Wind-solar-thermal storage complementary medium and long term optimization scheduling method and electronic equipment

Abstract

The invention provides a medium-and-long-term optimal scheduling method for wind-solar-thermal-storage complementation and electronic equipment, and belongs to the technical field of multi-energy system optimal scheduling, and the method comprises the steps: obtaining historical data and real-time data of a wind-solar-thermal-storage complementation system, constructing a total optimization objective function, and setting constraint conditions; based on the historical data and the real-time data, a medium and long term scheduling model is established according to the total optimization objective function and the constraint conditions; solving a mixed integer linear programming problem in the medium and long term scheduling model by adopting a branch and bound method to obtain a globally optimal solution; wherein the global optimal solution is an initial scheduling strategy; performing a simulation experiment based on a simulation scene of a medium-and-long-term scheduling time scale of the wind-solar-thermal-storage complementary system, and adjusting and optimizing an initial scheduling strategy according to a simulation result to obtain a satisfactory solution; wherein the satisfactory solution is a target scheduling strategy. According to the scheme, the effect of wind-solar-thermal-storage complementary optimization scheduling can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of multi-energy system optimization and scheduling, and specifically to a medium- and long-term optimization and scheduling method and electronic equipment for wind, solar, thermal and energy storage complementarity. Background Art

[0002] With the large-scale development of new energy in my country, the demand for medium- and long-term dispatch has become increasingly prominent, and the challenges faced have also increased. The most prominent are wind power generation and photovoltaic power generation. Due to the randomness and volatility of wind power and photovoltaic output, the obstruction of wind power and photovoltaic power generation has become more serious, further exacerbating the phenomenon of wind and photovoltaic abandonment. At the same time, new energy is seriously affected by weather conditions, and long-term adverse weather conditions may lead to unstable power supply.

[0003] To address these challenges, it is necessary to fully analyze the output characteristics of each renewable energy source under the condition of multiple energy sources participating, while considering the impact of the intermittent nature of renewable energy on thermal power units, so as to achieve multi-time scale wind, solar, thermal and storage complementary optimization scheduling.

[0004] At present, in the existing technology, most optimization scheduling research based on reinforcement learning only considers a single scheduling time scale, ignoring the differences in the time scales of different power generation units during actual operation, which seriously affects the optimization effect. Therefore, how to improve the effect of wind, solar, thermal and storage complementary optimization scheduling has become a problem to be solved. Summary of the invention

[0005] The purpose of this application is to provide a medium- and long-term optimal scheduling method for wind, solar, thermal and storage complementarity, which can solve the problem of poor effect of wind, solar, thermal and storage complementary optimal scheduling in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity, the method comprising: Obtain historical and real-time data of the wind-solar-thermal-storage complementary system, construct the overall optimization objective function, and set constraints; Based on historical data and real-time data, establish a medium- and long-term scheduling model according to the overall optimization objective function and constraints; The branch-and-bound method is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model and obtain the global optimal solution; the global optimal solution is the initial scheduling strategy; A simulation experiment is conducted based on the simulation scenario of the medium- and long-term scheduling time scale of the wind-solar-thermal-storage complementary system. According to the simulation results, the initial scheduling strategy is adjusted and optimized to obtain a satisfactory solution; among them, the satisfactory solution is the target scheduling strategy.

[0007] In a possible implementation of the first aspect, the historical data includes: historical meteorological data, historical load demand, and historical power generation output; the real-time data includes: real-time meteorological data, real-time load demand, and real-time power generation output.

[0008] In a possible implementation of the first aspect, the expression of the overall optimization objective function is as follows:

[0009] in, F ( Y ) represents the overall optimization objective function, min Indicates taking the minimum value, TC represents the operating cost of thermal power units, p Indicates the expected loss of wind and solar power.

[0010] In a possible implementation manner of the first aspect, the method further includes: According to the optimal unit start-up and shutdown plan and unit power generation plan within the preset optimization period, a unit combination model for renewable energy consumption is constructed, and its objective function expression is as follows:

[0011] in, Indicates the unit j On the operating day d In the running state, Indicates the unit j On the operating day d The boot status, Indicates the unit j On the operating day d The shutdown state, j Indicates the ordinal number of the unit, d Indicates the ordinal number of the running day, represents the weight coefficient of the optimization objective item, Indicates the number of thermal power units, Indicates the number of days for the optimization cycle. Indicates the unit j On the operating day d The startup cost, Indicates the unit j On the operating day d The downtime cost, Indicates the unit j On the operating day d fixed costs.

[0012] In a possible implementation of the first aspect, the expression of expected wind and solar power loss is as follows:

[0013] in, Indicates the penalty costs for abandoning wind and solar power. represents the weight coefficient of the optimization objective item, NN Indicates the number of wind farms, NNP Indicates the number of photovoltaic power stations, Represents wind farm n No. d The loss of electricity per day, Represents photovoltaic power station np No. d The loss of electricity per day, n represents the ordinal number of the wind farm, np Indicates the ordinal number of the PV power station.

[0014] In a possible implementation of the first aspect, the constraint conditions include: operating reserve constraint, power balance constraint, thermal power generation capacity constraint, wind power constraint and photovoltaic power constraint.

[0015] In a possible implementation of the first aspect, a relational expression for running the standby constraint is as follows:

[0016] in, NLG Indicates the number of thermal power units, Represents thermal power unit lg On the operating day h In the running state, lg Indicates the ordinal number of the thermal power unit, h Indicates the running day number, Represents thermal power unit lg The maximum technical output of NB represents the number of load nodes, d Indicates the ordinal number of the load node, Represents load node d The maximum load requirement, Indicates the operating day h Operational standby load requirements.

[0017] In a possible implementation of the first aspect, a relational expression of the power balance constraint is as follows:

[0018] in, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Photovoltaic power station np of power generation, Represents load node b Operation day d power demand.

[0019] In a possible implementation manner of the first aspect, the relational expression for the power generation capacity constraint of the thermal power unit is as follows:

[0020] in, Indicates thermal power start and stop unit lg Operation day d The minimum power generation capacity, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates thermal power start and stop unit lg Operation day d Maximum power generation.

[0021] In a possible implementation of the first aspect, the relational expression for wind power quantity constraint is as follows:

[0022]

[0023] in, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Wind Farm n The power loss, Indicates the operating day d Wind Farm n The predicted power generation.

