An energy storage optimal configuration method under power supply guarantee of a water-light complementary system considering water regulation constraints

Abstract

The application discloses a kind of energy storage optimization configuration method under water-light complementary system power supply considering water regulation constraint, belong to water-light complementary system planning technical field, this method includes: according to water-light complementary system framework modeling, obtain water-light complementary system model, based on water-light complementary system model establishes the energy storage double-layer optimization configuration model of water-light complementary system, based on multi-objective whale optimization algorithm and linear programming commercial solver Gurobi to the energy storage double-layer optimization configuration model of water-light complementary system is solved.The energy storage optimization configuration method under water-light complementary system power supply considering water regulation constraint provided by the application can realize the energy storage optimization configuration of water-light complementary system, while optimizing economy and clean energy abandoned power, guarantee maximum power supply.

Description

Technical Field

[0001] This invention relates to the field of planning technology for hydro-solar complementary systems, and in particular to a method for optimizing the energy storage configuration of hydro-solar complementary systems under the condition of power supply guarantee, taking into account water regulation constraints. Background Technology

[0002] With the increasing proportion of new energy grid connection and the advancement of clean energy transformation, photovoltaic (PV) power output is significantly random, fluctuating, and intermittent due to its dependence on sunlight intensity. Large-scale grid connection increases the operational difficulty of system power balance, thus restricting the efficiency of PV power generation absorption. Leveraging the flexible regulation characteristics of hydropower to smooth out fluctuations in PV output can effectively improve the stability of new energy grid connection and enhance PV absorption efficiency. The hydro-PV complementary operation mode is of great significance for promoting the green and low-carbon development of the new power system.

[0003] With the gradual improvement of water resource management systems, the demand for water intake in hydropower station reservoirs for comprehensive utilization is becoming increasingly stringent. Simultaneously, the proportion of photovoltaic power generation in hydro-solar hybrid systems is surging, leading to increasingly heavy hydropower regulation tasks. The water and power regulation needs of hydropower stations are mutually constraining, making energy storage configuration for hydro-solar hybrid systems essential to improve system regulation capabilities. Therefore, designing an optimized energy storage configuration method for hydro-solar hybrid systems that considers water regulation constraints while ensuring power supply is crucial. Summary of the Invention

[0004] The purpose of this invention is to provide an energy storage optimization configuration method for a hydro-solar complementary system that takes into account water dispatch constraints and ensures power supply. By considering the mutual constraints between water dispatch and power dispatch in the scheduling plan, the method optimizes the economic efficiency of energy storage configuration and the capacity for new energy absorption while ensuring power supply.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for optimizing energy storage configuration under power supply guarantee in a hydro-solar hybrid system considering water regulation constraints includes the following steps:

[0007] Step 1: Model the water-solar complementary system based on the framework to obtain the water-solar complementary system model;

[0008] Step 2: Establish a two-layer optimal configuration model for energy storage of the water-solar complementary system based on the water-solar complementary system model;

[0009] Step 3: Solve the energy storage dual-layer optimization configuration model of the water-solar hybrid system based on the multi-objective whale optimization algorithm and the commercial linear programming solver Gurobi.

[0010] Furthermore, in step 1, a model is obtained by modeling the water-solar complementary system based on the framework of the water-solar complementary system, specifically as follows:

[0011] A framework for a hydro-solar complementary system is established, including a source side, an energy storage side, and an upstream grid side. The source side includes photovoltaic clusters and cascade hydropower groups for generating power from the hydro-solar complementary system. The energy storage side includes electrochemical energy storage for storing excess energy when the hydro-solar complementary system generates a large amount of power and releasing the stored energy when power generation is insufficient. The upstream grid side includes upstream transmission lines for transmitting the power generated by the hydro-solar complementary system to the load center.

[0012] Modeling is based on a water-solar complementary system framework, including photovoltaic cluster and cascade hydropower generation models, electrochemical energy storage models, and transmission section models.

[0013] Furthermore, based on the framework of a photovoltaic-hydropower complementary system, the following constraints are established: photovoltaic clusters, cascaded hydropower groups, energy storage equipment, and transmission section limitations.

[0014] The output of a photovoltaic cluster is limited by the maximum predicted output and the minimum power generation constraints, specifically:

[0015] (1)

[0016] In the formula, for Periodic photovoltaic clusters Predicted power, for Periodic photovoltaic clusters contribution;

[0017] The output of a cascade hydropower group has upper and lower limits, specifically:

[0018] (2)

[0019] In the formula, for Periodic hydroelectric power station No. The upper limit of the power output of a reservoir of level 1 for Periodic hydroelectric power station No. The lower limit of the output of a reservoir of level 1. for Periodic hydroelectric power station No. The output of the reservoir;

[0020] The energy storage device charge and discharge constraint model is as follows:

[0021] (3)

[0022] (4)

[0023] In the formula, For the power capacity of energy storage equipment, for Charging power of time-limited energy storage devices for Discharge power of time-limited energy storage devices for The charging status of energy storage devices during a given time period is a 0-1 variable. for Discharge status of energy storage equipment during a given period (0-1 variable);

[0024] The transmission section constraints are as follows:

[0025] (5)

[0026] In the formula, for Power transmitted across a transmission section during a given time period. This represents the maximum power transmitted across the transmission section.

