Optimized operation method of small hydropower and photoelectric energy storage effect complementary system

Abstract

The invention discloses an optimized operation method of a small hydropower and photoelectric energy storage effect complementary system. According to the method, the runoff type small hydropower station is transformed to have the functions of adjusting the reservoir capacity, modeling the runoff type small hydropower station into an equivalent energy storage unit, and effectively replacing the functions of an electrochemical energy storage device and the like by using the reservoir capacity state corresponding to the energy storage charge state and the power generation flow corresponding to the charging and discharging power, so that the construction and maintenance cost of a large amount of energy storage equipment is saved; the potential of existing facilities of small hydropower stations is fully utilized, and the dependence of the system on an additional energy storage device is greatly reduced; by optimizing the operation model, the purpose is to minimize the total operation cost including the electricity purchasing cost, the network loss cost, the energy storage cost, the light abandoning punishment cost and the hydropower station transformation cost, compared with an existing model which only focuses on the power generation side cost, the multi-dimensional economic factors are comprehensively considered, the output of the small hydropower station and the photovoltaic power station is accurately dispatched, and the power generation efficiency is improved. The electricity purchased from a superior power grid is reduced, and the network loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of integrated energy optimization technology, specifically to an optimized operation method for a complementary system of small-scale hydropower and photovoltaic energy storage. Background Technology

[0002] As the installed capacity and power generation of new energy sources continue to increase, they are gradually replacing thermal power as the main force of the future energy system. However, due to environmental and meteorological factors, the output of renewable energy is characterized by strong randomness, large fluctuations and poor controllability. Large-scale grid connection will inevitably increase the power fluctuation of the power grid, which will bring great risks and challenges to the safe and stable operation of the system. As an important part of clean energy, photovoltaic power generation has developed rapidly in recent years, but its output is significantly affected by natural conditions such as sunshine and temperature, and has obvious intermittency and uncontrollability, resulting in frequent "curtailment" of solar power, which seriously restricts the efficient utilization of photovoltaic power generation.

[0003] One effective way to promote the grid connection and consumption of photovoltaic power is to integrate uncontrollable energy and dispatchable power sources into a complementary power generation system based on the complementary characteristics of different energy sources. Cascade hydropower, as a clean and efficient adjustable energy source, has advantages such as large regulation capacity, fast response speed and flexible operation. It is an important support for smoothing photovoltaic fluctuations and improving the absorption capacity. In existing research, hydro-photovoltaic complementary system (HPCS) has received widespread attention. Most studies focus on the role of hydropower in smoothing photovoltaic output fluctuations, optimizing the system's peak-shaving capacity and multi-time-scale scheduling strategies, and have achieved significant results.

[0004] However, most existing studies have not fully considered the transformation of run-of-river small hydropower stations into hydropower stations with regulation capabilities, nor have they systematically evaluated their incremental benefits in replacing energy storage and improving photovoltaic absorption rates. In the modeling process, the handling of the randomness of photovoltaic output is often simplified, lacking a mechanism for generating and reducing typical scenarios, which affects the robustness and practicality of the scheduling model. Existing optimization models focus more on generation-side costs and fail to comprehensively consider multi-dimensional economic factors such as electricity purchase costs, grid loss costs, energy storage construction and operation costs, curtailment penalties, and hydropower station transformation costs. A systematic optimization operation method has not yet been formed for the operation mechanism of small hydropower achieving "storage-like" functions through water level regulation and its supporting role in system voltage quality. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized operation method for a complementary system of small-scale hydropower and photovoltaic energy storage.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the operation of a complementary system combining small-scale hydropower and photovoltaic energy storage, comprising the following steps:

[0007] S1. Construct a small hydropower energy storage effect model, modeling the modified small hydropower with adjustable reservoir capacity as an equivalent energy storage unit, wherein the reservoir capacity state corresponds to the state of charge of the energy storage unit, and the controlled power generation flow of the hydropower station corresponds to the charging and discharging power of the energy storage unit.

[0008] S2. Establish an optimized operation model for the hydro-solar complementary system. The objective function model is to minimize the total system operating cost, including electricity purchase cost, grid loss cost, energy storage cost, curtailment penalty cost, and hydropower station renovation cost. A mathematical model is constructed by integrating the small hydropower energy storage effect model, the photovoltaic output model, and system operation constraints.

[0009] S3. Optimization and Instruction Generation: Based on future predicted photovoltaic output and inflow runoff data, the optimal operation model of the water-photovoltaic complementary system is solved to obtain the optimal power generation flow instruction for small hydropower and the output plan of photovoltaic power station for each time period of the day.

