Optimized dispatching method and system for wind-light-fire storage power generation base with pumped storage

Abstract

The invention discloses an optimal scheduling method and system for a wind, light and fire storage power generation base with pumped storage, and belongs to the technical field of new energy. The method comprises the steps that on the day-ahead time scale, the lowest combined operation cost of a multi-power-source power generation system serves as an optimization target, a first full-system power generation cost target function model is constructed, and according to the first full-system power generation cost target function model, a day-ahead scheduling plan of a wind-solar-thermal storage power generation base containing pumped storage is obtained; and on the intra-day ultra-short-term and real-time time scale, taking the minimum difference between the operation cost of each power supply and peak regulation compensation participated by each energy storage as an optimization target, constructing a second full-system power generation cost target function model, and according to the second full-system power generation cost target function model, adjusting the day-ahead scheduling plan so as to obtain the optimal power generation cost. And obtaining an intra-day-real-time scheduling plan of the wind-light-fire storage power generation base containing pumped storage, and finally issuing the intra-day-real-time scheduling plan to various types of power supplies for execution. According to the method, the active output plan of the multi-power-supply integrated power generation system is calculated on three time scales of day-ahead, day-ahead and real-time, so that coordinated scheduling of each power supply in the multi-power-supply integrated power generation system is realized, and safe and stable operation of a power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy, and particularly relates to an optimal scheduling method, system, device, medium and program for a wind-solar-thermal-storage power generation base including pumped storage. Background Art

[0002] New energy power generation technologies represented by wind power and photovoltaic power are new power generation technologies that replace traditional fossil energy. New energy power generation not only has rich resources and is environmentally friendly, but also helps to reduce dependence on external energy and enhance energy security. However, the large-scale grid connection of new energy power generation has also brought new challenges to the safe and stable operation of the power system. The random change characteristics of natural resources such as wind energy and solar energy determine that the output of new energy power generation has significant intermittency, volatility and uncertainty. The output power of wind power generation fluctuates greatly with the change of wind speed, while photovoltaic power generation highly depends on sunlight intensity and sunlight time. These natural conditions are difficult to accurately predict and control, resulting in the output power of new energy power generation being difficult to stably regulate. This output characteristic brings complexity and uncertainty to aspects such as frequency regulation, voltage control and power flow management of the power grid, increasing the difficulty of power grid dispatching and operation. Especially in a power system with a high proportion of new energy, the traditional power generation peak shaving and reserve capacity based on fossil energy may not be sufficient to cope with the large fluctuations of new energy power generation, which may lead to a decline in power grid stability and even cause local power outages or large-scale power grid accidents.

[0003] Therefore, how to effectively integrate new energy power generation and ensure the safe and stable operation of the power grid has become an urgent technical problem in the current energy field. Summary of the Invention

[0004] Aiming at the problem that the output of new energy power generation in the prior art is easily affected by the random changes of wind and light resources, and has strong intermittency, volatility and uncertainty, which poses a problem to the safe and stable operation of the power grid, the present invention provides an optimal scheduling method for a wind-solar-thermal-storage power generation base including pumped storage, and calculates and gives the active power output plan of the multi-source integrated power generation system on three time scales of day-ahead, intra-day and real-time, so as to realize the coordinated scheduling of each power source in the multi-source integrated power generation system and ensure the safe and stable operation of the power grid.

[0005] To achieve the above object, the present invention provides the following technical solutions.

[0006] In the first aspect, the present invention provides an optimal scheduling method for a wind-solar-thermal-storage power generation base including pumped storage, comprising: On the day-ahead time scale, with the lowest combined operation cost of the multi-source power generation system as the optimization goal, a first overall system power generation cost objective function model is constructed, and according to the first overall system power generation cost objective function model, a day-ahead scheduling plan for the wind-solar-thermal-storage power generation base including pumped storage is obtained; On the intraday ultra-short-term and real-time time scales, with the minimum difference between the operating costs of each power source and the peak shaving compensation of various energy storages as the optimization objective, a second full-system power generation cost objective function model is constructed. According to the second full-system power generation cost objective function model, the intraday-real-time scheduling plan of the wind-solar-thermal-hydrogen energy storage power generation base with pumped storage is obtained; Based on the day-ahead scheduling plan and the intraday-real-time scheduling plan, the particle swarm optimization algorithm is used to calculate the optimized scheduling output values of the day-ahead-intraday-real-time active power output; According to the optimized scheduling output values of the day-ahead-intraday-real-time active power output, the scheduling arrangement of the wind-solar-thermal-hydrogen energy storage power generation base with pumped storage is carried out.

[0007] As a further improvement of the present invention, on the day-ahead time scale, with the lowest combined operating cost of the multi-power generation system as the optimization objective, a first full-system power generation cost objective function model is constructed. According to the first full-system power generation cost objective function model, the day-ahead scheduling plan of the wind-solar-thermal-hydrogen energy storage power generation base with pumped storage is obtained, including: On the day-ahead time scale, with the lowest combined operating cost of the multi-power generation system as the optimization objective, a first full-system power generation cost objective function model is established ; Among them, represents the total operating cost of the power system; represents the operating cost of thermal power units; represents the operating cost of battery energy storage; represents the operating cost of solar thermal power plants; represents the operating cost of compressed air energy storage; represents the operating cost of pumped storage power plants; represents the penalty for wind and light abandonment and the penalty for load shedding; According to the first full-system power generation cost objective function model, the day-ahead scheduling plan of the wind-solar-thermal-hydrogen energy storage power generation base with pumped storage is obtained.

