scenario at the time period, the difference between the power generation plan and the planned output, represents the scenario at the time period, the combined output of wind power and photovoltaic power, represents the scenario at the time period, the power provided by the combined wind power and photovoltaic power to the hybrid pumped-storage for pumping, represents the scenario at the th cascade hydropower station at the time period, the output, represents the scenario at the time period, the power provided by all cascade hydropower stations for the hybrid pumped-storage for pumping, represents the scenario at the time period, the output of the hybrid pumped-storage.

[0043] S2.3: Set the corresponding constraint conditions, including: hydraulic and electrical constraints, power balance and ramp constraints, pumping and generation mutual exclusion constraints, grid power transmission constraints.

[0044] S2.3.1: Set the hydraulic and electrical constraints, and the specific formulas include:

[0045] Different from traditional cascade hydropower, the hybrid pumped storage shares the reservoirs of its upper and lower hydropower stations, so its generating head depends on the water levels of the reservoirs of its upper and lower hydropower stations. In addition, since the electric energy for pumping of the hybrid pumped storage comes from wind power, photovoltaic power and traditional hydropower, the specific pumping power model is different from the generating model. The specific content is as follows:

[0046] (6);

[0047] (7);

[0048] (8);

[0049] (9);

[0050] (10);

[0051] In the formula: and represent the hydropower conversion efficiency and the electro - water conversion efficiency respectively, represents the density of water, represents the acceleration of gravity, represents the scenario at the th time period of the th cascade hydropower station, represents the net head height of the hybrid pumped storage at the th time period of the scenario th cascade hydropower station, represents the generating flow rate of the hybrid pumped storage at the th time period of the scenario represents the scenario at the th and represent the reservoir water level and the reservoir tail water level of the th cascade hydropower station at the time period of the scenario and represent the reservoir water level of the upper hydropower station and the reservoir water level of the lower hydropower station of the hybrid pumped storage at the th time period of the scenario represents the scenario at the Total pumping power during the period;

[0052] (11);

[0053] (12);

[0054] (13);

[0055] (14);

[0056] (15);

[0057] Where: represents the set of hydropower stations, including conventional hydropower stations, the upper-level hydropower stations corresponding to hybrid pumped-storage, and the lower-level hydropower stations corresponding to hybrid pumped-storage, where represents a single hydropower station among them, and respectively represent the reservoir storage capacities of the upper-level and lower-level hydropower stations corresponding to hybrid pumped-storage, represents the scenario at the th cascade hydropower station, the reservoir storage capacity at the th period, represents the scenario at the th hydropower station, the reservoir inflow at the th period, represents the scenario at the th hydropower station, the natural inflow of the reservoir at the th period, represents the scenario at the th hydropower station, the reservoir outflow at the th period, represents the scenario at the th hydropower station, the power generation flow at the th period, represents the scenario at the th hydropower station, the water discharge of abandoned water at the th period; and respectively represent the natural inflows of the reservoirs of the upper-level and lower-level hydropower stations corresponding to hybrid pumped-storage at the th period under the scenario; and respectively represent the natural inflows of the reservoirs of the upper-level and lower-level hydropower stations corresponding to hybrid pumped-storage at the th period under the scenario; ​The reservoir outflows of the upper and lower hydropower stations corresponding to the time - period hybrid pumped - storage Scenario The natural inflow of the reservoir of the n - th cascade hydropower station in the scenario The reservoir outflow of the n - th cascade hydropower station in the

[0058] (16);

[0059] Where: and represent the reservoir water levels at the beginning and end of the operation period of the n - th cascade hydropower station in the scenario respectively, and represent the reservoir water levels at the beginning and end of the operation period of the n - th cascade hydropower station, and represent the allowable fluctuation ranges of the reservoir water levels at the beginning and end of the operation period of the n - th cascade hydropower station;

[0060] (17);

[0061] (18);

[0062] (19);

[0063] Where: and represent the non - linear fitting coefficients of the water level - storage capacity and tail - water level - discharge of the hydropower station reservoir respectively, and represent the average reservoir storage capacity and average reservoir discharge of the n - th cascade hydropower station in the scenario respectively, and represent the dead water level and normal operation water level of the n - th cascade hydropower station reservoir respectively;

[0064] (20);

[0065] (21);

[0066] Where: and respectively represent the maximum generating flow rates of the th cascade hydropower station and the pumped-storage power station with a combined cycle;

[0067] (22);

[0068] Wherein: and respectively represent the lower and upper limits of the reservoir discharge flow rates of the th cascade hydropower station.