[0024] In a possible implementation of the first aspect, the photovoltaic power constraint is expressed as follows:

[0025]

[0026] in, Indicates the operating day d Photovoltaic power station np of power generation, Indicates the operating day d Photovoltaic power station np The power loss, Indicates the operating day d Photovoltaic power station np The predicted power generation.

[0027] In a possible implementation of the first aspect, a branch and bound method is used to solve a mixed integer linear programming problem in a medium- and long-term scheduling model to obtain a global optimal solution, including: Solve the mixed integer linear programming problem to obtain an initial feasible solution, and divide the mixed integer linear programming problem into multiple sub-problems; each sub-problem is a branch; At each branch node, node selection is performed based on the objective function value and constraint conditions, and nodes whose objective function value of the solution to the relaxed problem is greater than the current upper bound or whose relaxed problem does not meet the preset conditions are pruned; For each subproblem remaining after pruning, if the optimal solution is a feasible solution and the objective function value is better than the current optimal feasible solution, then the current upper bound is updated to the objective function value of the optimal solution. At the same time, if the objective function value of the relaxed solution is greater than the current lower bound, then the current lower bound is updated to the objective function value of the relaxed solution, and the iteration is performed step by step. When the absolute difference between the upper and lower bounds is less than or equal to the preset threshold, or when the relative difference between the upper and lower bounds is less than the preset threshold, the iteration ends and the current optimal feasible solution is taken as the global optimal solution.

[0028] In a possible implementation of the first aspect, a calculation formula for the relative difference between the upper bound and the lower bound is as follows:

[0029] in, Gap Indicates the relative difference between the upper bound and the lower bound.

[0030] In a possible implementation of the first aspect, the simulation scenario includes: Typical weekly scenes in spring, typical weekly scenes in summer, typical weekly scenes in autumn, typical weekly scenes in winter, typical monthly scenes in spring, typical monthly scenes in summer, typical monthly scenes in autumn, typical monthly scenes in winter, typical seasonal scenes in spring, typical seasonal scenes in summer, typical seasonal scenes in autumn, typical seasonal scenes in winter and annual scenes.

[0031] In a possible implementation manner of the first aspect, simulation parameters of the simulation experiment include: penalty fees for wind and solar power abandonment, unit startup costs, unit shutdown costs, and unit fixed costs.

[0032] In a second aspect, the embodiment of the present application provides a medium- and long-term optimization scheduling device for wind, solar, thermal and storage complementarity, the device comprising: The acquisition unit is used to obtain historical data and real-time data of the wind-solar-thermal-storage complementary system, build the overall optimization objective function, and set constraints; Establishing a unit for establishing a medium- and long-term scheduling model based on historical data and real-time data according to the overall optimization objective function and constraints; A solving unit is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model by using a branch and bound method to obtain a global optimal solution; wherein the global optimal solution is an initial scheduling strategy; The optimization unit is used to conduct simulation experiments based on simulation scenarios of the medium- and long-term scheduling time scale of the wind-solar-thermal-storage complementary system, and adjust and optimize the initial scheduling strategy according to the simulation results to obtain a satisfactory solution; among which, the satisfactory solution is the target scheduling strategy.

[0033] In a third aspect, an embodiment of the present application 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 medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity according to any one of the above-mentioned first aspects is implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity according to any one of the above-mentioned first aspects.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity according to any one of the first aspects above.

[0036] This application scheme first obtains historical data and real-time data of the wind, solar, thermal and storage complementary system, constructs an overall optimization objective function, and sets constraints. Secondly, based on the historical data and real-time data, a medium- and long-term scheduling model is established according to the overall optimization objective function and constraints. Then, the branch and bound method is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model to obtain the initial scheduling strategy. Finally, the initial scheduling strategy is adjusted and optimized according to the simulation experimental results of the simulation scenario of the medium- and long-term scheduling time scale.

[0037] This application scheme takes into account the differences in different scheduling time scales during the actual operation of the wind-solar-thermal-storage complementary system, and optimizes the scheduling strategy by conducting simulation experiments on simulation scenarios of medium- and long-term scheduling time scales, thereby improving the effect of wind-solar-thermal-storage complementary optimized scheduling, and has strong ease of use and practicality.

[0038] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 It is a schematic diagram of the steps of the medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity provided in an embodiment of the present application; Figure 2 It is a curve diagram of the predicted electricity volume of new energy in the spring scenario of the quarterly plan provided in the embodiment of the present application; Figure 3 It is a curve diagram of the renewable energy power generation in the spring scenario of the quarterly plan provided in the embodiment of the present application; Figure 4 is a curve diagram of the total power generation plan for the spring scenario of the quarterly plan provided in an embodiment of the present application; Figure 5 It is a curve diagram of a power generation plan of a thermal power unit in a spring scenario of a quarterly plan provided in an embodiment of the present application; Figure 6 This is a schematic diagram of the startup state of thermal power unit 1# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Figure 7 This is a schematic diagram of the shutdown state of thermal power unit 1# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Figure 8 This is a schematic diagram of the start-up state of thermal power unit 2# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig. 9 This is a schematic diagram of the shutdown state of thermal power unit 2# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig.10 This is a schematic diagram of the start-up state of thermal power unit 3# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig.11 This is a schematic diagram of the shutdown state of thermal power unit 3# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig.12 This is a schematic diagram of the startup state of thermal power unit 4# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig.13 This is a schematic diagram of the shutdown state of thermal power unit 4# in the spring scenario of the quarterly plan provided in an embodiment of the present application; Fig.14 It is a schematic diagram of the operating status of a thermal power unit in a spring scenario of a quarterly plan provided in an embodiment of the present application; Fig.15It is a schematic diagram of a thermal power unit operation plan for a spring scenario of a quarterly plan provided in an embodiment of the present application; Fig.16 It is a curve diagram of the predicted electricity volume of new energy in the annual planning scenario provided in the embodiment of the present application; Fig.17 It is a curve diagram of the renewable energy power generation in the annual planning scenario provided in the embodiment of the present application; Fig.18 It is a curve diagram of the total power generation plan of the annual plan scenario provided in the embodiment of the present application; Fig.19 It is a curve diagram of the power generation plan of the thermal power unit in the annual plan scenario provided in the embodiment of the present application; Fig. 20 This is a schematic diagram of the startup state of thermal power unit 1# in the annual planning scenario provided in an embodiment of the present application; Fig.21 This is a schematic diagram of the shutdown state of thermal power unit 1# in the annual planning scenario provided in an embodiment of the present application; Fig. 22 This is a schematic diagram of the startup state of thermal power unit 2# in the annual planning scenario provided in an embodiment of the present application; Fig.23 This is a schematic diagram of the shutdown state of thermal power unit 2# in the annual planning scenario provided in an embodiment of the present application; Fig.24 This is a schematic diagram of the startup state of thermal power unit 3# in the annual planning scenario provided in an embodiment of the present application; Fig.25 This is a schematic diagram of the shutdown state of thermal power unit 3# in the annual planning scenario provided in an embodiment of the present application; Fig.26 This is a schematic diagram of the startup state of thermal power unit 4# in the annual planning scenario provided in an embodiment of the present application; Fig. 27 This is a schematic diagram of the shutdown state of thermal power unit 4# in the annual planning scenario provided in an embodiment of the present application; Fig.28 It is a schematic diagram of the operating status of a thermal power unit in the annual planning scenario provided by an embodiment of the present application; Fig.29 It is a schematic diagram of the operation plan of a thermal power unit in the annual plan scenario provided in an embodiment of the present application; Fig.30 It is a structural schematic diagram of a medium- and long-term optimization scheduling device for wind, solar, thermal and storage complementarity provided in an embodiment of the present application; Fig.31 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or photovoltaic components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, photovoltaic components and / or combinations thereof.

