Method, device and equipment for real-time intelligent operation of multi-energy complement of hydroelectric, wind, solar and storage

Abstract

To provide a real-time intelligent operation method for multi-energy complementation of hydraulic power / wind power / photovoltaic power / power storage which implements a simultaneous optimization dispatch of hydraulic power / wind power / photovoltaic power / power storage while defining minimization of remaining load distribution of a power network as a dispatch goal, advantageously achieves an optimal operation control scheme of hydraulic power generation before and after hydraulic power / wind power / photovoltaic power / power storage combination and reduces a peak / bottom difference of a remaining load of the power network.SOLUTION: A method includes the steps of: collecting operation data of a multi-energy complementation system in a basin; defining a day as a dispatch period and a time as a dispatch time zone, defining minimization of remaining load distribution of a power network as a dispatch goal and constructing a previous day simultaneous optimization dispatch model while simultaneously taking restrictions of an operation and restrictions of a power system into accounts; and implementing a real-time intelligent operation dispatch for multi-energy complementation on the basis of the previous day simultaneous optimization dispatch model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the technical field of multi-energy power generation dispatch optimization, and in particular to a method, device and equipment for real-time intelligent operation of multi-energy complementation of hydropower, wind power, solar power and storage. [Background technology]

[0002] Solar and wind power have advantages such as safety, reliability, and pollution-free operation, but they also suffer from disadvantages such as instability, intermittency, and randomness. Large-scale system interconnections affect the stable operation of the power grid and make peak shifting difficult. Hydropower units are relatively easy to control, can be started and adjusted quickly, and their reservoirs have a certain storage capacity, making them a clean energy source with strong adjustability. Given the characteristics of hydropower and new energy generation, many of China's clean energy complementary cooperation demonstration centers rely on large-scale construction in river basins, locating wind and solar power generation around the river basin. The flexible adjustability of hydropower and the flexibility of large-scale storage plants can smooth out the output fluctuations of wind and solar power generation, forming a stable combined output of hydropower, wind, solar, and storage power that can be transmitted overseas. This is currently an important integrated energy service business model for power generation companies.

[0003] For large-scale clean energy systems, by studying the complementary characteristics of hydropower, wind power, solar power, and storage, it is necessary to determine an appropriate cascade complementary model for hydropower, wind power, solar power, and storage, implement simultaneous optimized dispatching of hydropower, wind power, solar power, and storage, realize an optimal operation control scheme for hydropower generation before and after hydropower, wind power, solar power, and storage synthesis, and ensure the security and good consumption capacity of the power grid while building a green and clean energy system, thereby improving the utilization rate of clean energy. This has become an urgent technical challenge for those skilled in the art. Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention has been made in view of the above circumstances, and provides a method, device and equipment for real-time intelligent operation of multi-energy complementation of hydropower, wind power, solar power and energy storage, which facilitates simultaneous optimized dispatching of hydropower, wind power, solar power and energy storage, facilitates the realization of an optimal operation control scheme for hydropower before and after hydropower, wind power, solar power and energy storage combination, ensures the safety and good consumption capacity of the power grid, helps to improve the utilization rate of clean energy, and solves the above technical problems. [Means for solving the problem]

[0005] In order to achieve the above object, the present invention employs the following technical means: In a first aspect, an embodiment of the present invention provides a real-time intelligent operation method for hydropower, wind power, solar power and storage multi-energy complementation, the method being executed by a computer, Step S1 of collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power generation + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric power, wind power, solar power, and storage; Step S2: based on the operation data, construct a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with the day as the dispatch period, the time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; Step S3: solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; and performing real-time intelligent operation dispatch of the hydropower, wind power, solar power, and storage energy complement based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; The method includes:

[0006] Preferably, in step S1, the operating data includes the load of the power grid, the remaining load of the power grid, hydroelectric power output, wind power output, solar power output, stored power, pumped storage power, the number of power plants, operating water level, power generation flow rate, pumped storage flow rate, and line capacity.

[0007] Preferably, in step S2, the constructed day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy includes an objective function and a constraint condition, [1] Objective function The remaining load distribution of the power grid is minimal,

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[0008] Preferably, in step S3, an improved moth-flame optimization algorithm is used to solve a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy, and hybrid coding is used during the model solving process, in which, in the case of a conventional hydropower plant, the discharge volume is selected as the decision variable, and in the case of a hybrid power generation and storage plant, the power output / pumped storage power is selected as the decision variable.

[0009] Preferably, the improved moth flame optimization algorithm is an improvement of the moth flame optimization algorithm in three aspects: update formula, linear flight path heuristic, and flame population update strategy.

[0010] Preferably, in step S3, based on the result of the simultaneous optimization dispatch of hydropower, wind power, photovoltaic power, and storage, an analysis is performed to obtain a storage operation strategy for a hybrid power generation and storage plant having the following configuration: (1) When the load is at the bottom of the load range, if the output of wind and solar energy increases beyond the threshold, the pumped hydroelectric power of the hybrid power generation and storage plant is increased to reduce the amount of output suppression. If the output of wind and solar energy decreases beyond the threshold, the pumped hydroelectric power is decreased and the pumping time is increased to meet the power demand during the peak load range. (2) When the load is in the peak hours, if the output of wind and solar energy increases beyond the threshold, reduce the output of the hybrid power generation and storage plant and increase the power generation time, and if the output of wind and solar energy decreases beyond the threshold, increase the output of the hybrid power generation and storage plant; (3) When the hybrid power generation and storage plant is shut down and the output of wind and solar energy increases beyond the threshold, it will operate to pump water to reduce the amount of output curtailment, and when the output of wind and solar energy decreases beyond the threshold, it will operate to generate electricity, level out the output of the power grid, and meet the operational requirements of the power grid.