[0027] Furthermore, in step 2, a two-layer optimal configuration model for energy storage of the hydro-solar complementary system is established based on the hydro-solar complementary system model, specifically as follows:

[0028] A two-layer optimal configuration model for energy storage of a water-solar complementary system is established based on the water-solar complementary system model. The two-layer optimal configuration model for energy storage of the water-solar complementary system includes an upper-layer model and a lower-layer model.

[0029] Furthermore, the upper-level model focuses on the economics of energy storage configuration. Waste of electricity from hydro-solar hybrid systems To optimize the objectives and set constraints, the optimization objective for the economic efficiency of energy storage configuration is as follows:

[0030] (6)

[0031] In the formula, Economic efficiency of energy storage configuration The minimum value, For the capacity of energy storage equipment, The unit power cost of energy storage equipment, The unit capacity cost of energy storage equipment, This is the investment recovery factor. The annual interest rate is For the expected service life;

[0032] The target for the amount of power wasted by the hydro-solar hybrid system is:

[0033] (7)

[0034] In the formula, Wasted power of water-solar hybrid systems The maximum value, for Periodic hydroelectric power station No. The power generation sequence of the first-level reservoirs, for Periodic photovoltaic clusters The actual power generation sequence, For hydroelectric power station Total quantity For reservoir The total series, For photovoltaic clusters Total quantity For scheduling time period identifier, For typical daily scheduling periods, As a typical day marker, This represents the total number of scenes.

[0035] Furthermore, the constraints of the upper-level model include the capacity and power constraints of the energy storage devices, specifically:

[0036] (8)

[0037] (9)

[0038] In the formula, This represents the upper limit of the capacity for energy storage equipment to be deployed. This represents the lower limit for the capacity of energy storage equipment to be constructed. This refers to the upper limit of the power capacity for energy storage equipment. This represents the lower limit of the power capacity required for the construction of energy storage devices.

[0039] Furthermore, the lower-level model uses the power transmission volume of the water-solar hybrid system as an example. To optimize the objective, constraints are set, where the optimization objective is:

[0040] (10)

[0041] In the formula, for Periodic hydroelectric power station No. The output of the reservoir; for Periodic photovoltaic clusters of effort.

[0042] Furthermore, the constraints of the lower-level model include cascade hydropower operation constraints, energy storage operation constraints, and power balance constraints. Among these, the cascade hydropower operation constraints include water balance constraints, power generation flow constraints, reservoir capacity constraints, water level constraints, hydraulic connection constraints between cascade hydropower reservoirs, and water demand constraints for water allocation. Specifically:

[0043] Water balance constraints:

[0044] (11)

[0045] In the formula, for Hydropower station at the end of the period No. The water storage capacity of the reservoir is as follows: for Periodic hydroelectric power station No. Level 1 reservoir capacity, for Periodic hydroelectric power station No. Inflow rate of the reservoir. for Periodic hydroelectric power station No. The power generation flow of the reservoir. for Periodic hydroelectric power station No. The discharge flow of the reservoir. The scheduling time interval;

[0046] Power generation flow constraints:

[0047] (12)

[0048] In the formula, for Periodic hydroelectric power station No. The upper limit of the power generation flow of the reservoir. for Periodic hydroelectric power station No. Lower limit of power generation flow rate for Class A reservoirs;

[0049] Reservoir capacity constraints:

[0050] (13)

[0051] In the formula, for Periodic hydroelectric power station No. Upper limit of reservoir capacity; for Periodic hydroelectric power station No. Lower limit of reservoir capacity;

[0052] Water level constraints:

[0053] (14)

[0054] In the formula, for Periodic hydroelectric power station No. Upper limit of water level of reservoir level 1 for Periodic hydroelectric power station No. Level 1 reservoir water level for Periodic hydroelectric power station No. Lower limit of water level in Class I reservoirs;

[0055] Hydraulic connection constraints between cascade hydropower reservoirs:

[0056] (15)

[0057] In the formula, for Periodic hydroelectric power station No. Inflow rate of a Class 1 reservoir; for Periodic hydroelectric power station No. Level 1 reservoir and the first Inflow within the +1 level reservoir;

[0058] Water demand constraints in water allocation:

[0059] (16)

[0060] (17)

[0061] (18)

[0062] In the formula, For hydroelectric power station The minimum water demand for engineering construction must be taken into account; For hydroelectric power station The actual water consumption for engineering construction must be taken into account; For hydroelectric power station The minimum ecological water intake requirement must be taken into account. For hydroelectric power station The actual amount of ecological water intake that needs to be taken into account; For hydroelectric power station The minimum water demand for irrigation must be taken into account. For hydroelectric power station The actual amount of irrigation water used must be taken into account;

[0063] Energy storage operation constraints:

[0064] (19)

[0065] (20)

[0066] (twenty one)

[0067] (twenty two)

[0068] In the formula, for Output of time-limited energy storage equipment for Time-of-use energy storage devices store electricity. Improve the charging efficiency of energy storage devices. The discharge efficiency of energy storage devices. This represents the lower limit of the amount of electricity that an energy storage device can store. ;

[0069] Power balance constraints:

[0070] (twenty three)

[0071] In the formula, for Hydropower stations located at the cross section during the period of efforts, for Photovoltaic clusters located in the cross section during the period of efforts, for Power transmitted across a transmission section during a given time period.

