Joint optimization scheduling method and device for wind-solar-thermal storage power system

Abstract

The invention relates to the technical field of power systems, in particular to a joint optimization scheduling method and device for a wind-solar-thermal storage power system. The method comprises the steps that unit data of all target units are acquired, the target units comprise a wind power unit, a photovoltaic power unit, a thermal power unit and an energy storage power unit, and the unit data comprise unit parameters, network parameters, load data, wind power output and photovoltaic power generation output; taking the minimum operation cost of the wind-solar-thermal storage power system as a target, and constructing a joint optimization scheduling model and constraint conditions; and based on the unit data, the joint optimization scheduling model and the constraint condition, solving the optimal scheduling cost and the thermal power generating unit time sequence power, the energy storage generator set time sequence charging and discharging power, the wind turbine generator set available power, the wind turbine generator set actual consumption power, the photovoltaic generator set available power and the photovoltaic generator set actual consumption power in each simulation period. Therefore, the technical problem of how to improve renewable energy consumption can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and apparatus for joint optimization scheduling of wind, solar, thermal and energy storage power systems. Background Art

[0002] With resource depletion and increasing environmental pollution, accelerating the development and utilization of clean energy has become an inevitable trend. However, the inherent uncertainty in the output of new energy sources makes grid connection and consumption increasingly difficult. To address the grid connection challenges of renewable energy sources such as wind and solar power, the concept of multi-energy complementary power generation systems has been proposed. These systems integrate multiple energy generation methods across regions to form a comprehensive energy power generation system, fully leveraging the advantages of multi-energy complementarity and coordination. They possess enormous development potential in regional energy system construction and improving system reliability.

[0003] Wind power, photovoltaic power, thermal power, and energy storage power generation methods differ significantly in both time and space, but they also exhibit strong complementarity. For example, wind power generation is affected by wind speed, with more power generated at night and less during the day, while photovoltaic power generation is affected by sunlight, generating electricity only during the day. Temporally, wind and photovoltaic power generation have a natural complementary characteristic. Fully exploring the complementary and coordinated operation modes of different energy sources in dispatching methods is beneficial for achieving cascaded utilization and complementary advantages of energy from multiple perspectives, improving overall system efficiency, and has significant practical and economic value.

[0004] Therefore, there is an urgent need for a joint optimization scheduling method and device for wind, solar, thermal and energy storage power systems to solve the technical problem of how to improve the absorption of renewable energy. Summary of the Invention

[0005] To address the technical challenge of improving the absorption of renewable energy, this invention provides a method and apparatus for the joint optimization scheduling of wind, solar, thermal, and energy storage power systems.

[0006] In a first aspect, embodiments of the present invention provide a joint optimization scheduling method for wind-solar-thermal-storage power systems, comprising:

[0007] Acquire the unit data of each target unit; wherein, the target units include wind turbine units, photovoltaic generator units, thermal power units and energy storage generator units, and the unit data includes unit parameters, network parameters, load data, wind power output and photovoltaic power output;

[0008] With the goal of minimizing the operating cost of the wind-solar-thermal-storage power system, a joint optimization scheduling model and constraints for the wind-solar-thermal-storage power system are constructed.

[0009] Based on the unit data, the joint optimization scheduling model, and the constraints, the optimal scheduling cost and the time-series power of thermal power units, the time-series charging and discharging power of energy storage generator units, the available power of wind turbine units, the actual power absorbed by wind turbine units, the available power of photovoltaic generator units, and the actual power absorbed by photovoltaic generator units in each simulation period are obtained.

[0010] Secondly, embodiments of the present invention also provide a joint optimization scheduling device for wind-solar-thermal-storage power systems, comprising:

[0011] The acquisition module is used to acquire the unit data of each target unit; wherein, the target units include wind turbine units, photovoltaic generator units, thermal power units and energy storage generator units, and the unit data includes unit parameters, network parameters, load data, wind power output and photovoltaic power output;

[0012] The module is used to construct a joint optimization scheduling model and constraints for the wind-solar-thermal-storage power system with the goal of minimizing the operating cost of the wind-solar-thermal-storage power system.

