Renewable energy high-proportion power grid dispatching method and device

Abstract

The invention relates to the technical field of energy storage system configuration, and particularly provides a renewable energy source high-proportion power grid dispatching method and device, and the method comprises the steps: solving a first-stage optimization model corresponding to a power system, obtaining an energy storage planning result, and planning the power system through the energy storage planning result; a second-stage optimization model corresponding to the planned power system is obtained and solved, and an optimization result is obtained; based on the optimization result, obtaining a power grid scheduling strategy, and performing optimization scheduling on the planned power system by using the power grid scheduling strategy; according to the technical scheme provided by the invention, collaborative configuration of energy storage planning at the power grid side is realized, the utilization rate of renewable energy sources is improved, the operation cost of the system is reduced, and the problem of power grid peak regulation caused by large-scale grid connection of the renewable energy sources is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system configuration, and in particular to a method and device for dispatching a power grid with a high proportion of renewable energy. Background Art

[0002] With the global energy shortage, the installed capacity of renewable energy sources such as wind power and photovoltaic power generation has increased rapidly. However, the output of wind and photovoltaic power generation is intermittent and fluctuating. Large-scale grid connection will lead to problems with power system security, stability, and economic operation.

[0003] Based on this, technicians have proposed configuring energy storage systems within the power grid to address the technical challenges brought about by the rapid development of renewable energy. Battery energy storage systems are characterized by high flexibility, bidirectional power adjustment, and fast response speed. By rationally configuring energy storage systems within the power system, technicians can promote the absorption of renewable energy generation, alleviate renewable energy fluctuations, assist in frequency and peak regulation, and reduce carbon emissions. However, based on the configuration and optimized complementary coordinated control of battery energy storage systems, how to further explore the cost-effectiveness of energy storage systems from the perspective of the power grid, while also meeting the needs of market-based electricity transactions and promoting the active participation of energy storage power stations in grid dispatching, there is still no good configuration and control strategy. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes a method and device for dispatching a power grid with a high proportion of renewable energy.

[0005] In a first aspect, a method for dispatching a power grid with a high proportion of renewable energy is provided, the method comprising:

[0006] Solving the first-stage optimization model corresponding to the power system to obtain energy storage planning results, and using the energy storage planning results to plan the power system;

[0007] Obtain and solve the second-stage optimization model corresponding to the planned power system to obtain the optimization results;

[0008] Based on the optimization results, a power grid dispatching strategy is obtained, and the power grid dispatching strategy is used to optimize the planned power system;

[0009] The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

[0010] Preferably, the energy storage planning result includes: the configured capacity and maximum charge and discharge power of the energy storage at each node in the power system.

[0011] Preferably, the first-stage optimization model includes: a first objective function aiming at minimizing the total investment cost of energy storage and its corresponding first constraint condition.

[0012] Furthermore, the first objective function is as follows:

[0013] minC T =C inv +C om +C rep

[0014] In the above formula, C T is the total investment cost of the energy storage system, C inv is the initial construction cost, C om is the operation and maintenance cost, C rep Replacement costs for related equipment.

[0015] Furthermore, the initial construction costs are as follows:

[0016]

[0017] The operation and maintenance costs are as follows:

[0018]

[0019] The replacement costs of the relevant equipment are as follows:

[0020]

[0021] In the above formula, y e is the equivalent annual value conversion coefficient, r is the conversion rate, N is the planned number of years, B is the total number of nodes, c E 、c P are the cost per unit capacity and charge / discharge power, E i,rate and P i,rate are the configured capacity and maximum charge and discharge power of energy storage at node i, C om,f 、C om,v are fixed and variable operation and maintenance costs, c f 、c v are the unit fixed and variable maintenance costs of energy storage, P i,ch (t) and P i,dis (t) are the charging and discharging power of the energy storage at node i during period t, Δt is the optimization step length, k is the replacement time of the energy storage, T life is the life cycle of the energy storage system, K is the total replacement time of the energy storage, and T is the optimization period.