[0043] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0046] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0047] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0048] The structural reform of the energy supply side has become the top priority of my country's energy transformation and development. It will optimize the energy structure by rationally controlling the total energy consumption, increasing the proportion of clean energy, and the coordinated development and utilization of multiple energies.

[0049] With the large-scale development of new energy in my country, the demand for medium- and long-term dispatch has become increasingly prominent, and the challenges faced have also increased. The most prominent are wind power generation and photovoltaic power generation. Due to the randomness and volatility of wind power and photovoltaic output, the obstruction of wind power and photovoltaic power generation has become more serious, further exacerbating the phenomenon of wind and photovoltaic abandonment. At the same time, new energy is seriously affected by weather conditions, and long-term adverse weather conditions may lead to unstable power supply.

[0050] To address these challenges, it is necessary to fully analyze the output characteristics of each renewable energy source under the condition of multiple energy sources participating, while considering the impact of the intermittent nature of renewable energy on thermal power units, so as to achieve optimal scheduling of wind, solar, thermal and power storage on multiple time scales.

[0051] The optimization and dispatching technologies of multi-energy power systems are developing rapidly to adapt to the high proportion of new energy access and improve the operating efficiency of the system. These technologies include the optimization planning of energy storage systems, the scenario generation method based on artificial intelligence, and the dispatching model considering the complementary characteristics of multiple energy sources. For example, VAE (Variational Auto Encoder) is used to generate renewable energy output scenarios, and the monthly power generation plan optimization method based on the power aggregation-decomposition model.

[0052] In addition, there are also studies focusing on improving the friendly performance of new energy systems, such as improving the inertial response and frequency regulation capabilities of new energy power stations through grid-type characteristic upgrades. The development of these technologies is crucial to building a clean, efficient, and intelligent new power system, and helps achieve a green and low-carbon transformation of energy.

[0053] my country is now facing the challenge of multi-time scale optimization scheduling, covering long-term planning such as monthly, quarterly and annual. At present, in the existing technology, most optimization scheduling research based on reinforcement learning only considers a single scheduling time scale, ignoring the differences in the time scales of different power generation units during actual operation, which seriously affects the optimization effect. Therefore, how to improve the effect of wind, solar, thermal and storage complementary optimization scheduling has become a problem to be solved.

[0054] In response to the above-mentioned defects, an embodiment of the present application provides a medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity. First, the historical data and real-time data of the wind, solar, thermal and storage complementary system are obtained, the overall optimization objective function is constructed, and constraints are set. Secondly, based on the historical data and real-time data, a medium- and long-term scheduling model is established according to the overall optimization objective function and constraints. Then, the branch and bound method is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model to obtain the initial scheduling strategy. Finally, the initial scheduling strategy is adjusted and optimized according to the simulation experiment results of the simulation scenario of the medium- and long-term scheduling time scale.

[0055] This application scheme takes into account the differences in different scheduling time scales during the actual operation of the wind-solar-thermal-storage complementary system, and optimizes the scheduling strategy by conducting simulation experiments on simulation scenarios of medium- and long-term scheduling time scales, thereby improving the effect of wind-solar-thermal-storage complementary optimized scheduling, and has strong ease of use and practicality.

[0056] The specific process implemented by this application is introduced below through specific embodiments.

[0057] See also Figure 1 , Figure 1 Schematic diagram of the steps of the medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity provided in the embodiment of the present application. Figure 1 As shown, the method may include the following steps: S101, obtain historical data and real-time data of the wind, solar, thermal and energy storage complementary system, construct the overall optimization objective function, and set constraints.

[0058] In one embodiment, the wind, photovoltaic, thermal and energy storage complementary system includes: a wind power generation system, a photovoltaic power generation system, a thermal power system and an energy storage system.

[0059] According to an implementation of the present application, historical data includes: historical meteorological data, historical load demand and historical power generation output; real-time data includes: real-time meteorological data, real-time load demand and real-time power generation output. Among them, meteorological data includes: wind speed, irradiance, temperature, humidity and other data.

[0060] In one embodiment, medium- and long-term scheduling goals are defined to determine the optimal start-up and shutdown plans for thermal power units to minimize operating costs, maximize the absorption of wind power and photovoltaic power, and reduce wind and solar power abandonment.

[0061] According to one embodiment of the present application, the expression of the overall optimization objective function is as follows:

[0062] in, F ( Y ) represents the overall optimization objective function, min Indicates taking the minimum value, TC represents the operating cost of thermal power units, p Indicates the expected loss of wind and solar power.

[0063] In some embodiments, when the expected wind and solar power loss is the smallest, the wind power and photovoltaic power consumption are the largest. From the perspective of total volume, the sum of wind and solar power consumption and wind and solar power loss is equal to the theoretical power generation of wind and solar power installed capacity. When the grid's consumption capacity is improved and it can accommodate more wind and solar power generation, the wind and solar power consumption will increase, and the corresponding wind and solar power loss will decrease; conversely, if the grid's consumption capacity is insufficient, the wind and solar power consumption will decrease, and the wind and solar power loss will increase.

