A short-term optimal scheduling method for hydro-photovoltaic complementary system participating in power auxiliary service

Abstract

The present invention relates to the field of electrical communication technology, and specifically to a short-term optimization scheduling method for a hydro-photovoltaic complementary system to participate in power auxiliary services, comprising establishing a short-term scheduling multi-objective optimization model; solving the short-term scheduling multi-objective optimization model based on a decomposed multi-objective evolutionary algorithm to obtain a solution set; and processing the solution set using an improved Chebyshev method to obtain an optimal solution. When establishing the short-term scheduling multi-objective optimization model, the present invention establishes an objective function from the two aspects of minimizing power generation fluctuations and maximizing power generation economic benefits, and incorporates into the objective function the consideration of the hydro-photovoltaic complementary system participating in the active power balance auxiliary service of the power grid, thereby enabling the output active power of the complementary system to follow the power grid load fluctuation trend within a certain range, thereby improving the power generation efficiency of the hydro-photovoltaic complementary system and further enhancing the friendliness of the complementary system in accessing the power grid. The problem of poor power generation efficiency of the hydro-photovoltaic complementary system is solved.

Description

Technical Field

[0001] The present invention relates to the field of electrical communication technology, and in particular to a short-term optimization scheduling method for a water-photovoltaic complementary system participating in power auxiliary services. Background Art

[0002] my country boasts abundant hydropower resources and significant development potential in hydropower generation. However, renewable resources are primarily distributed in remote areas, leading to regional source-load mismatches, severe wind and solar curtailment, and other issues. These issues severely impact the quality and economic benefits of renewable energy generation, hindering the development of new energy generation technologies. Therefore, the complementary operation of hydropower and photovoltaic systems could be considered. This approach, while ensuring efficient generation, could leverage the regulatory capabilities of hydropower to compensate for photovoltaic power generation, reduce system output fluctuations, and ensure the integration and absorption of regional renewable energy generation.

[0003] The peak-shaving capacity of a hydropower-solar hybrid system primarily refers to the ability of the unit system to track load changes. It is a comprehensive reflection of factors such as the system's unit start-up and shutdown time, output variation amplitude, and output adjustment speed. Currently, research focuses on the short-term scheduling of hydropower-solar hybrid systems, using the system's peak-shaving capacity as the objective function and establishing scheduling models to solve the problem. However, such research primarily considers the complementary operation of a single large-capacity hydropower station and photovoltaic power generation, lacking the full utilization of cascaded small hydropower resources. Therefore, a short-term daily total load distribution model for cascaded hydropower stations can be established, and the optimization objectives can be developed from a single objective to multiple objectives. For example, a scaled optimization scheduling model for hydropower-solar hybrid systems can be established based on system coordination and economic efficiency.

[0004] However, the optimization objectives involved in such multi-objective optimization scheduling research usually consider the power generation characteristics or economic efficiency of the hydro-photovoltaic complementary system in isolation, weakening the connection between the hydro-photovoltaic complementary system and the power grid. There is a lack of consideration of the hydro-photovoltaic complementary system's participation in power auxiliary services such as peak load regulation and frequency regulation under grid-connected conditions, thereby reducing the power generation efficiency of the hydro-photovoltaic complementary system. Summary of the Invention

[0005] The purpose of the present invention is to provide a short-term optimization scheduling method for a hydro-photovoltaic complementary system participating in power auxiliary services, aiming to solve the problem of poor power generation efficiency of the hydro-photovoltaic complementary system.

[0006] To achieve the above objectives, the present invention provides a short-term optimization scheduling method for a hydro-photovoltaic complementary system participating in power auxiliary services, comprising the following steps:

[0007] Establish a short-term scheduling multi-objective optimization model;

[0008] Solving the short-term scheduling multi-objective optimization model based on a decomposed multi-objective evolutionary algorithm to obtain a solution set;

[0009] The improved Chebyshev method is used to process the solution set to obtain the optimal solution.