[0022] Furthermore, the first constraint condition is as follows:

[0023]

[0024] In the above formula, E i,max and P i,max are the maximum capacity and power of energy storage allowed at node i, V is the budget limit, and μ is the energy storage charging and discharging limit coefficient. When μ = 1, energy storage is allowed to charge and discharge, and when μ = 0, energy storage is prohibited from charging and discharging.

[0025] Preferably, the second-stage optimization model includes: a second objective function aiming at minimizing the system operation cost and its corresponding second constraint condition.

[0026] Furthermore, the second objective function is as follows:

[0027] minC sys =C DG +C gen +C cur +C em

[0028] In the above formula, C sys is the system operating cost, C DG is the operating cost of renewable energy, C gen is the operating cost of conventional units, C cur is the cost of curtailing wind and solar power, C em For environmental costs.

[0029] Furthermore, the operating costs of renewable energy are as follows:

[0030]

[0031] The operating costs of the conventional units are as follows:

[0032]

[0033] The costs of curtailing wind and solar power are as follows:

[0034]

[0035] The environmental costs are as follows:

[0036]

[0037] In the above formula, c wt and c pv are the unit output costs of wind power and photovoltaic power, P wt,t and P pv,t are the output power of wind power and photovoltaic power generation during period t, C f is the coal consumption cost; C res is the standby cost of conventional units; u g,tis the start and stop state of the conventional unit during period t. When the generator is in shutdown state, u g,t is 0, otherwise u g,t is 1, P g,t 、 and are the power output, upper reserve and lower reserve capacity of the conventional unit g during period t, respectively. g 、b g and c g are the energy consumption coefficient of the power generation function of the conventional unit g, α g , β g are the costs of upper and lower spare capacity provided by conventional unit g, C cur,wt and C cur,pv are the total cost of curtailed electricity of wind power and photovoltaic power, c cur,wt and c cur,pv are the curtailment costs of wind power wt and photovoltaic power pv, P wt,t,max and P pv,t,max are the maximum outputs of wind power wt and photovoltaic power pv during period t, P wt,t and P pv,t are the output of wind power wt and photovoltaic power generation pv during period t, d g is the CO2 emission coefficient per unit power generation of conventional unit g, ε is the distribution coefficient of CO2 emission power, K C is the carbon trading price, T is the scheduling period set, N g For conventional unit collection.

[0038] Furthermore, the second constraint conditions include: conventional power flow constraint, node voltage constraint, branch current constraint, renewable energy generator limitation, energy storage system constraint and spinning reserve constraint.

[0039] Furthermore, the renewable energy generators are restricted as follows:

[0040]

[0041] The energy storage system constraints are as follows:

[0042]

[0043] The spinning reserve constraints are as follows:

[0044]

[0045] In the above formula, u ch,t and u dis,t are the charge and discharge states of the energy storage system during period t, represented by 0 and 1 state quantities, respectively. ch,i,t and P dis,i,tare the charging and discharging power of the energy storage system at node i during period t. i,t-1 and E i,t are the energy stored in the energy storage system at node i during periods t-1 and t, respectively, η ch and η dis are the charge and discharge efficiency of the storage system, ω D 、ω wt and ω pv are the prediction error coefficients of load, wind power and photovoltaic power generation, and are the upper and lower reserve capacities of the energy storage system at node i during period t, P i,rate is the maximum charge and discharge power of energy storage at node i, Δt is the optimization step size, B is the total number of nodes, D i,t Let the load power at node i in time period t be.

[0046] In a second aspect, a dispatching device for a power grid with a high proportion of renewable energy is provided, the dispatching device for a power grid with a high proportion of renewable energy comprising:

[0047] a first analysis module for solving a first-stage optimization model corresponding to the power system, obtaining an energy storage planning result, and using the energy storage planning result to plan the power system;

[0048] The second analysis module is used to obtain and solve the second-stage optimization model corresponding to the planned power system to obtain the optimization results;

[0049] A third analysis module is used to obtain a power grid dispatching strategy based on the optimization result, and optimize the planned power system using the power grid dispatching strategy;

[0050] The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

[0051] In a third aspect, a computer device is provided, comprising: one or more processors;

[0052] The processor is configured to store one or more programs;

[0053] When the one or more programs are executed by the one or more processors, the method for dispatching a power grid with a high proportion of renewable energy is implemented.