[0072] Furthermore, step 3: solving the energy storage two-layer optimization configuration model of the hydro-solar hybrid system based on the multi-objective whale optimization algorithm and the commercial linear programming solver Gurobi, specifically includes:

[0073] Step 31: Set algorithm parameters, including population size, number of iterations, and boundary values ​​for energy storage power and capacity planning;

[0074] Step 32: The upper-level model generates random individuals using the multi-objective whale algorithm, which contain energy storage capacity and power planning information. This information is then passed to the lower-level mixed integer model.

[0075] Step 33: The lower-level model uses the received power and capacity configuration parameters of the energy storage system as known parameters, and uses a commercial solver to solve the lower-level optimization model, and feeds back the obtained hydro-solar-storage power generation to the upper level.

[0076] Step 34: The upper-level model calculates the energy storage configuration economy of the upper-level objective function based on the hydro-solar-storage power generation fed back from the lower-level model and the energy storage system configuration information contained in random individuals. Waste of electricity from water-solar hybrid systems The solution results are then stored in the external archive of the multi-objective whale algorithm.

[0077] Step 35: Execute the multi-objective whale algorithm optimization strategy, repeatedly run steps 32, 33, and 34 until the number of iterations reaches the set maximum number, and output the Pareto optimal solution set of the external archive.

[0078] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0079] The energy storage optimization configuration method for a hydro-solar complementary system under power supply guarantee, considering water regulation constraints, provided by this invention, solves the problem in the prior art of "the increasingly heavy task of hydropower regulation and the mutual constraints between the water regulation and power regulation needs of hydropower stations". This method constructs a hydro-solar complementary system model based on the analysis of the hydro-solar complementary system framework, and then constructs an energy storage configuration model for the hydro-solar complementary system. The upper-level model takes the average daily investment cost of energy storage and the amount of abandoned electricity as optimization objectives, aiming to balance the energy storage configuration cost with the power supply guarantee capacity of the hydro-solar complementary system. The lower-level model is a collaborative operation model of the hydro-solar complementary system and the energy storage system. The model considers the water demand of hydropower regulation to better fit the actual engineering of the hydro-solar complementary system in the region. The multi-objective whale optimization algorithm and the commercial solver Gurobi are used for solving. Finally, based on design examples during the wet, normal and dry seasons, comparative analysis verifies that the model proposed in this invention can achieve the optimization of energy storage configuration economy and new energy consumption capacity while ensuring power supply. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a flowchart of the energy storage optimization configuration method for a water-solar hybrid system under power supply guarantee considering water regulation constraints, according to the present invention.

[0082] Figure 2This is a framework diagram of a water-solar complementary system according to an embodiment of the present invention;

[0083] Figure 3 This is a schematic diagram of the two-layer planning model structure according to an embodiment of the present invention;

[0084] Figure 4 This is a flowchart of the solution process for the bi-level programming model according to an embodiment of the present invention;

[0085] Figure 5 These are photovoltaic power output forecast diagrams for different typical days according to embodiments of the present invention;

[0086] Figure 6 The Pareto front solution of the multi-objective function in this embodiment of the invention is shown.

[0087] Figure 7 This is a diagram showing the scheduling results of Scheme 1 in this embodiment of the invention;

[0088] Figure 8 This is a diagram showing the scheduling results of Scheme 2 in this embodiment of the invention;

[0089] Figure 9 This is a diagram showing the scheduling result of Scheme 3 in an embodiment of the present invention. Detailed Implementation

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0092] As shown in the figure, this invention provides an energy storage optimization configuration method for a hydro-solar hybrid system under power supply guarantee, taking into account water regulation constraints, including the following steps:

[0093] Step 1: Model the system based on the framework of the water-solar hybrid system to obtain the water-solar hybrid system model, specifically:

[0094] Establish a framework for a water-solar hybrid system, the framework as follows: Figure 2As shown, it includes a source side, an energy storage side, and an upstream grid side. The source side includes photovoltaic clusters and cascade hydropower groups for generating power from the hydro-solar hybrid system. The energy storage side includes electrochemical energy storage for storing excess energy when the hydro-solar hybrid system generates a large amount of power and releasing the stored energy when power generation is insufficient. The upstream grid side includes upstream transmission lines for transmitting the power generated by the hydro-solar hybrid system to the load center, but there are transmission limitations.

[0095] Modeling is based on the framework of a photovoltaic-hydropower complementary system, including a photovoltaic cluster and cascade hydropower generation model, an electrochemical energy storage model, and a transmission section model.