[0069] S2.3.2: Set power balance and ramp constraints, and the specific formulas include:

[0070] (23);

[0071] Wherein: represents the electric energy transmitted from the combined wind power and photovoltaic power to the pumped-storage power station with a combined cycle during the th time period under scenario ; represents the electric energy transmitted from the combined wind power and photovoltaic power to the power grid during the th time period under scenario ; represents the curtailed electric energy of the combined wind power and photovoltaic power during the th time period under scenario ;

[0072] (24);

[0073] (25);

[0074] (26);

[0075] Wherein: represents the total power transmitted from the cascade hydropower station group to the power grid during the th time period under scenario , represents the curtailed electric energy of the cascade hydropower station group during the th time period under scenario , and respectively represent the lower and upper limits of the output of the th cascade hydropower station, represents the ramp capacity of the th cascade hydropower station;

[0076] (27);

[0077] (28);

[0078] (29);

[0079] Wherein: represents the scenario the electric energy purchased by the hybrid pumped-storage from the power market represents the scenario the hybrid pumped-storage transmits electric energy to the power grid at the time period represents the scenario the curtailed electricity of the hybrid pumped-storage at the time period and respectively represent the lower limit and upper limit of the output of the hybrid pumped-storage

[0080] S2.3.3: Set the mutual exclusion constraint between pumping and generating, and the specific formula includes:

[0081] (30);

[0082] (31);

[0083] Wherein: represents the scenario the output of the upper-level hydropower station of the hybrid pumped-storage at the time period

[0084] S2.3.4: Set the power transmission constraint of the power grid, and the specific formula is expressed as:

[0085] (32);

[0086] Wherein: is the upper limit of the channel capacity for electric energy transmission and grid connection

[0087] Furthermore, the purpose of constructing and solving the optimization model is to manage the peak shaving demand on the grid side and the curtailment and load shedding of hydropower in a complex cascade water, wind, solar, and pumped-storage power system. By the first optimization objective (minimizing the peak-valley difference of the remaining load) to meet the peak shaving demand on the grid side, the second optimization objective (minimizing the sum of curtailment and load shedding) to meet the demand of reducing complementary operation risks and suppressing new energy fluctuations, and combining various constraints (such as hydraulic and electrical constraints, power balance and ramping constraints, pumping and generating mutual exclusion constraints, grid power transmission constraints), it is ensured that the operation modes of cascade hydropower stations and hybrid pumped-storage power stations in the basin can reasonably utilize water energy to dispatch output and control water levels on the premise of meeting physical and operation limitations. This step can not only effectively meet the peak shaving demand on the grid side while reducing curtailment and load shedding on the hydropower side, promoting the stable operation and reliability of the system. At the same time, the scenario set generated based on prediction errors can effectively characterize the uncertainty of new energy, give full play to the flexible regulation advantages of hybrid pumped-storage, maximize the utilization of renewable energy such as wind energy and photovoltaic energy, and achieve environmental benefits. In addition, through linearization processing, the complex nonlinear model is converted into a linearized model, which helps to improve the speed and accuracy of solution, ensures the efficiency of real-time calculation of the system, and makes the formulation of dispatching decisions more efficient.

[0088] S3: Model solution and dispatching instruction generation: Perform linearization processing on the optimization model, convert the original complex mixed-integer nonlinear programming model with high dimensions, multiple constraints, and multiple variables into a mixed-integer linear programming optimization model. After importing dispatching scenario data, call the solver to solve and generate the Pareto solution set using the constraint method, and select the solution of the optimization model from the Pareto solution set based on the fuzzy entropy weight method to generate dispatching instructions for cascade hydropower stations and hybrid pumped-storage power stations in the basin. The specific process is as follows:

[0089] S3.1: The steps of selecting the solution of the optimization model from the Pareto solution set based on the fuzzy entropy weight method are as follows:

[0090] (33);

[0091] In the formula: represents the membership degree value of the minimization objective, represents an optimal solution of the objective on the Pareto solution set, represents the maximum value of all solution sets under the objective, represents the minimum value of all solution sets under the

[0092] (34);

[0093] In the formula: represents the normalized membership degree value of the th solution in the solution set, where the maximum value is regarded as the optimal solution; represents the weight value of the objective on the Pareto solution set, represents the number of objectives, represents the number of Pareto solutions, represents the number of Pareto solutions; is the th solution under the is the th solution under the

[0094] Through the above steps, the optimal solution of the parameters of the cascade hydropower station and the optimal solution of the parameters of the hybrid pumped-storage power station are obtained.

[0095] S3.2: The scheduling instructions for the cascade hydropower station and the hybrid pumped-storage power station generated include: based on the optimal solution of the parameters of the cascade hydropower station and the optimal solution of the parameters of the hybrid pumped-storage power station, generating the start-stop states of specific conventional hydropower units and hybrid pumped-storage units, the reservoir water level fluctuation conditions of the hydropower station, and the power scheduling instructions;

[0096] S3.2.1: The optimal solution of the parameters of the hydropower station includes: output power, water level change state, power generation flow rate, and water discharge flow rate;

[0097] S3.2.2: The optimal solution of the parameters of the hybrid pumped-storage power station includes: the pumping state, power generation state, pumping power, power generation power, pumping flow rate, and power generation flow rate of the pumped-storage power station.