[0054] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the method for dispatching a power grid with a high proportion of renewable energy is implemented.

[0055] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0056] The present invention provides a method and device for dispatching a power grid with a high proportion of renewable energy, comprising: solving a first-stage optimization model corresponding to the power system to obtain an energy storage planning result, and using the energy storage planning result to plan the power system; obtaining a second-stage optimization model corresponding to the planned power system and solving it to obtain an optimization result; based on the optimization result, obtaining a power grid dispatching strategy, and using the power grid dispatching strategy to optimize and dispatch the planned power system; wherein the optimization result includes: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node. The technical solution provided by the present invention realizes the coordinated configuration of energy storage planning on the power grid side, helps to improve the utilization rate of renewable energy, reduce the operating cost of the system, and solve the power grid peak regulation problem caused by the large-scale grid connection of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of the main steps of the method for dispatching a power grid with a high proportion of renewable energy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1

[0061] See attached Figure 1 , Figure 1 This is a flow chart of the main steps of a method for dispatching a power grid with a high proportion of renewable energy according to an embodiment of the present invention. Figure 1 As shown, the method for dispatching a power grid with a high proportion of renewable energy in an embodiment of the present invention mainly includes the following steps:

[0062] Step S101: solving the first-stage optimization model corresponding to the power system to obtain energy storage planning results, and using the energy storage planning results to plan the power system;

[0063] Step S102: obtaining and solving the second-stage optimization model corresponding to the planned power system to obtain an optimization result;

[0064] Step S103: Based on the optimization result, a power grid dispatching strategy is obtained, and the power grid dispatching strategy is used to optimize the planned power system.

[0065] The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

[0066] In this embodiment, the energy storage planning result includes: the configured capacity and maximum charge and discharge power of the energy storage at each node in the power system.

[0067] In this embodiment, the first-stage optimization model includes: a first objective function with the goal of minimizing the total investment cost of energy storage and its corresponding first constraint condition.

[0068] In one embodiment, the first objective function is as follows:

[0069] minC T =C inv +C om +C rep

[0070] In the above formula, C T is the total investment cost of the energy storage system, C inv is the initial construction cost, C om is the operation and maintenance cost, C rep Replacement costs for related equipment.

[0071] In one embodiment, the initial construction cost is as follows:

[0072]

[0073] The operation and maintenance costs are as follows:

[0074]

[0075] The replacement costs of the relevant equipment are as follows:

[0076]

[0077] In the above formula, y e is the equivalent annual value conversion coefficient, r is the conversion rate, N is the planned number of years, B is the total number of nodes, c E 、c P are the cost per unit capacity and charge / discharge power, E i,rate and Pi,rate are the configured capacity and maximum charge and discharge power of energy storage at node i, C om,f 、C om,v are fixed and variable operation and maintenance costs, c f 、c v are the unit fixed and variable maintenance costs of energy storage, P i,ch (t) and P i,dis (t) are the charging and discharging power of the energy storage at node i during period t, Δt is the optimization step length, k is the replacement time of the energy storage, T life is the life cycle of the energy storage system, K is the total replacement time of the energy storage, and T is the optimization period.

[0078] In one embodiment, the first constraint is as follows:

[0079]

[0080] In the above formula, E i,max and P i,max are the maximum capacity and power of energy storage allowed at node i, V is the budget limit, and μ is the energy storage charging and discharging limit coefficient. When μ = 1, energy storage is allowed to charge and discharge, and when μ = 0, energy storage is prohibited from charging and discharging.

[0081] In this embodiment, the second-stage optimization model includes: a second objective function with the goal of minimizing the system operation cost and its corresponding second constraint condition.