[0013] The solution module is used to solve for the optimal scheduling cost and the time-series power of thermal power units, the time-series charging and discharging power of energy storage generator units, the available power of wind turbine units, the actual power absorbed by wind turbine units, the available power of photovoltaic generator units, and the actual power absorbed by photovoltaic generator units in each simulation period, based on the unit data, the joint optimization scheduling model, and the constraints.

[0014] This invention provides a joint optimization scheduling method and apparatus for wind-solar-thermal-storage power systems. Based on unit data of target units such as wind turbines, photovoltaic generators, thermal power units, and energy storage generators, as well as a constructed joint optimization scheduling model and constraints for the wind-solar-thermal-storage power system, the optimal scheduling cost and the time-series power of thermal power units, the time-series charging and discharging power of energy storage generators, the available power of wind turbines, the actual power absorbed by wind turbines, the available power of photovoltaic generators, and the actual power absorbed by photovoltaic generators can be obtained in each simulation period. This approach can fully leverage the advantages of multi-energy complementarity and multi-energy coordination, and has great development potential in regional energy system construction, improving renewable energy absorption, mitigating renewable energy fluctuations, achieving energy conservation and emission reduction, and improving system reliability. Attached Figure Description

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flowchart of the joint optimization scheduling method for wind, solar, thermal, and energy storage power systems provided in this embodiment of the invention;

[0017] Figure 2 This is a hardware architecture diagram of the electronic equipment containing the joint optimization dispatching device for the wind-solar-thermal-storage power system provided in this embodiment of the invention;

[0018] Figure 3 This is a structural diagram of the joint optimization dispatching device for wind, solar, thermal, and energy storage power systems provided in this embodiment of the invention. Detailed Implementation

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Please refer to Figure 1 This invention provides a joint optimization scheduling method for wind, solar, thermal, and energy storage power systems, the method comprising:

[0021] Step 100: Obtain the unit data for each target unit; wherein, the target units include wind turbines, photovoltaic generators, thermal power units and energy storage generators, and the unit data includes unit parameters, network parameters, load data, wind power output and photovoltaic power output;

[0022] Step 102: With the goal of minimizing the operating cost of the wind-solar-thermal-storage power system, construct a joint optimization scheduling model and constraints for the wind-solar-thermal-storage power system.

[0023] Step 104: Based on the unit data, joint optimization scheduling model and constraints, solve for the optimal scheduling cost and the time-series power of thermal power units, time-series charging and discharging power of energy storage generator units, available power of wind turbine units, actual power absorbed by wind turbine units, available power of photovoltaic generator units, and actual power absorbed by photovoltaic generator units in each simulation period.

[0024] In this invention, most related technologies focus on the operational characteristics of individual power generation systems such as wind, solar, thermal, and energy storage. Furthermore, most determine dispatch strategies from the perspective of improving renewable energy volatility and achieving energy conservation and emission reduction, without mentioning how to formulate a joint dispatch strategy for wind, solar, hydro, thermal, and energy storage systems to improve renewable energy absorption. This invention, based on unit data from target units such as wind turbines, photovoltaic generators, thermal power units, and energy storage generators, and a constructed joint optimization dispatch model and constraints for a wind-solar-thermal-energy-storage power system, can solve for the optimal dispatch cost and the time-series power of thermal power units, the time-series charging and discharging power of energy storage generators, the available power of wind turbines, the actual absorbed power of wind turbines, the available power of photovoltaic generators, and the actual absorbed power of photovoltaic generators in each simulation period. This approach can fully leverage the advantages of multi-energy complementarity and multi-energy coordination, and has enormous development potential in regional energy system construction, improving renewable energy absorption, mitigating renewable energy volatility, achieving energy conservation and emission reduction, and enhancing system reliability.