[0098] Furthermore, based on the solution of the optimization model, the scheduling instructions for the cascade hydropower station and the hybrid pumped-storage power station in the basin are generated, aiming to achieve accurate and efficient power scheduling. By generating the start-stop states of specific conventional hydropower units and hybrid pumped-storage units, the reservoir water level fluctuation conditions of the hydropower station, and the power scheduling instructions through the optimal solutions of the cascade hydropower station and the hybrid pumped-storage power station in the basin obtained by solving, the operation modes of the cascade hydropower units and the hybrid pumped-storage units in the basin can be reasonably controlled in different time periods, and the supply-demand balance can be achieved.

[0099] Example 2, an embodiment of the present invention, provides a hybrid pumped-storage optimization scheduling system that takes into account peak shaving and risk aversion, including:

[0100] Referring to Figure 2, which is a schematic structural diagram of a hybrid pumped - storage optimization scheduling system that takes into account peak shaving and risk aversion provided by an embodiment of the present invention. As shown in the figure, the hybrid pumped - storage optimization scheduling system 10 that takes into account peak shaving and risk aversion includes a data acquisition module 101, a data processing module 102, a model construction module 103, a solution module 104, and a control and scheduling module 105. The specific units of each module are as follows:

[0101] The data acquisition module 101 is used to obtain historical data of wind power and photovoltaic output in the cascade basin, historical data of the flow of reservoirs of each cascade hydropower station in the cascade basin, and historical data of the load of the receiving - end power grid.

[0102] The data processing module 102 is used for data prediction and performs splitting, scenario generation, reduction, and combination based on the predicted data to obtain scheduling scenario data.

[0103] Optionally, the data processing module 102 includes a data prediction unit 1021 and a scenario generation unit 1022, where:

[0104] The data prediction unit 1021 is used to generate initial scheduling data. Based on the historical data of wind power and photovoltaic output in the cascade basin, historical data of the flow of reservoirs of each cascade hydropower station in the cascade basin, and historical data of the load of the receiving - end power grid obtained by the data acquisition module 101, it predicts the day - ahead prediction data of wind power and photovoltaic output in the cascade basin, the day - ahead prediction data of the flow of reservoirs of each cascade hydropower station in the cascade basin, and the day - ahead prediction data of the load of the receiving - end power grid within a future set scheduling period (24 hours) as the initial scheduling data.

[0105] The scenario generation unit 1022 is used to generate scheduling scenario data. Based on the day - ahead prediction data part of wind power and photovoltaic output in the initial scheduling data obtained by the data prediction unit 1021, it uses the Latin hypercube stratified random sampling method combined with its prediction error to simulate and obtain a scenario set with a specific number of wind power and photovoltaic output prediction data, and uses the iterative self - organizing data analysis algorithm to cluster the scenario set of the wind power and photovoltaic output prediction data, generating a representative scenario set of wind power and photovoltaic output prediction data representing the uncertainty of new energy and the probability of each scenario; and combines it with the day - ahead prediction data of the flow of reservoirs of each cascade hydropower station in the cascade basin and the day - ahead prediction data of the load of the receiving - end power grid in the initial scheduling data to generate scheduling scenario data.

[0106] The model construction module 103 is used to establish a hybrid pumped - storage optimization model that takes into account peak shaving and risk aversion and perform linearization processing to convert the original mixed - integer nonlinear programming model into a mixed - integer linear programming model.

[0107] Optionally, the model construction module 103 includes a model generation unit 1031 and a linearization processing unit 1032, where:

[0108] The model generation unit 1031 is used to establish a hybrid pumped-storage optimization model that takes into account peak shaving and risk aversion. The hybrid pumped-storage optimization model that takes into account peak shaving and risk aversion includes a first optimization objective, a second optimization objective, and constraint conditions. The first optimization objective is to minimize the peak-valley difference of the remaining load, and the second optimization objective is to minimize the sum of the curtailed electricity and the load shedding amount. The constraint conditions include hydraulic and electrical constraints, power balance and ramp constraints, pumping and generating mutual exclusion constraints, and grid power transmission constraints.

[0109] The linearization processing unit 1032 is used to linearize the non-linear part in the hybrid pumped-storage optimization model that takes into account peak shaving and risk aversion, and convert the original mixed-integer non-linear programming model into a mixed-integer linear programming model.

[0110] The solution module 104 uses a solver to combine with the constraint method to solve and generate a Pareto solution set, and determines the solution of the optimization model based on the fuzzy entropy weight method in the solution set.