[0064] According to one embodiment of the present application, the method further includes: According to the optimal unit start-up and shutdown plan and unit power generation plan within the preset optimization period, a unit combination model for high-proportion renewable energy consumption is constructed. The expression of its objective function is as follows:

[0065] in, Indicates the unit j On the operating day d In the running state, Indicates the unit j On the operating day d The boot status, Indicates the unit j On the operating day d The shutdown state, j Indicates the ordinal number of the unit, d Indicates the ordinal number of the running day, represents the weight coefficient of the optimization objective item, Indicates the number of thermal power units, Indicates the number of days for the optimization cycle. Indicates the unit j On the operating day d The startup cost, Indicates the unit j On the operating day d The downtime cost, Indicates the unit j On the operating day d fixed costs.

[0066] In some embodiments, considering that wind power and photovoltaic clean energy can replace thermal power generation and reduce the system coal consumption cost, the expression of the total optimization objective function is taken as the loss of clean energy. According to one embodiment of the present application, the expression of the expected loss of wind and photovoltaic power is as follows:

[0067] in, Indicates the penalty costs for abandoning wind and solar power. represents the weight coefficient of the optimization objective item, NN Indicates the number of wind farms, NNP Indicates the number of photovoltaic power stations, Represents wind farm n No. d The loss of electricity per day, Represents photovoltaic power station np No. d The loss of electricity per day, n represents the ordinal number of the wind farm, np Indicates the ordinal number of the PV power station.

[0068] According to an implementation mode of the present application, the constraint conditions include: operation reserve constraint, power balance constraint, thermal power generation capacity constraint, wind power constraint and photovoltaic power constraint.

[0069] In one embodiment, the operating limitations of thermal power units are taken into account, the output fluctuations of wind power and photovoltaic power are predicted and incorporated, and constraints are set from five aspects: operating reserve, power balance, thermal power generation capacity, wind power and photovoltaic power.

[0070] In order to ensure that the power grid can respond quickly when facing load fluctuations, prediction errors and equipment failures, according to an implementation of the present application, the relationship of setting the operating reserve constraint is as follows:

[0071] in, NLG Indicates the number of thermal power units, Represents thermal power unit lg On the operating day h In the running state, lg Indicates the ordinal number of the thermal power unit, h Indicates the running day number, Represents thermal power unit lg The maximum technical output of NB represents the number of load nodes, d Indicates the ordinal number of the load node, Represents load node d The maximum load requirement, Indicates the operating dayh Operational standby load requirements.

[0072] In order to ensure that the power supply matches the power demand and avoid power shortage or surplus, according to an implementation of the present application, the relationship of setting the power balance constraint is as follows:

[0073] in, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Photovoltaic power station np of power generation, Represents load node b Operation day d power demand.

[0074] In order to ensure that the output of the thermal power unit does not exceed its physical and technical limitations and also meets market demand and environmental standards, according to an implementation method of the present application, the relationship between the power generation capacity constraints of the thermal power unit is set as follows:

[0075] in, Indicates thermal power start and stop unit lg Operation day d The minimum power generation capacity, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates thermal power start and stop unit lg Operation day d Maximum power generation.

[0076] In order to ensure that the power system can adapt to the unstable output of wind power and reduce the phenomenon of wind power abandonment, according to an implementation method of the present application, the relationship of setting the wind power quantity constraint is as follows:

[0077]

[0078] in, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Wind Farm n The power loss, Indicates the operating dayd Wind Farm n The predicted power generation.

[0079] According to an implementation of the present application, the photovoltaic power constraint relationship is as follows:

[0080]

[0081] in, Indicates the operating day d Photovoltaic power station np of power generation, Indicates the operating day d Photovoltaic power station np The power loss, Indicates the operating day d Photovoltaic power station np The predicted power generation.

[0082] S102, based on historical data and real-time data, a medium- and long-term scheduling model is established according to the overall optimization objective function and constraints.

[0083] Among them, the medium- and long-term scheduling model is a multi-energy complementary scheduling model, namely, a wind, solar, thermal and storage complementary scheduling model.

[0084] In some embodiments, historical data and real-time data provide input information for the model, the objective function clarifies the optimization direction of the model, and the constraints ensure the feasibility and safety of the model solution. These four are interrelated and interact with each other, forming a complete framework of the medium- and long-term scheduling model.

[0085] In some embodiments, when establishing a medium- and long-term scheduling model, the output characteristics, constraints and regulation capabilities of each energy system are analyzed, historical data and real-time data are collected, and long-term start-up and shutdown costs are considered.

[0086] S103, using the branch and bound method to solve the mixed integer linear programming problem in the medium- and long-term scheduling model, and obtain a global optimal solution; wherein the global optimal solution is an initial scheduling strategy.

[0087] According to an implementation of the present application, a branch and bound method is used to solve a mixed integer linear programming problem in a medium- and long-term scheduling model to obtain a global optimal solution, including: The mixed integer linear programming problem is solved to obtain an initial feasible solution, and the mixed integer linear programming problem is divided into multiple sub-problems, that is, the mixed integer linear programming problem is branched to obtain multiple branches, wherein each sub-problem is a branch.

[0088] At each branch node, node selection is performed based on the objective function value and constraint conditions, and nodes whose objective function value of the solution to the relaxed problem is greater than the current upper bound or whose relaxed problem does not meet the preset conditions are pruned, thereby reducing the calculation of invalid branches. Among them, the relaxed problem that does not meet the preset conditions is unsolvable or infeasible.

[0089] For each subproblem remaining after pruning, if the optimal solution is a feasible solution and the objective function value is better than the current optimal feasible solution, the current upper bound is updated to the objective function value of the optimal solution. At the same time, if the objective function value of the relaxed solution is greater than the current lower bound, the current lower bound is updated to the objective function value of the relaxed solution, and the iteration is performed step by step. The update of the upper bound is based on the better feasible solution found, and the update of the lower bound is based on the solution of the relaxed problem to gradually improve its reliability.

[0090] During the entire iteration process, the gap between the upper and lower bounds is gradually narrowed. When the absolute difference between the upper and lower bounds is less than or equal to the preset threshold, or when the relative difference between the upper and lower bounds is less than the preset threshold, the iteration ends and the current optimal feasible solution is taken as the global optimal solution.

[0091] In some embodiments, the branch and bound algorithm has three key steps: branching, node selection, and pruning. Among them, the pruning operation can reduce the number of QPs (Optimal solutions) that the algorithm needs to solve, and improve the solution speed of the model. In addition, starting the iteration from the upper and lower bounds close to the optimal value can prune a large number of branches, which can greatly increase the number of pruning branches and accelerate the convergence of the algorithm.