[0025] In terms of optimal scheduling solutions, the joint optimal scheduling of wind-solar-thermal-storage power systems belongs to the category of uncertain optimal scheduling problems in power systems, exhibiting typical characteristics of large-scale, discrete, nonlinear, and non-convex mathematical programming problems. System decision variables include both discrete and continuous variables, and each generating unit has its own operational and physical constraints. Therefore, this problem is mathematically a complex mixed-integer programming problem, mathematically difficult to solve to obtain a globally optimal solution. CPLEX integrates the advantages of various algorithms, enabling it to solve multivariate, complex, mixed-integer programming problems in a relatively short time. In particular, its mixed-integer optimization program is effective in solving many practical mixed-integer programming problems. The CPLEX solver can be directly applied to solving numerous optimization problems or models and is compatible with many optimization software and languages.

[0026] Described below Figure 1 The execution method of each step is shown.

[0027] For step 100:

[0028] In one embodiment of the present invention, the unit parameters include the node number of the network where each target unit is located, maximum output, minimum output, coal consumption coefficient, uphill ramp rate, downhill ramp rate, minimum shutdown duration, minimum startup duration, shutdown cost, and startup cost.

[0029] Network parameters include the first and last node numbers, branch reactance, maximum line load, and minimum line load;

[0030] Load data includes the load size within each scheduling time period.

[0031] The specific formats for unit parameters are shown in Table 1, network parameters are shown in Table 2, and load data are shown in Table 2.

[0032] Table 1

[0033]

[0034] Table 2

[0035]

[0036] Table 3

[0037]

[0038]

[0039] In one embodiment of the present invention, the wind power output is determined by the following formula:

[0040]

[0041] In the formula, υ t υ in υ off υ R The wind speeds at time t, cut-off speed, cut-off speed, and rated speed of the wind turbine are, in order, m / s; P WR The rated output of the wind turbine is in MW.

[0042] The output power of photovoltaic power generation is determined by the following formula:

[0043]

[0044] Where S t 、S max 、S C The solar irradiance, maximum solar irradiance, and fixed-point solar irradiance of the photovoltaic power generation unit at time t are, in W / m², respectively. 2 ;P SR The rated output of the photovoltaic generator set is expressed in MW.

[0045] Regarding step 102:

[0046] In one embodiment of the present invention, the joint optimization scheduling model is determined by the following formula:

[0047]

[0048] In the formula, minC represents the minimum operating cost; T represents the optimal scheduling time; N g , N pv 、N batThe numbers are listed in order: thermal power units, wind power units, photovoltaic power units, and energy storage power units. The operating cost coefficients are, in order, thermal power units, wind power units, photovoltaic power units, and energy storage power units; μ i (t) represents the operating state of thermal power unit i during time period t, μ i (t) = 1 indicates that the unit is started, μ i (t) = 0 indicates that the unit is shut down; The costs are, in order, the start-up cost and the shutdown cost of a thermal power unit; P i,t , P bat The output of thermal power units, wind power units, photovoltaic power units, and energy storage power units in the following order are during time period t; a i 、b i c i The following are the coal consumption coefficients.

[0049] This joint optimization scheduling model aims to minimize the operating cost of the power system, while also considering the operating costs of wind power, photovoltaic power, and energy storage.