[0011] In a second aspect, an embodiment of the present invention also provides a real-time intelligent operation device for hydropower, wind power, solar power and storage multi-energy complementary, which uses the above-mentioned real-time intelligent operation method for hydropower, wind power, solar power and storage multi-energy complementary to implement real-time intelligent operation dispatch of hydropower, wind power, solar power and storage multi-energy complementary, and the device includes: A data collection module for collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power plant + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric, wind, solar and storage power; A model construction module for constructing a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy based on operation data, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with day as the dispatch period, time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; and an operation dispatch module for solving and setting a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and energy storage, and for implementing real-time intelligent operation dispatch of multi-energy complementation of hydropower, wind power, solar power, and energy storage based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and energy storage.

[0012] In a third aspect, an embodiment of the present invention also provides an electronic device comprising a processor and a memory, wherein the memory stores machine-executable instructions executable by the processor, and the processor executes the machine-executable instructions to implement the above-mentioned real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage.

[0013] In a fourth aspect, an embodiment of the present invention provides a computer-readable recording medium storing a computer program, which, when executed by a processor, implements the above-mentioned real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage. [Effects of the Invention]

[0014] Compared with the prior art, the present invention has at least the following advantageous effects: The real-time intelligent operation method for the multi-energy complementation of hydropower, wind power, solar power and storage provided by the present invention sets the dispatch goal as the minimum remaining load balancing of the power grid, and at the same time takes into account the energy operation constraints of hydropower, wind power, solar power and storage and the power system constraints to establish a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power and storage, which facilitates the simultaneous optimization dispatch of hydropower, wind power, solar power and storage, which is advantageous in realizing the optimal operation control scheme for hydropower before and after hydropower, wind power, solar power and storage synthesis, and is also advantageous in reducing the peak / bottom difference of the remaining load of the power grid, ensuring the security and good consumption capacity of the power grid and improving the utilization rate of clean energy.

[0015] Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention may be realized and attained through the structure particularly pointed out in the description and the accompanying drawings.

[0016] Hereinafter, the technical means of the present invention will be described in more detail with reference to the accompanying drawings and embodiments.

[0017] In order to clearly explain the embodiments of the present invention or the technical means in the prior art, the accompanying drawings that need to be used to depict the embodiments or the prior art will be briefly described below. The accompanying drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other accompanying drawings based on these accompanying drawings without any creative activity.

[0018] The accompanying drawings are intended to provide a further understanding of the present invention, constitute a part of the specification, and are used to interpret the present invention together with the embodiments of the present invention, and are not intended to limit the present invention in any way. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a flowchart of a real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage provided by an embodiment of the present invention; [Figure 2] 1 is a schematic configuration diagram of a real-time intelligent operation device for multi-energy complementation of hydropower, wind power, solar power and storage provided by an embodiment of the present invention; [Figure 3] 1 is a schematic configuration diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] In order to make the objectives, technical means and advantages of the embodiments of the present invention clearer and more apparent, the technical means in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention. However, it goes without saying that the described embodiments are only some embodiments of the present invention and not all embodiments.

[0021] In describing the present invention, it should be noted that some flows described in the specification and accompanying drawings of this application include multiple operations that appear in a specific order, but it should be clearly understood that these operations can be performed in an order different from that described herein or can be performed in parallel. Note that ordinal numbers are used merely for descriptive purposes and should not be understood to indicate or imply relative importance.

[0022] Therefore, the following detailed description of the embodiments of the present invention as illustrated in the accompanying drawings is not intended to limit the scope of the invention sought to be protected, but is intended to represent only selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without any creative activity fall within the scope of protection of the present invention.

[0023] As shown in FIG. 1, the present invention provides a real-time intelligent operation method for multi-energy complementary use of hydropower, wind power, solar power and storage energy, the method being executed by a computer: Step S1 of collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power generation + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric power, wind power, solar power, and storage; Step S2: based on the operation data, construct a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with the day as the dispatch period, the time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; Step S3: solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; and performing real-time intelligent operation dispatch of the hydropower, wind power, solar power, and storage energy complement based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; The method includes:

[0024] Hereinafter, a specific embodiment of the method of the present invention will be described in detail, taking the multi-energy complement of hydropower, wind power, solar power and storage in the Beipanjiang River Basin as an example.

[0025] 1. Collecting driving data In this embodiment, a simultaneous optimized dispatch of hydropower, wind power, solar power, and storage is implemented based on the integration of renewable energy in the Beipan River Basin. The target is the basin's cascaded hydropower, the basin's cascaded hydropower + hybrid pumped storage power generation and storage station, and the hydropower-wind-solar-storage multi-energy complementary system. Typical dispatch scenarios are constructed according to different inflow conditions and meteorological and environmental conditions, and operation data of the basin's cascaded hydropower station, the basin's cascaded hydropower + hybrid power generation and storage station, and the hydropower-wind-solar-storage multi-energy complementary system is collected. The operation data includes the power grid load, remaining power grid load, hydropower output, wind power output, solar power output, storage power, pumped storage power, number of power plants, operating water level, power generation flow rate, pumped storage flow rate, and line capacity.