[0111] Optionally, the solution module 104 includes a model solution unit 1041 and an optimal solution selection unit 1042, where:

[0112] The model solution unit 1041 is used to call a solver and combine with the constraint method to solve, so as to generate a uniformly distributed Pareto solution set;

[0113] The optimal solution selection unit 1042 is used to select the optimal solution in the Pareto solution set generated by the model solution unit 1041 through the fuzzy entropy weight method, and obtain the optimal solution of the cascade hydropower station parameter state and the optimal solution of the hybrid pumped-storage power station parameter state

[0114] The control and scheduling module 105 generates scheduling instructions for the cascade hydropower stations and the hybrid pumped-storage power stations in the basin based on the optimal solution of the cascade hydropower station parameter state and the optimal solution of the hybrid pumped-storage power station parameter state obtained by the solution module 104.

[0115] Embodiment 3, referring to FIGS. 3(a), 3(b), 3(c), and 3(d), is an embodiment of the present invention. Based on the hybrid pumped-storage optimization scheduling method and system proposed by the present invention, scientific demonstration is carried out through simulation experiments and comprehensive effect evaluation. In order to verify the feasibility of the present invention, the day-ahead scheduling operation effects in different seasons are studied. In addition, the optimization and improvement effects of the method proposed in this invention patent on guiding the operation of the system before and after installing the hybrid pumped-storage power station are also studied to verify the beneficial effects of the method proposed in this invention patent and its applicability to the introduction of the hybrid pumped-storage power station.

[0116] Taking a cascade water-wind-solar complementary system in a certain basin as the research object, this system includes cascade hydropower stations, wind power stations and photovoltaic power stations. Among them, the cascade hydropower stations in the research object take three cascaded hydropower stations, namely GZ, MMY and DQ, as references to construct application examples. Currently, there are 4 hydroturbine units in each of the GZ Power Station, MMY Power Station and DQ Power Station. By installing pumped-storage units between the GZ and MMY power stations, a cascade hybrid pumped-storage power station is formed. The specific power station and unit parameters are shown in Table 1.

[0117] In terms of basic water conservancy parameters, they include: water level-storage capacity relationship, tail water level-outflow relationship, power-head-flow relationship, and the starting water level fixed value of each hydropower station reservoir within the four-season scheduling cycle, the fluctuating range of the final water level, the inflow of the uppermost hydropower station reservoir, and the sectional flow of each cascaded hydropower station reservoir.

[0118] System parameters include: taking 24 hours as the scheduling cycle and 1 hour as the time scale. In addition, the hybrid pumped-storage power station includes 4 vertical shaft single-stage reversible Francis turbine generator units with a capacity of 200 MW each, and the total installed capacity is 800 MW.

[0119] Table 1 Basic parameters of cascade hydropower stations

[0120] 。

[0121] Historical data of wind power and photovoltaic power output, historical flow data of each cascade hydropower station reservoir, and historical data of the receiving-end power grid load in this basin in the past three years were collected. The data are all in hourly time steps, and a total of 26,280 time steps are included.

[0122] Based on the historical data collected by the system, the day-ahead prediction data of wind power and photovoltaic power output in the cascade basin, the day-ahead prediction data of the flow of each cascade hydropower station reservoir in the cascade basin, and the day-ahead prediction data of the load of the receiving-end power grid are predicted as the initial scheduling data. Taking the wind power and photovoltaic power output prediction data part in the obtained initial scheduling data as the basis, the Latin hypercube stratified random sampling method is used to combine its prediction error to simulate and obtain a scenario set with a specific number of wind power and photovoltaic power output prediction data. The iterative self-organizing data analysis algorithm is used to cluster the generated wind power and photovoltaic power output scenarios to generate a representative scenario set and the probability of each scenario of wind power and photovoltaic power output prediction data representing the uncertainty of new energy for typical days in each season; and it is combined with the day-ahead prediction data of the flow of each cascade hydropower station reservoir in the cascade basin and the day-ahead prediction data of the load of the receiving-end power grid in the initial scheduling data to generate scheduling scenario data.

[0123] Subsequently, a hybrid pumped-storage optimization model that takes into account both peak shaving and risk aversion is established. The first optimization objective (aiming to minimize the peak-valley difference of the remaining load) is used to meet the peak shaving demand on the grid side, and the second optimization objective (aiming to minimize the sum of curtailed power and load shedding) is used to meet the requirements of reducing the complementary operation risk and suppressing the fluctuations of new energy, and combined with various constraints (such as hydraulic and electrical constraints, power balance and ramp constraints, mutual exclusion constraints between pumping and generating, and grid power transmission constraints).