[0008] As a further improvement of the present invention, on the intraday ultra-short-term and real-time time scales, with the minimum difference between the operating costs of each power source and the peak shaving compensation of various energy storages as the optimization objective, a second full-system power generation cost objective function model is constructed. According to the second full-system power generation cost objective function model, the intraday-real-time scheduling plan of the wind-solar-thermal-hydrogen energy storage power generation base with pumped storage is obtained, including: On the intraday ultra-short-term and real-time time scales, by adopting the intraday-real-time rolling scheduling coordination scheduling method, and performing ultra-short-term scheduling for the next 15 minutes to 4 hours and real-time scheduling for the next 15 minutes every 15 minutes, with the minimum difference between the operating costs of each power source and the peak shaving compensation of various energy storages as the optimization objective, a second full-system power generation cost objective function model is constructed ; Among them, represents the total operating cost of the power system; represents the operating cost of thermal power units; represents the operating cost of battery energy storage; represents the operating cost of a solar thermal power station; represents the operating cost of compressed air energy storage; represents the operating cost of a pumped - storage power station; represents the penalty for wind and photovoltaic curtailment and load shedding penalty; represents the peak - shaving compensation for the energy storage system; According to the second overall system power generation cost objective function model, obtain the intraday - real - time dispatch plan of the wind - solar - thermal - storage power generation base with pumped - storage.

[0009] As a further improvement of the present invention, based on the day - ahead dispatch plan and the intraday - real - time dispatch plan, the particle swarm optimization algorithm is used to calculate the optimized dispatch output value of the day - ahead - intraday - real - time active power output, including: Based on the day - ahead dispatch plan and the intraday - real - time dispatch plan, the wind - solar new energy power prediction data, and the load of the multi - power - source power generation system, using the particle swarm optimization algorithm, with a 15 - minute time interval, rolling - calculate and solve the optimized dispatch output value of the day - ahead - intraday - real - time active power output of the multi - power - source power generation system for the next 15 minutes to 4 hours.

[0010] As a further improvement of the present invention, the day - ahead dispatch plan and the intraday - real - time dispatch plan need to satisfy the output constraint conditions of the multi - power - source power generation system; The output constraint conditions of the multi - power - source power generation system include the power balance constraint of the multi - power - source power generation system, the wind - solar new energy power generation output constraint, the thermal power unit output constraint, the battery energy storage device constraint, the solar thermal power station output constraint, the pumped - storage power station output constraint, and the compressed air energy storage system constraint.

[0011] As a further improvement of the present invention, the wind - solar new energy power prediction data includes day - ahead prediction data and ultra - short - term prediction data.

[0012] From a second aspect, the present invention provides an optimized dispatch system for a wind - solar - thermal - storage power generation base with pumped - storage, including: A day - ahead dispatch plan module: used to, on the day - ahead time scale, with the lowest combined operating cost of the multi - power - source power generation system as the optimization goal, construct the first overall system power generation cost objective function model, and according to the first overall system power generation cost objective function model, obtain the day - ahead dispatch plan of the wind - solar - thermal - storage power generation base with pumped - storage; Intraday real-time scheduling module: It is used to construct the second full-system power generation cost objective function model with the minimum difference between the operating costs of each power source and the peak shaving compensation of various energy storages as the optimization objective on the intraday ultra-short-term and real-time time scales. According to the second full-system power generation cost objective function model, obtain the intraday-real-time scheduling plan of the wind-solar-thermal-storage power generation base with pumped-storage energy storage; Calculation of dispatch output module: It is used to calculate the optimized dispatch output value of the day-ahead-intraday-real-time active power output based on the day-ahead scheduling plan and the intraday-real-time scheduling plan, using the particle swarm optimization algorithm; Dispatch arrangement module: It is used to make dispatch arrangements for the wind-solar-thermal-storage power generation base with pumped-storage energy storage according to the optimized dispatch output value of the day-ahead-intraday-real-time active power output.

[0013] From a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the optimization scheduling method for a wind-solar-thermal-storage power generation base with pumped-storage energy storage are implemented.

[0014] From a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the optimization scheduling method for a wind-solar-thermal-storage power generation base with pumped-storage energy storage are implemented.