[0096] Based on the framework of a photovoltaic-hydropower complementary system, the following constraints are established: photovoltaic clusters, cascade hydropower groups, energy storage equipment, and transmission section limitations.

[0097] The output of photovoltaic clusters is constrained by the maximum predicted output, specifically:

[0098] (1)

[0099] In the formula, for Periodic photovoltaic clusters Predicted power, for Periodic photovoltaic clusters contribution;

[0100] The output of a cascade hydropower group is determined by the technical conditions of each hydropower station's generating units, and there are upper and lower limits constrained, specifically:

[0101] (2)

[0102] In the formula, for Periodic hydroelectric power station No. The upper limit of the power output of a reservoir of level 1 for Periodic hydroelectric power station No. The lower limit of the output of a reservoir of level 1. for Periodic hydroelectric power station No. The output of the reservoir;

[0103] The charging and discharging power of energy storage devices is limited by the amount of energy storage capacity deployed, and the constraint model is as follows:

[0104] (3)

[0105] (4)

[0106] In the formula, For the power capacity of energy storage equipment, for Charging power of time-limited energy storage devices for Discharge power of time-limited energy storage devices for The charging status of energy storage devices during a given time period is a 0-1 variable. for Discharge status of energy storage equipment during a given period (0-1 variable);

[0107] The transmission section constraints are as follows:

[0108] (5)

[0109] In the formula, for Power transmitted across a transmission section during a given time period. This represents the maximum power transmitted across the transmission section.

[0110] Step 2: Establish a two-layer optimal configuration model for energy storage in the hydro-solar complementary system based on the hydro-solar complementary system model, specifically as follows:

[0111] A two-layer optimal configuration model for energy storage in a hydro-solar hybrid system is established based on the existing model. This model comprises an upper-layer model and a lower-layer model, as shown in the diagram below. Figure 3 As shown;

[0112] The upper-level model optimizes the energy storage configuration of the hydro-solar complementary system by taking the economic efficiency and the amount of abandoned electricity as optimization objectives. The lower-level operation model considers the connection between the cascade hydropower dispatching and the power dispatching, the operation of energy storage equipment and the transmission section of the upper-level power grid, and optimizes the operation plan of the complementary system with the goal of maximizing the sum of typical daily power transmission. The operation results are fed back to the upper-level multi-objective optimization model. Through the optimization iteration of the upper and lower two-level models, the method of energy storage configuration of the hydro-solar complementary system is solved.

[0113] The upper-level model focuses on the economics of energy storage configuration. Waste of electricity from hydro-solar hybrid systems To optimize the objectives and set constraints, the optimization objective for the economic efficiency of energy storage configuration is as follows:

[0114] (6)

[0115] In the formula, Economic efficiency of energy storage configuration The minimum value, For the capacity of energy storage equipment, The unit power cost of energy storage equipment, The unit capacity cost of energy storage equipment, This is the investment recovery factor. The annual interest rate is For the expected service life;

[0116] The target for the amount of power wasted by the hydro-solar hybrid system is:

[0117] (7)

[0118] In the formula, Wasted power of water-solar hybrid systems The maximum value, for Periodic hydroelectric power station No. The power generation sequence of the first-level reservoirs, for Periodic photovoltaic clusters The actual power generation sequence, For hydroelectric power station Total quantity For reservoir The total series, For photovoltaic clusters Total quantity For scheduling time period identifier, For typical daily scheduling periods, As a typical day marker, This represents the total number of scenes.

[0119] Furthermore, the constraints of the upper-level model include the capacity and power constraints of the energy storage devices, specifically:

[0120] (8)

[0121] (9)

[0122] In the formula, This represents the upper limit of the capacity for energy storage equipment to be deployed. This represents the lower limit for the capacity of energy storage equipment to be constructed. This refers to the upper limit of the power capacity for energy storage equipment. This represents the lower limit of the power capacity required for the construction of energy storage devices.

[0123] The lower-level model uses the power transmission volume of the water-solar hybrid system as an example. To optimize the objective, the objective function includes the output of cascade hydropower and the output of the photovoltaic cluster:

[0124] (10)

[0125] In the formula, for Periodic hydroelectric power station No. The output of the reservoir; for Periodic photovoltaic clusters of effort.

[0126] The constraints of the lower-level model include cascade hydropower operation constraints, energy storage operation constraints, and power balance constraints. Among these, cascade hydropower operation constraints include water balance constraints, power generation flow constraints, reservoir capacity constraints, water level constraints, hydraulic connection constraints between cascade hydropower reservoirs, and water demand constraints for water allocation. Specifically:

[0127] Water balance constraints:

[0128] (11)

[0129] In the formula, for Hydropower station at the end of the period No. The water storage capacity of the reservoir is as follows: for Periodic hydroelectric power station No. Level 1 reservoir capacity, for Periodic hydroelectric power station No. Inflow rate of the reservoir. for Periodic hydroelectric power station No. The power generation flow of the reservoir. for Periodic hydroelectric power station No. The discharge flow of the reservoir. The scheduling time interval;

[0130] Power generation flow constraints:

[0131] (12)

