Wind power and energy storage planning method and system considering hour-level robust unit combination

Abstract

The invention discloses a wind power and energy storage planning method and system considering hour-level robust unit combination. The method comprises the following steps: establishing a power system planning problem mathematical model containing wind power and energy storage; solving the electric power system planning problem mathematical model by using a parallel period splitting algorithm to obtain a specific operation plan of the electric power system; and based on a specific operation plan of the power system, solving by using a gradient descent method of a nested genetic algorithm to obtain an optimal capacity plan. Reference is provided for a power system to formulate a wind power and energy storage planning scheme through hour-level solution of a long-period problem, and the wind power consumption and the energy storage working efficiency are increased under the condition that the economic cost is reduced as much as possible.

Description

Background Art

[0002] Traditional thermal power generating units such as coal-fired units generate a large amount of carbon emissions during the power generation process, which is one of the main causes of climate change. Therefore, clean and renewable energy sources are being developed in large quantities to increase energy supply and reduce carbon emissions. Energy storage, as one of the important assets to mitigate the uncertainty of renewable energy, has also been greatly developed. In this case, the expansion planning of renewable energy and energy storage systems becomes very important for power grid companies and investors. Under the optimal planning decisions of different objective functions, the utilization rate of renewable energy and the profit of investors will be maximized respectively. The core difficulty in solving the capacity planning problem is how to accurately evaluate the economic benefits / operating cost expectations brought about by the experimental capacity planning decisions. In order to solve the planning problem, many factors are generally considered, including geographical characteristics, climate characteristics, incentive policies, and power system operation constraints, among which the power system operation constraints have a greater impact on the planning results.

[0003] Among the existing technical methods, there are two-tier capacity expansion planning methods that incorporate the renewable energy market into the operation of the power system, there are also methods that use data-driven scenario generation methods for transmission capacity expansion planning problems, and there are also technologies that embed computationally expensive simulation models to simulate unit combination problems.

[0004] In existing methods, multi-stage operation processes are not well considered for power systems with transmission constraints. In other words, multi-stage robust unit commitment and economic dispatch issues are not properly involved in these planning methods. Therefore, in actual multi-stage operation of power systems, the planning results may be uneconomical or unsafe.

[0005] In the process of short-term multi-stage operation of the power system, the uncertainty of renewable energy must be fully considered to ensure the multi-stage robustness of the solution, especially the unpredictability and robustness of the solution. In addition, in the process of solving the long-term hourly robust transmission security constrained unit commitment problem (TCUC) and its economic dispatch problem, the amount of calculation should be controlled within a reasonable range. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a wind power energy storage planning method and system considering the hourly robust unit combination problem in view of the above-mentioned deficiencies in the prior art, so as to solve the technical problem that the prior art does not consider the actual multi-stage operation process of the power system.

[0007] The present invention adopts the following technical solutions:

[0008] The wind power and energy storage planning method considering hourly robust unit combination includes the following steps:

[0009] S1. Establish a mathematical model for power system planning problems including wind power and energy storage;

[0010] S2, using a parallel cycle splitting algorithm to solve the mathematical model of the power system planning problem obtained in step S1 to obtain a specific operation plan of the power system;

[0011] S3. Based on the specific operation plan of the power system, the optimal capacity planning is solved using the gradient descent method of the nested genetic algorithm.

[0012] Preferably, step S1 specifically includes:

[0013] Constructing the objective function with the goal of minimizing investment-related costs and operation-related costs;

[0014] Determine investment and construction-related constraints, including limiting the total investment, the available investment range for wind farms and energy storage stations; limiting the total capacity of wind power generation to be less than the maximum value of wind power generation; and limiting the total transmission power to within the transmission power limit when the connection constraint is to transmit power to the main grid as the positive direction, including all nodes of wind power and energy storage;

[0015] The system operation process is characterized by the full-scenario feasible transmission safety constraint unit combination and economic dispatch problem, and the system operation cost is calculated in the process of solving the long-term full-scenario feasible unit combination problem and the corresponding economic dispatch problem.

[0016] Preferably, the objective function is:

[0017]

[0018] Among them, cap new represents the expansion planning decision, c pla is the total planning cost, c inv is the investment and construction cost, c fom is the fixed operation and maintenance cost, c sal is the residual value within the planning period, c om For running costs.