[0015] From a fifth aspect, the present invention provides a computer program product including computer instructions, and when the computer instructions are executed by a processor, the steps of the optimization scheduling method for a wind-solar-thermal-storage power generation base with pumped-storage energy storage are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: On the day-ahead time scale, with the minimum combined operation cost of a multi-power generation system as the core optimization goal, a first overall system power generation cost objective function model is constructed. This strategy fully considers the long-term prediction ability of new energy power generation and the flexible dispatching potential of conventional thermal power units. Through the solution of an optimization algorithm, a reasonable day-ahead dispatching plan can be formulated in advance. It not only ensures the minimization of power generation cost but also effectively alleviates the power supply-demand mismatch problem caused by the uncertainty of wind and light resources. Through advance planning, the power dispatching department can allocate various power sources more calmly, reduce the additional costs and environmental impacts caused by emergency adjustments, and promote the optimal allocation of resources and the sustainable development of the environment. Secondly, on the intraday ultra-short-term and real-time time scales, a more refined optimization goal is introduced - to minimize the difference between the operation costs of each power source and the peak shaving compensation of various energy storage systems participating in peak shaving, and a second overall system power generation cost objective function model is constructed. The fast response ability and energy storage characteristics of energy storage facilities such as pumped storage power stations are fully utilized as a "buffer" to regulate the fluctuations of wind and light output, effectively suppressing the short-term fluctuations of new energy power generation and improving the frequency stability and voltage quality of the power grid. At the same time, by optimizing the economic compensation mechanism for energy storage to participate in peak shaving, the active participation of energy storage facilities in the power market is encouraged, and the peak shaving pressure on conventional thermal power units is reduced. Based on the day-ahead dispatching plan and the intraday-real-time dispatching plan, this invention uses the particle swarm optimization algorithm, an advanced intelligent optimization technology, to calculate the optimized dispatching output values of active power for day-ahead, intraday, and real-time. The particle swarm optimization algorithm shows unique advantages in solving complex, non-linear, and multi-constrained optimization problems with its good global search ability and fast convergence. In summary, this invention realizes the accurate calculation and dynamic adjustment of the active power output of a multi-power integrated power generation system, ensuring the close matching of the power generation plan and the actual demand at various time scales. This refined dispatching strategy not only significantly improves the operation efficiency and energy utilization rate of the system but also effectively reduces the risk of power grid accidents caused by improper dispatching, ensuring the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure of the present invention in any way. In the drawings: Figure 1 is a schematic flow chart of an optimized dispatching method for a wind-solar-thermal-energy storage power generation base with pumped storage of the present invention; Figure 2 is a multi-time scale dispatching strategy flow chart of an optimized dispatching method for a wind-solar-thermal-energy storage power generation base with pumped storage of the present invention; Figure 3 is a diagram of the output power ratio of each power source system in the day-ahead dispatching in the embodiment of the present invention; Figure 4 is a diagram of the output power ratio of each power source system in the intraday-real-time dispatching in the embodiment of the present invention; Figure 5 It is a schematic structural diagram of an optimized dispatching system for a wind-solar-thermal energy storage power generation base with pumped storage in the present invention; Figure 6 It is a schematic diagram of an electronic device in an embodiment of the present invention. Specific embodiments

[0018] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. The described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Aiming at the problem that new energy power generation in the existing technology is vulnerable to the random changes of wind and light resources, with strong intermittency, volatility and uncertainty in output, which poses a problem to the safe and stable operation of the power grid, the present invention provides an optimized dispatching method for a wind-solar-thermal energy storage power generation base with pumped storage, as Figure 1 shown, the method includes: S100: On the day-ahead time scale, with the lowest combined operation cost of the multi-power generation system as the optimization goal, construct a first full-system power generation cost objective function model, and obtain the day-ahead dispatching plan of the wind-solar-thermal energy storage power generation base with pumped storage according to the first full-system power generation cost objective function model; S200: On the intra-day ultra-short-term and real-time time scales, with the minimum difference between the operation costs of each power source and the peak shaving compensation participated by various energy storages as the optimization goal, construct a second full-system power generation cost objective function model, and obtain the intra-day-real-time dispatching plan of the wind-solar-thermal energy storage power generation base with pumped storage according to the second full-system power generation cost objective function model; S300: Based on the day-ahead dispatching plan and the intra-day-real-time dispatching plan, adopt the particle swarm optimization algorithm to calculate the optimized dispatching output value of the day-ahead-intra-day-real-time active power output; S400: According to the optimized dispatching output value of the day-ahead-intra-day-real-time active power output, make dispatching arrangements for the wind-solar-thermal energy storage power generation base with pumped storage.

[0021] The present invention calculates and gives the active power output plan of the multi - power integrated power generation system on three time scales of day - ahead, within - day and real - time, so as to realize the coordinated dispatching of each power source in the multi - power integrated power generation system and ensure the safe and stable operation of the power grid.

[0022] The following further explains the present invention in conjunction with specific drawings.

[0023] An optimal dispatching method for a wind - solar - thermal - storage power generation base with pumped - storage energy, of the present invention, comprises the following steps: S1: Establish the day - ahead active power output optimal regulation objective function of the multi - power generation system On the day - ahead (24h) time scale, with the lowest combined operation cost of the multi - power generation system as the optimization objective, establish the first whole - system power generation cost objective function including the operation cost F1 of thermal power units, the operation cost F2 of battery energy storage, the operation cost F3 of solar thermal power plants, the operation cost F4 of compressed air energy storage, the operation cost F5 of pumped - storage power plants, the penalty for wind and light abandonment and the penalty for load shedding F6.

[0024] S2: Establish the within - day - real - time active power output optimal regulation objective function of the multi - power generation system As Figure 2 shown, on the within - day ultra - short - term (next 15min - 4h) and real - time (0 - 15min) time scales, adopt a within - day - real - time rolling scheduling coordinated dispatching method, perform an ultra - short - term scheduling (15min / point) for the next 15min - 4h and a real - time scheduling (5min / point) for the next 0min - 15min every 15min. With the minimum difference between the operation cost of each power source and the peak - shaving compensation of various energy storages as the optimization objective, establish the second whole - system power generation cost objective function including the operation cost F1 of thermal power units, the operation cost F2 of battery energy storage, the operation cost F3 of solar thermal power plants, the operation cost F4 of compressed air energy storage, the operation cost F5 of pumped - storage power plants, the penalty for wind and light abandonment and the penalty for load shedding F6, and the peak - shaving compensation F7 of energy storage.