[0010] S4. Perform complementary operation. Based on the optimal instructions obtained from the solution, control the small hydropower station to reduce its output during peak photovoltaic output periods to store water, and increase its output during off-peak photovoltaic output periods to supplement power, thereby achieving optimized operation of hydropower-solar synergy.

[0011] As a preferred embodiment, the objective function model is: , , In the formula, The total operating cost of the distribution network; For electricity purchase costs; For network loss costs; For energy storage operating costs; For the construction and maintenance costs of energy storage; The cost of penalties for abandoning light; Costs associated with the renovation of cascade hydropower stations; for The active power supplied by the upstream power grid to the distribution network at any given time; for Unit cost of electricity purchased per unit of time; Power capacity; Cost per unit power; Energy capacity; This is the unit energy cost coefficient; To balance the system cost coefficients; The installation and construction cost coefficient is used to summarize the energy storage construction cost as the daily construction cost of energy storage. This is the cost coefficient for wasted light. , for Forecasted and actual power output of photovoltaic systems during the specified time period; Costs for the renovation of hub buildings; Costs for the modification of hydro-generator sets and auxiliary equipment; Costs for upgrading electrical and control systems; Other expenses.

[0012] As a preferred embodiment, the system operating constraints are: a. Constraints on hydropower output characteristics: , In the formula, The density of water; For hydroelectric power station Power generation efficiency; for Periodic hydroelectric power station The water purifier head; for Periodic hydroelectric power station The power generation flow rate; b. Water balance constraints: , In the formula, For hydroelectric power station exist Storage capacity for a given period of time; , For hydroelectric power station exist Inflow and outflow during different time periods; For hydroelectric power station A collection of upstream connected power stations; For upstream hydropower station Water flow stagnation time; The length of a single time period during the scheduling period; c. Constraints on power generation and water discharge: , , In the formula, , For the first The minimum and maximum power generation flow rates of the hydroelectric power station; , For the first The upper and lower limits of the discharge flow of the hydropower station; d. Reservoir capacity constraints of hydropower stations: , , In the formula, , For hydroelectric power station Upper and lower limits of storage capacity; , For the first The initial and final reservoir capacities for the daily scheduling of the hydropower station; e. Hydropower head and water level constraints: , , , In the formula, , for The water level in front of the dam and the tailrace level of the hydropower station during the specified time period; For head loss; formula , These represent the functional relationships between the upstream water level and the reservoir capacity of the hydropower station, and between the tailrace water level and the downstream flow rate, respectively. f. Unit start-up and shutdown duration constraints: , In the formula, For hydroelectric power station exist The startup operation variable within the time period, with a value of 1 indicating the start of startup; For hydroelectric power station exist The shutdown operation variable for a given time period, where a value of 1 indicates a shutdown operation; , For hydroelectric power station Minimum start-up and shutdown duration of the indoor unit; g. Hydropower station output constraints: , In the formula, , For hydroelectric power station exist Upper and lower limits of output during different time periods; For hydroelectric power station exist The start-up and shutdown status variables of the generating units during the time period; A value of 1 indicates the power-on state, and 0 indicates the power-off state; h. Energy storage operation constraints: , , , , In the formula, , A Boolean variable characterizing the charging and discharging states of an energy storage device, taking the value 0 or 1; , Energy conversion efficiency during charging and discharging of electrochemical energy storage; The rated capacity for electrochemical energy storage; , These are the upper and lower limits of the state of charge (SOC) for electrochemical energy storage; i. Constraints on renewable energy output: , In the formula, For photovoltaics The maximum active power that can be generated at any given time.

[0013] As a preferred embodiment, the system operation constraints also include power flow constraints, specifically: a. Using the Distflow power flow model, establish the following power flow constraint model based on branch power: , , , In the formula, , For nodes The sum of active and reactive power on the power supply and energy storage sides; , For nodes Active and reactive loads at the location; , These represent the active and reactive power on the line. , , The square of the current amplitude, resistance, and inductive reactance on the line; For nodes The square of the voltage amplitude at that point; Due to the presence of nonlinear equality constraints, the DistFlow model is non-convex, making it difficult to find the global optimum. To make the model convex, the equality constraints need to be adjusted. It can be relaxed to an inequality constraint: , Thus, the DistFlow model is relaxed into a second-order cone programming model. Since the second-order cone model is a typical convex optimization problem, there are already mature commercial solvers available for solving it. Furthermore, in radial distribution networks, the DistFlow model is equivalent to the power flow equations based on the nodal voltage method. When both sides of the inequality constraints are equal, the relaxed DistFlow model can approximate the nonlinear power flow equations without loss. b. Boundary condition constraints for safe operation: , In the formula, This represents the maximum value of the branch current. , These are the upper and lower limits of the node voltage; , These represent the maximum and minimum values ​​for purchasing electricity from the main grid.