[0132] In the formula, for Periodic hydroelectric power station No. The upper limit of the power generation flow of the reservoir. for Periodic hydroelectric power station No. Lower limit of power generation flow rate for Class A reservoirs;

[0133] Reservoir capacity constraints:

[0134] (13)

[0135] In the formula, for Periodic hydroelectric power station No. Upper limit of reservoir capacity; for Periodic hydroelectric power station No. Lower limit of reservoir capacity;

[0136] Water level constraints:

[0137] (14)

[0138] In the formula, for Periodic hydroelectric power station No. Upper limit of water level of reservoir level 1 for Periodic hydroelectric power station No. Level 1 reservoir water level for Periodic hydroelectric power station No. Lower limit of water level in Class I reservoirs;

[0139] Hydraulic connection constraints between cascade hydropower reservoirs:

[0140] (15)

[0141] In the formula, for Periodic hydroelectric power station No. Inflow rate of a Class 1 reservoir; for Periodic hydroelectric power station No. Level 1 reservoir and the first Inflow within the +1 level reservoir;

[0142] Water allocation tasks include hydropower station basin engineering construction, ecological maintenance, and irrigation water use. Water allocation requirements are constrained by the following:

[0143] (16)

[0144] (17)

[0145] (18)

[0146] In the formula, For hydroelectric power station The minimum water demand for engineering construction must be taken into account; For hydroelectric power station The actual water consumption for engineering construction must be taken into account; For hydroelectric power station The minimum ecological water intake requirement must be taken into account. For hydroelectric power station The actual amount of ecological water intake that needs to be taken into account; For hydroelectric power station The minimum water demand for irrigation must be taken into account. For hydroelectric power station The actual amount of irrigation water used must be taken into account;

[0147] Energy storage operations must adhere to energy conservation principles, and the stored electricity must not exceed or fall below permissible values. Energy storage operation constraints include:

[0148] (19)

[0149] (20)

[0150] (twenty one)

[0151] (twenty two)

[0152] In the formula, for Output of time-limited energy storage equipment for Time-of-use energy storage devices store electricity. Improve the charging efficiency of energy storage devices. The discharge efficiency of energy storage devices. This represents the lower limit of the amount of electricity that an energy storage device can store. ;

[0153] The power transmission capacity of the upstream power grid is provided by cascade hydropower, photovoltaic power, and energy storage. The power balance constraint is:

[0154] (twenty three)

[0155] In the formula, for Hydropower stations located at the cross section during the period of efforts, for Photovoltaic clusters located in the cross section during the period of efforts, Transmission section Power transmitted during a given time period.

[0156] Step 3: Solve the energy storage two-layer optimization configuration model of the hydro-solar hybrid system based on the multi-objective whale optimization algorithm and the commercial linear programming solver Gurobi. Specifically, this includes:

[0157] The optimal configuration of energy storage in a hydro-solar hybrid system considering water regulation constraints is a multi-objective, multi-constraint linear integer bilevel programming problem. Intelligent algorithms, when handling multi-objective problems, possess advantages such as high parallel computation efficiency and strong solution adaptability. The multi-objective whale optimization algorithm can switch between three different hunting strategies, has strong global search capabilities, and can effectively solve complex multi-objective optimization problems. Therefore, this invention uses the multi-objective whale optimization algorithm and the commercial solver Gurobi, and programs it in Matlab. Specifically, step 31 involves setting algorithm parameters, including population size, number of iterations, and energy storage. Step 32: The upper-layer model generates random individuals using the multi-objective whale algorithm, containing energy storage capacity and power planning information, and passes this information to the lower-layer mixed integer model; Step 33: The lower-layer model uses the received energy storage system power and capacity configuration parameters as known parameters, and solves the lower-layer optimization model using a commercial solver, and feeds back the obtained hydro-solar-storage power generation to the upper layer; Step 34: The upper-layer model calculates the energy storage configuration economy of the upper-layer objective function using the hydro-solar-storage power generation fed back by the lower-layer model and the energy storage system configuration information contained in the random individuals. Waste of electricity from water-solar hybrid systems The solution results are stored in the external archive of the multi-objective whale algorithm; Step 35: Execute the multi-objective whale algorithm optimization strategy, repeatedly run steps 32, 33, and 34 until the maximum number of iterations is reached, and output the Pareto optimal solution set in the external archive; The specific solution process is as follows Figure 4 As shown.

[0158] This invention provides an embodiment to verify the rationality of the planning method described herein. The hydropower stations are located on two tributaries within a unified river basin. The first tributary has two hydropower stations: Hydropower Station No. 1 with an installed capacity of 200MW and Hydropower Station No. 2 with an installed capacity of 116.1MW. The second tributary also has two hydropower stations: Hydropower Station No. 3 with an installed capacity of 50MW and Hydropower Station No. 4 with an installed capacity of 248MW. The typical daily photovoltaic output is as follows: Figure 5 As shown in Table 1, the power transmission limit of the hydro-solar hybrid system is set at 300MW, and the energy storage technology and economic parameters are shown in Table 1.