[0025] S3: Establish the output constraint conditions of the multi - power generation system Considering the output characteristics of various power sources and the start - stop and operating condition conversion characteristics of energy storage, establish their respective output constraint conditions, specifically including the power balance constraint of the multi - power generation system, the output constraint of wind - solar new energy power generation, the output constraint of thermal power units, the constraint of battery energy storage equipment, the output constraint of solar thermal power plants, the output constraint of pumped - storage power plants and the constraint of compressed air energy storage system.

[0026] S4: Solve the day - ahead - within - day - real - time active power output optimal dispatching output value of the multi - power generation system Taking the load curve of the multi - power generation system or the dispatching plan instruction curve, and the power prediction curves of wind and solar new energy (including two time scales of day - ahead and ultra - short - term) as inputs, using the particle swarm optimization algorithm, with a time interval of 15 minutes, rolling calculation is carried out to solve the optimized dispatching output values of the multi - power generation system for the next 15 minutes to 4 hours in day - ahead, intra - day and real - time active power output.

[0027] In summary, according to the above calculation steps, the optimized dispatching output values of the multi - power generation system for day - ahead, intra - day and real - time active power output are finally obtained.

[0028] The following is a further detailed description of the present invention: S1: Establish the objective function for optimizing the day - ahead active power output of the multi - power generation system On the day - ahead (24 - hour) time scale, with the lowest combined operation cost of the multi - power generation system as the optimization objective, establish the first overall system power generation cost objective function, as shown in formula (1).

[0029] (1) In the formula, represents the total operation cost of the power system; represents the operation cost of thermal power units; represents the operation cost of battery energy storage; represents the operation cost of solar thermal power plants; represents the operation cost of compressed air energy storage; represents the operation cost of pumped - storage power plants; represents the penalty for wind and light curtailment and load shedding penalty.

[0030] Among them, the operation cost of thermal power units is expressed as shown in formula (2) and formula (3): (2) (3) In the formula, represents the coal consumption cost of thermal power units; represents the start - up and shut - down cost of thermal power units; , , represent the thermal power unit fuel cost coefficient; represents the number of thermal power units; represents the cost of a single start - up or shut - down of a thermal power unit; represents the start - up and shut - down cost coefficient of thermal power units; represents the start - up and shut - down state of thermal power units.

[0031] The expression of the operation cost of battery energy storage is as shown in formula (4): (4) Wherein, represents the charging cost coefficient of battery energy storage; represents the operating cost coefficient of battery discharging.

[0032] The operating cost expression of the solar thermal power station is as shown in formula (5): (5) Wherein, represents the power generation cost coefficient of the solar thermal unit; represents the start-up cost coefficient of the solar thermal unit.

[0033] The operating cost expression of the compressed air energy storage system is as shown in formula (6): (6) Wherein, represents the operating cost coefficient of compressed air energy storage.

[0034] The operating cost expression of the pumped-storage power station is as shown in formula (7): (7) Wherein, represents the cost coefficient of pumping water by the pumped-storage unit; represents the cost coefficient of power generation by the pumped-storage unit.

[0035] The penalty term includes the penalty for wind and light curtailment and the penalty for load shedding. The purpose is to improve the new energy consumption rate on the premise of ensuring power supply reliability, as shown in formula (8): (8) Wherein, represents the penalty for wind and light curtailment; represents the penalty for load shedding.

[0036] According to the first full-system power generation cost objective function model, an optimal power output combination is found, so that the combined operating cost of the multi-power generation system reaches the lowest on the day-ahead time scale, thereby obtaining the day-ahead scheduling plan of the wind-solar-thermal-energy storage power generation base with pumped-storage and the power prediction data of wind and solar new energy.

[0037] S2: Establish the objective function for optimizing the regulation of the in-day - real-time active power output of the multi-power generation system The in-day - real-time rolling scheduling coordination scheduling method proposed in this application is as follows: After the end of the kth in-day scheduling period, real-time scheduling with a resolution of 5 minutes is started within the first 15 minutes of this scheduling period, and then the (k + 1)th in-day scheduling and the real-time scheduling of the first 15 minutes of this period are carried out, and finally all the in-day scheduling of the day is completed.

[0038] The specific steps of the intra-day real-time scheduling strategy process are as follows: Step 1: Perform intra-day scheduling (scheduling interval: 4h, granularity: 15min) based on the intra-day prediction data of wind power, photovoltaic power, and load, and obtain the output plans of each power source and the charge-discharge plans of each energy storage within the scheduling interval.

[0039] Step 2: Based on the intra-day scheduling plan, perform real-time energy storage adjustment according to the deviation between the real-time prediction data and the intra-day prediction data of wind power, photovoltaic power, and load (scheduling interval: 15min, granularity: 5min).

[0040] Step 3: Execute the scheduling program in a rolling manner until the daily scheduling plan is completed.