[0050] In one embodiment of the present invention, the constraint conditions are determined by the following formula:

[0051]

[0052] In the formula, N L P represents the total number of load nodes. d，t Let d be the load demand of load node d in time period t. The maximum and minimum outputs of thermal power unit i are, in order. The uphill and downhill ramp rates of thermal power unit i are given in order, Δt represents the operating period, ρ represents the thermal reserve coefficient, and T represents the thermal reserve coefficient. i，on 、T i，off The minimum start-up duration and minimum shutdown duration for thermal power unit i are, in order, H. i，on H i，off The following are the start-up cost coefficient and shutdown cost coefficient for thermal power unit i, respectively, P bat (i,t) represents the real-time power of energy storage unit i during time period t. The maximum discharge power and maximum charging power of energy storage unit i are given by SOC(i,t), and the SOC(i,t) is the charge of energy storage unit i in time period t. min SOC max The minimum and maximum charge capacities of energy storage unit i are, respectively, P. l,t 、P l,min 、P l,maxThe loads of branch l in time period t, the minimum load of branch l, and the maximum load of branch l are, in order, G. l-i G is used to describe the impact of the injected power at load node i on branch l. l-j Used to describe the effect of load node j on the output power transfer of branch l.

[0053] Under the above constraints, the sum of the real-time power of thermal power, wind power, photovoltaic, and energy storage units equals the real-time power demand of the load; in order to meet safe and economical operation, the power generation of thermal power units should meet the upper and lower power limits.

[0054] In terms of optimal scheduling modeling, due to the enormous scale of large power systems, the optimal scheduling model is a large-scale, nonlinear, and multi-stage optimization model, making power flow calculations for large power grids extremely complex and time-consuming, sometimes even leading to non-convergence. To address this issue, by constructing the aforementioned constraints, the dimensionality of the mathematical programming problem can be reduced, allowing the optimal scheduling problem to be solved in a shorter time.

[0055] In terms of optimal scheduling solutions, the joint optimal scheduling of wind-solar-thermal-storage power systems belongs to the category of uncertain optimal scheduling problems in power systems, exhibiting typical characteristics of large-scale, discrete, nonlinear, and non-convex mathematical programming problems. System decision variables include both discrete and continuous variables, and each generating unit has its own operational and physical constraints. Therefore, this problem is mathematically a complex mixed-integer programming problem, mathematically difficult to solve to obtain a globally optimal solution. CPLEX integrates the advantages of various algorithms, enabling it to solve multivariate, complex, mixed-integer programming problems in a relatively short time. In particular, its mixed-integer optimization program is effective in solving many practical mixed-integer programming problems. The CPLEX solver can be directly applied to solving numerous optimization problems or models and is compatible with many optimization software and languages.

[0056] In other words, considering that solving the aforementioned joint optimal scheduling model is a nonlinear, non-convex, multi-constraint optimization problem, where the optimal scheduling problem includes unit start-up and shutdown scheduling and static economic scheduling subproblems, thus determining the start-up, shutdown, and output of the units on the next scheduling day, the solution method can be to use the yalmip toolbox to call the CPLEX solver to obtain the optimal solution to the problem. This solution method has many advantages, including sufficient consideration of factors, good complementary coordination, high overall system efficiency, scientific methodology, high accuracy of results, fast computation, and good compatibility.

[0057] like Figure 2 , Figure 3As shown, this invention provides a joint optimization dispatching device for wind, solar, thermal, and energy storage power systems. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for a joint optimization dispatching device of a wind-solar-thermal-storage power system provided in an embodiment of the present invention. Except for... Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0058] like Figure 3 As shown in the figure, this embodiment provides a joint optimization dispatching device for wind, solar, thermal, and energy storage power systems. The device includes:

[0059] The acquisition module 300 is used to acquire the unit data of each target unit; wherein, the target units include wind turbine units, photovoltaic generator units, thermal power units and energy storage generator units, and the unit data includes unit parameters, network parameters, load data, wind power output and photovoltaic power output;

[0060] Module 302 is used to construct a joint optimization scheduling model and constraints for the wind-solar-thermal-storage power system with the goal of minimizing the operating cost of the wind-solar-thermal-storage power system.

[0061] The solution module 304 is used to solve for the optimal scheduling cost and the time-series power of thermal power units, the time-series charging and discharging power of energy storage generator units, the available power of wind turbine units, the actual power absorbed by wind turbine units, the available power of photovoltaic generator units, and the actual power absorbed by photovoltaic generator units in each simulation period based on the unit data, the joint optimization scheduling model, and the constraints.