[0026] 2. Construction of a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy In this embodiment, the day is the dispatch period, the time is the dispatch time slot, and the focus is on peak shifting and bottom-up of the multi-energy complementary system of hydropower, wind power, solar power, and storage, with the dispatch goal being to minimize the remaining load distribution of the power grid, and taking into account the operational constraints of hydropower, wind power, solar power, and storage energy and the constraints of the power system, a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage is constructed.

[0027] [1] Objective function: The remaining load distribution of the power grid is minimal,

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[0028] 1) Water level constraints in the upper reservoir Z min up,g ≦Z up,t ≦Z max up,g Z min up,p ≦Z up,t ≦Z max up,p In the formula, Z up,t is the operating water level of the upper reservoir of the hybrid power generation and storage plant during time t, Z max up,g and Z min up,g are the upper and lower limits of the operating water level under the upper reservoir power generation mode, and Z max up,p and Z min up,p are the upper and lower limits of the operating water level under the upper reservoir pumping mode, respectively, 2) Water level constraints in the lower reservoir Z min down,g ≦Z down,t ≦Z max down,g Z min down,p ≦Z down,t ≦Z max down,p In the formula, Z down,t is the operating water level of the lower reservoir of the hybrid power generation and storage plant during time t, Z max down,g and Zmin down,g are the upper and lower limits of the operating water level under the lower reservoir power generation mode, and Z max down,p and Z min down,p are the upper and lower limits of the operating water level under the lower reservoir pumping mode, respectively, 3) Power generation flow rate constraints Q min g ≦Q g,t ≦Q max g In the formula, Q g,t is the power generation flow rate of the hybrid power generation and storage plant in time period t, Q max g and Q min g are the upper and lower limits of the power generation flow rate, respectively, 4) Pumping flow rate constraints Q min p ≦Q p,t ≦Q max p In the formula, Q p,t is the pumping flow rate of the hybrid power generation and storage plant in time period t, Q max p and Q min p are the upper and lower limits of the pumping flow rate, respectively, 5) Power generation output constraints N min g ≦N g,t ≦N max g In the formula, N g,t is the power generation output of the hybrid power generation and storage plant in time period t, N max g and N min g are the upper and lower limits of the power generation output, respectively, 6) Pumped storage power constraints N min p ≦N p,t ≦N max p In the formula, N p,tis the pumped power of the hybrid power generation and storage plant in time period t, N max p and N min p are the upper and lower limits of pumped storage power, respectively, 7) Operation mode constraints of hybrid power generation and storage plants I g t +I p t ≦1 In the formula, I g t and I p t is a variable between 0 and 1, indicating whether the hybrid power generation and storage plant is in power generation mode or pumping mode at time t, where 1 is "yes" and 0 is "no." The pump turbines of the hybrid power generation and storage plant are connected to the upper and lower reservoirs through the water transmission system, and at the same time, the pumping power generation status of the hybrid power generation and storage plant are mutually exclusive. 8) Restrictions on capacity of external transmission lines

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[0029] 3. Setting the boundary and solution for simultaneous day-ahead optimization of hydropower, wind power, solar power, and storage energy (1) Model solution target In this embodiment, the dispatch targets of the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and energy storage include Shannipoh Hydropower Station, Guangzhao Hydropower Station, Mamaya Hydropower Station, Dongqing Hydropower Station, Guangma Hybrid Power Generation and Storage Station, and wind and solar power stations that transmit their combined output overseas, and specific information is shown in the table below.

[0030] Table 1. Targets for day-ahead simultaneous optimization dispatch of hydropower, wind power, solar power, and storage energy [Table 1]

[0031] (2) Relevant operating boundaries of the Kōma hybrid pumped storage power plant In this embodiment, the Guangma Hybrid Power Generation and Storage Station is a daytime regulating pumped storage power generation and storage station, mainly responsible for tasks such as peak shifting, bottom-up, power storage, frequency regulation, phase regulation and emergency backup in the Guizhou power grid. The installed capacity of the power station is 800,000 kW, and the dedicated power generation storage capacity is 14.43 million m 3 Under the condition of constant speed development, the power consumption of the pumped storage power generation unit in pumping mode always remains at the rated pumped storage power and cannot be adjusted, while the power generation in generating mode can be adjusted according to the demands of frequency regulation and peak shifting of the power grid.

[0032] Because the Guangma Hybrid Power Generation and Storage Plant uses the Guangzhao and Mamaya Reservoirs as the upper and lower reservoirs, it must avoid being subject to requirements such as navigation and environmental protection standard flow rates, which were originally the responsibility of the Guangzhao and Mamaya Reservoirs. At this stage, the characteristic water levels of the Guangzhao and Mamaya reservoirs will not be changed, ensuring that the Guangzhao Reservoir's original tasks remain unchanged. The pumped storage power generation unit of the upper reservoir, the Guangzhao Reservoir, operates at a water level between 715m and 745m in power generation mode, with power generation halted when the reservoir water level drops to 715m. The pumped storage power generation unit operates at a water level between 691m and 745m in pumping mode, with pumping halted when the reservoir water level reaches 745m. The operating water level of the pumped storage power generation unit in the power generation mode of the lower reservoir, the Mamaya Reservoir, ranges from 580m to 585m, and power generation is stopped when the reservoir water level reaches 585m. The operating water level of the pumped storage power generation unit in the pumping mode ranges from 582m to 585m, and pumping is stopped when the reservoir water level drops to 582m.