[0041] During the intra-day scheduling stage, to ensure the economy of system operation, the intra-day scheduling goal is to minimize the operating costs of each generator set and energy storage system. To ensure the reliability and security of system operation, load shedding penalties are also added to the goal to ensure continuous power supply to the load. In addition, to address the challenges brought by new energy grid connection to power system peak regulation, battery energy storage, compressed air energy storage, and pumped storage power stations are used for peak regulation, and corresponding peak regulation compensation is set. The multi-time scale scheduling strategy flow chart is shown in Figure 1 Establish an intra-day real-time active power output optimization control objective function for the multi-power generation system, as shown in formula (9); (9) In the formula, represents the peak regulation compensation of the energy storage system, and the specific calculation method is shown in formula (10).

[0042] (10) In the formula, represents the peak regulation income coefficient of the battery energy storage; represents the peak regulation income coefficient of the compressed air energy storage; represents the peak regulation income coefficient of the pumped storage unit.

[0043] After the intra-day scheduling of a certain period is completed, real-time optimization adjustment within this scheduling period begins. Since intra-day prediction needs to predict the wind and photovoltaic power output for the next 4h, the prediction accuracy is not high, and there is an error between the obtained scheduling result and the actual situation. Therefore, in the real-time control stage, the real-time prediction values of wind power, photovoltaic power, and load are input at a resolution of 5min, and the planned output of each energy storage unit obtained during the intra-day scheduling process is adjusted using the real-time prediction values. The steps of the real-time scheduling stage can be summarized as follows: Step 1: After the kth intra-day scheduling is completed, immediately calculate the real-time power regulation amount of the system through the real-time prediction data of wind power, photovoltaic power, and load (prediction interval: 15min, resolution: 5min).

[0044] Step 2: Calculate the maximum charge and discharge power of each energy storage system according to the real-time operating status of pumped-storage, battery energy storage, and compressed air energy storage.

[0045] Step 3: Divide the system regulation capacity and allocate the real-time power regulation amount of the system among each energy storage system.

[0046] Step 4: Return to Step 1 and execute iteratively until all intraday-real-time scheduling is completed.

[0047] S3: Establish the output constraint conditions of the multi-power generation system Considering the output characteristics of various power sources and the start-stop and operating condition conversion characteristics of energy storage, establish their respective output constraint conditions as follows: a) Real-time power regulation amount of the system The calculation method of the real-time power regulation amount of the system is shown in formula (10). This formula comprehensively measures the error between the intraday prediction value and the real-time prediction value. At the same time, it is also the adjustment amount required to meet the real-time power fluctuation based on the intraday energy storage output plan: (10) In the formula: represents the real-time power regulation amount in the j-th real-time adjustment stage; represents the real-time predicted value of the load in the j-th stage; represents the real-time predicted value of wind power in the j-th stage; represents the real-time predicted value of photovoltaic power in the j-th stage.

[0048] b) Maximum charge and discharge power of the energy storage system The maximum charge and discharge power of the energy storage system are shown in formula (11) and formula (12); this formula represents the power regulation capacity of the energy storage system in the j-th real-time adjustment stage.

[0049] (11) (12) In the formula: represents the maximum charge of the battery energy storage in the j-th real-time adjustment stage; represents the maximum charge of the compressed air energy storage in the j-th real-time adjustment stage; represents the maximum charge of the pumped-storage power station in the j-th real-time adjustment stage; represents the maximum discharge of the battery energy storage in the j-th real-time adjustment stage; represents the maximum discharge of the compressed air energy storage in the j-th real-time adjustment stage; represents the maximum discharge of the pumped-storage power station in the j-th real-time adjustment stage.

[0050] The maximum charge and discharge power of the energy storage system is determined by the state of the system at the current moment. Based on the SOC of the system at the current moment, the battery energy storage can calculate the maximum charge and discharge power as shown in formula (13);

[0051] (13) In the formula: represents the capacity of the battery energy storage system; represents the SOC of the battery energy storage in the j-th real-time adjustment stage.

[0052] The maximum charge and discharge power of the compressed air energy storage system is limited by the capacity of the heat storage device, the air pressure in the gas storage tank, and the maximum 5-minute ramp rate at the current moment, as shown in formula (14).

[0053] (14) In the formula: represents the maximum compression power restricted by the upper limit of the air pressure in the gas storage tank; represents the maximum power generation power restricted by the lower limit of the air pressure in the gas storage tank; represents the maximum 5-minute ramp rate of the compressed air energy storage system. Its value can be calculated by formula (15).

[0054] (15) In the formula, represents the maximum compression power restricted by the upper limit of the heat storage capacity of the heat storage device; represents the maximum power generation power restricted by the lower limit of the heat storage capacity of the heat storage device, and its value can be calculated by formula (16).

[0055] (16) In the formula: is the relationship between the pressure change rate and the power generation power determined by formula (14) and formula (15); is the relationship between the pressure change rate and the compression power determined by formula (14) and formula (15); is the relationship between the heat release power of the heat exchanger and the power generation power determined by formula (14) and formula (16); is the relationship between the heat absorption power of the heat exchanger and the compression power determined by formula (14) and formula (16).

[0056] The maximum charge and discharge power of the pumped-storage power station is restricted by the reservoir capacity and the maximum 5-minute ramp rate at the current moment, as shown in formula (17).