[0062] In this embodiment of the invention, the acquisition module 300 can be used to execute step 100 in the above method embodiment, the construction module 302 can be used to execute step 102 in the above method embodiment, and the solution module 304 can be used to execute step 104 in the above method embodiment.

[0063] In one embodiment of the present invention, the unit parameters include the node number of the network where each target unit is located, maximum output, minimum output, coal consumption coefficient, uphill ramp rate, downhill ramp rate, minimum shutdown duration, minimum startup duration, shutdown cost, and startup cost.

[0064] The network parameters include the first and last node numbers, branch reactance, maximum line load, and minimum line load.

[0065] The load data includes the load size within each scheduling time period.

[0066] In one embodiment of the present invention, the wind power output is determined by the following formula:

[0067]

[0068] In the formula, υ t υ in υ off υ R The wind speeds at time t, cut-off speed, cut-off speed, and rated speed of the wind turbine are, in order, m / s; P WR The rated output of the wind turbine is in MW.

[0069] The photovoltaic power output is determined by the following formula:

[0070]

[0071] Where S t 、S max 、S C The solar irradiance, maximum solar irradiance, and fixed-point solar irradiance of the photovoltaic power generation unit at time t are, in W / m², respectively. 2 ;P SR The rated output of the photovoltaic generator set is expressed in MW.

[0072] In one embodiment of the present invention, the joint optimization scheduling model is determined by the following formula:

[0073]

[0074] In the formula, minC represents the minimum operating cost; T represents the optimal scheduling time; N g , N pv 、N bat The numbers are listed in order: thermal power units, wind power units, photovoltaic power units, and energy storage power units. The operating cost coefficients are, in order, thermal power units, wind power units, photovoltaic power units, and energy storage power units; μ i (t) represents the operating state of thermal power unit i during time period t, μ i (t) = 1 indicates that the unit is started, μ i (t) = 0 indicates that the unit is shut down; The costs are, in order, the start-up cost and the shutdown cost of a thermal power unit; P i,t , P bat The output of thermal power units, wind power units, photovoltaic power units, and energy storage power units in the following order are during time period t; a i 、b i c i The following are the coal consumption coefficients.

[0075] In one embodiment of the present invention, the constraint condition is determined by the following formula:

[0076]

[0077] In the formula, N L P represents the total number of load nodes. d，t Let d be the load demand of load node d in time period t. The maximum and minimum outputs of thermal power unit i are, in order. The uphill and downhill ramp rates of thermal power unit i are given in order, Δt represents the operating period, ρ represents the thermal reserve coefficient, and T represents the thermal reserve coefficient. i，on 、T i，off The minimum start-up duration and minimum shutdown duration for thermal power unit i are, in order, H. i，on H i，off The following are the start-up cost coefficient and shutdown cost coefficient for thermal power unit i, respectively, P bat (i,t) represents the real-time power of energy storage unit i during time period t. The maximum discharge power and maximum charging power of energy storage unit i are given by SOC(i,t), and the SOC(i,t) is the charge of energy storage unit i in time period t. min SOC max The minimum and maximum charge capacities of energy storage unit i are, respectively, P. l,t 、P l,min 、P l,max The loads of branch l in time period t, the minimum load of branch l, and the maximum load of branch l are, in order, G. l-i G is used to describe the impact of the injected power at load node i on branch l. l-j Used to describe the effect of load node j on the output power transfer of branch l.

[0078] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a joint optimization dispatching device for wind, solar, thermal, and energy storage power systems. In other embodiments of the present invention, a joint optimization dispatching device for wind, solar, thermal, and energy storage power systems may include more or fewer components than illustrated, or combine some components, split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0079] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0080] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a joint optimization scheduling method for wind, solar, thermal, and energy storage power systems according to any embodiment of this invention.