[0033] (3) Model solution settings In this embodiment, the day-ahead simultaneous optimization dispatch model for hydropower, wind, solar, and storage energy is solved using an improved Mohsin optimization algorithm. Hybrid coding is used in the model solution process. For conventional hydropower plants such as Shanipo, Guangzhao, Mamaya, and Dongqing, the discharge volume is selected as the decision variable. For the Guangma hybrid power generation and storage plant, the power output / pumped storage power is selected as the decision variable. In this embodiment, the model prioritizes wind and solar energy consumption and corrects the output upper limit of the hydropower plant and the hybrid power generation and storage plant by subtracting the corresponding combined wind and solar power output from the off-site transmission line capacity. Before calculating the power output or pumped storage power of the Guangma hybrid power generation and storage plant, the dispatch process for the cascaded conventional hydropower plant is first completed, and then the dispatch process for the Guangma hybrid power generation and storage plant is calculated. The state variables of the upper and lower reservoir hydropower plants are corrected based on the pumped storage power generation process.

[0034] The improved moth flame optimization algorithm used in the present invention will now be described.

[0035] 1) Moth Flame Optimization Algorithm The moth flame optimization algorithm is an intelligent evolutionary algorithm inspired by the horizontal orientation mechanism of moths. When moths fly at night, they orient themselves by flying at a certain angle to the moonlight, and because moonlight appears as almost parallel light, moths are able to fly in a straight line. When moths fly around a point light source, because they fly at a certain angle to the light, their flight path becomes spiral, and they eventually hit the point light source, which is the "moths attracted to lights" phenomenon.

[0036] Inspired by moths attracted to lights, Mirjalili proposed the Moth Flame Optimization Algorithm (MFO) in 2015. The MFO algorithm consists of a moth population and a flame population. The moth population is a general-purpose population in evolutionary algorithms, while the flame population is a population consisting of the historical optimization of all moth individuals during the evolutionary process, and is the same size as the moth population. Moth individuals are updated using a spiral function centered on the flame individual, and if the newly generated moth individual is superior to the flame individual, the new moth individual replaces the flame individual. The spiral update function is as follows:

[0037]

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[0038] 2) Improved Moth Flame Optimization Algorithm The reasons for the poor convergence of the MFO are: (1) The center of the spiral update formula is the flame individual F ij However, the moth M ij F ij Unless it is updated to a better position, the center of the update equation will not change, which significantly limits the algorithm's ability to overcome premature convergence. [2] Since flame individuals are the historical optimal solutions of the corresponding moth individuals, and the updates of moth individuals are only influenced by the corresponding flame individuals, i.e., there is no interaction between moth individuals, the optimization results will also fall into local optimal solutions.

[0039] To address the problem of poor convergence of the MFO algorithm, this invention proposes an improved moth-flame optimization algorithm (IMFO), which improves the MFO algorithm from three aspects: update formula, linear flight path heuristic, and flame population update strategy.

[0040] (a) Improvement of the update formula The IMFO spiral update formula is generally similar to the MFO algorithm, but also has three improvements:

[0041] [1] The center of the spiral is the flame F ij The average value of the flame and moth individuals was 0.5 (F ij +M ij ) Moth Individual M ij Since changes with each iteration, the center of the spiral also changes with each iteration, which helps to escape local optima.

[0042] [2] The formula for calculating the distance influence parameter c was improved through repeated experiments and trial and error.

[0043] [3] Distance D between the moth and the flame ij The absolute value sign of D has been removed. ij If is constrained to be positive, the search process reduces the search potential by half.

[0044] The improved spiral update formula is as follows:

[0045]

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[0046] (b) Linear Flight Path Heuristic The MFO algorithm only takes inspiration from the moth's spiral flight path, not its linear flight path. In an embodiment of the present invention, the creation of a lunar population Mo is further inspired. In a single-objective optimization algorithm, the lunar population Mo can be a population composed of the current global optimization in the entire search process, and the population size is 1. In a multi-objective optimization algorithm, the lunar population Mo is an external file set maintained by a specific multi-objective mechanism, and the population size is the same as the moth population.

[0047] The linear update formula for the moth is:

[0048] M ij =(M ij +Mo rj ) / 2 In the formula, Mo rj is the jth decision variable of the rth lunar individual, where r means that the individuals participating in the update are randomly selected from the lunar population. The lunar population represents the current optimal solution or optimal solution set in the entire search process, so linear updates accelerate convergence.

[0049] (c) Flame population renewal strategy In the single-objective optimization algorithm, the newly generated moth individuals are optimized by the flame individuals F through the target value. i Direct comparison with the flame individual F i . Let M′ be the newly generated moth individual. i In the multi-objective optimization algorithm, the newly generated moth individual M′ can be determined based only on the value of the objective function. i and Flame Individual F i The newly generated moth individual M′ i Flame Individual F i If ,dominates,F i M′ i Replace with F i is M' i If you control F i is unchanged, and F i and M′ i If they do not dominate each other, Fi To replace the r This improvement not only preserves the newly generated superior individuals, but also strengthens the interaction between individuals, which helps to speed up convergence and avoid falling into a local optimum solution.