[0057] (17) In the formula: represents the maximum 5-minute ramp rate of the pumped-storage unit; represents the maximum water storage power restricted by the upper limit of the reservoir capacity; represents the maximum power generation power restricted by the lower limit of the reservoir capacity, and its value can be calculated by formula (18).

[0058] (18) In the formula: is the relationship between the reservoir capacity and the power generation power determined by formula (17) and formula (18); is the relationship between the reservoir capacity and the water storage power determined by formula (17) and formula (18).

[0059] c) Real-time power regulation of the energy storage system By comparing the maximum charge and discharge powers of the energy storage system with the real-time power regulation amount of the system, the real-time adjustment stage can be divided into 4 working conditions, namely: the energy storage system has sufficient discharge power, insufficient discharge power, sufficient charge power, and insufficient charge power.

[0060] When the system has sufficient discharge power, the real-time power balance can be satisfied only by relying on the regulation ability of the energy storage. The criterion for this operating condition is as shown in formula (19). At this time, only the load power deficit needs to be reasonably distributed among the three energy storage systems. The power distribution principle is that "the abundant units generate more power, and the critical units generate less or no power" to make the operating states of each energy storage as consistent as possible. The specific distribution method is as shown in formulas (20) to (22). Except for this condition, the energy storage distribution methods of other conditions are also the same.

[0061] (19) (20) (21) (22) In the formula: represents the output of the pumped storage unit in the Sth daily dispatch period; represents the output of the battery energy storage in the Sth daily dispatch period; represents the output of the compressed air energy storage in the Sth daily dispatch period; represents the output of the pumped storage unit in the jth real-time dispatch period; represents the output of the battery energy storage in the jth real-time dispatch period; represents the output of the compressed air energy storage in the jth real-time dispatch period.

[0062] When the system has insufficient discharge power, the criterion for this operating condition is as shown in formula (23).

[0063] (23) When the system has sufficient charging power, the criterion for this operating condition is shown in formula (24).

[0064] (24) When the system has insufficient charging power, the criterion for this operating condition is shown in formula (25). At this time, in order to meet the real-time power balance, the output of wind power and photovoltaic power also needs to be reduced, and the specific methods are shown in formulas (26) to (27).

[0065] (25) (26) (27) In the formula: represents the output of wind power in the jth real-time scheduling period; represents the output of photovoltaic power in the jth real-time scheduling period.

[0066] S4: Solve the optimal scheduling output values of the day-ahead, intra-day, and real-time active power outputs of the multi-source power generation system Taking the scheduling plan curve (day-ahead) and the wind-solar new energy power prediction curve (including two time scales of day-ahead and ultra-short-term) as inputs, using the particle swarm optimization algorithm, with a time interval of 15 minutes, rolling calculation is carried out to solve the optimal scheduling output values of the day-ahead, intra-day, and real-time active power outputs of the multi-source power generation system for the next 15 minutes to 4 hours.

[0067] S5: Arrange the dispatching of the wind-solar-thermal-storage power generation base according to the optimal scheduling output values of the day-ahead, intra-day, and real-time active power outputs of the multi-source power generation system.

[0068] The following further illustrates the present invention through specific embodiments: Taking the wind-solar-thermal-storage power generation base with pumped storage as an example, assume that the installed capacity of wind power is 80MW, the installed capacity of photovoltaic power is 50MW, the installed capacity of thermal power units is 300MW, the installed capacity of battery energy storage is 25MW / 50WMh, the installed capacity of the pumped storage power station is 100MW, the installed capacity of the solar thermal power station is 50MW, and the installed capacity of the compressed air energy storage system is 50MW. The output data of the day-ahead wind power and photovoltaic prediction modules are used as the input data for tracking the day-ahead wind power and photovoltaic. The main parameters of other units are shown in Tables 1 - 5.

[0069] Table 1 Parameters of the pumped storage power station

[0070] Table 2 Parameters of thermal power units

[0071] Table 3 Parameters of electrochemical energy storage

[0072] Table 4 Parameters of the solar thermal power station

[0073] Table 5 Parameters of compressed air energy storage

[0074] By using this method, the optimal scheduling output values of the active power output of the wind-solar-thermal-storage power generation base with pumped-storage energy storage are solved on the day-ahead and intraday-real-time time scales. On the day-ahead time scale, the scheduling results of the active power output values of each power supply system are as Figure 3 shown. One calculated value is solved every 1 hour. The thermal power units carry the base load with a basically constant output. Each type of energy storage fully exerts its power regulation ability at the peak load time from 11:00 to 22:00, so that the entire multi-power generation system meets the supply-demand balance during the day-ahead scheduling stage.

[0075] On the intraday-real-time time scale, the scheduling results of the active power output values of each power supply system are as Figure 4 . One calculated value is solved every 15 minutes. On the intraday-real-time time scale, due to the certain deviation between the day-ahead power prediction of wind and light and the ultra-short-term power prediction, after comprehensively considering the operation constraint conditions of each energy storage, the deviation active power amount is allocated to obtain the corrected optimal scheduling output value of the active power output.