[0124] By calling the solver and combining the constraint method to generate the Pareto solution set, the fuzzy entropy weight method is used to select the optimal solution from the Pareto solution set. The optimal solutions for the four seasons are shown in Table 2. From the results in the table, it can be seen that in all seasons, the numerical values of the first and second optimization objectives of the system with the hybrid pumped-storage system are lower than those of the system without the hybrid pumped-storage system. Since both the first and second optimization objectives are optimal with minimization, the hybrid pumped-storage optimization scheduling method and system proposed in this patent can achieve the coordinated optimization of the risk of curtailment and load shedding on the hydropower side and the peak shaving demand on the grid side, and is applicable to the multi-objective regulation requirements of the cascade basin after the introduction of the hybrid pumped-storage power station.

[0125] Table 2 Numerical values of optimization objectives under the optimal solutions for the four seasons

[0126] 。

[0127] The scenarios in each seasonal scenario set were multiplied by their corresponding probabilities, and the day-ahead scheduling results for four different seasons were obtained and discussed in detail, as shown in Figures 3(a), 3(b), 3(c), and 3(d). Generally speaking, the power generation plan of the system with the hybrid pumped-storage system is consistent with the load trend of the receiving-end power grid. The hybrid pumped-storage system pumps water for energy storage at the load valley and generates electricity concentratedly at the load peak. Taking spring as an example, as can be seen from Figure 3(a), at the two load valleys near Period 4 and Period 14 in spring, the hybrid pumped-storage system pumps water to fill the valley, raising the remaining load valleys; while at the two peaks near Period 11 and Period 20, the hybrid pumped-storage system adjusts the peak by generating electricity, causing the peak of the system with the hybrid pumped-storage system to decrease. The characteristics of summer, autumn, and winter are similar to those of spring and will not be elaborated here. In summer shown in Figure 3(b) and autumn shown in Figure 3(c), due to abundant water inflow, the remaining load is relatively flat; while in spring shown in Figure 3(a) and winter shown in Figure 3(d), due to less water inflow, the change trend of the remaining load is similar to the load change. From the optimization results of the four seasons in Figure 3, it can be seen that the system with the hybrid pumped-storage system has better peak regulation performance in all four seasons than the system without the hybrid pumped-storage system. In addition, while meeting the peak regulation requirements, the system with the hybrid pumped-storage system reduces the deviation between the actual output and the power generation plan by virtue of the flexible regulation ability of the hybrid pumped-storage system, effectively reducing the risks of curtailment and load loss. This proves the effectiveness of the hybrid pumped-storage optimal scheduling method proposed in this patent, which takes into account both peak regulation and risk aversion.

[0128] Table 3 Data table of various indicators under the optimal solutions of the four seasons

[0129] 。

[0130] According to the results shown in Table 3, under the hybrid pumped-storage optimal scheduling method and system proposed in the present invention, which takes into account both peak regulation and risk aversion, the overall performance of the system with the hybrid pumped-storage system is better in all scenarios. Its curtailment and load loss amounts are lower than those of the system without the hybrid pumped-storage system, especially in reducing curtailment. This shows that the hybrid pumped-storage optimal scheduling method that takes into account both peak regulation and risk aversion can effectively utilize the energy storage characteristics of the hybrid pumped-storage system to hedge the impact of new energy prediction uncertainty. In addition, the solution operation time of the present invention is short and the solution efficiency is high. It can provide the optimal day-ahead scheduling plan in a short time and is suitable for real-time application in actual power systems. By simultaneously meeting the peak regulation requirements on the grid side and reducing the curtailment and load loss risks on the hydropower side, it gives full play to the flexible regulation advantages of the hybrid pumped-storage system and ensures the safety, stability, and reliability of the power system.

Claims

1. A hybrid pumped storage optimization dispatching method that takes into account both peak load regulation and risk avoidance, characterized in that: The following steps are involved: Step 1: Data processing: Obtain historical data of cascade basin hydropower, wind and solar power stations and grid loads, generate initial dispatch data through prediction, and generate dispatch scenario data based on the initial dispatch data; Step 2: Construct a hybrid pumped storage optimization model that takes into account both peak load regulation and risk avoidance: determine the first optimization goal by minimizing the peak-to-valley difference of the remaining load to meet the peak load regulation demand on the grid side; determine the second optimization goal by minimizing the sum of the amount of abandoned power and the amount of lost load to meet the needs of reducing the risk of complementary operation and smoothing the fluctuations of wind power and photovoltaic power, and set corresponding constraints; Step 3: Model solving and dispatch instruction generation: Linearize the hybrid pumped-storage optimization model that takes into account both peak load regulation and risk avoidance, and convert the original high-dimensional, multi-constraint, multi-variable complex mixed integer nonlinear programming model into a mixed integer linear programming model. After importing the dispatch scenario data, call the solver to generate a Pareto solution set in combination with the constraint method, and determine the solution of the optimization model in the Pareto solution set based on the fuzzy entropy weight method to generate dispatch instructions for the basin cascade hydropower stations and hybrid pumped-storage power stations.