[0082] In one embodiment, the second objective function is as follows:

[0083] minC sys =C DG +C gen +C cur +C em

[0084] In the above formula, C sys is the system operating cost, C DG is the operating cost of renewable energy, C gen is the operating cost of conventional units, C cur is the cost of curtailing wind and solar power, C em For environmental costs.

[0085] In one embodiment, the renewable energy operating cost is as follows:

[0086]

[0087] The operating costs of the conventional units are as follows:

[0088]

[0089] The costs of curtailing wind and solar power are as follows:

[0090]

[0091] The environmental costs are as follows:

[0092]

[0093] In the above formula, c wt and c pv are the unit output costs of wind power and photovoltaic power, P wt,t and P pv,t are the output power of wind power and photovoltaic power generation during period t, C f is the coal consumption cost; C res is the standby cost of conventional units; u g,t is the start and stop state of the conventional unit during period t. When the generator is in shutdown state, u g,t is 0, otherwise u g,t is 1, P g,t 、 and are the power output, upper reserve and lower reserve capacity of the conventional unit g during period t, respectively. g 、b g and c g are the energy consumption coefficient of the power generation function of the conventional unit g, α g , β g are the costs of upper and lower spare capacity provided by conventional unit g, C cur,wt and C cur,pv are the total cost of curtailed electricity of wind power and photovoltaic power, c cur,wt and c cur,pv are the curtailment costs of wind power wt and photovoltaic power pv, P wt,t,max and P pv,t,max are the maximum outputs of wind power wt and photovoltaic power pv during period t, P wt,t and P pv,t are the output of wind power wt and photovoltaic power generation pv during period t, d g is the CO2 emission coefficient per unit power generation of conventional unit g, ε is the distribution coefficient of CO2 emission power, K C is the carbon trading price, T is the scheduling period set, N g For conventional unit collection.

[0094] In one embodiment, the second constraints include: conventional power flow constraints, node voltage constraints, branch current constraints, renewable energy generator limitations, energy storage system constraints, and spinning reserve constraints.

[0095] In one embodiment, the renewable energy generator is constrained as follows:

[0096]

[0097] The energy storage system constraints are as follows:

[0098]

[0099] The spinning reserve constraints are as follows:

[0100]

[0101] In the above formula, u ch,t and u dis,t are the charge and discharge states of the energy storage system during period t, represented by 0 and 1 state quantities, respectively. ch,i,t and P dis,i,t are the charging and discharging power of the energy storage system at node i during period t. i,t-1 and E i,t are the energy stored in the energy storage system at node i during periods t-1 and t, respectively, η ch and η dis are the charge and discharge efficiency of the storage system, ω D 、ω wt and ω pv are the prediction error coefficients of load, wind power and photovoltaic power generation, and are the upper and lower reserve capacities of the energy storage system at node i during period t, P i,rate is the maximum charge and discharge power of energy storage at node i, Δt is the optimization step size, B is the total number of nodes, D i,t Let the load power at node i in time period t be.

[0102] In one specific implementation, the first-stage optimization model is solved using an improved Sunflower optimization algorithm to determine the location, capacity, and maximum charge and discharge power of the energy storage system connected to the grid. The second-stage optimization model is solved using MATLAB connected to YALMIP and the CPLEX commercial solver to determine the optimal grid dispatch strategy. The improved Sunflower algorithm is specifically:

[0103] By introducing the Levy flight mechanism, the performance of the Sunflower optimization algorithm can be significantly improved, effectively preventing it from falling into local convergence. This mechanism allows random search techniques to regulate local search, ultimately solving the problem of premature convergence. The formula for the Levy flight mechanism Le(w) is:

[0104]

[0105] Where: w represents the step size, the parameter τ is in the range [0, 2], and the value is 1.5, where A and B follow the normal distribution N(0, σ 2 ), σ 2 is the variance of the normal distribution, A / B obeys the normal distribution N(0,σ 2 ), Γ is the Gamma function.

[0106] After the Levy flight mechanism is implemented, the position update function of the new plant is as follows:

[0107]

[0108] Where: and are the positions of the sunflowers at the i-th iteration and the i+1-th iteration, The direction of the sunflower facing the sun, S i For sunflowers in the direction The step length of the movement.