[0010] The establishment of a short-term scheduling multi-objective optimization model includes:

[0011] Establish the objective function of power generation fluctuation and power generation economic benefits;

[0012] Provide constraints on water balance, reservoir storage and release, hydropower station output, and water level fluctuation;

[0013] A short-term scheduling multi-objective optimization model is established based on the objective function and the constraint conditions.

[0014] The decomposition-based multi-objective evolutionary algorithm solves the short-term scheduling multi-objective optimization model to obtain a solution set, including:

[0015] The multi-objective evolutionary algorithm utilizes the sub-problems of the short-term scheduling multi-objective optimization model to perform genetic mutation and optimization on the solutions of the short-term scheduling multi-objective optimization model to obtain a solution set.

[0016] In each iteration of genetic variation and optimization, the multi-objective evolutionary algorithm saves the current optimal solution of each sub-problem and the corresponding objective function value, the currently found optimal value of each objective function and all non-dominated solutions.

[0017] The non-dominated solutions are stored in an external population.

[0018] The present invention provides a short-term optimization scheduling method for a hydro-photovoltaic complementary system participating in power auxiliary services. The method establishes a short-term scheduling multi-objective optimization model; solves the short-term scheduling multi-objective optimization model based on a decomposition-based multi-objective evolutionary algorithm to obtain a solution set; and processes the solution set using an improved Chebyshev method to obtain an optimal solution. When establishing the short-term scheduling multi-objective optimization model, the present invention establishes an objective function from the two aspects of minimizing power generation fluctuations and maximizing power generation economic benefits, and incorporates into the objective function the consideration of the hydro-photovoltaic complementary system participating in the active power balance auxiliary service of the power grid, so that the active power output of the complementary system follows the power grid load fluctuation trend within a certain range, thereby improving the power generation efficiency of the hydro-photovoltaic complementary system and further enhancing the friendliness of the complementary system in accessing the power grid. The method solves the problem of poor power generation efficiency of the hydro-photovoltaic complementary system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a schematic diagram of a trapezoidal water-photovoltaic complementary system.

[0021] Figure 2 It is the flow chart of MOEA / D algorithm.

[0022] Figure 3 It is a schematic diagram of the topology of the basin's cascade water-photovoltaic complementary system.

[0023] Figure 4 It is a schematic diagram of the constraints of the hydropower station.

[0024] Figure 5 It is a schematic diagram of the daily natural water flow of cascade hydropower stations.

[0025] Figure 6 This is a schematic diagram of the daily power generation curves of three typical photovoltaic power stations.

[0026] Figure 7 It is a schematic diagram of a typical daily load curve of the power grid.

[0027] Figure 8 It is a schematic diagram of the Pareto solution set of three typical scenarios.

[0028] Figure 9 This is a schematic diagram of the daily output process of water, light, and water-light complementary systems in three typical scenarios.

[0029] Figure 10 This is a schematic diagram of the daily output curves of various hydropower stations under sunny day scenarios.

[0030] Figure 11 This is a flow chart of a short-term optimization scheduling method for a water-photovoltaic complementary system participating in power auxiliary services provided by the present invention. DETAILED DESCRIPTION

[0031] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0032] See also Figures 1 to 11The present invention provides a short-term optimization scheduling method for a water-solar complementary system to participate in power auxiliary services, comprising the following steps:

[0033] S1 establishes a short-term scheduling multi-objective optimization model;

[0034] The specific method is:

[0035] S11 establishes the objective function of power generation fluctuation and power generation economic benefits;

[0036] This paper constructs a multi-objective optimization model for the short-term scheduling of a cascaded hydropower-solar hybrid system. Hydropower is used as a complementary power source to smooth out PV output power fluctuations. After smoothing out the fluctuations in the system's total output, the system's total output is aligned as closely as possible with grid load fluctuations within a certain range. Simultaneously, the goal is to maximize the system's economic benefits. The objective function consists of minimizing power generation fluctuations and maximizing economic benefits.