[0014] As a preferred embodiment, the implementation method and scale correspondence of the small hydropower energy storage effect model are as follows:

[0015] Small hydropower achieves the capacity substitution function of energy storage systems through the regulating capacity of its reservoirs. The regulating capacity of the reservoir is equivalent to the rated energy storage capacity of the energy storage system. The specific substitution scale is determined by the total amount of reservoir capacity available for daily regulation between the dead capacity and the normal water level.

[0016] Small hydropower stations achieve the power substitution function of energy storage systems through the rated installed capacity of their generator units. The installed capacity of the hydropower units is equivalent to the rated charging and discharging power of the energy storage system. During system operation, the small hydropower stations reduce their power generation during peak photovoltaic output periods to store water, achieving an equivalent "charging" process. During off-peak photovoltaic output periods, they increase their power generation to release water, achieving an equivalent "discharging" process.

[0017] The specific scale of small hydropower replacing energy storage is determined through the following methods:

[0018] The scale of alternative energy storage capacity is determined by the energy value corresponding to the product of the reservoir's available regulating capacity and the power generation head;

[0019] The capacity of alternative energy storage is determined by the installed capacity of small hydropower.

[0020] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method described above.

[0021] A storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] 1. This invention transforms run-of-river small hydropower plants to enable them to regulate reservoir capacity, models them as equivalent energy storage units, and uses the reservoir capacity state to correspond to the energy storage state of charge and the power generation flow rate to correspond to the charging and discharging power. This effectively replaces the functions of electrochemical energy storage devices. This design not only saves a lot of construction and maintenance costs for energy storage equipment, but also makes full use of the potential of existing small hydropower facilities, greatly reduces the system's dependence on additional energy storage devices, and significantly improves the economic efficiency of energy utilization.

[0024] 2. This invention optimizes the operating model to minimize the total operating cost, including electricity purchase cost, grid loss cost, energy storage cost, curtailment penalty cost, and hydropower station renovation cost. Compared with existing models that only focus on generation-side costs, this invention comprehensively considers multiple economic factors. By precisely scheduling the output of small hydropower and photovoltaic power, it reduces the amount of electricity purchased from the upper-level grid, lowers grid loss, minimizes curtailment to avoid penalty costs, and reasonably distributes hydropower station renovation costs across the operating cycle, thus achieving optimal control of the system's economic cost throughout its entire life cycle.

[0025] 3. This invention utilizes the synergistic and complementary operation of small hydropower and photovoltaic power. During peak photovoltaic power output periods, the output of small hydropower is reduced to store water, while during off-peak periods, the output is increased to supplement power. This effectively mitigates the intermittent and random fluctuations in photovoltaic power output. This synergistic mechanism significantly reduces the impact of photovoltaic power output fluctuations on the power grid, solves the problem of frequent "curtailment" in traditional photovoltaic grid connection, significantly improves the absorption capacity of photovoltaic power generation, and promotes the efficient utilization of clean energy.

[0026] 4. This invention incorporates constraints based on the DistFlow power flow model into the optimization model and transforms it into a convex optimization model through relaxation, thereby ensuring the rationality of the power flow distribution and the reliability of the solution. At the same time, the flexible adjustment capability of small hydropower can provide power support for the system, effectively maintain the node voltage within a reasonable range, avoid risks such as branch current overload, ensure the safe and stable operation of the radial distribution network, and improve the voltage quality and operational reliability of the system.

[0027] 5. This invention solves the model based on future predicted photovoltaic power output and inflow runoff data, which can accurately generate the optimal operating instructions for each time period of the day, adapting to the time scale requirements of actual scheduling scenarios. The model includes multi-dimensional constraints such as hydropower output characteristics, water balance, reservoir capacity, and unit start-up and shutdown, which comprehensively consider the actual constraints of small hydropower and photovoltaic operation, ensuring the feasibility and effectiveness of scheduling instructions, and improving the adaptability and practicality of the optimization method in actual engineering applications. Attached Figure Description

[0028] Figure 1 This is a diagram of the power distribution system of the present invention, which includes cascaded small hydropower and photovoltaic power.

[0029] Figure 2 This is a photovoltaic power output prediction diagram for the present invention;

[0030] Figure 3 This is a diagram showing the optimized scheduling results for daytime in scenario 1 of this invention.

[0031] Figure 4 This is a diagram showing the optimized scheduling results for daytime in scenario 2 of this invention.