[0092] In some embodiments, the branch and bound method has an upper bound and a lower bound, and is continuously updated as the node quadratic programming problem is solved in sequence. Whenever a feasible solution (mixed integer solution) of the original problem is obtained, if the objective function of the solution is less than the current optimal feasible solution, then the upper bound is updated, and the lower bound is updated in a similar manner.

[0093] In some embodiments, the branch-and-bound method is an iterative algorithm that solves the QP problem at each iteration. When the upper and lower bounds are very close (usually set to ), the iteration ends; or the Gap (relative difference) of the branch and bound method is calculated. It is usually set that when the Gap < 0.1%, the current optimal feasible solution is taken as the global optimal solution of the mixed integer linear programming problem (the clamping criterion of the upper and lower bounds ensures that it converges to the global optimum), and the branch and bound method is terminated immediately.

[0094] According to one implementation of the present application, the calculation formula for the relative difference between the upper bound and the lower bound is as follows:

[0095] in, GapIndicates the relative difference between the upper bound and the lower bound.

[0096] S104, conduct simulation experiments based on simulation scenarios of the medium- and long-term scheduling time scale of the wind-solar-thermal-storage complementary system, and adjust and optimize the initial scheduling strategy according to the simulation results to obtain a satisfactory solution; wherein the satisfactory solution is the target scheduling strategy.

[0097] In one embodiment, based on the simulation results obtained from simulation experiments on simulation scenarios with medium- and long-term scheduling time scales, the initial scheduling strategy is adjusted and optimized, and ultimately a better scheduling strategy is obtained, thereby maximizing the new energy consumption of the wind, solar, thermal and storage complementary system and minimizing the operating cost of the wind, solar, thermal and storage complementary system.

[0098] In some embodiments, the impact of meteorological factors such as load demand changes in different seasons and time periods, wind speed, sunshine, etc. on the output of renewable energy is taken into consideration, typical weekly, monthly, seasonal and annual simulation scenarios of spring, summer, autumn and winter are designed, and simulation experiments are conducted to evaluate the effectiveness of scheduling strategies in different seasons from multiple time scales.

[0099] In some embodiments, the simulation results of 13 simulation scenarios are analyzed to verify the effectiveness of the medium- and long-term scheduling model and the accuracy of the algorithm, and to analyze the impact of changes in key parameters on system scheduling. According to the simulation results, the scheduling strategy is adjusted and optimized, and finally a satisfactory solution with the minimum operating cost and the maximum absorption of wind power and photovoltaic power is obtained.

[0100] According to one embodiment of the present application, the simulation scenarios include: a typical weekly scene in spring, a typical weekly scene in summer, a typical weekly scene in autumn, a typical weekly scene in winter, a typical monthly scene in spring, a typical monthly scene in summer, a typical monthly scene in autumn, a typical monthly scene in winter, a typical seasonal scene in spring, a typical seasonal scene in summer, a typical seasonal scene in autumn, a typical seasonal scene in winter and an annual scene.

[0101] In one embodiment, specific simulation scenarios are designed as follows: typical week scenarios (typical week in spring, typical week in summer, typical week in autumn, typical week in winter), typical month scenarios (typical month in spring, typical month in summer, typical month in autumn, typical month in winter), typical season scenarios (spring, summer, autumn, winter), and annual scenarios, totaling 13.

[0102] According to an implementation manner of the present application, simulation parameters of the simulation experiment include: penalty fees for wind and solar power abandonment, unit startup costs, unit shutdown costs, and unit fixed costs.

[0103] In some embodiments, simulation parameters are set, including wind and solar power abandonment penalty fees, unit costs, etc. Unit costs include: unit startup cost, unit shutdown cost, and unit fixed cost. The base is designed to have a thermal power installed capacity of 4*660MW, a wind power installed capacity of 425MW, a photovoltaic installed capacity of 75MW, and an energy storage power station capacity of 140MW*2h, with new energy accounting for more than 18%.

[0104] Among them, the penalty fee for curtailing wind and solar power is 2,000 yuan / MWh, the unit startup cost is 1.25 million yuan, the unit shutdown cost is 1.1 million yuan, and the unit fixed cost is 940,000 yuan.

[0105] According to an implementation of the present application, a simulation experiment is described by taking a typical spring season scene as an example, and the simulation results are as follows: Figure 2 : is a curve diagram of the predicted electricity volume of new energy in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 2 As shown, the red curve represents the predicted wind power generation, the blue curve represents the predicted photovoltaic power generation, and the green curve represents the total predicted new energy power generation.

[0106] In the simulation of the typical spring season, a representative time period within the season is selected, and the start and stop plan of the thermal power units is calculated based on the predicted power generation of wind and light and the load power demand. Under the premise of ensuring the execution of the power plan, the new energy consumption capacity is improved and the operating cost is reduced.

[0107] Figure 3 is a curve diagram of the renewable energy power generation in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 3 As shown, the blue curve represents the power generation of renewable energy, and the green curve represents the total predicted power generation of renewable energy.

[0108] Figure 4 : is a curve diagram of the total power generation plan for the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 4 As shown, the red curve represents the load power demand, the blue curve represents the total thermal power generation, and the green curve represents the total new energy power generation.

[0109] Figure 5 is a curve diagram of a power generation plan of a thermal power unit in a spring scenario of a quarterly plan provided in an embodiment of the present application, such as Figure 5 As shown, the red curve represents the total power generation of thermal power, the blue curve represents the power generation of thermal power unit 1, the black curve represents the power generation of thermal power unit 2, the light green straight line represents the power generation of thermal power unit 3, and the dark green straight line represents the power generation of thermal power unit 4. The light green straight line and the dark green straight line coincide.

[0110] from Figure 3It can be seen that considering the prediction accuracy and the randomness and uncertainty of wind and solar output, a 95% confidence parameter is set to improve the reliability of the system. In addition, in this scenario, the maximum wind and solar consumption can be achieved by adjusting the thermal power output plan.

[0111] Figure 6 1 is a schematic diagram of the start-up state of thermal power unit 1# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 6 As shown in the figure, the startup status of the unit on the 21st day is 1, and the startup status of the unit at other times is 0, indicating that the unit was started on the 21st day and was not started at other times. The startup status of the unit from 0 to 1 means that the unit starts slowly and finally reaches the fully started state.