[0159] Table 1. Economic and technical parameters of energy storage systems

[0160]

[0161] The Pareto solution set, composed of the planning results obtained from solving the multi-objective programming model of the water-solar hybrid system using the multi-objective whale optimization algorithm and the commercial solver Gurobi, is as follows: Figure 6As shown, Figure 6 As can be seen from this, in the problem of energy storage optimization configuration of this hydro-solar complementary system, the two objectives of energy storage configuration economy and system curtailment are not a simple linear relationship. The increase in energy storage configuration cost may not necessarily promote the reduction of the total curtailment of the system. In addition to economic factors, it also depends on the reasonable ratio of power and capacity of the energy storage system.

[0162] For the Pareto front plot of the above multi-objective function, it is necessary to select the optimal solution. Based on the objective values ​​of the Pareto feasible solution set in the external archive of the multi-objective whale algorithm, this invention uses the TOPSIS method to sort and determine the actual ideal solution and actual negative ideal solution for each objective dimension. The final actual ideal solution and actual negative ideal solution are shown in Table 2.

[0163] Table 2 Optimal Solution Based on TOPSIS Method

[0164]

[0165] To verify the multi-objective planning method for energy storage configuration of a water-solar hybrid system considering water regulation constraints proposed in this invention, the following three different schemes were set up for comparative analysis;

[0166] Option 1: Minimum power curtailment energy storage optimization configuration scheme without considering water regulation constraints.

[0167] Option 2: Minimum power curtailment energy storage optimization configuration scheme considering water regulation constraints.

[0168] Scheme 3: The multi-objective energy storage optimization configuration scheme for a water-solar complementary system considering water regulation constraints proposed in this invention.

[0169] The configuration results of each scheme are shown in Table 3, and the water dispatching tasks are shown in Table 4.

[0170] Table 3 Energy storage configuration results for each scheme

[0171]

[0172] As shown in Table 3, Scheme 1 ignores the water dispatching requirement constraint, eliminating the need to consider water dispatching tasks during hydropower generation scheduling. This results in higher hydropower operation flexibility, the lowest energy storage configuration cost, and the least total wind and solar curtailment. Scheme 2 considers the water dispatching constraint, limiting the flexibility of power dispatching. Because it aims at the total power generation of the hydro-solar system, it requires large-scale energy storage to increase total power generation. Compared to Scheme 1, its average daily energy storage investment cost increases by 105,000 yuan, and the curtailed energy increases by 212.67 MWh. Scheme 3 considers the water dispatching constraint while optimizing both economy and total power generation. Compared to Scheme 2, its energy storage configuration cost decreases by 22.38%, and the curtailed energy increases by 3.33%. The analysis concludes that considering the water dispatching constraint has a significant impact on the planning results. Using a multi-objective programming method can better balance system economy and curtailment.

[0173] Table 4 Comparison of water diversion task completion under different schemes

[0174]

[0175] The values ​​in Table 4 are the ratios of the actual water intake scheduling results to the minimum demand. Scheme 1, which does not consider water dispatch constraints, shows that the water supply of most hydropower stations far exceeds the demand during the wet season, while the water supply is insufficient during the dry season. Schemes 2 and 3, which take into account water dispatch demand constraints, can meet the water dispatch demand in each typical day scenario and can ensure water supply needs even during the dry season.

[0176] Scheduling results of different scheduling schemes are as follows Figures 7-9 As shown in the dispatch results, all schemes exhibit significant power curtailment during the high-water season. This curtailment can be mitigated by configuring energy storage systems to store surplus electricity during peak hydro-solar periods and release it during periods of lower hydro-solar output. Scheme 1, which does not consider water dispatch constraints, is not subject to water usage restrictions, resulting in significantly less total curtailment compared to Schemes 2 and 3. During the normal water season, Scheme 1 experiences curtailment only at 2 PM due to its smaller energy storage capacity. Schemes 2 and 3 achieve complete absorption of hydro-solar output through flexible energy storage charging and discharging. During the dry season, reduced natural water inflow lowers hydropower generation levels, and hydro-solar output does not exceed the transmission section limit during any period, resulting in no power curtailment.

[0177] This invention focuses on a hydro-solar hybrid system, incorporating the water intake requirements of water diversion tasks into the operational constraints of cascade hydropower. Addressing the power curtailment phenomenon caused by transmission line limitations in the hydro-solar hybrid system, it proposes an energy storage system to enhance the power supply capacity of the system. To balance the economics of energy storage configuration with the power transmission capacity of the hydro-solar hybrid system, a method for configuring energy storage in a hydro-solar hybrid system considering water diversion constraints is proposed. The following conclusions are drawn through numerical examples:

[0178] Energy storage configuration schemes that do not consider water diversion constraints have lower energy storage levels and stronger power supply capabilities, but they do not reflect the actual situation of the studied watershed being constrained by water diversion. Configuration schemes that consider water diversion tasks require larger-scale energy storage to supplement the flexible operation capability of the hydro-solar complementary system and enhance power supply capabilities. Energy storage configuration schemes that only optimize the external transmission capacity of the hydro-solar complementary system achieve limited external transmission capacity improvement at a high economic cost. In contrast, the multi-objective energy storage optimization configuration scheme provided by this invention achieves a balance between the economic efficiency of energy storage configuration and the power transmission capacity of the hydro-solar complementary system through Parato feasible solutions and Topsis ranking methods.