[0037] The short-term scheduling cycle of the hydro-photovoltaic complementary system is divided into multiple equal time periods t, and the power generation fluctuation objective function is established as follows:

[0038]

[0039] Where: F (MW) represents the output power fluctuation of the hydropower-photovoltaic hybrid system; in this paper, the length of each time period is set to 15 minutes, T is the total number of time periods in the scheduling period, which is 96; i represents the cascade hydropower station number; n is the number of cascade hydropower stations; and The sum of (MW) is the total output of the hydro-photovoltaic complementary power generation system in the tth period; (MW) is the average design output of the water-photovoltaic complementary system during period t.

[0040] The average design output of the hydro-photovoltaic system in period t in formula (1) This will be affected by the grid load curve. That is, when the grid load is light, the total output target of the complementary system can be reduced, while during periods of heavy load such as noon and night, the total output of the complementary system can be increased to participate in active power balancing services. The specific calculation method is as follows:

[0041]

[0042] Where: (MW) is the average load (power) of the grid during period t. The coefficient K represents the output range of the hydro-solar hybrid system for participating in ancillary services. That is, K times the output of the hydro-solar hybrid system is used to participate in the active power balance ancillary service of the grid after optimized scheduling.

[0043] The total daily power generation economic benefit of the hydro-photovoltaic complementary system can be expressed as:

[0044]

[0045] Where: B(￥) is the total daily power generation benefit of the cascade hydropower-photovoltaic complementary system in the basin; T is the total number of time periods in the scheduling period; Δt is the length of the uniform time period, which is 900 when the unit is 15 minutes; n is the number of cascade hydropower stations; (MW) is the average hydropower output of hydropower station i in period t; (MW) is the average photovoltaic output of the photovoltaic power station in period t; and (￥ / MW·h) are the hydropower and photovoltaic grid-connected electricity prices and active power balancing ancillary service fees that change over time.

[0046] S13 provides constraints on water balance, reservoir storage and release, hydropower station output, and water level fluctuation.

[0047] Specifically, the water balance constraints are as follows:

[0048]

[0049] Where: S i,t 、S i,t+1 (m3) are the water storage capacity of hydropower station i at the beginning and end of period t; I i,t (m3 / s) is the average inflow of water to hydropower station i during period t; the use of water released from the reservoir for purposes other than power generation is not considered. and R i,t (m³ / s) are the average discharge and abandonment flows of hydropower station i during period t, and their sum is the outflow. This constraint establishes an equation for the water balance of cascade hydropower stations both within and between hydropower stations.

[0050] The reservoir storage and release constraints are as follows:

[0051]

[0052] Where: The reservoir storage and release limits within a single day will not be affected by time, and (m3) is the minimum and maximum storage capacity of hydropower station i, which respectively represent the dead storage capacity of the reservoir and the maximum storage capacity for flood control during the flood season. and (m3 / s) are the minimum and maximum water release flows required by hydropower station i to ensure downstream flood control safety and ecological requirements.

[0053] The output constraints of the hydropower station are as follows:

[0054]

[0055] Where: (MW) are the minimum output and maximum expected output of hydropower station i in period t. At the same time, this constraint gives the output calculation method of the hydropower station, where (m3 / s) is the average power generation flow of hydropower station i in period t; Hi(m) is the average net head of hydropower station i, ignoring head loss; g is the gravity coefficient, usually taken as 9.81×10-3; η is the power generation coefficient of the hydropower station.

[0056] The water level fluctuation constraints are as follows:

[0057]

[0058] Where: Z i,t (m) is the reservoir water level of hydropower station i at time t, f(S i,t ) is the reservoir water level-storage capacity relationship function; the large fluctuation of the water level at the beginning and end of the day in the daily regulation power station is not conducive to the power generation operation of the next day, so it is set (m) is the maximum fluctuation of reservoir water level at the beginning and end of day i.

[0059] S14 establishes a short-term scheduling multi-objective optimization model based on the objective function and the constraints.