2. The hybrid pumped storage optimization scheduling method taking into account both peak load regulation and risk avoidance according to claim 1 is characterized in that: The step 1 is specifically as follows: Step 1.1: Obtain historical data of hydropower, wind and solar power stations and grid loads in cascade basins, including historical data of wind power and photovoltaic output in cascade basins, historical flow data of reservoirs of cascade hydropower stations in cascade basins, and historical data of grid loads at receiving ends; Step 1.2: Forecast the day-ahead forecast data of wind power and photovoltaic power output in the cascade basin, the day-ahead forecast data of the flow of each cascade hydropower station reservoir in the cascade basin, and the day-ahead forecast data of the load of the receiving power grid as the initial dispatching data; Step 1.3: Take the wind power and photovoltaic output forecast data in the obtained initial dispatch data as the basis, assume that the wind power and photovoltaic output forecast errors obey the normal distribution and are independent, and and Respectively represent The forecast error distribution of wind power and photovoltaic output in different time periods; Indicates that the variables in the formula are normally distributed; For the The wind power output forecast error for each period, For the PV output prediction error for each time period; The forecast error of wind power and photovoltaic combined output in period t is expressed as: ； Subsequently, the Latin hypercube stratified random sampling method is used in combination with its prediction error to simulate and obtain a scenario set with a specific number of wind power and photovoltaic output forecast data, and the iterative self-organizing data analysis algorithm is used to cluster the generated scenario set of wind power and photovoltaic output forecast data to generate a representative scenario set of wind power and photovoltaic output forecast data that characterizes the uncertainty of new energy and the probability of each scenario; and it is combined with the day-ahead forecast data of the flow of each cascade hydropower station reservoir in the cascade basin in the initial scheduling data and the day-ahead forecast data of the load of the receiving power grid to generate scheduling scenario data.

3. The hybrid pumped storage optimization scheduling method taking into account both peak load regulation and risk avoidance according to claim 2 is characterized in that: In step 2, the first optimization objective is obtained by minimizing the peak-to-valley difference of the residual load, and the formula is expressed as: ； ； Where: Represents the peak-to-valley difference of the residual load, subscript represents a certain period of time during the scheduling period, , Represents the total time of the entire scheduling period; Representative The remaining load of the period, Represents the original load of the power grid, Representative Power generation plan for each time period; The second optimization objective is obtained by minimizing the sum of the amount of abandoned power and the amount of lost load, and the formula is expressed as: ； ； ； Where: Represents a cascade hydropower station in the basin. Represents a collection of cascade hydropower stations in the basin, ; Represents the total amount of power loss and load loss, Representative scene, A collection of scenes representing each season. , Representative scenes The corresponding probability is Representative scenes Next The amount of power wasted during the period, Representative scenes Next The amount of load loss during the period, Representative scenes Next The difference between the power generation plan and the planned output during the period, Representative scenes Next Wind power and photovoltaic power generation are combined during this period. Representative scenes Next The power provided by wind power and photovoltaic power to the hybrid pumped storage hydropower in the period, Representative scenes Next Cascade hydropower station Output during the period, Representative scenes All cascade hydropower stations are under The period is the power provided by the hybrid pumped storage. Representative scenes Hybrid pumped storage Output during the period.