[0179] In summary, by considering the mutual constraints between water and electricity dispatching in the scheduling plan, this invention achieves both optimized energy storage configuration and new energy absorption capacity while ensuring power supply, thus possessing high practical value and promotional significance.

[0180] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for optimizing energy storage configuration under power supply guarantee in a hydro-solar hybrid system considering water regulation constraints, characterized in that, Includes the following steps: Step 1: Model the water-solar complementary system based on the framework to obtain the water-solar complementary system model; Step 2: Establish a two-layer optimal configuration model for energy storage of the water-solar complementary system based on the water-solar complementary system model; Step 3: Solve the energy storage dual-layer optimization configuration model of the water-solar hybrid system based on the multi-objective whale optimization algorithm and the commercial linear programming solver Gurobi.

2. The energy storage optimization configuration method for a hydro-solar hybrid system considering water regulation constraints under power supply guarantee, as described in claim 1, is characterized in that... In step 1, a model is created based on the framework of the water-solar complementary system to obtain the water-solar complementary system model, specifically as follows: A framework for a hydro-solar complementary system is established, including a source side, an energy storage side, and an upstream grid side. The source side includes photovoltaic clusters and cascade hydropower groups for generating power from the hydro-solar complementary system. The energy storage side includes electrochemical energy storage for storing excess energy when the hydro-solar complementary system generates a large amount of power and releasing the stored energy when power generation is insufficient. The upstream grid side includes upstream transmission lines for transmitting the power generated by the hydro-solar complementary system to the load center. Modeling is based on a water-solar complementary system framework, including photovoltaic cluster and cascade hydropower generation models, electrochemical energy storage models, and transmission section models.

3. The energy storage optimization configuration method for a hydro-solar hybrid system considering water regulation constraints under power supply guarantee, as described in claim 2, is characterized in that... Based on the framework of a photovoltaic-hydropower complementary system, the following constraints are established: photovoltaic clusters, cascade hydropower groups, energy storage equipment, and transmission section limitations. The output of a photovoltaic cluster is limited by the maximum predicted output and the minimum power generation constraints, specifically: （1） In the formula, for Periodic photovoltaic clusters Predicted power, for Periodic photovoltaic clusters contribution; The output of a cascade hydropower group has upper and lower limits, specifically: （2） In the formula, for Periodic hydroelectric power station No. The upper limit of the power output of a reservoir of level 1 for Periodic hydroelectric power station No. The lower limit of the output of a reservoir of level 1. for Periodic hydroelectric power station No. The output of the reservoir; The energy storage device charge and discharge constraint model is as follows: （3） （4） In the formula, For the power capacity of energy storage equipment, for Charging power of time-limited energy storage devices for Discharge power of time-limited energy storage devices for The charging status of energy storage devices during a given time period is a 0-1 variable. for Discharge status of energy storage equipment during a given period (0-1 variable); The transmission section constraints are as follows: （5） In the formula, for Power transmitted across a transmission section during a given time period. This represents the maximum power transmitted across the transmission section.

4. The energy storage optimization configuration method for a hydro-solar hybrid system considering water regulation constraints under power supply guarantee, as described in claim 1, is characterized in that... In step 2, a two-layer optimal configuration model for energy storage of the hydro-solar complementary system is established based on the hydro-solar complementary system model, specifically as follows: A two-layer optimal configuration model for energy storage of a water-solar complementary system is established based on the water-solar complementary system model. The two-layer optimal configuration model for energy storage of the water-solar complementary system includes an upper-layer model and a lower-layer model.

5. The energy storage optimization configuration method for a hydro-solar complementary system considering water regulation constraints under power supply guarantee, as described in claim 4, is characterized in that... The upper-level model focuses on the economics of energy storage configuration. Waste of electricity from hydro-solar hybrid systems To optimize the objectives and set constraints, the optimization objective for the economic efficiency of energy storage configuration is as follows: （6） In the formula, Economic efficiency of energy storage configuration The minimum value, For the capacity of energy storage equipment, The unit power cost of energy storage equipment, The unit capacity cost of energy storage equipment, This is the investment recovery factor. The annual interest rate is For the expected service life; The target for the amount of power wasted by the hydro-solar hybrid system is: （7） In the formula, Wasted power of water-solar hybrid systems The maximum value, for Periodic hydroelectric power station No. The power generation sequence of the first-level reservoirs, for Periodic photovoltaic clusters The actual power generation sequence, For hydroelectric power station Total quantity For reservoir The total series, For photovoltaic clusters Total quantity For scheduling time period identifier, For typical daily scheduling periods, As a typical day marker, This represents the total number of scenes.