[0076] It can be seen from the implementation cases that this application proposes an optimization control method for a multi-power integrated power generation system for a wind-solar-thermal-storage power generation base with pumped-storage energy storage. This method takes the lowest overall system operation cost as the optimization goal, and fully considers the output characteristics of various power sources and the start-stop and condition conversion characteristics of energy storage. The power sources include wind power, photovoltaic power, solar thermal power and thermal power units, and the energy storage includes pumped-storage energy storage, battery energy storage and compressed air. The active power output planned curves of the multi-power integrated power generation system are calculated and given on the day-ahead, intraday and real-time time scales, so as to realize the coordinated scheduling of each power source in the multi-power integrated power generation system. This application has strong applicability and has guiding significance and practical value for large-scale wind-solar-thermal-storage power generation projects.

[0077] The second object of the present invention is to propose an optimization scheduling system for a wind-solar-thermal-storage power generation base with pumped-storage energy storage, as Figure 5 shown, including: Day-ahead scheduling plan module 101: For the day-ahead time scale, with the lowest combined operation cost of the multi-power generation system as the optimization goal, a first overall system power generation cost objective function model is constructed, and according to the first overall system power generation cost objective function model, the day-ahead scheduling plan of the wind-solar-thermal-storage power generation base with pumped-storage energy storage is obtained; Intraday Real-time Scheduling Module 201: It is used to construct the second whole-system power generation cost objective function model with the minimum difference between the operating costs of each power source and the peak shaving compensation of various energy storages as the optimization objective on the intraday ultra-short-term and real-time time scales, and obtain the intraday-real-time scheduling plan of the wind-solar-thermal-storage power generation base with pumped-storage according to the second whole-system power generation cost objective function model; Calculation of Scheduling Output Module 301: It is used to calculate the optimized scheduling output value of active power for day-ahead - intraday - real-time based on the day-ahead scheduling plan and the intraday-real-time scheduling plan by using the particle swarm optimization algorithm; Scheduling Arrangement Module 401: It is used to make scheduling arrangements for the wind-solar-thermal-storage power generation base with pumped-storage according to the optimized scheduling output value of active power for day-ahead - intraday - real-time.

[0078] As Figure 6 shown, the third object of the present invention is to provide an electronic device, which includes: a processor 501, a memory 502 and a display screen 503. Among them, the memory 502 and the display screen 503 are both connected to the processor 501, such as connected through a bus 504. Optionally, the electronic device may further include a transceiver 505. It should be noted that in practical applications, the transceiver 505 is not limited to one, and the structure of this electronic device does not constitute a limitation to the embodiments of the present application.

[0079] The processor 501 may be a CPU (Central Processing Unit, central processing unit), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure content of the present application. The processor 501 may also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0080] The bus 504 may include a path for transmitting information between the above components. The bus 504 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard architecture) bus, etc. The bus 504 may be divided into an address bus, a data bus, a control bus, etc.

[0081] The memory 502 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0082] The memory 502 is used to store the application program code for implementing the solution of this application and is controlled by the processor 501 for execution. The processor 501 is used to execute the application program code stored in the memory 502 to implement the content shown in the foregoing method embodiments.

[0083] Figure 6 The illustrated electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0084] The fourth object of the present invention is to provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements each process of the method embodiment as described above. Figure 1 For example, a memory including instructions, and the above instructions can be executed by the processor of the electronic device to complete the above method.

[0085] A computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, 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 disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical coding device, and any combination of the above.

[0086] The fifth object of the present invention is to provide a computer program product comprising computer instructions which, when executed by a processor, implement each of the processes of the method embodiments described above and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. Figure 1 The processes of the method embodiments shown above, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0087] Upon reading the above description, many embodiments and many applications other than the provided examples will be apparent to those skilled in the art. Therefore, the scope of this teaching should not be determined with reference to the above description, but rather should be determined with reference to the full scope of the foregoing claims and the equivalents thereof. For the sake of completeness, all articles and references, including applications and published disclosures, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended to waive that subject matter, nor should it be considered that the applicant has not considered that subject matter to be part of the disclosed inventive subject matter.

[0088] The above is a further detailed description of the present invention. It cannot be determined that the specific embodiments of the present invention are limited thereto. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as falling within the protection scope determined by the claims submitted for the present invention.

Claims

1. A method for optimizing the dispatching of wind, solar, thermal and power generation bases with pumped storage, characterized in that: include: On the day-ahead time scale, taking the lowest joint operation cost of the multi-power generation system as the optimization goal, the first system-wide power generation cost objective function model is constructed. Based on the first system-wide power generation cost objective function model, the day-ahead dispatch plan of the wind, solar, thermal and energy storage power generation base with pumped storage is obtained. On the intraday ultra-short-term and real-time time scales, the optimization goal is to minimize the difference between the operating cost of each power source and the compensation for various types of energy storage participating in peak regulation, and to construct a second system-wide power generation cost objective function model. Based on the second system-wide power generation cost objective function model, the intraday-real-time dispatch plan of the wind, solar, thermal and storage power generation base with pumped storage is obtained. Based on the day-ahead dispatch plan and the intraday-real-time dispatch plan, the particle swarm optimization algorithm is used to calculate the day-ahead-intraday-real-time active output optimization dispatch output value; According to the day-ahead, intra-day and real-time active output optimization dispatching output value, dispatching arrangements are made for wind, solar and thermal power generation bases including pumped storage.