[0060] S2 solves the short-term scheduling multi-objective optimization model based on a decomposed multi-objective evolutionary algorithm to obtain a solution set;

[0061] Specifically, the multi-objective evolutionary algorithm uses the subproblems of the short-term scheduling multi-objective optimization model to perform genetic mutation and optimization on the solutions to the model, obtaining a solution set. During each iteration of genetic mutation and optimization, the multi-objective evolutionary algorithm saves the current optimal solution and corresponding objective function value for each subproblem, the currently found optimal value for each objective function, and all non-dominated solutions. These non-dominated solutions are stored in an external population.

[0062] This paper uses a multi-objective evolutionary algorithm based on decomposition (MOEA / D) to solve the short-term optimization model of a hydro-photovoltaic hybrid system, obtaining a Pareto Front (PF) solution set for decision makers. Minimizing power fluctuations in the hybrid system and maximizing economic benefits are used as two conflicting objective functions of the MOEA / D algorithm.

[0063] In each generation of calculation, the MOEA / D algorithm optimizes N objectives simultaneously. The neighborhood of a weight vector is defined as the set of its nearest weight vectors. The algorithm uses the solutions to the subproblems in the domain of each solution to perform genetic mutation and optimization on the solution. The non-dominated solutions found for each subproblem are saved in the external group (EP). In each iteration, the MOEA / D algorithm needs to save the following information: the current optimal solution to each subproblem and its corresponding objective function value; the currently found optimal value of each objective function; EP, that is, all non-dominated solutions found so far. When the termination condition is reached, the required Pareto solution set will be output.

[0064] S3 uses the improved Chebyshev method to process the solution set to obtain the optimal solution.

[0065] Specifically, the present invention uses a modified Chebyshev method to address multi-objective problems, transforming the Pareto front approximation problem into multiple scalar optimization problems. Each Pareto optimal solution has a corresponding weight vector. Therefore, by varying the weight vector, different Pareto optimal solutions can be obtained.

[0066] The present invention is described in detail below with reference to an example: a case study is to use a cascade hydropower station in a certain river basin and a local photovoltaic power station for complementarity. The calculation scheduling period is 1 day (0:00 to 24:00), the calculation period is 15 minutes, and there are 96 periods in total. The cascade hydropower stations in this river basin are in series topology, and each station has daily regulation capacity. The topological relationship of the hydropower station group in the river basin is as follows: Figure 3 shown.

[0067] The model is subject to four constraints: water balance of cascade hydropower stations, reservoir storage and release, hydropower station output, and water level fluctuation. The water balance constraint essentially establishes an equation relationship between the internal and inter-station hydropower balance. The reservoir storage and release constraint essentially means that each cascade hydropower station has a water level limit (minimum reservoir capacity). and maximum storage capacity (m3)) and the minimum and maximum water release flow restrictions. The essence of the hydropower station output constraint is that each hydropower station has a minimum and maximum output, and its output size is related to the water release flow and the head height. The water level fluctuation constraint is to hope that the daily hydropower station has a better regulation ability and can maintain the water level fluctuation amplitude at the beginning and end of the day as small as possible. Therefore, the boundary conditions of the model mainly include water level restriction, water release flow restriction, normal water level, output restriction, average net head and output coefficient. The constraints of each hydropower station are detailed in Figure 4 .

[0068] The present invention assumes that the water level at the beginning of each day of each hydropower station is the normal water level, and the water level-storage capacity relationship curve of each hydropower station is known.i+1,t The water flow rate is 5m3 / s. In the calculation, the water flow lag time is taken as 1 period, that is, the water released from the upstream hydropower station reaches the downstream hydropower station in the next period. The natural water flow rate of the hydropower station within 1 day is as follows: Figure 5 shown.

[0069] Photovoltaic power generation is affected by many factors such as weather and season. Based on the irradiation data of a 50MW photovoltaic power station in the basin during the power generation period, the daily power generation curves of the photovoltaic power station under three typical meteorological conditions: sunny, cloudy and rainy days are fitted, as shown in the figure below: Figure 6 shown.

[0070] According to the setting of the minimum power generation fluctuation objective function, a typical daily load curve of the power grid is selected as the reference of the output of the cascade hydro-photovoltaic complementary system, such as Figure 7 shown.