[0050] 4. Real-time intelligent operational dispatch results of multi-energy complementation of hydropower, wind power, solar power and storage energy In this embodiment, a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy is relied upon, and a real-time simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy can be constructed for minute-by-minute dispatch time slots. Taking into account different inflow water conditions, typical days during non-flood and flood periods are selected, and sunny and windy weather conditions are combined to analyze the regulating effect of the Guangma hybrid pumped storage power generation and storage plant. Since only the measured output data from the solar power plant currently available contains minute-scale data, the relevant time-scale data is downsampled to obtain minute-scale data, which is then input as data for the real-time simultaneous optimization dispatch model.

[0051] During grid load troughs, the cascade conventional hydropower plant does not discharge water or generate electricity except for the necessary river maintenance flow, while the Guangma hybrid power generation and storage plant implements "bottom-up" operation during grid load troughs by pumping. In a typical scenario, the more abundant the inflow and the longer the pumping mode lasts, the greater the pumped power. Under clear, windy weather conditions, the output of the hydropower-wind-solar-storage multi-energy complementary system during the grid load troughs during the daytime is primarily solar power output. While the cascade conventional hydropower plant primarily implements peak shifting during the nighttime peak, the Guangma hybrid power generation and storage plant also operates in pumping mode during the load peaks of the two grids to further smooth out the fluctuations in the remaining grid load. The results of the real-time simultaneous optimized dispatch of hydropower-wind-solar-storage are shown in Table 2.

[0052] Table 2. Peak shift statistical results of real-time simultaneous optimization dispatch of hydropower, wind power, solar power, and storage energy. [Table 2]

[0053] As can be seen from the statistical peak shift results of the real-time simultaneous optimized dispatch of hydropower, wind power, solar power, and energy storage, the reduction rates of the peak / valley difference in the typical scenarios of non-flood season and flood season were 56.0% and 56.1%, respectively, and the remaining load balance of the power grid was 6797.84 and 5648.45, respectively. In short, under the two typical dispatch scenarios, the multi-energy complementary system of hydropower, wind power, solar power, and energy storage reduced the peak / valley difference of the power grid load to roughly the same extent, but the remaining load balance of the flood season dispatch result was smaller, and the remaining load process was smoother.

[0054] 5. Simultaneous optimization of dispatching and operation of hydropower, wind power, solar power, and storage energy In this embodiment, based on the results of simultaneous optimized dispatch of hydropower, wind power, solar power, and storage, the input timing and duration rhythm of Guangma hybrid pumped storage power generation and storage power generation and pumped storage mode are analyzed and inducted, and a simultaneous optimized dispatch operation strategy for hydropower, wind power, solar power, and storage is provided.

[0055] [1] Peak shifting and bottom-up operation strategies During flood seasons, the Guangma Hybrid Pumped Storage Power Plant participates in peak-shift operation of the daytime peak system, operating bottom-up during the morning trough and the normal interval between the two peaks. During non-flood seasons, the Guangma Hybrid Pumped Storage Power Plant participates in peak-shift operation of the daytime and nighttime peak system, operating bottom-up during the morning trough and the normal interval between the two peaks. However, the pumped storage power and continuous operating time must be appropriately reduced compared to flood seasons. At the same time, the pumping mode of the Guangma Hybrid Pumped Storage Power Plant and the effects of wind and solar energy output characteristics, as well as the fluctuations in the maximum daytime water levels of the Guangzhao and Mamaya Hydropower Plants on typical days during dry, flood, and normal water periods, must be taken into consideration to ensure adequate water storage capacity during dispatch planning, and the peak-shift operating times of the Guangma Hybrid Pumped Storage Power Plant and the Guangzhao and Mamaya Hydropower Plants must be centrally determined to avoid hydropower curtailment at the plants due to water level exceeding the limit or inconsistencies in the peak-shift times of upstream and downstream plants. In addition to meeting basic requirements such as river maintenance flow, simultaneous operation of pumping at the Guangma Hybrid Power Generation and Storage Plant and power generation at the Guangshao Hydroelectric Power Plant should be avoided as much as possible.

[0056] (2) Energy storage operation strategy The Guangma Hybrid Pumped Storage Power Plant has a storage function, which can smooth out unstable wind and solar energy output, reduce the impact of random wind and solar energy output on the power grid, and increase the utilization rate of new energy.

[0057] (1) When the load is at its bottom, if the output of wind and solar energy suddenly increases, the pumped hydroelectric power of the Guangma hybrid power generation and storage plant can be increased to reduce the amount of output suppression; if the output of wind and solar energy suddenly decreases, the pumped hydroelectric power can be reduced and the pumping time can be increased to meet the power demand during peak hours.

[0058] (2) When the load is at peak hours, if the output of wind and solar energy suddenly increases, the output of the Guangma hybrid power generation and storage plant can be reduced and the power generation time can be increased; if the output of wind and solar energy suddenly decreases, the output of the Guangma hybrid power generation and storage plant can be increased.