[0032] Figure 5 This is a diagram showing the optimized scheduling results for daytime in scenario 3 of this invention.

[0033] Figure 6 This is a diagram showing the optimized scheduling results for daytime in scenario 4 of this invention.

[0034] Figure 7 This is a diagram showing the optimized scheduling results for the nighttime period in Scenario 1 of this invention.

[0035] Figure 8 This is a diagram showing the optimized scheduling results for the nighttime period in scenario 2 of this invention.

[0036] Figure 9 This is a diagram showing the optimized scheduling results for nighttime period in scenario 3 of this invention.

[0037] Figure 10 This is a diagram showing the optimized scheduling results for nighttime period in scenario 4 of this invention.

[0038] Figure 11 This is a flowchart illustrating the implementation of the scheduling method of the present invention. Detailed Implementation

[0039] 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.

[0040] This invention provides an optimized operation method for a complementary system combining small-scale hydropower and photovoltaic energy storage, comprising the following steps:

[0041] S1. Construct a small hydropower energy storage effect model, modeling the modified small hydropower with adjustable reservoir capacity as an equivalent energy storage unit, wherein the reservoir capacity state corresponds to the state of charge of the energy storage unit, and the controlled power generation flow of the hydropower station corresponds to the charging and discharging power of the energy storage unit.

[0042] S2. Establish an optimized operation model for the hydro-solar complementary system. The objective function model is to minimize the total system operating cost, including electricity purchase cost, grid loss cost, energy storage cost, curtailment penalty cost, and hydropower station renovation cost. A mathematical model is constructed by integrating the small hydropower energy storage effect model, the photovoltaic output model, and system operation constraints.

[0043] S3. Optimization and Instruction Generation: Based on future predicted photovoltaic output and inflow runoff data, the optimal operation model of the water-photovoltaic complementary system is solved to obtain the optimal power generation flow instruction for small hydropower and the output plan of photovoltaic power station for each time period of the day.

[0044] S4. Perform complementary operation. Based on the optimal instructions obtained from the solution, control the small hydropower station to reduce its output during peak photovoltaic output periods to store water, and increase its output during off-peak photovoltaic output periods to supplement power, thereby achieving optimized operation of hydropower-solar synergy.

[0045] The objective function model is:

[0046]

[0047]

[0048] In the formula, The total operating cost of the distribution network; For electricity purchase costs; For network loss costs; For energy storage operating costs; For the construction and maintenance costs of energy storage; The cost of penalties for abandoning light; Costs associated with the renovation of cascade hydropower stations; for The active power supplied by the upstream power grid to the distribution network at any given time; for Unit cost of electricity purchased per unit of time; Power capacity; Cost per unit power; Energy capacity; This is the unit energy cost coefficient; To balance the system cost coefficients; The installation and construction cost coefficient is used to summarize the energy storage construction cost as the daily construction cost of energy storage. This is the cost coefficient for wasted light. , for Forecasted and actual power output of photovoltaic systems during the specified time period; Costs for the renovation of hub buildings; Costs for the modification of hydro-generator sets and auxiliary equipment; Costs for upgrading electrical and control systems; Other expenses.

[0049] The system's operational constraints are:

[0050] a. Constraints on hydropower output characteristics:

[0051]

[0052] In the formula, The density of water; For hydroelectric power station Power generation efficiency; for Periodic hydroelectric power station The water purifier head; for Periodic hydroelectric power station The power generation flow rate;

[0053] b. Water balance constraints:

[0054]

[0055] In the formula, For hydroelectric power station exist Storage capacity for a given period of time; , For hydroelectric power station exist Inflow and outflow during different time periods; For hydroelectric power station A collection of upstream connected power stations; For upstream hydropower station Water flow stagnation time; The length of a single time period during the scheduling period;

[0056] c. Constraints on power generation and water discharge:

[0057]

[0058]

[0059] In the formula, , For the first The minimum and maximum power generation flow rates of the hydroelectric power station; , For the first The upper and lower limits of the discharge flow of the hydropower station;

[0060] d. Reservoir capacity constraints of hydropower stations:

[0061]

[0062]

[0063] In the formula, , For hydroelectric power station Upper and lower limits of storage capacity; , For the first The initial and final reservoir capacities for the daily scheduling of the hydropower station;

[0064] e. Hydropower head and water level constraints:

[0065]

[0066]

[0067]

[0068] In the formula, , for The water level in front of the dam and the tailrace level of the hydropower station during the specified time period; For head loss; formula , These represent the functional relationships between the upstream water level and the reservoir capacity of the hydropower station, and between the tailrace water level and the downstream flow rate, respectively.