[0109] Example 2

[0110] Based on the same inventive concept, the present invention further provides a dispatching device for a power grid with a high proportion of renewable energy, the dispatching device for a power grid with a high proportion of renewable energy comprising:

[0111] a first analysis module for solving a first-stage optimization model corresponding to the power system, obtaining an energy storage planning result, and using the energy storage planning result to plan the power system;

[0112] The second analysis module is used to obtain and solve the second-stage optimization model corresponding to the planned power system to obtain the optimization results;

[0113] A third analysis module is used to obtain a power grid dispatching strategy based on the optimization result, and optimize the planned power system using the power grid dispatching strategy;

[0114] The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

[0115] Preferably, the energy storage planning result includes: the configured capacity and maximum charge and discharge power of the energy storage at each node in the power system.

[0116] Preferably, the first-stage optimization model includes: a first objective function aiming at minimizing the total investment cost of energy storage and its corresponding first constraint condition.

[0117] Furthermore, the first objective function is as follows:

[0118] minC T =C inv +C om +C rep

[0119] In the above formula, C T is the total investment cost of the energy storage system, C inv is the initial construction cost, C om is the operation and maintenance cost, C rep Replacement costs for related equipment.

[0120] Furthermore, the initial construction costs are as follows:

[0121]

[0122] The operation and maintenance costs are as follows:

[0123]

[0124] The replacement costs of the relevant equipment are as follows:

[0125]

[0126] In the above formula, y e is the equivalent annual value conversion coefficient, r is the conversion rate, N is the planned number of years, B is the total number of nodes, c E 、c P are the cost per unit capacity and charge / discharge power, E i,rate and P i,rate are the configured capacity and maximum charge and discharge power of energy storage at node i, C om,f 、C om,v are fixed and variable operation and maintenance costs, c f 、c v are the unit fixed and variable maintenance costs of energy storage, P i,ch (t) and P i,dis (t) are the charging and discharging power of the energy storage at node i during period t, Δt is the optimization step length, k is the replacement time of the energy storage, T life is the life cycle of the energy storage system, K is the total replacement time of the energy storage, and T is the optimization period.

[0127] Furthermore, the first constraint condition is as follows:

[0128]

[0129] In the above formula, E i,max and P i,maxare the maximum capacity and power of energy storage allowed at node i, V is the budget limit, and μ is the energy storage charging and discharging limit coefficient. When μ = 1, energy storage is allowed to charge and discharge, and when μ = 0, energy storage is prohibited from charging and discharging.

[0130] Preferably, the second-stage optimization model includes: a second objective function aiming at minimizing the system operation cost and its corresponding second constraint condition.

[0131] Furthermore, the second objective function is as follows:

[0132] minC sys =C DG +C gen +C cur +C em

[0133] In the above formula, C sys is the system operating cost, C DG is the operating cost of renewable energy, C gen is the operating cost of conventional units, C cur is the cost of curtailing wind and solar power, C em For environmental costs.

[0134] Furthermore, the operating costs of renewable energy are as follows:

[0135]

[0136] The operating costs of the conventional units are as follows:

[0137]

[0138] The costs of curtailing wind and solar power are as follows:

[0139]

[0140] The environmental costs are as follows:

[0141]

[0142] In the above formula, c wt and c pv are the unit output costs of wind power and photovoltaic power, P wt,t and P pv,t are the output power of wind power and photovoltaic power generation during period t, C f is the coal consumption cost; C res is the standby cost of conventional units; u g,t is the start and stop state of the conventional unit during period t. When the generator is in shutdown state, u g,t is 0, otherwise u g,t is 1, Pg,t 、 and are the power output, upper reserve and lower reserve capacity of the conventional unit g during period t, respectively. g 、b g and c g are the energy consumption coefficient of the power generation function of the conventional unit g, α g , β g are the costs of upper and lower spare capacity provided by conventional unit g, C cur,wt and C cur,pv are the total cost of curtailed electricity of wind power and photovoltaic power, c cur,wt and c cur,pv are the curtailment costs of wind power wt and photovoltaic power pv, P wt,t,max and P pv,t,max are the maximum outputs of wind power wt and photovoltaic power pv during period t, P wt,t and P pv,t are the output of wind power wt and photovoltaic power generation pv during period t, d g is the CO2 emission coefficient per unit power generation of conventional unit g, ε is the distribution coefficient of CO2 emission power, K C is the carbon trading price, T is the scheduling period set, N g For conventional unit collection.