[0071] Based on the known information such as the daily output of photovoltaic power stations and the water inflow of hydropower stations, the cascade hydropower stations are considered to complement the daily output power of photovoltaic power stations. Through the complementary system optimization scheduling model established in this paper, considering the two goals of minimizing output power fluctuations and maximizing economic benefits of power generation, the MOEA / D algorithm is adopted to solve the water-photovoltaic complementary system in three typical scenarios of sunny days, cloudy days and rainy days. Among them, the coefficient K of the auxiliary service of active balance of the complementary system is taken as 0.1. The Pareto solution set under the three typical scenarios of sunny days, cloudy days and rainy days is as follows Figure 8 shown.

[0072] Depend on Figure 8 From the Pareto solution distribution in the equation, it can be seen that the smaller the power fluctuation of the hydro-photovoltaic complementary system, the greater the economic benefit is in a contradictory relationship. If the output power fluctuation is to be minimized, a certain degree of economic efficiency will inevitably be lost. A compromised satisfactory solution is selected in each of the three scenarios for analysis. Combining equations (1) and (3), the power fluctuation index value of the hydro-photovoltaic complementary system in the sunny scenario is 2.3MW, and the economic benefit value is 1.169 million yuan; the power fluctuation index value in the cloudy scenario is 1.7MW, and the economic benefit value is 1.128 million yuan; the power fluctuation index value in the rainy scenario is 2.4MW, and the economic benefit value is 1.086 million yuan. In the three weather scenarios of sunny, cloudy and rainy, from the theoretical minimum power fluctuation solution along the Pareto distribution to the selected compromised satisfactory solution, the power fluctuation value increases by 2.2MW, 1.6MW and 2.3MW respectively, and the economic benefit increases by 1.28%, 1.24% and 1.47% accordingly, which illustrates the trade-off relationship between system power fluctuation and economic benefit in the selected satisfactory solution.

[0073] Figure 9The figures show the daily output of a hydropower station, a photovoltaic power station, and a hydropower-photovoltaic hybrid system for three typical days, along with the system's daily output design curves. The figures show that the hydropower station can adjust its output in a timely manner to smooth out fluctuations in photovoltaic output. The minimum hydropower output for the three typical scenarios of sunny, cloudy, and rainy days is 62.2 MW, 72.0 MW, and 86.0 MW, corresponding to the time periods 52 (13:00-13:15), 53 (13:15-13:30), and 56 (14:00-14:15), respectively. The maximum output is 112.1 MW, 108.4 MW, and 105.2 MW, corresponding to the time periods 85 (21:15-21:30), 86 (21:30-21:45), and 84 (21:00-21:15), respectively. Further analysis shows that the optimized minimum and maximum hydropower output periods coincide with the peak photovoltaic output periods and the peak grid load periods, respectively.

[0074] In addition, in order to verify and analyze the intraday optimization scheduling process of the hydro-photovoltaic complementary system, taking a typical sunny day scenario as an example, the intraday output and water level changes of the cascade hydropower stations are further analyzed. Figure 10 As shown in the figure, the output of each hydropower station is within the constraints, with minimal output fluctuations, except for Hydropower Station II. During the nighttime period (00:00-06:00), when photovoltaic output is low, the cascade hydropower stations must collectively generate higher output to compensate. During this period, water inflow is relatively balanced, with Hydropower Station III contributing the highest output. Hydropower Station II's output is relatively balanced, and Hydropower Stations I and III are undergoing a water storage process. During the first daytime load peak (09:00-10:00), when photovoltaic output is high, Hydropower Station I continues to store water, while Hydropower Stations II and III contribute to the task of compensating for photovoltaic power and participating in peak load regulation. When photovoltaic output reaches its maximum, Hydropower Station I releases some of its stored water, allowing Hydropower Station II to take over, resulting in a corresponding decrease in its output. During the evening, when photovoltaic output declines and coincides with the grid's late peak load period (around 21:00), the three hydropower stations contribute jointly to compensate for photovoltaic power and provide auxiliary services for active power balancing. On a sunny day, the average daily output of hydropower stations I, II, and III shows an overall trend from small to large. This is because there is water inflow between stations, and the water inflow flow into hydropower stations II and III is relatively larger.