[0112] Figure 7 1 is a schematic diagram of the shutdown state of thermal power unit 1# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 7 As shown in the figure, the shutdown status of the unit on the 14th day is 1, and the shutdown status of the unit at other times is 0, indicating that the unit was shut down on the 21st day and did not shut down at other times. The shutdown status of the unit from 0 to 1 indicates that the unit is shut down slowly and finally reaches a complete shutdown state.

[0113] Figure 8 2 is a schematic diagram of the start-up state of thermal power unit 2# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Figure 8 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig. 9 2 is a schematic diagram of the shutdown state of thermal power unit 2# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig. 9 As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0114] Fig.10 3 is a schematic diagram of the start-up state of thermal power unit 3# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig.10 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig.11 3 is a schematic diagram of the shutdown state of thermal power unit 3# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig.11 As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0115] Fig.12 4 is a schematic diagram of the start-up state of the thermal power unit 4# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig.12 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig.13 4 is a schematic diagram of the shutdown state of thermal power unit 4# in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig.13As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0116] Fig.14 : is a schematic diagram of the operating status of a thermal power unit in a spring scenario of a quarterly plan provided in an embodiment of the present application, such as Fig.14 As shown, the red dotted line represents the operating status of unit 1, the blue straight line represents the operating status of unit 2, the black straight line represents the operating status of unit 3, and the green straight line represents the operating status of unit 4. The blue straight line, the black straight line and the green straight line coincide with each other.

[0117] Figures 6 to 12 The base was provided with a start-up and shutdown plan for four thermal power units, under which the operation economy was maximized. The start-up order of the thermal power units was set as follows: 4# thermal power unit, 3# thermal power unit, 2# thermal power unit, 1# thermal power unit, so Fig.14 It can be seen that when the base load is oversupplied, the 1# thermal power unit is stopped first to ensure the base power generation plan.

[0118] Fig.15 Schematic diagram of the operation plan of a thermal power unit in the spring scenario of the quarterly plan provided in the embodiment of the present application, such as Fig.15 As shown, the number of machines started from the 14th to the 20th day is 3, and the number of machines started during the rest of the time is 4.

[0119] Taking into account the load conditions and unit operating costs, we can get Fig.15 From the above-mentioned base electricity plan arrangement, it can be seen that the method proposed in the embodiment of the present application can ensure the feasibility of the base electricity plan, and at the same time, it shows that the method has a promoting effect on improving the new energy consumption capacity.

[0120] According to one implementation of the present application, the annual simulation results are as follows: In the simulation of a typical week of the year, a representative time period within the year is selected, and the start and stop plan of the thermal power units is calculated based on the predicted wind and solar power generation and the load power demand. Under the premise of ensuring the execution of the power plan, the new energy absorption capacity is improved and the operating costs are reduced.

[0121] Fig.16 : is a curve diagram of the predicted electricity volume of new energy in the annual planning scenario provided in the embodiment of the present application, such as Fig.16 As shown, the red curve represents the predicted wind power generation, the blue curve represents the predicted photovoltaic power generation, and the green curve represents the total predicted new energy power generation.

[0122] from Fig.16It can be seen that considering the prediction accuracy and the randomness and uncertainty of wind and solar output, a 95% confidence parameter is set to improve the reliability of the system. In addition, in this scenario, the maximum wind and solar consumption can be achieved by adjusting the thermal power output plan.

[0123] Fig.17 is a curve diagram of the renewable energy power generation in the annual planning scenario provided in the embodiment of the present application, such as Fig.17 As shown, the blue curve represents the power generation of renewable energy, and the green curve represents the total predicted power generation of renewable energy.

[0124] Fig.18 is a curve diagram of the total power generation plan of the annual plan scenario provided in the embodiment of the present application, such as Fig.18 As shown, the red curve represents the load power demand, the blue curve represents the total thermal power generation, and the green curve represents the total new energy power generation.

[0125] Fig.19 is a curve diagram of the power generation plan of a thermal power unit in the annual plan scenario provided in the embodiment of the present application, such as Fig.19 As shown, the light red curve represents the total power generation of thermal power, the blue curve represents the power generation of thermal power unit 1, the black curve represents the power generation of thermal power unit 2, the light green straight line represents the power generation of thermal power unit 3, and the dark red straight line represents the power generation of thermal power unit 4. The light green straight line and the dark red straight line coincide.

[0126] Fig. 20 1 is a schematic diagram of the startup state of thermal power unit 1# in the annual planning scenario provided in the embodiment of the present application, such as Fig. 20 As shown, the startup status of the unit on the 24th, 115th, 175th and 250th days is 1, and the startup status of the unit at the rest of the time is 0, indicating that the unit is started on the 24th, 115th, 175th and 250th days, and is not started at the rest of the time.

[0127] Fig.21 1 is a schematic diagram of the shutdown state of thermal power unit 1# in the annual planning scenario provided in the embodiment of the present application, such as Fig.21 As shown, the shutdown status of the unit on the 15th, 101st, 170th and 240th days is 1, and the shutdown status of the unit at other times is 0, indicating that the unit is shut down on the 15th, 101st, 170th and 240th days, and is not shut down at other times.

[0128] Fig. 22 2 is a schematic diagram of the startup state of thermal power unit 2 in the annual planning scenario provided in the embodiment of the present application, such as Fig. 22 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig.23 2 is a schematic diagram of the shutdown state of thermal power unit 2# in the annual planning scenario provided in the embodiment of the present application, such as Fig.23As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0129] Fig.24 3 is a schematic diagram of the startup state of thermal power unit 3# in the annual planning scenario provided in the embodiment of the present application, such as Fig.24 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig.25 3 is a schematic diagram of the shutdown state of thermal power unit 3# in the annual planning scenario provided in the embodiment of the present application, such as Fig.25 As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0130] Fig.26 4 is a schematic diagram of the startup state of the thermal power unit 4# in the annual planning scenario provided in the embodiment of the present application, such as Fig.26 As shown, the startup status of the unit has always been 0, indicating that the unit has not been started. Fig. 27 4# shutdown state diagram of the thermal power unit in the annual planning scenario provided in the embodiment of the present application, such as Fig. 27 As shown, the shutdown status of the unit has always been 0, indicating that the unit has not been shut down.

[0131] Fig.28 Schematic diagram of the operating status of a thermal power unit in the annual planning scenario provided by an embodiment of the present application, such as Fig.28 As shown, the red dotted line represents the operating status of unit 1, the blue straight line represents the operating status of unit 2, the black straight line represents the operating status of unit 3, and the green straight line represents the operating status of unit 4. The blue straight line, the black straight line and the green straight line coincide with each other.