[0059] (3) When the Guangma Hybrid Power Generation and Storage Plant is shut down and wind and solar energy output suddenly increases, it can operate to pump water to reduce the amount of output curtailment; when wind and solar energy output suddenly decreases, it can operate to generate electricity, level out the output of the power grid, and meet the operational requirements of the power grid.

[0060] From the description of the above embodiments, those skilled in the art can understand that the present invention provides a real-time intelligent operation method for the multi-energy complementation of hydropower, wind power, solar power and energy storage. In the present invention, the dispatch goal is to minimize the remaining load distribution of the power grid, and at the same time, a day-ahead joint optimization dispatch model for hydropower, wind power, solar power and energy storage is established taking into account the energy operation constraints of hydropower, wind power, solar power and energy storage and the power system constraints, which facilitates the joint optimization dispatch of hydropower, wind power, solar power and energy storage, and is advantageous in realizing the optimal operation control scheme for hydropower before and after hydropower, wind power, solar power and energy storage combination. This has an obvious effect of reducing the peak / bottom difference of the remaining load of the power grid, which is helpful in ensuring the effective operation of the power grid, ensuring the security and good consumption capacity of the power grid, and also improving the utilization rate of clean energy.

[0061] In addition, as shown in FIG. 2, the present invention also provides a real-time intelligent operation device for hydropower, wind power, solar power and storage of multi-energy complementary, which is used in the real-time intelligent operation method for hydropower, wind power, solar power and storage of multi-energy complementary in the above-mentioned embodiment, and performs real-time intelligent operation dispatch of hydropower, wind power, solar power and storage of multi-energy complementary, and the device comprises: A data collection module for collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power plant + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric, wind, solar and storage power; A model construction module for constructing a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy based on operation data, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with day as the dispatch period, time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; and an operation dispatch module for solving and setting a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and energy storage, and for implementing real-time intelligent operation dispatch of multi-energy complementation of hydropower, wind power, solar power, and energy storage based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and energy storage.

[0062] The realization principles and technical effects of the device provided by the embodiments of the present invention are the same as those of the aforementioned method embodiments. However, for the sake of simplicity, for the parts not mentioned in the system embodiments, reference can be made to the corresponding contents in the aforementioned method embodiments, and detailed descriptions thereof will be omitted here.

[0063] As shown in FIG. 3, an embodiment of the present invention also provides an electronic device used for real-time intelligent operation of multi-energy complementation of hydropower, wind power, solar power and storage, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10.

[0064] In some embodiments, the processor 10 may be configured as an integrated circuit, for example, a single packaged integrated circuit, or multiple integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, and is connected to the various components of the entire electronic device using various interfaces and circuits. The processor 10 runs or executes programs or modules stored in the memory 11 and accesses data stored in the memory 11 to perform various functions of the electronic device and process data.

[0065] In addition, an embodiment of the present invention also provides a recording medium storing one or more computer-readable programs, the one or more programs including instructions that, when executed by a computer, cause the computer to perform the real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage in the above-mentioned embodiment.

[0066] In embodiments of the present invention, the recording medium may be, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of recording media include a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination thereof.

[0067] Those skilled in the art will appreciate that embodiments of the present invention may be provided as a method, an apparatus, or a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. The present invention may also take the form of a computer program product embodied on one or more computer-usable recording media (including, but not limited to, disk storage devices, CD-ROMs, optical storage devices, etc.) having computer-usable program code thereon.

[0068] It should be noted that the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "one" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.

[0069] Each embodiment in this specification is described step by step, and the main points of each embodiment are the differences from other embodiments, and the same or similar parts between each embodiment can be mutually referenced.

[0070] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0071] (Addendum) (Appendix 1) A real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage, the method being executed by a computer: Step S1 of collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power generation + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric power, wind power, solar power, and storage; Step S2: based on the operation data, construct a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with the day as the dispatch period, the time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; Step S3: solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; and performing real-time intelligent operation dispatch of the hydropower, wind power, solar power, and storage energy complement based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy; A real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage, characterized by comprising:

[0072] (Appendix 2) The real-time intelligent operation method for hydropower, wind power, solar power and storage multi-energy complementarity described in Appendix 1, characterized in that in step S1, the operating data includes the load of the power grid, the remaining load of the power grid, hydropower output, wind power output, solar power output, stored power, pumped storage power, number of power plants, operating water level, power generation flow rate, pumped storage flow rate and line capacity.

[0073] (Appendix 3) In step S2, the constructed day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy includes an objective function and constraints, [1] Objective function The remaining load distribution of the power grid is minimal,

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[0074] (Appendix 4) The real-time intelligent operation method for multi-energy complementarity of hydropower, wind power, solar power and energy storage described in Appendix 3, characterized in that in step S3, an improved moth-flame optimization algorithm is used to solve a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power and energy storage, and hybrid coding is used during the model solving process, and in the case of a conventional hydropower plant, the discharge volume is selected as the decision variable, and in the case of a hybrid power generation and energy storage plant, the power generation output / pumped hydroelectric power is selected as the decision variable.

[0075] (Appendix 5) The real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and energy storage described in Appendix 4 is characterized in that the improved moth flame optimization algorithm is an improvement of the moth flame optimization algorithm in three aspects: update formula, linear flight path heuristic and flame population update strategy.