[0069] f. Unit start-up and shutdown duration constraints:

[0070]

[0071] In the formula, For hydroelectric power station exist The startup operation variable within the time period, with a value of 1 indicating the start of startup; For hydroelectric power station exist The shutdown operation variable for a given time period, where a value of 1 indicates a shutdown operation; , For hydroelectric power station Minimum start-up and shutdown duration of the indoor unit;

[0072] g. Hydropower station output constraints:

[0073]

[0074] In the formula, , For hydroelectric power station exist Upper and lower limits of output during different time periods; For hydroelectric power station exist The start-up and shutdown status variables of the generating units during the time period; A value of 1 indicates the power-on state, and 0 indicates the power-off state;

[0075] h. Energy storage operation constraints:

[0076]

[0077]

[0078]

[0079]

[0080] In the formula, , A Boolean variable characterizing the charging and discharging states of an energy storage device, taking the value 0 or 1; , Energy conversion efficiency during charging and discharging of electrochemical energy storage; The rated capacity for electrochemical energy storage; , These are the upper and lower limits of the state of charge (SOC) for electrochemical energy storage;

[0081] i. Constraints on renewable energy output:

[0082]

[0083] In the formula, For photovoltaics The maximum active power that can be generated at any given time.

[0084] System operational constraints also include power flow constraints, specifically:

[0085] a. Using the Distflow power flow model, establish the following power flow constraint model based on branch power:

[0086]

[0087]

[0088]

[0089] In the formula, , For nodes The sum of active and reactive power on the power supply and energy storage sides; , For nodes Active and reactive loads at the location; , These represent the active and reactive power on the line. , , The square of the current amplitude, resistance, and inductive reactance on the line; For nodes The square of the voltage amplitude at that point;

[0090] Due to the presence of nonlinear equality constraints, the DistFlow model is non-convex, making it difficult to find the global optimum. To make the model convex, the equality constraints need to be adjusted. It can be relaxed to an inequality constraint:

[0091]

[0092] Thus, the DistFlow model is relaxed into a second-order cone programming model. Since the second-order cone model is a typical convex optimization problem, there are already mature commercial solvers available for solving it. Furthermore, in radial distribution networks, the DistFlow model is equivalent to the power flow equations based on the nodal voltage method. When both sides of the inequality constraints are equal, the relaxed DistFlow model can approximate the nonlinear power flow equations without loss.

[0093] b. Boundary condition constraints for safe operation:

[0094]

[0095] In the formula, This represents the maximum value of the branch current. , These are the upper and lower limits of the node voltage; , These represent the maximum and minimum values ​​for purchasing electricity from the main grid.

[0096] The implementation methods and scale correspondence of the small hydropower energy storage effect model are as follows:

[0097] Small hydropower achieves the capacity substitution function of energy storage systems through the regulating capacity of its reservoirs. The regulating capacity of the reservoir is equivalent to the rated energy storage capacity of the energy storage system. The specific substitution scale is determined by the total amount of reservoir capacity available for daily regulation between the dead capacity and the normal water level.

[0098] Small hydropower stations achieve the power substitution function of energy storage systems through the rated installed capacity of their generator units. The installed capacity of the hydropower units is equivalent to the rated charging and discharging power of the energy storage system. During system operation, the small hydropower stations reduce their power generation during peak photovoltaic output periods to store water, achieving an equivalent "charging" process. During off-peak photovoltaic output periods, they increase their power generation to release water, achieving an equivalent "discharging" process.

[0099] The specific scale of small hydropower replacing energy storage is determined through the following methods:

[0100] The scale of alternative energy storage capacity is determined by the energy value corresponding to the product of the reservoir's available regulating capacity and the power generation head;

[0101] The capacity of alternative energy storage is determined by the installed capacity of small hydropower.

[0102] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method described above.

[0103] A storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0104] This invention constructs a multi-scenario stochastic optimization model to quantitatively combine the total system operating cost with typical scenarios of uncertain photovoltaic output, and utilizes the regulation capacity of cascaded small hydropower to achieve "storage-like" operation. It provides system decision-makers with optimized scheduling schemes that take into account economy, reliability and photovoltaic absorption rate under different regulation capacity configurations and renovation budget conditions.

[0105] Among them, power distribution systems that include cascaded small hydropower and photovoltaic power, such as Figure 1 As shown, based on the power grid load data in Yuexi County, Sichuan Province, my country, an IEEE 33-node test system was constructed for simulation analysis. The scheduling cycle was set as 10 hours from 7 am to 5 pm, and the data was discretized at 1-minute intervals.