[0143] Furthermore, the second constraint conditions include: conventional power flow constraint, node voltage constraint, branch current constraint, renewable energy generator limitation, energy storage system constraint and spinning reserve constraint.

[0144] Furthermore, the renewable energy generators are restricted as follows:

[0145]

[0146] The energy storage system constraints are as follows:

[0147]

[0148] The spinning reserve constraints are as follows:

[0149]

[0150] In the above formula, u ch,t and u dis,t are the charge and discharge states of the energy storage system during period t, represented by 0 and 1 state quantities, respectively. ch,i,t and P dis,i,t are the charging and discharging power of the energy storage system at node i during period t. i,t-1 and E i,t are the energy stored in the energy storage system at node i during periods t-1 and t, respectively, ηch and η dis are the charge and discharge efficiency of the storage system, ω D 、ω wt and ω pv are the prediction error coefficients of load, wind power and photovoltaic power generation, and are the upper and lower reserve capacities of the energy storage system at node i during period t, P i,rate is the maximum charge and discharge power of energy storage at node i, Δt is the optimization step size, B is the total number of nodes, D i,t Let the load power at node i in time period t be.

[0151] Example 3

[0152] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for dispatching a power grid with a high proportion of renewable energy in the above embodiment.

[0153] Example 4

[0154] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for dispatching a power grid with a high proportion of renewable energy in the above embodiment.

[0155] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for dispatching a power grid with a high proportion of renewable energy, characterized in that: The method comprises: Solving the first-stage optimization model corresponding to the power system to obtain energy storage planning results, and using the energy storage planning results to plan the power system; Obtain and solve the second-stage optimization model corresponding to the planned power system to obtain the optimization results; Based on the optimization results, a power grid dispatching strategy is obtained, and the power grid dispatching strategy is used to optimize the planned power system; The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

2. The method according to claim 1, wherein The energy storage planning results include: the configured capacity and maximum charge and discharge power of energy storage at each node in the power system.

3. The method according to claim 1, wherein The first-stage optimization model includes: a first objective function aiming at minimizing the total investment cost of energy storage and its corresponding first constraint condition.

4. The method according to claim 3, wherein The first objective function is as follows: minC T =C inv +C om +C rep In the above formula, C T is the total investment cost of the energy storage system, C inv is the initial construction cost, C om is the operation and maintenance cost, C rep Replacement costs for related equipment.

5. The method according to claim 4, wherein The initial construction costs are as follows: The operation and maintenance costs are as follows: The replacement costs of the relevant equipment are as follows: In the above formula, y e is the equivalent annual value conversion coefficient, r is the conversion rate, N is the planned number of years, B is the total number of nodes, c E 、c P are the cost per unit capacity and charge / discharge power, E i,rate and P i,rate are the configured capacity and maximum charge and discharge power of energy storage at node i, C om,f 、C om,v are fixed and variable operation and maintenance costs, c f 、c v are the unit fixed and variable maintenance costs of energy storage, P i,ch (t) and P i,dis (t) are the charging and discharging power of the energy storage at node i during period t, Δt is the optimization step length, k is the replacement time of the energy storage, T life is the life cycle of the energy storage system, K is the total replacement time of the energy storage, and T is the optimization period.

6. The method according to claim 5, wherein The first constraint is as follows: In the above formula, E i,max and P i,max are the maximum capacity and power of energy storage allowed at node i, V is the budget limit, and μ is the energy storage charging and discharging limit coefficient. When μ = 1, energy storage is allowed to charge and discharge, and when μ = 0, energy storage is prohibited from charging and discharging.