[0076] (Appendix 6) In step S3, the results of the simultaneous optimization dispatch of hydropower, wind power, solar power, and storage are analyzed, (1) When the load is at the bottom of the load range, if the output of wind and solar energy increases beyond the threshold, the pumped hydroelectric power of the hybrid power generation and storage plant is increased to reduce the amount of output suppression. If the output of wind and solar energy decreases beyond the threshold, the pumped hydroelectric power is decreased and the pumping time is increased to meet the power demand during the peak load range. (2) When the load is in a peak load period, if the output of the wind and solar energy increases beyond a threshold, the output of the hybrid power generation and storage plant is reduced and the power generation time is increased, and if the output of the wind and solar energy decreases beyond a threshold, the output of the hybrid power generation and storage plant is increased; (3) When the hybrid power generation and storage plant is in a stopped state and the output of the wind and solar energy increases beyond a threshold, it operates to pump water to reduce the amount of output curtailment, and when the output of the wind and solar energy decreases beyond a threshold, it operates to generate electricity, leveling the output of the power grid and meeting the operating requirements of the power grid; A real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and energy storage as described in Appendix 4, characterized by obtaining a storage operation strategy for a hybrid power generation and storage station having the above configuration.

[0077] (Appendix 7) A real-time intelligent operation device for the complementary use of hydropower, wind power, solar power and storage energy, which uses a real-time intelligent operation method for the complementary use of hydropower, wind power, solar power and storage energy as set forth in any one of appendices 1 to 6 to perform real-time intelligent operation dispatching for the complementary use of hydropower, wind power, solar power and storage energy; A data collection module for collecting operation data of the basin cascade hydroelectric power plant, the basin cascade hydroelectric power plant + hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric, wind, solar and storage power; A model construction module for constructing a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy based on operation data, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with day as the dispatch period, time as the dispatch time slot, and the minimum remaining load balancing of the power grid as the dispatch target; an operation dispatch module for solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage of electricity, and for implementing real-time intelligent operation dispatch of multi-energy complementation of hydropower, wind power, solar power, and storage of electricity based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage of electricity; An apparatus comprising:

[0078] (Appendix 8) An electronic device comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement a real-time intelligent operation method for multi-energy complementation using hydropower, wind power, solar power, and storage batteries as set forth in any one of Supplementary Notes 1 to 6.

[0079] (Appendix 9) A computer-readable recording medium storing a computer program, the computer program being characterized in that, when executed by a processor, the computer program implements a real-time intelligent operation method for multi-energy complementation using hydropower, wind power, solar power, and storage as described in any one of appendices 1 to 6.

Claims

1. A real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power, and storage, the method being executed by a computer: Step S1: collecting operation data of a basin cascade hydroelectric power plant, a basin cascade hydroelectric power plant and a hybrid power generation and storage plant, and a multi-energy complementary system of hydroelectric power, wind power, solar power, and storage; Step S2: based on the operation data, construct a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy, taking into account the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power system, with the day as the dispatch period, the time as the dispatch time slot, and the minimum remaining load distribution of the power grid as the dispatch target; Step S3: solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage of electricity, and performing real-time intelligent operation dispatch of the hydropower, wind power, solar power, and storage of electricity based on the day-ahead simultaneous optimization dispatch model; Including, In step S1, the operation data includes a load on the power grid, a remaining load on the power grid, a hydroelectric power output, a wind power output, a solar power output, stored power, pumped storage power, the number of power plants, an operating water level, a power generation flow rate, a pumped storage flow rate, and a line capacity; In step S2, the constructed day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage power includes an objective function and constraint conditions, [1] Objective function The remaining load distribution of the power grid is minimal, [Equation 1] [Where F is the distribution of the remaining load of the power grid, Ct is the remaining load of the power grid at time slot t, Dt is the load of the power grid at time slot t, Pm,t is the power output of hydropower plant m at time slot t, Pn,t is the power output or pumped hydroelectric power of hybrid power generation and storage plant n at time slot t, Pi,t is the power output of solar power plant i at time slot t, Pj,t is the power output of wind power plant j at time slot t, M, N, I, and J are the numbers of hydropower plants, hybrid power generation and storage plants, solar power plants, and wind power plants in the hydropower-wind-solar-storage multi-energy complementary system, respectively, T is the total number of time slots in the dispatch period, and T = 24.] [2] Constraints 1) Water level constraints in the upper reservoir Z min up, g ≦Z up, t ≦Z max up, g Z min up, p ≦Z up, t ≦Z max up, p [Wherein, Z up,t is the operating water level of the upper reservoir of the hybrid power generation / storage plant during time period t, Z max up,g and Z min up,g are the upper and lower limits of the operating water level in the upper reservoir power generation mode, respectively, and Z max up,p and Z min up,p are the upper and lower limits of the operating water level in the upper reservoir pumping mode, respectively.] 2) Water level constraints in the lower reservoir Z min down, g ≦Z down, t ≦Z max down, g Z min down, p ≦Z down, t ≦Z max down, p [Wherein, Z down,t is the operating water level of the lower reservoir of the hybrid power generation / storage plant during time period t, Z max down,g and Z min down,g are the upper and lower limits of the operating water level in the lower reservoir power generation mode, respectively, and Z max down,p and Z min down,p are the upper and lower limits of the operating water level in the lower reservoir pumping mode, respectively.] 3) Power generation flow rate constraints Q min g ≦Q g,t ≦Q max g [In the formula, Q g,t is the power generation flow rate of the hybrid power generation / storage plant in time period t, and Q max g and Q min g are the upper and lower limits of the power generation flow rate, respectively.] 4) Pumping flow rate constraints Q min p ≦Q p, t ≦Q max p [In the formula, Q p,t is the pumped water flow rate of the hybrid power generation / storage plant in time period t, and Q max p and Q min p are the upper and lower limits of the pumped water flow rate, respectively.] 5) Power generation output constraints N min g ≦N g, t ≦N max g [In the formula, N g,t is the power generation output of the hybrid power generation / storage plant in time period t, and N max g and N min g are the upper and lower limits of the power generation output, respectively.] 6) Pumped storage power constraints N min p ≦N p, t ≦N max p [In the formula, N p,t is the pumped storage power of the hybrid power generation / storage plant in time period t, and N max p and N min p are the upper and lower limits of the pumped storage power, respectively.] 7) Restrictions on operation modes of hybrid power generation and storage plants I g t +I p t ≦1 [where I g t and I pt are variables between 0 and 1 indicating whether the hybrid power generation and storage plant is in power generation mode or pumping mode during time period t, with 1 being "yes" and 0 being "no."] 8) Restrictions on capacity of external transmission lines [Equation 2] [In the formula, L n represents the capacity of the external transmission line of the hybrid power generation / storage plant n, and I n and J n represent the number of solar power plants and wind power plants that transmit the combined output of the hybrid power generation / storage plant n externally, respectively.] A real-time intelligent operation method for the multi-energy complementation of hydropower, wind power, solar power and storage, characterized by:

2. The real-time intelligent operation method for the multi-energy complementation of hydropower, wind power, solar power and storage of claim 1, characterized in that in step S3, an improved moth-flame optimization algorithm is used to solve a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power and storage, and hybrid coding is used during the model solving process, and in the case of a conventional hydropower station, the discharge volume is selected as the decision variable, and in the case of a hybrid power generation and storage station, the power output / pumped hydroelectric power is selected as the decision variable.

3. The real-time intelligent operation method for multi-energy complementation of hydropower, wind power, solar power and storage power as described in claim 2, characterized in that the improved moth flame optimization algorithm is an improvement of the moth flame optimization algorithm in three aspects: update formula, linear flight path heuristic and flame population update strategy.

4. In step S3, analysis is performed based on the results of simultaneous optimization dispatch of hydropower, wind power, solar power, and storage power. (1) When the load is at the bottom of the load range, if the output of wind or solar energy increases beyond a threshold, the pumped hydroelectric power of the hybrid power generation and storage plant is increased to reduce the amount of output suppression; if the output of wind or solar energy decreases beyond a threshold, the pumped hydroelectric power is decreased and the pumping time is increased to meet the power demand during the peak load range; (2) during peak load periods, if the output of the wind and solar energy increases beyond a threshold, reduce the output of the hybrid power generation and storage plant and increase the power generation time, and if the output of the wind and solar energy decreases beyond a threshold, increase the output of the hybrid power generation and storage plant; (3) When the hybrid power generation and storage plant is in a stopped state and the output of the wind and solar energy increases beyond a threshold, the hybrid power generation and storage plant operates to pump water in order to reduce the amount of output suppression, and when the output of the wind and solar energy decreases beyond a threshold, the hybrid power generation and storage plant operates to generate electricity, thereby leveling the output of the power grid and meeting the operational requirements of the power grid. The real-time intelligent operation method for the multi-energy complementation of hydropower, wind power, solar power and storage as claimed in claim 2, characterized in that the storage operation strategy of the hybrid power generation and storage station having the above configuration is obtained.

5. A real-time intelligent operation device for the complementary multi-energy of hydropower, wind power, solar power and storage energy, which uses the real-time intelligent operation method for the complementary multi-energy of hydropower, wind power, solar power and storage energy as claimed in any one of claims 1 to 4 to implement the real-time intelligent operation dispatch of the complementary multi-energy of hydropower, wind power, solar power and storage energy; a data collection module for collecting operation data of the basin cascade hydroelectric power plant, the combination of the basin cascade hydroelectric power plant and the hybrid power generation and storage plant, and the multi-energy complementary system of hydroelectric power, wind power, solar power and storage; a model construction module for constructing a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage energy based on the operation data, taking day as the dispatch period, time as the dispatch time slot, and the minimum remaining load distribution of the power grid as the dispatch target, while considering the constraints of hydropower, wind power, solar power, and storage energy operation and the constraints of the power grid; an operation dispatch module for solving a day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage of electricity, and for performing real-time intelligent operation dispatch of multi-energy complementation of hydropower, wind power, solar power, and storage of electricity based on the day-ahead simultaneous optimization dispatch model for hydropower, wind power, solar power, and storage of electricity; An apparatus comprising:

6. An electronic device comprising a processor and a memory, wherein the memory stores machine-executable instructions executable by the processor, and the processor executes the machine-executable instructions to implement a real-time intelligent operation method for multi-energy complementation using hydropower, wind power, solar power, and storage power as described in any one of claims 1 to 4.

7. A computer-readable recording medium storing a computer program, the computer program being characterized in that, when executed by a processor, the computer program implements a real-time intelligent operation method for multi-energy complementation using hydropower, wind power, solar power, and storage as described in any one of claims 1 to 4.

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