6. The energy storage optimization configuration method for a hydro-solar complementary system considering water regulation constraints under power supply guarantee, as described in claim 5, is characterized in that... The constraints of the upper-level model include the capacity and power constraints of the energy storage equipment, specifically: （8） （9） In the formula, This represents the upper limit of the capacity for energy storage equipment to be deployed. This represents the lower limit for the capacity of energy storage equipment to be constructed. This refers to the upper limit of the power capacity for energy storage equipment. This represents the lower limit of the power capacity required for the construction of energy storage devices.

7. The energy storage optimization configuration method for a water-solar hybrid system considering water regulation constraints under power supply guarantee, as described in claim 4, is characterized in that... The lower-level model uses the power transmission volume of the water-solar hybrid system as an example. To optimize the objective, constraints are set, where the optimization objective is: （10） In the formula, for Periodic hydroelectric power station No. The output of the reservoir. for Periodic photovoltaic clusters of effort.

8. The energy storage optimization configuration method for a hydro-solar hybrid system considering water regulation constraints under power supply guarantee, as described in claim 7, is characterized in that... The constraints of the lower-level model include cascade hydropower operation constraints, energy storage operation constraints, and power balance constraints. Among these, cascade hydropower operation constraints include water balance constraints, power generation flow constraints, reservoir capacity constraints, water level constraints, hydraulic connection constraints between cascade hydropower reservoirs, and water demand constraints for water allocation. Specifically: Water balance constraints: （11） In the formula, for Hydropower station at the end of the period No. The water storage capacity of the reservoir is as follows: for Periodic hydroelectric power station No. Level 1 reservoir capacity, for Periodic hydroelectric power station No. Inflow rate of the reservoir. for Periodic hydroelectric power station No. The power generation flow of the reservoir. for Periodic hydroelectric power station No. The discharge flow of the reservoir. The scheduling time interval; Power generation flow constraints: （12） In the formula, for Periodic hydroelectric power station No. The upper limit of the power generation flow of the reservoir. for Periodic hydroelectric power station No. Lower limit of power generation flow rate for Class A reservoirs; Reservoir capacity constraints: （13） In the formula, for Periodic hydroelectric power station No. Upper limit of reservoir capacity; for Periodic hydroelectric power station No. Lower limit of reservoir capacity; Water level constraints: （14） In the formula, for Periodic hydroelectric power station No. Upper limit of water level of reservoir level 1 for Periodic hydroelectric power station No. Level 1 reservoir water level for Periodic hydroelectric power station No. Lower limit of water level in Class I reservoirs; Hydraulic connection constraints between cascade hydropower reservoirs: （15） In the formula, for Periodic hydroelectric power station No. Inflow rate of a Class 1 reservoir; for Periodic hydroelectric power station No. Level 1 reservoir and the first Inflow within the +1 level reservoir; Water demand constraints in water allocation: （16） （17） （18） In the formula, For hydroelectric power station The minimum water demand for engineering construction must be taken into account. For hydroelectric power station The actual water usage for engineering construction must be taken into account. For hydroelectric power station The minimum ecological water intake requirement must be taken into account. For hydroelectric power station The actual amount of ecological water intake that needs to be taken into account. For hydroelectric power station The minimum irrigation water demand that needs to be considered. For hydroelectric power station The actual amount of irrigation water used must be taken into account; Energy storage operation constraints: （19） （20） （21） （22） In the formula, for Output of time-limited energy storage equipment for Time-of-use energy storage devices store electricity. Improve the charging efficiency of energy storage devices. The discharge efficiency of energy storage devices. This represents the lower limit of the amount of electricity that an energy storage device can store. ; Power balance constraints: （23） In the formula, for Hydropower stations located at the cross section during the period of efforts, for Photovoltaic clusters located in the cross section during the period of efforts, for Power transmitted across a transmission section during a given time period.

9. The energy storage optimization configuration method for a hydro-solar complementary system considering water regulation constraints under power supply guarantee, as described in claim 8, is characterized in that... In step 3: the energy storage two-layer optimization configuration model of the hydro-solar hybrid system is solved based on the multi-objective whale optimization algorithm and the commercial linear programming solver Gurobi, specifically including: Step 31: Set algorithm parameters, including population size, number of iterations, and boundary values ​​for energy storage power and capacity planning; Step 32: The upper-level model generates random individuals using the multi-objective whale algorithm, which contain energy storage capacity and power planning information. This information is then passed to the lower-level mixed integer model. Step 33: The lower-level model uses the received power and capacity configuration parameters of the energy storage system as known parameters, and uses a commercial solver to solve the lower-level optimization model, and feeds back the obtained hydro-solar-storage power generation to the upper level. Step 34: The upper-level model calculates the energy storage configuration economy of the upper-level objective function based on the hydro-solar-storage power generation fed back from the lower-level model and the energy storage system configuration information contained in random individuals. Waste of electricity from water-solar hybrid systems The solution results are then stored in the external archive of the multi-objective whale algorithm. Step 35: Execute the multi-objective whale algorithm optimization strategy, repeatedly run steps 32, 33, and 34 until the number of iterations reaches the set maximum number, and output the Pareto optimal solution set of the external archive.

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