[0075] The beneficial effects of the present invention are: starting from the two objective functions of minimizing power generation fluctuation and maximizing power generation economic benefits, considering the participation of the hydro-photovoltaic complementary system in the auxiliary service of active power balance of the power grid, by establishing a short-term multi-objective optimization scheduling model for the cascade hydro-photovoltaic complementary system, and taking into account the water volume constraints of the cascade hydropower stations and the three typical daily power generation characteristics of the photovoltaic power stations: sunny, cloudy and rainy, a decomposition-based multi-objective evolutionary algorithm is used to solve the problem, and the intraday optimization scheduling of the cascade hydro-photovoltaic complementary system under three typical scenarios is verified and analyzed. It can effectively smooth out the intermittent and volatility of photovoltaic power generation, take into account both power fluctuation and power generation economic benefits, and is of great significance to helping the green economy of the power grid to operate stably.

[0076] The above disclosure is merely a preferred embodiment of a short-term optimization scheduling method for a hydro-photovoltaic hybrid system participating in power auxiliary services according to the present invention. This is certainly not intended to limit the scope of the present invention. Persons skilled in the art will understand that any equivalent changes made by implementing all or part of the above-described embodiments in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A short-term optimization scheduling method for a hydro-photovoltaic complementary system participating in power auxiliary services, characterized in that: The following steps are involved: Establish a short-term scheduling multi-objective optimization model; Solving the short-term scheduling multi-objective optimization model based on a decomposed multi-objective evolutionary algorithm to obtain a solution set; The solution set is processed by using the improved Chebyshev method to obtain the optimal solution; The establishment of a short-term scheduling multi-objective optimization model includes: The objective function of power generation fluctuation and power generation economic benefits is established. The short-term scheduling cycle of the hydro-photovoltaic complementary system is divided into multiple equal time periods t. The power generation fluctuation objective function is established as follows: （1） Where: F represents the output power fluctuation of the hydropower-photovoltaic hybrid system; the length of each time period is set to 15 minutes; T is the total number of time periods in the scheduling period, which is 96; i represents the cascade hydropower station number; n is the number of cascade hydropower stations; and The sum is the total output of the hydro-photovoltaic complementary power generation system in the tth period; is the average design output of the hydro-photovoltaic complementary system in period t; The specific calculation method is as follows: （2） Where: is the average load of the grid during period t; the coefficient K represents the output range of the hydro-solar hybrid system for participating in the auxiliary service, that is, K times the output of the hydro-solar hybrid system is used to participate in the grid active power balance auxiliary service after optimized scheduling; Provide constraints on water balance, reservoir storage and release, hydropower station output, and water level fluctuation; A short-term scheduling multi-objective optimization model is established based on the objective function and the constraint conditions.

2. The short-term optimization scheduling method for a water-solar complementary system participating in power auxiliary services according to claim 1, characterized in that: The decomposition-based multi-objective evolutionary algorithm solves the short-term scheduling multi-objective optimization model to obtain a solution set, including: The multi-objective evolutionary algorithm utilizes the sub-problems of the short-term scheduling multi-objective optimization model to perform genetic mutation and optimization on the solutions of the short-term scheduling multi-objective optimization model to obtain a solution set.

3. The short-term optimization scheduling method for a water-solar complementary system participating in power auxiliary services according to claim 2, characterized in that: In each iteration of genetic variation and optimization, the multi-objective evolutionary algorithm saves the current optimal solution of each subproblem and the corresponding objective function value, the currently found optimal value of each objective function and all non-dominated solutions.

4. The short-term optimization scheduling method for a water-solar complementary system participating in power auxiliary services according to claim 3, characterized in that: The non-dominated solutions are stored in the external population.

Images