[0081] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a joint optimization scheduling method for wind, solar, thermal, and energy storage power systems according to any embodiment of this invention.

[0082] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0083] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0084] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0085] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0086] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the functions of any of the embodiments described above.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0088] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, 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 various embodiments of the present invention.

Claims

1. A joint optimization dispatching method for a wind, solar, thermal and energy storage power system, characterized in that: include: Acquire the unit data of each target unit; wherein the target units include wind turbines, photovoltaic generators, thermal generators and energy storage generators, and the unit data include unit parameters, network parameters, load data, wind power output and photovoltaic power output; Taking the minimization of the operating cost of the wind-solar-thermal-storage power system as the goal, a joint optimization dispatch model and constraint conditions of the wind-solar-thermal-storage power system are constructed; Based on the unit data, the joint optimization scheduling model and the constraints, the optimal scheduling cost and the sequential power of the thermal power unit in each simulation period, the sequential charging and discharging power of the energy storage generator set, the available power of the wind turbine unit, the actual power consumption of the wind turbine unit, the available power of the photovoltaic generator unit, and the actual power consumption of the photovoltaic generator unit are solved.

2. The method according to claim 1, characterized in that The unit parameters include the node number of the network where each target unit is located, the maximum output, the minimum output, the coal consumption coefficient, the upward climbing rate, the downward climbing rate, the minimum shutdown duration, the minimum startup duration, the shutdown cost and the startup cost; The network parameters include the first and last node numbers, branch reactance, line maximum load and line minimum load; The load data includes the load size in each scheduling time period.

3. The method according to claim 2, characterized in that The wind power output is determined by the following formula: In the formula, υ t ,υ in ,υ off ,υ R are the wind speed at time t, cut-in wind speed, cut-off wind speed and rated wind speed of the wind turbine, m / s; P WR is the rated output of the wind turbine, MW; The photovoltaic power generation output is determined by the following formula: In the formula, S t , S max , S C They are the solar radiation at time t, the maximum solar radiation and the fixed-point solar radiation of the photovoltaic generator set, W / m 2 ;P SR is the rated output of the photovoltaic generator set, MW.

4. The method according to claim 3, characterized in that: The joint optimization scheduling model is determined by the following formula: Where minC is the minimum operating cost; T is the optimized scheduling time; N g , N pv 、N bat They are the number of thermal power units, wind power units, photovoltaic power units and energy storage power units, respectively; They are the operating cost coefficients of thermal power generation units, wind power generation units, photovoltaic power generation units and energy storage power generation units respectively; μ i (t) is the operating status of thermal power unit i in period t, μ i (t)=1 means the unit is turned on, μ i (t) = 0 means the unit is shut down; are the startup cost and shutdown cost of thermal power units; P i,t , P bat They are the output of thermal power generation unit, wind power generation unit, photovoltaic power generation unit and energy storage power generation unit in time period t; a i 、b i 、c i They are coal consumption coefficient.

5. The method according to claim 4, characterized in that The constraint condition is determined by the following formula: Where N L is the total number of load nodes, P d，t is the load demand of load node d in period t, are the maximum and minimum output of thermal power unit i, respectively. are the upward climbing rate and downward climbing rate of thermal power unit i, Δt is the operating period, ρ is the hot standby coefficient, T i，on , T i，off They are the minimum startup duration and shutdown duration of thermal power unit i, H i，on , H i，off are the startup cost coefficient and shutdown cost coefficient of thermal power unit i, P bat (i,t) is the real-time power of energy storage unit i in time period t, are the maximum discharge power and maximum charging power of energy storage unit i, SOC(i,t) is the charge of energy storage unit i in time period t, SOC min , SOC max are the minimum charge and maximum charge of energy storage unit i, P l,t , P l,min , P l,max are the load of branch l in time period t, the minimum load of branch l and the maximum load of branch l, G l-i It is used to describe the impact of the injected power of load node i on branch l, G l-j Used to describe the impact of load node j on the output power transfer of branch l.