[0132] Figures 20 to 27 The base was provided with a start-up and shutdown plan for four thermal power units, under which the operation economy was maximized. The start-up order of the thermal power units was set as follows: 4# thermal power unit, 3# thermal power unit, 2# thermal power unit, 1# thermal power unit, so Fig.28 It can be seen that when the base load is oversupplied, the 1# thermal power unit is stopped first to ensure the base power plan.

[0133] Fig.29 is a schematic diagram of the operation plan of a thermal power unit in the annual plan scenario provided in an embodiment of the present application, such as Fig.29 As shown, the number of machines started on the 20th, 105th, 170th and 245th days is 3, and the number of machines started at other times is 4.

[0134] Taking into account the load conditions and unit operating costs, we can get Fig.29The annual operation plan of thermal power units. From the electricity plan arrangement of the above base, it can be seen that the method proposed in this application scheme can ensure the feasibility of the base electricity plan, and at the same time it shows that the method has a promoting effect on improving the new energy consumption capacity.

[0135] The medium- and long-term optimization scheduling method for wind, solar, thermal and storage complementarity provided in the embodiment of the present application first obtains historical data and real-time data of the wind, solar, thermal and storage complementary system, constructs an overall optimization objective function, and sets constraints. Secondly, based on the historical data and real-time data, a medium- and long-term scheduling model is established according to the overall optimization objective function and constraints. Then, the branch and bound method is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model to obtain an initial scheduling strategy. Finally, the initial scheduling strategy is adjusted and optimized according to the simulation experiment results of the simulation scenario of the medium- and long-term scheduling time scale.

[0136] The multi-time-scale medium- and long-term dispatch planning carried out in this application is the control strategy required by the current wind, solar, thermal and storage complementary system. This strategy has a relatively wide range of applications and can support the preparation of dispatch plans for multiple time series. At the same time, it can reduce the loss of clean energy caused by wind and solar power abandonment, reduce power generation costs, and improve system regulation capabilities, which will have significant benefits for the development of the power system.

[0137] This application scheme can optimize the medium- and long-term scheduling strategy of the wind-solar-thermal-storage complementary system by simulating typical scenarios in the four seasons and scenarios throughout the year, evaluate the effectiveness of scheduling strategies in different seasons from multiple time scales, minimize operating costs, maximize the absorption of wind power and photovoltaics, reduce wind and solar power abandonment, and meet the needs of long-term planning.

[0138] This application proposes a comprehensive optimization scheduling strategy for the medium- and long-term time scale of wind, solar, thermal and storage complementarity. This application takes into account typical scenarios in the four seasons and scenarios throughout the year, and can meet the needs of long-term planning, which has not been fully integrated and optimized in previous studies.

[0139] The optimization scheduling strategy in this application scheme is aimed at the wind, solar, thermal and storage complementary system, taking into account various cost factors such as the start-up and shutdown costs, operating costs and expected loss of new energy electricity of thermal power units, and clearly puts forward the optimization goals of minimizing operating costs and maximizing new energy consumption.

[0140] This application proposal designs 13 typical operating scenarios, including typical weeks, typical months, typical seasons and annual scenarios for the four seasons of spring, summer, autumn and winter, and comprehensively evaluates the scheduling strategies of the wind, solar, thermal and storage complementary system under different time scales and seasonal conditions.

[0141] In the algorithm part, this application proposes to use the branch and bound method to solve the mixed integer linear programming problem in the multi-energy optimization complementary scheduling model. By setting upper and lower bounds and monitoring the convergence of the algorithm, a satisfactory solution is finally obtained.

[0142] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0143] Corresponding to the method of the above embodiment, Fig.30 1 is a schematic diagram of the structure of the medium- and long-term optimization scheduling device for wind, solar, thermal and storage complementarity provided in the embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown.

[0144] Reference Fig.30 , the device comprises: An acquisition unit 301 is used to acquire historical data and real-time data of the wind-solar-thermal-storage complementary system, construct an overall optimization objective function, and set constraint conditions; Establishing unit 302, for establishing a medium- and long-term scheduling model based on historical data and real-time data according to the overall optimization objective function and constraints; A solving unit 303 is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model by using a branch and bound method to obtain a global optimal solution; wherein the global optimal solution is an initial scheduling strategy; The optimization unit 304 is used to conduct simulation experiments based on simulation scenarios of the medium- and long-term scheduling time scale of the wind-solar-thermal-storage complementary system, and adjust and optimize the initial scheduling strategy according to the simulation results to obtain a satisfactory solution; wherein the satisfactory solution is the target scheduling strategy.

[0145] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0146] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0147] Fig.31 Schematic diagram of the structure of the electronic device 31 provided in the embodiment of the present application. Fig.31 As shown, the electronic device 31 of this embodiment includes: at least one processor 311 ( Fig.31 Only one is shown in the figure), a memory 313, and a computer program 312 stored in the memory 313 and executable on at least one processor 311, and when the processor 311 executes the computer program 312, the steps in the above method embodiment are implemented.

[0148] The electronic device 31 may be a computing device such as a desktop computer, a notebook, a PDA, or a mobile phone. The electronic device 31 may include, but is not limited to, a processor 311 and a memory 313. Those skilled in the art will appreciate that Fig.31 It is only an example of the electronic device 31 and does not constitute a limitation on the electronic device 31. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0149] The processor 311 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware photovoltaic modules, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0150] In some embodiments, the memory 313 may be an internal storage unit of the electronic device 31, such as a hard disk or memory of the electronic device 31. In other embodiments, the memory 313 may also be an external storage device of the electronic device 31, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital card (SecureDigital, SD), a flash card (Flash Card), etc. equipped on the electronic device 31. Further, the memory 313 may also include both an internal storage unit of the electronic device 31 and an external storage device. The memory 313 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as program codes of a computer program, etc. The memory 313 may also be used to temporarily store data that has been output or is to be output.

[0151] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, when the present application implements all or part of the processes in the above-mentioned embodiment method, it can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps applied to the above-mentioned method embodiment. Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may at least include: any entity or device that can carry the computer program code to the computing device / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, such as USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electric carrier signals and telecommunication signals.

[0152] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0153] An embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the steps in the above-mentioned method embodiments.