[0106] Photovoltaic power output is significantly affected by environmental and meteorological factors, exhibiting considerable randomness and volatility. To improve the robustness and practicality of the optimization scheduling model, Monte Carlo simulation is employed to address this uncertainty. First, based on the predicted solar radiation data for the following day and considering a 10% prediction error, 300 initial photovoltaic power output scenarios were generated using Monte Carlo simulation. Subsequently, a scenario reduction technique based on Euclidean distance was applied to aggregate the initial scenarios into five representative typical scenarios, the probability distribution of which is shown in Table 1. This approach accurately characterizes the randomness of photovoltaic power output while significantly improving the solution efficiency of the subsequent optimization model. The predicted photovoltaic power output is as follows: Figure 2 As shown.

[0107] Table 1 Probability Distribution

[0108] The parameters of each power supply device used in the power distribution system model are as follows: the installed capacity of distributed photovoltaic power is 4MW, connected to node 8; the total installed capacity of cascaded small hydropower is 5.64MW, connected to nodes A26, A29 and A33 respectively. The peak-valley time-of-use electricity price adopted in the system is shown in Table 2, which can effectively guide the charging and discharging behavior of hydropower and energy storage, and play a role in peak shaving and valley filling.

[0109] Table 2 Peak-Valley Time-of-Use Electricity Prices

[0110] Based on scenario 1, the results are shown in Table 3.

[0111] Table 3 Results

[0112] In this scenario, all three hydropower stations are run-of-river hydropower stations without regulation capacity, and the system is not equipped with energy storage. Therefore, a serious curtailment of solar power occurs during the peak solar power output period (13:00–15:00). The operating results of different scenarios during the peak solar power output period (13:00–15:00) are shown in Table 4.

[0113] Table 4. Operational results during peak volt-output periods under different scenarios.

[0114] Scenario 1 is the baseline scenario, Scenario 2 is the scenario for configuring energy storage, Scenario 3 is the scenario for modifying a hydropower station, and Scenario 4 is the scenario for modifying two hydropower stations. The scenario settings are shown in Table 5.

[0115] Table 5 Scene Settings

[0116] The optimized scheduling results for daytime periods in four scenarios are as follows: Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the scheduling results for the nighttime period are as follows: Figure 7 , Figure 8 , Figure 9 , Figure 10 As shown, the implementation process of the scheduling method is as follows: Figure 11 As shown.

[0117] (1) Optimized operation results of the baseline scenario

[0118] Using the minimum total system operating cost as the objective function and reasonably constructing constraints, including DistFlow power flow constraints, system safety operation constraints, cascade small hydropower operation constraints, and new energy output constraints, we optimized the solution for Scenario 1 (all hydropower stations are run-of-river and have no energy storage). The final result is that the total system operating cost under this benchmark scenario is 17,800.52 yuan. The operating results of different scenarios during the peak photovoltaic output period are shown in Table 4. In this scenario, due to the lack of regulation capability, a serious curtailment phenomenon occurred during the peak photovoltaic output period 4, with a curtailment penalty cost as high as 17,800.52 yuan and a photovoltaic absorption rate of only 80.58%.

[0119] (2) Analysis of optimized operation results of energy storage scenario (Scenario 2)

[0120] In Scenario 2, the system is equipped with 1.88MWh of energy storage. Optimized operation results show that the energy storage system effectively diverts peak photovoltaic output through a "low-storage, high-output" operation strategy. Compared with Scenario 1, the photovoltaic grid integration rate has increased significantly from 80.58% to 99.94%, and the curtailment penalty cost has decreased to 46.69 yuan. However, due to the high daily construction and operation cost of the energy storage equipment (25210.08 yuan), the total system cost rises to 25256.77 yuan. The mean and variance of voltage fluctuations are shown in Table 6. In Scenario 2, the mean voltage of each node is between 1.02 and 1.05 pu, which is within the acceptable range.

[0121] (3) Analysis of optimized operation results in the scenario of upgrading a hydropower station (Scenario 3)

[0122] In Scenario 3, the run-of-river hydropower station H1 was transformed into a hydropower station with a regulating reservoir capacity of 4090.74 m³ (approximately 2 hours of regulating capacity). Optimized operation results show that the transformed hydropower station, through a water level regulation mechanism, adopts a "storage water and generate less power" strategy during peak photovoltaic output periods and "increase power generation" during peak nighttime load periods, achieving a flexible regulation function similar to energy storage. Compared with the baseline Scenario 1, the photovoltaic absorption rate increased by 19.38 percentage points, reaching 99.96%. More importantly, compared with Scenario 2, which is equipped with energy storage, under the premise of achieving a similar absorption rate, the total system cost of this scenario is 14838.46 yuan, which is 41.25% lower than that of Scenario 2, demonstrating excellent economic efficiency.