7. The method according to claim 1, wherein The second-stage optimization model includes: a second objective function with the goal of minimizing the system operation cost and its corresponding second constraint condition.

8. The method according to claim 7, wherein The second objective function is as follows: minC sys =C DG +C gen +C cur +C em In the above formula, C sys is the system operating cost, C DG is the operating cost of renewable energy, C gen is the operating cost of conventional units, C cur is the cost of curtailing wind and solar power, C em For environmental costs.

9. The method according to claim 8, wherein The renewable energy operating costs are as follows: The operating costs of the conventional units are as follows: The costs of curtailing wind and solar power are as follows: The environmental costs are as follows: In the above formula, c wt and c pv are the unit output costs of wind power and photovoltaic power, P wt,t and P pv,t are the output power of wind power and photovoltaic power generation during period t, C f is the coal consumption cost; C res is the standby cost of conventional units; u g,t is the start and stop state of the conventional unit during period t. When the generator is in shutdown state, u g,t is 0, otherwise u g,t is 1, P g,t 、 and are the power output, upper reserve and lower reserve capacity of the conventional unit g during period t, respectively. g 、b g and c g are the energy consumption coefficient of the power generation function of the conventional unit g, α g , β g are the costs of upper and lower spare capacity provided by conventional unit g, C cur,wt and C cur,pv are the total cost of curtailed power generation from wind power and photovoltaic power, c cur,wt and c cur,pv are the curtailment costs of wind power wt and photovoltaic power pv, P wt,t,max and P pv,t,max are the maximum outputs of wind power wt and photovoltaic power pv during period t, P wt,t and P pv,t are the output of wind power wt and photovoltaic power generation pv during period t, d g is the CO2 emission coefficient per unit power generation of conventional unit g, ε is the distribution coefficient of CO2 emission power, K C is the carbon trading price, T is the scheduling period set, N g For conventional unit collection.

10. The method according to claim 9, wherein The second constraint conditions include: conventional power flow constraint, node voltage constraint, branch current constraint, renewable energy generator limitation, energy storage system constraint and spinning reserve constraint.

11. The method according to claim 10, wherein The renewable energy generator restrictions are as follows: The energy storage system constraints are as follows: The spinning reserve constraints are as follows: In the above formula, u ch,t and u dis,t are the charge and discharge states of the energy storage system during period t, represented by 0 and 1 state quantities, respectively. ch,i,t and P dis,i,t are the charging and discharging power of the energy storage system at node i during period t. i,t-1 and E i,t are the energy stored in the energy storage system at node i during periods t-1 and t, respectively, η ch and η dis are the charge and discharge efficiency of the storage system, ω D 、ω wt and ω pv are the prediction error coefficients of load, wind power and photovoltaic power generation, and are the upper and lower reserve capacities of the energy storage system at node i during period t, P i,rate is the maximum charge and discharge power of energy storage at node i, Δt is the optimization step size, B is the total number of nodes, D i,t Let the load power at node i in time period t be.

12. A device for dispatching a power grid with a high proportion of renewable energy according to any one of claims 1 to 11, characterized in that: The device comprises: a first analysis module for solving a first-stage optimization model corresponding to the power system, obtaining an energy storage planning result, and using the energy storage planning result to plan the power system; The second analysis module is used to obtain and solve the second-stage optimization model corresponding to the planned power system to obtain the optimization results; A third analysis module is used to obtain a power grid dispatching strategy based on the optimization result, and optimize the planned power system using the power grid dispatching strategy; The optimization results include: the output power of wind and photovoltaic power generation, the start and stop status of conventional units, the power output of conventional units, the charge and discharge status of the energy storage system, and the charge and discharge power of the energy storage system at the node.

13. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for dispatching a power grid with a high proportion of renewable energy as claimed in any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the method for dispatching a power grid with a high proportion of renewable energy is implemented as described in any one of claims 1 to 11.

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