[0154] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0156] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. The device / electronic device embodiments described above are merely schematic, and the division of the above modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or photovoltaic components can be combined or integrated into another system, and some features can be ignored and not executed. Another point is that the indirect coupling, direct coupling or communication connection between each other shown or discussed can be an indirect coupling, direct coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0157] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A medium- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity, characterized in that: The method comprises: Obtain historical and real-time data of the wind-solar-thermal-storage complementary system, construct the overall optimization objective function, and set constraints; Based on the historical data and the real-time data, according to the overall optimization objective function and the constraint conditions, a medium- and long-term scheduling model is established; Using the branch and bound method, the mixed integer linear programming problem in the medium- and long-term scheduling model is solved to obtain a global optimal solution; wherein the global optimal solution is an initial scheduling strategy; A simulation experiment is conducted based on a simulation scenario of the medium- and long-term scheduling time scale of a wind-solar-thermal-storage complementary system. According to the simulation results, the initial scheduling strategy is adjusted and optimized to obtain a satisfactory solution; wherein the satisfactory solution is the target scheduling strategy.

2. The mid- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity according to claim 1 is characterized in that: The historical data includes: historical meteorological data, historical load demand and historical power generation output; the real-time data includes: real-time meteorological data, real-time load demand and real-time power generation output.

3. The mid- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity according to claim 1 is characterized in that: The expression of the overall optimization objective function is as follows: in, F ( Y ) represents the overall optimization objective function, min Indicates taking the minimum value, TC represents the operating cost of thermal power units, p Indicates the expected loss of wind and solar power.

4. The mid- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity according to claim 3 is characterized in that: The method further comprises: According to the optimal unit start-up and shutdown plan and unit power generation plan within the preset optimization period, a unit combination model for renewable energy consumption is constructed, and its objective function expression is as follows: in, Indicates the unit j On the operating day d In the running state, Indicates the unit j On the operating day d The boot status, Indicates the unit j On the operating day d The shutdown state, j Indicates the ordinal number of the unit, d Indicates the ordinal number of the running day, represents the weight coefficient of the optimization objective item, Indicates the number of thermal power units, Indicates the number of days for the optimization cycle. Indicates the unit j On the operating day d The startup cost, Indicates the unit j On the operating day d The downtime cost, Indicates the unit j On the operating day d fixed costs; The expression of the expected loss of wind and solar power is as follows: in, Indicates the penalty costs for abandoning wind and solar power. represents the weight coefficient of the optimization objective item, NN Indicates the number of wind farms, NNP Indicates the number of photovoltaic power stations, Represents wind farm n No. d The loss of electricity per day, Represents photovoltaic power station np No. d The loss of electricity per day, n represents the ordinal number of the wind farm, np Indicates the ordinal number of the PV power station.

5. The mid- and long-term optimal scheduling method for wind, solar, thermal and energy storage complementarity according to claim 1 is characterized in that: The constraints include: operation reserve constraints, power balance constraints, thermal power generation capacity constraints, wind power constraints and photovoltaic power constraints.

6. The mid- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity according to claim 5 is characterized in that: The relationship between the running standby constraint is as follows: in, NLG Indicates the number of thermal power units, Represents thermal power unit lg On the operating day h In the running state, lg Indicates the ordinal number of the thermal power unit, h Indicates the running day number, Represents thermal power unit lg The maximum technical output of NB represents the number of load nodes, d Indicates the ordinal number of the load node, Represents load node d The maximum load requirement, Indicates the operating day h Operational reserve load requirements; The relationship between the power balance constraint is as follows: in, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Photovoltaic power station np of power generation, Represents load node b Operation day d The power demand; The relational expression for the power generation capacity constraint of the thermal power unit is as follows: in, Indicates thermal power start and stop unit lg Operation day d The minimum power generation capacity, Indicates the operating day d Thermal power start and stop unit lg of power generation, Indicates thermal power start and stop unit lg Operation day d Maximum power generation capacity; The relationship between the wind power consumption constraint is as follows: in, Indicates the operating day d Wind Farm n of power generation, Indicates the operating day d Wind Farm n The power loss, Indicates the operating day d Wind Farm n The predicted power generation; The relationship between the photovoltaic power constraints is as follows: in, Indicates the operating day d Photovoltaic power station np of power generation, Indicates the operating day d Photovoltaic power station np The power loss, Indicates the operating day d Photovoltaic power station np The predicted power generation.

7. The mid- and long-term optimal dispatching method for wind, solar, thermal and energy storage complementarity according to claim 1 is characterized in that: The branch and bound method is used to solve the mixed integer linear programming problem in the medium- and long-term scheduling model and obtain the global optimal solution, including: Solving the mixed integer linear programming problem to obtain an initial feasible solution, and dividing the mixed integer linear programming problem into a plurality of sub-problems; wherein each sub-problem is a branch; At each branch node, node selection is performed based on the objective function value and constraint conditions, and nodes whose objective function value of the solution to the relaxed problem is greater than the current upper bound or whose relaxed problem does not meet the preset conditions are pruned; For each subproblem remaining after pruning, if the optimal solution is a feasible solution and the objective function value is better than the current optimal feasible solution, then the current upper bound is updated to the objective function value of the optimal solution. At the same time, if the objective function value of the relaxed solution is greater than the current lower bound, then the current lower bound is updated to the objective function value of the relaxed solution, and the iteration is performed step by step. When the absolute difference between the upper and lower bounds is less than or equal to a preset threshold, or when the relative difference between the upper and lower bounds is less than a preset threshold, the iteration is terminated and the current optimal feasible solution is used as the global optimal solution.

8. The mid- and long-term optimal dispatching method for wind, solar, thermal and energy storage complementarity according to claim 7 is characterized in that: The relative difference between the upper bound and the lower bound is calculated as follows: in, Gap Indicates the relative difference between the upper bound and the lower bound.

9. The medium- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity according to any one of claims 1 to 8, characterized in that: The simulation scenarios include: a typical weekly scenario in spring, a typical weekly scenario in summer, a typical weekly scenario in autumn, a typical weekly scenario in winter, a typical monthly scenario in spring, a typical monthly scenario in summer, a typical monthly scenario in autumn, a typical monthly scenario in winter, a typical seasonal scenario in spring, a typical seasonal scenario in summer, a typical seasonal scenario in autumn, a typical seasonal scenario in winter, and an annual scenario; The simulation parameters of the simulation experiment include: penalty costs for wind and solar power abandonment, unit startup costs, unit shutdown costs and unit fixed costs.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the medium- and long-term optimization scheduling method for wind, solar, thermal and energy storage complementarity as described in any one of claims 1-9.

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