4. The hybrid pumped storage optimization scheduling method taking into account both peak load regulation and risk avoidance according to claim 3 is characterized in that: In step 2, the constraints include hydraulic and electrical constraints, power balance and ramp constraints, pumping and power generation mutual exclusion constraints, and power transmission constraints of the power grid; The hydraulic and electrical constraints are expressed as follows: ； ； ； ； ； Where: and represent the water-to-electricity conversion efficiency and the electricity-to-water conversion efficiency, respectively. represents the density of water, represents the acceleration due to gravity, Representative scenes Next Cascade hydropower station The net head height of the time period, Representative scenes Hybrid Pumped Storage The net head height of the time period, Representative scenes Next Cascade hydropower station The power generation flow during the period, Representative scenes Hybrid Pumped Storage The power generation flow during the period, Representative scenes Hybrid Pumped Storage Pumping flow rate during the period, and Representing scenes Next Cascade hydropower station The reservoir water level and the reservoir tailwater level during the period, and Representing scenes Hybrid Pumped Storage The reservoir water level of the upper hydropower station and the reservoir water level of the lower hydropower station during the period; Representative scenes Hybrid Pumped Storage Total pumping power during the period; ； ； ； ； ； Where: represents a set of hydropower stations, which includes conventional hydropower stations, upper-level hydropower stations corresponding to hybrid pumped storage, and lower-level hydropower stations corresponding to hybrid pumped storage. Represents a single hydroelectric power station within it; and They represent the reservoir capacities of the upper and lower hydropower stations corresponding to the hybrid pumped storage system. Representative scenes Next Cascade hydropower station The reservoir capacity during the period, Representative scenes Next Hydropower Station No. Reservoir inflow during the period, Representative scenes Next Hydropower Station No. The natural water flow of the reservoir during the period, Representative scenes Next Hydropower Station No. The outflow of the reservoir during the period, Representative scenes Next Hydropower Station No. The power generation flow during the period, Representative scenes Next Hydropower Station No. The amount of water abandoned during the period; and Representing scenes Next The natural water inflow from the reservoirs of the upstream and downstream hydropower stations corresponding to the period-time hybrid pumped storage; and Representing scenes Next The outflow of the reservoirs of the upper and lower hydropower stations corresponding to the hybrid pumped storage in the time period; Scenario Next Cascade hydropower station Natural water inflow from reservoirs during certain periods of time; Scenario Next Cascade hydropower station Reservoir outflow during the period; Δ t Indicates that from t The time difference between the first period and the next period, i.e., the time difference between the t+1 period; ； Where: and Representing scenes Next The reservoir water level at the beginning of the dispatch period and the reservoir water level at the end of the dispatch period for each cascade hydropower station. and Respectively represent The reservoir water level at the beginning of the dispatch period and the reservoir water level at the end of the dispatch period for each cascade hydropower station. and Respectively represent The permissible fluctuation range of reservoir water level at the beginning and end of the dispatch period of each cascade hydropower station; ； ； ； Where: and They represent the nonlinear fitting coefficients of the water level-storage capacity and tailwater level-discharge flow of the hydropower station reservoir, and Representing scenes Next Cascade hydropower station The average reservoir storage capacity and average reservoir discharge during the period, and Respectively represent Dead water level and normal operating water level of each cascade hydropower station reservoir; Z n,i,t Representative scenes Next Cascade hydropower station Reservoir water level during the period; ； ； Where: and Respectively represent Maximum power generation flow of cascade hydropower stations and hybrid pumped storage; ； Where: and Respectively represent The lower and upper limits of the outflow flow from the reservoirs of each cascade hydropower station; The power balance and climbing constraint formula is expressed as: ； Where: Representative scenes Next The electricity transmitted from wind power and photovoltaic power to hybrid pumped storage during the period; Representative scenes Next The electricity transmitted to the grid by wind power and photovoltaic power during the period, Representative scenes Wind power and photovoltaic Power abandonment during time periods; ； ； ； Where: Representative scenes The lower cascade hydropower station group is in The total power transmitted to the grid during the period, Representative scenes The lower cascade hydropower station group is in Power abandonment during the period, and Respectively represent The lower and upper limits of the output of each cascade hydropower station, Representative The climbing capacity of each cascade hydropower station; ； ； ； Where: Representative scenes The hybrid pumped storage system purchases electricity from the electricity market. Representative scenes Hybrid pumped storage The amount of electrical energy transmitted to the grid during the period, Representative scenes Hybrid pumped storage Power abandonment during the period, and They represent the lower and upper limits of the hybrid pumped storage output respectively; The formula for the mutual exclusion constraint between pumping and power generation is expressed as: ； ； Where: Representative scenes The upper level of the hybrid pumped storage hydropower station Output during the time period; The formula of the power transmission constraint of the power grid is expressed as: ； Where: It is the upper limit of the channel capacity for power transmission and grid connection.

5. The hybrid pumped storage optimization scheduling method taking into account both peak load regulation and risk avoidance according to claim 1 is characterized in that: Step 3 is as follows: Step 3.1: Determine the solution of the optimization model in the Pareto solution set based on the fuzzy entropy weight method. The specific steps are as follows: ； Where: represents the membership value of the minimization objective, Represents the Pareto solution set An optimal solution to the goal, represent The maximum value of all solution sets under the target, represent The minimum value among all solutions under the target; Calculate the standardized membership value : ； Where: represents the standardized membership value, and its maximum value is regarded as the best solution; Represents the Pareto solution set The weight of the target, Represents the number of targets, represents the number of Pareto solutions; for The kth solution under the target, for Target A solution; Through the above steps, the optimal solution of the parameter state of the cascade hydropower station and the optimal solution of the parameter state of the hybrid pumped storage power station are obtained; Step 3.2: Generate dispatching instructions for cascade hydropower stations and hybrid pumped storage power stations in the basin: Generating dispatching instructions for cascade hydropower stations and hybrid pumped-storage power stations in the river basin, including: generating specific start-stop states of conventional hydropower units and hybrid pumped-storage units, reservoir water level fluctuation conditions of hydropower stations, and power dispatching instructions based on the optimal solution of the parameter states of the cascade hydropower stations and the optimal solution of the parameter states of the hybrid pumped-storage power stations; The optimal solution of the hydropower station parameter state includes: output power, water level change state, power generation flow, and abandoned water flow; The optimal solution of the parameter state of the hybrid pumped-storage power station includes the pumping state, power generation state, pumping power, power generation power, pumping flow and power generation flow of the pumped-storage power station.