2. The optimization scheduling method for a wind-solar-thermal power generation base with pumped storage according to claim 1 is characterized in that: On the day-ahead time scale, taking the lowest joint operation cost of the multi-power generation system as the optimization goal, a first system-wide power generation cost objective function model is constructed, and according to the first system-wide power generation cost objective function model, a day-ahead dispatch plan of the wind, solar, thermal and storage power generation base with pumped storage is obtained, including: On the day-ahead time scale, the first system-wide power generation cost objective function model is established with the lowest joint operation cost of the multi-power generation system as the optimization goal. ; in, represents the total operating cost of the power system; represents the operating cost of thermal power units; represents the battery energy storage operating cost; represents the operating cost of a CSP plant; represents the operating cost of compressed air energy storage; represents the operating cost of a pumped storage power station; Indicates the penalty for wind and solar power abandonment and load shedding penalty; According to the first system-wide power generation cost objective function model, the day-ahead dispatch plan of the wind, solar, thermal and energy storage power generation base with pumped storage is obtained.

3. The optimization scheduling method for a wind-solar-thermal power generation base with pumped storage according to claim 1 is characterized in that: In the intraday ultra-short-term and real-time time scales, the optimization goal is to minimize the difference between the operating cost of each power source and the compensation for peak load regulation of various energy storages, and to construct a second system-wide power generation cost objective function model. According to the second system-wide power generation cost objective function model, the intraday-real-time dispatch plan of the wind, solar, thermal and storage power generation base with pumped storage is obtained, including: On the intraday ultra-short-term and real-time time scales, by adopting the intraday-real-time rolling scheduling coordination scheduling method, and performing ultra-short-term scheduling for the next 15 minutes and 4 hours and real-time scheduling for the next 15 minutes every 15 minutes, the second full-system power generation cost objective function model is constructed with the minimum difference between the operating cost of each power source and the peak-shaving compensation of various energy storage participating in the optimization goal. ; in, represents the total operating cost of the power system; represents the operating cost of thermal power units; represents the battery energy storage operating cost; represents the operating cost of a CSP plant; represents the operating cost of compressed air energy storage; represents the operating cost of a pumped storage power station; Indicates the penalty for wind and solar power abandonment and load shedding penalty; Indicates peak load compensation of energy storage system; According to the second system-wide power generation cost objective function model, the intraday-real-time dispatch plan of the wind-solar-thermal power generation base with pumped storage is obtained.

4. The optimization scheduling method for a wind-solar-thermal-storage power generation base with pumped storage according to claim 1 is characterized in that: The method of using a particle swarm optimization algorithm based on the day-ahead scheduling plan and the intraday-real-time scheduling plan to calculate the day-ahead-intraday-real-time active output optimization scheduling output value includes: Based on the day-ahead dispatch plan and intraday-real-time dispatch plan, wind and solar power forecast data and multi-power generation system load, the particle swarm optimization algorithm is adopted to calculate and solve the day-ahead-intraday-real-time active output optimization dispatch value of the multi-power generation system in the next 15 minutes to 4 hours with a rolling calculation at 15 minutes interval.

5. The method for optimizing the dispatching of a wind-solar-thermal-storage power generation base with pumped storage according to claim 4 is characterized in that: The day-ahead scheduling plan and the intraday-real-time scheduling plan need to meet the output constraints of the multi-power generation system; The output constraints of the multi-power generation system include power balance constraints of the multi-power generation system, wind and solar energy power generation output constraints, thermal power unit output constraints, battery energy storage equipment constraints, solar thermal power station output constraints, pumped storage power station output constraints and compressed air energy storage system constraints.

6. The method for optimizing the dispatching of a wind-solar-thermal-storage power generation base with pumped storage according to claim 4, characterized in that: The wind and solar energy new energy power forecast data includes day-ahead forecast data and ultra-short-term forecast data.

7. An optimization dispatching system for wind, solar, thermal and power generation bases with pumped storage, characterized in that: include: Day-ahead dispatch planning module: It is used to construct the first system-wide power generation cost objective function model on the day-ahead time scale with the lowest joint operation cost of the multi-power generation system as the optimization goal. According to the first system-wide power generation cost objective function model, the day-ahead dispatch plan of the wind, solar, thermal and storage power generation base with pumped storage is obtained; Intraday real-time dispatch module: It is used to construct the second system-wide power generation cost objective function model on the intraday ultra-short-term and real-time time scales, taking the minimum difference between the operating cost of each power source and the compensation for peak load regulation of various energy storage as the optimization goal, and obtain the intraday-real-time dispatch plan of the wind, solar, thermal and storage power generation bases including pumped storage according to the second system-wide power generation cost objective function model; Calculation and dispatch output module: It is used to calculate the day-ahead, day-intraday, and real-time active output optimization dispatch output value based on the day-ahead dispatch plan and the intraday-real-time dispatch plan, using the particle swarm optimization algorithm; Dispatching module: It is used to optimize the dispatching output value according to the day-ahead, intra-day and real-time active output, and to dispatch wind, solar and thermal power generation bases including pumped storage.

8. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for optimizing the scheduling of a wind, solar, thermal and power generation base with pumped storage as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for optimizing scheduling of a wind, solar, and thermal power generation base with pumped storage as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which, when executed by a processor, implement the steps of a method for optimizing the scheduling of a wind, solar, and thermal power generation base with pumped storage as described in any one of claims 1 to 6.

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