6. A joint optimization dispatching device for a wind, solar, thermal and storage power system, characterized in that: include: An acquisition module is used to acquire the unit data of each target unit; wherein the target units include wind turbines, photovoltaic generators, thermal generators and energy storage generators, and the unit data include unit parameters, network parameters, load data, wind power output and photovoltaic power output; A construction module is used to construct a joint optimization dispatching model and constraint conditions of the wind, solar, thermal and storage power system with the goal of minimizing the operating cost of the wind, solar, thermal and storage power system; A solution module is used to solve the optimal scheduling cost and the sequential power of the thermal power unit in each simulation cycle, the sequential charging and discharging power of the energy storage generator set, the available power of the wind turbine unit, the actual power consumption of the wind turbine unit, the available power of the photovoltaic generator unit, and the actual power consumption of the photovoltaic generator unit based on the unit data, the joint optimization scheduling model and the constraints.

7. The device according to claim 6, characterized in that The unit parameters include the node number of the network where each target unit is located, the maximum output, the minimum output, the coal consumption coefficient, the upward climbing rate, the downward climbing rate, the minimum shutdown duration, the minimum startup duration, the shutdown cost and the startup cost; The network parameters include the first and last node numbers, branch reactance, line maximum load and line minimum load; The load data includes the load size in each scheduling time period.

8. The device according to claim 7, characterized in that The wind power output is determined by the following formula: In the formula, υ t ,υ in ,υ off ,υ R are the wind speed at time t, cut-in wind speed, cut-off wind speed and rated wind speed of the wind turbine, m / s; P WR is the rated output of the wind turbine, MW; The photovoltaic power generation output is determined by the following formula: In the formula, S t , S max , S C They are the solar radiation at time t, the maximum solar radiation and the fixed-point solar radiation of the photovoltaic generator set, W / m 2 ;P SR is the rated output of the photovoltaic generator set, MW.

9. The device according to claim 8, characterized in that The joint optimization scheduling model is determined by the following formula: Where minC is the minimum operating cost; T is the optimized scheduling time; N g , N pv 、N bat They are the number of thermal power units, wind power units, photovoltaic power units and energy storage power units, respectively; They are the operating cost coefficients of thermal power generation units, wind power generation units, photovoltaic power generation units and energy storage power generation units respectively; μ i (t) is the operating status of thermal power unit i in period t, μ i (t)=1 means the unit is turned on, μ i (t) = 0 means the unit is shut down; are the startup cost and shutdown cost of thermal power units; P i,t , P bat They are the output of thermal power generation unit, wind power generation unit, photovoltaic power generation unit and energy storage power generation unit in time period t; a i 、b i 、c i They are coal consumption coefficient.

10. The device according to claim 9, characterized in that The constraint condition is determined by the following formula: Where N L is the total number of load nodes, P d，t is the load demand of load node d in period t, are the maximum and minimum output of thermal power unit i, respectively. are the upward climbing rate and downward climbing rate of thermal power unit i, Δt is the operating period, ρ is the hot standby coefficient, T i，on , T i，off They are the minimum startup duration and shutdown duration of thermal power unit i, H i，on , H i，off are the startup cost coefficient and shutdown cost coefficient of thermal power unit i, P bat (i,t) is the real-time power of energy storage unit i in time period t, are the maximum discharge power and maximum charging power of energy storage unit i, SOC(i,t) is the charge of energy storage unit i in time period t, SOC min , SOC max are the minimum charge and maximum charge of energy storage unit i, P l,t , P l,min , P l,max are the load of branch l in time period t, the minimum load of branch l and the maximum load of branch l, G l-i It is used to describe the impact of the injected power of load node i on branch l, G l-j Used to describe the impact of load node j on the output power transfer of branch l.

Images