6. A hybrid pumped storage optimization dispatching system that takes into account both peak load regulation and risk avoidance, characterized in that: include: Data acquisition module (101): used to acquire historical data of wind power and photovoltaic power output in the cascade basin, historical flow data of reservoirs of cascade hydropower stations in the cascade basin, and historical data of load on the receiving end power grid; Data processing module (102): used for data prediction, and splitting, scene generation, reduction and combination based on the predicted data to obtain scheduling scene data; Model building module (103): used to establish a hybrid pumped storage optimization model that takes into account both peak load regulation and risk avoidance, and to perform linearization processing to convert the original mixed integer nonlinear programming model into a mixed integer linear programming model; Solving module (104): generating a Pareto solution set by combining a solver with a constraint method, and determining a solution of the optimization model in the solution set based on a fuzzy entropy weight method; A control and dispatching module (105) generates dispatching instructions for the cascade hydropower stations and the hybrid pumped-storage power stations in the basin based on the optimal solutions for the parameter states of the cascade hydropower stations and the optimal solutions for the parameter states of the hybrid pumped-storage power stations obtained by the solution module (104).

7. The hybrid pumped storage optimization dispatching system taking into account both peak load regulation and risk avoidance according to claim 6 is characterized in that: The data processing module (102) comprises a data prediction unit (1021) and a scene generation unit (1022); The data prediction unit (1021) is used to generate initial dispatching data, based on the historical data of wind power and photovoltaic power output in the cascade basin, the historical data of flow of reservoirs of cascade hydropower stations in the cascade basin, and the historical data of load of the receiving-end power grid acquired by the data acquisition module (101), to predict the day-ahead forecast data of wind power and photovoltaic output in the cascade basin, the day-ahead forecast data of flow of reservoirs of cascade hydropower stations in the cascade basin, and the day-ahead forecast data of load of the receiving-end power grid within a future set dispatching period, as the initial dispatching data; The scenario generation unit (1022) is used to generate scheduling scenario data. Based on the day-ahead forecast data of wind power and photovoltaic output in the initial scheduling data obtained by the data prediction unit (1021), a Latin hypercube stratified random sampling method is used in combination with its prediction error to simulate and obtain a scenario set with a specific number of wind power and photovoltaic output forecast data. The generated scenario set of wind power and photovoltaic output forecast data is clustered using an iterative self-organizing data analysis algorithm to generate a representative scenario set of wind power and photovoltaic output forecast data that characterizes the uncertainty of new energy and the probability of each scenario. The scenario set is combined with the day-ahead forecast data of the flow of the reservoirs of each cascade hydropower station in the cascade basin and the day-ahead forecast data of the load of the receiving power grid in the initial scheduling data to generate scheduling scenario data.

8. The optimization scheduling system according to claim 6, characterized in that: The model building module (103) includes a model generation unit (1031) and a linearization processing unit (1032); The model generation unit (1031) is used to establish a hybrid pumped storage optimization model that takes into account both peak load regulation and risk avoidance, wherein the hybrid pumped storage optimization model that takes into account both peak load regulation and risk avoidance includes a first optimization target, a second optimization target and constraint conditions, the first optimization target is to minimize the peak-to-valley difference of the remaining load, and the second optimization target is to minimize the sum of the amount of abandoned electricity and the amount of lost load; the constraint conditions include hydraulic and electric power constraints, power balance and ramp constraints, pumping and power generation mutual exclusion constraints, and power transmission constraints of the power grid; The linearization processing unit (1032) is used to perform linearization processing on the nonlinear part of the hybrid pumped storage optimization model that takes into account both peak load regulation and risk avoidance, and convert the original mixed integer nonlinear programming model into a mixed integer linear programming model.

9. The optimization scheduling system according to claim 6, characterized in that: The solution module (104) comprises a model solution unit (1041) and an optimal solution selection unit (1042); The model solving unit (1041) is used to call the solver and solve in combination with the constraint method to generate a uniformly distributed Pareto solution set; The optimal solution selection unit (1042) is used to select the optimal solution in the Pareto solution set generated by the model solving unit (1041) through a fuzzy entropy weight method, so as to obtain the optimal solution for the parameter state of the cascade hydropower station and the optimal solution for the parameter state of the hybrid pumped storage power station.

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