[0123] In terms of voltage quality, this solution demonstrates unique advantages: analysis of voltage fluctuation variance at key nodes (A26, A29, A33, A8) shows that, compared to scenario 2, scenario 3 significantly reduces the voltage fluctuation variance at each node. Specifically, at node A26, the variance is reduced by 5.11%; at node A29, by 1.10%; at node A33, by 0.72%; and at node A8 with photovoltaic access, by 1.42%. This proves that the upgraded small hydropower station, due to its use of synchronous generators, can not only regulate active power but also naturally provide strong reactive power compensation and inertial support, thereby significantly enhancing the voltage stability of the distribution network.

[0124] (4) Analysis of the optimized operation results of the scenario of upgrading two hydropower stations (Scenario 4)

[0125] In Scenario 4, hydropower station H2 was further modified. The operational results showed that the system's regulation flexibility was further enhanced, the photovoltaic absorption rate increased to 99.97%, and the voltage support effect was also enhanced. Compared with Scenario 2, the voltage fluctuation variance at nodes A26, A29, A33, and A8 in Scenario 4 decreased by 5.41%, 1.66%, 1.75%, and 1.48%, respectively, and the voltage quality was further improved. However, compared with Scenario 3, which only modified H1, the total system cost increased by 37.46% to 23,728.16 yuan. Although its total cost was still 6.05% lower than that of Scenario 2, which configured energy storage, the marginal benefit decreased. This indicates that although modifying multiple hydropower stations can improve performance, it is necessary to comprehensively evaluate its economic efficiency and incremental benefits.

[0126] Table 6. Mean and Variance of Voltage Fluctuations

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the operation of a complementary system combining small-scale hydropower and photovoltaic energy storage, characterized in that, Includes the following steps: S1. Construct a small hydropower energy storage effect model, modeling the modified small hydropower with adjustable reservoir capacity as an equivalent energy storage unit, wherein the reservoir capacity state corresponds to the state of charge of the energy storage unit, and the controlled power generation flow of the hydropower station corresponds to the charging and discharging power of the energy storage unit. S2. Establish an optimized operation model for the hydro-solar complementary system. The objective function model is to minimize the total system operating cost, including electricity purchase cost, grid loss cost, energy storage cost, curtailment penalty cost, and hydropower station renovation cost. A mathematical model is constructed by integrating the small hydropower energy storage effect model, the photovoltaic output model, and system operation constraints. S3. Optimization and Instruction Generation: Based on future predicted photovoltaic output and inflow runoff data, the optimal operation model of the water-photovoltaic complementary system is solved to obtain the optimal power generation flow instruction for small hydropower and the output plan of photovoltaic power station for each time period of the day. S4. Perform complementary operation. Based on the optimal instructions obtained from the solution, control the small hydropower station to reduce its output during peak photovoltaic output periods to store water, and increase its output during off-peak photovoltaic output periods to supplement power, thereby achieving optimized operation of hydropower-solar synergy.

2. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage as described in claim 1, characterized in that: The objective function model is as follows: ， ， In the formula, The total operating cost of the distribution network; For electricity purchase costs; For network loss costs; For energy storage operating costs; For the construction and maintenance costs of energy storage; The cost of penalties for abandoning light; Costs associated with the renovation of cascade hydropower stations; for The active power supplied by the upstream power grid to the distribution network at any given time; for Unit cost of electricity purchased per unit of time; Power capacity; Cost per unit power; Energy capacity; This is the unit energy cost coefficient; To balance the system cost coefficients; The installation and construction cost coefficient is used to summarize the energy storage construction cost as the daily construction cost of energy storage. This is the cost coefficient for wasted light. , for Forecasted and actual power output of photovoltaic systems during the specified time period; Costs for the renovation of hub buildings; Costs for the modification of hydro-generator sets and auxiliary equipment; Costs for upgrading electrical and control systems; Other expenses.

3. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage as described in claim 1, characterized in that: The system operation constraints are as follows: a. Constraints on hydropower output characteristics: ， In the formula, The density of water; For hydroelectric power station Power generation efficiency; for Periodic hydroelectric power station The water purifier head; for Periodic hydroelectric power station The power generation flow rate; b. Water balance constraints: ， In the formula, For hydroelectric power station exist Storage capacity for a given period of time; , For hydroelectric power station exist Inflow and outflow during different time periods; For hydroelectric power station A collection of upstream connected power stations; For upstream hydropower station Water flow stagnation time; The length of a single time period during the scheduling period; c. Constraints on power generation and water discharge: ， ， In the formula, , For the first The minimum and maximum power generation flow rates of the hydroelectric power station; , For the first The upper and lower limits of the discharge flow of the hydropower station; d. Reservoir capacity constraints of hydropower stations: ， ， In the formula, , For hydroelectric power station Upper and lower limits of storage capacity; , For the first The initial and final reservoir capacities for the daily scheduling of the hydropower station; e. Hydropower head and water level constraints: ， ， ， In the formula, , for The water level in front of the dam and the tailrace level of the hydropower station during the specified time period; For head loss; formula , These represent the functional relationships between the upstream water level and the reservoir capacity of the hydropower station, and between the tailrace water level and the downstream flow rate, respectively. f. Unit start-up and shutdown duration constraints: ， In the formula, For hydroelectric power station exist The startup operation variable within the time period, with a value of 1 indicating the start of startup; For hydroelectric power station exist The shutdown operation variable for a given time period, where a value of 1 indicates a shutdown operation; , For hydroelectric power station Minimum start-up and shutdown duration of the indoor unit; g. Hydropower station output constraints: ， In the formula, , For hydroelectric power station exist Upper and lower limits of output during different time periods; For hydroelectric power station exist The start-up and shutdown status variables of the generating units during the time period; A value of 1 indicates the power-on state, and 0 indicates the power-off state; h. Energy storage operation constraints: ， ， ， ， In the formula, , A Boolean variable characterizing the charging and discharging states of an energy storage device, taking the value 0 or 1; , Energy conversion efficiency during charging and discharging of electrochemical energy storage; The rated capacity for electrochemical energy storage; , These are the upper and lower limits of the state of charge (SOC) for electrochemical energy storage; i. Constraints on renewable energy output: ， In the formula, For photovoltaics The maximum active power that can be generated at any given time.

4. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage as described in claim 1, characterized in that: The system operation constraints also include power flow constraints, specifically: a. Use the Distflow power flow model; b. Boundary condition constraints for safe operation.

5. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage effect according to claim 4, characterized in that: Using the Distflow power flow model, the following power flow constraint model is established based on branch power: ， ， ， In the formula, , For nodes The sum of active and reactive power on the power supply and energy storage sides; , For nodes Active and reactive loads at the location; , These represent the active and reactive power on the line. , , The square of the current amplitude, resistance, and inductive reactance on the line; For nodes The square of the voltage amplitude at that point; Due to the presence of nonlinear equality constraints, the DistFlow model is non-convex, making it difficult to find the global optimum. To make the model convex, the equality constraints need to be adjusted. It can be relaxed to an inequality constraint: ， Thus, the DistFlow model is relaxed into a second-order cone programming model. Since the second-order cone model is a typical convex optimization problem, there are already mature commercial solvers available for solving it. Furthermore, in radial distribution networks, the DistFlow model is equivalent to the power flow equations based on the nodal voltage method. When both sides of the inequality constraints are equal, the relaxed DistFlow model can approximate the nonlinear power flow equations without loss.

6. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage effect according to claim 4, characterized in that: Boundary condition constraints for safe operation: ， In the formula, This represents the maximum value of the branch current. , These are the upper and lower limits of the node voltage; , These represent the maximum and minimum values ​​for purchasing electricity from the main grid.

7. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage as described in claim 1, characterized in that: The implementation method and scale correspondence of the small hydropower energy storage effect model are as follows: Small hydropower achieves the capacity substitution function of energy storage systems through the regulating capacity of its reservoirs. The regulating capacity of the reservoir is equivalent to the rated energy storage capacity of the energy storage system. The specific substitution scale is determined by the total amount of reservoir capacity available for daily regulation between the dead capacity and the normal water level. Small hydropower stations achieve the power substitution function of energy storage systems through the rated installed capacity of their generator units. The installed capacity of the hydropower units is equivalent to the rated charging and discharging power of the energy storage system. During system operation, the small hydropower stations reduce their power generation during peak photovoltaic power output periods to store water, thus achieving an equivalent "charging" process. During off-peak photovoltaic power output periods, they increase their power generation to release water, thus achieving an equivalent "discharging" process.

8. The optimized operation method of a complementary system of small-scale hydropower and photovoltaic energy storage effect according to claim 7, characterized in that: The specific scale of small hydropower replacing energy storage is determined through the following methods: The scale of alternative energy storage capacity is determined by the energy value corresponding to the product of the reservoir's available regulating capacity and the power generation head; The capacity of alternative energy storage is determined by the installed capacity of small hydropower.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the method as described in any one of claims 1-8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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