[0019] Preferably, the investment and construction related constraints are as follows:

[0020]

[0021] in, C inv,total , are the lower and upper limits of the total investment cost, C total,w , is the lower and upper limits of wind power investment costs, Ctotal,sto , are the lower and upper limits of energy storage investment costs, For the existing wind power capacity, The current energy storage charging and discharging power capacity, is the upper limit of naturally available wind, TL , K is the lower and upper limits of the line transmission capacity from the planning node to the main network, new is the newly added wind power set, k is the wind power index, J new is the newly added wind power set, j is the wind power index, N is the planning-related node set, n is the node index, is the existing energy storage charging and discharging power capacity of node n, Add energy storage charging and discharging power capacity for node n, is the existing wind power capacity of node n, Add new wind power capacity to node n, is the existing energy storage charging and discharging power capacity of node j, Add energy storage charging and discharging power capacity for node j, They are the unit investment and construction costs of wind power, energy storage charging and discharging power, and energy storage capacity.

[0022] Preferably, in the day-ahead stage, the objective function of the unit commitment model that is feasible in all scenarios is:

[0023]

[0024] Among them, p min ,p max are two auxiliary variables, S,ds represents the scenario set and scenario index, I,di represents the unit set and unit index, T,t represents the time period set and time period index, M,m represents the load set and load index, α s is the weight of scene s, z ,i,t is the on / off status of unit i in period t, p i,t,s is the power generation of unit i in time period t under scenario s, is the startup cost and fuel cost of unit i, β m,t is the penalty price for load shedding, is the load shedding power of load m in period t;

[0025] In the intraday scheduling stage, the objective function of the unit commitment model that is feasible in all scenarios is:

[0026]

[0027] Where p is the generating power of the unit, represents the objective function of the economic dispatch problem, and p represents the power generation vector of the firing unit.

[0028] Preferably, the constraints in the day-ahead phase are as follows:

[0029] The amount of wind power generated is less than the energy that can be provided by natural wind volume, and the amount of wind power generated is less than the installed capacity of wind power:

[0030]

[0031]

[0032] in, is the power generation of wind farm k in time period t under scenario s, is the energy that can be provided by the natural wind volume of wind farm k in time period t under scenario s;

[0033] Load balancing constraints:

[0034]

[0035] Among them, d m,t is the actual load power of load m in time period t;

[0036] Line transmission security constraints:

[0037]

[0038] Among them, F l is the line transmission capacity, Γ is the transmission power distribution factor matrix, Γ U ,Γ W ,Γ D The matrix part corresponding to the unit, wind power and load;

[0039] Power generation limit constraints

[0040]

[0041] Among them, P i , is the physical lower and upper bounds of the generating capacity of the unit, p min ,p max are two auxiliary variables, which are the scheduling lower and upper bounds;

[0042] Unit climbing constraints:

[0043]

[0044]

[0045] in, is the climbing ability limit, τ is the time period length, which is 1 hour in the present invention, is the startup and shutdown ramp power limit of unit i, P i is the rated power of unit i;

[0046] Minimum on / off time constraints for the unit:

[0047]

[0048] Among them, G i is the number of time periods that unit i needs to keep running at the beginning of scheduling, is the minimum number of startup periods of unit i, O i is the number of time periods that unit i needs to remain shut down at the beginning of scheduling, τ i is the minimum number of shutdown periods for unit i.

[0049] Preferably, the constraints of the intraday scheduling phase are as follows:

[0050] Various basic bounds:

[0051]

[0052] in, is the charging power of energy storage j in time period t under scenario s, is the discharge power of energy storage j in time period t under scenario s;

[0053] The operation mechanism of energy storage system:

[0054]

[0055] Among them, μ j , is the lower and upper limits of the energy storage capacity of energy storage j, e j,t,s is the energy storage capacity of energy storage j in time period t under scenario s, η ch is the charging power coefficient, η dis is the discharge power coefficient;

[0056] Load balancing constraint: the total power generation of thermal power units, wind power units, and energy storage is equal to the total load power:

[0057]

[0058] Transmission security constraints on lines

[0059]

[0060] in, It is the part corresponding to the energy storage node in the transmission power distribution factor matrix.

[0061] Preferably, in step S2, the time period of the long-period scheduling problem is [1, T], which is continuously and evenly divided into K short periods, that is, the following series of periods: [1, t1], [t1+1, t2], [t2+1, t3], …, [t K-1 +1,t K ]; With the splitting of the cycle, the original problem is converted into several independent short scheduling cycle sub-problems. The short scheduling cycle sub-problems are solved separately at the same time. After the optimal solution of each short cycle sub-problem is obtained, they are arranged in the original time sequence, and based on the requirements of the unit operation constraints, the scheduling decision is adjusted for several time periods of every two adjacent sub-problems. This is repeated K-1 times in total to obtain a feasible solution to the original problem.

[0062] Preferably, step S3 specifically includes:

[0063] S301, considering the impact of a single planning location on the total planning cost, select the initial trial planning decision in proportion within the capacity range limited by the constraint conditions in step S1;

[0064] S302. The total planning cost under the experimental plan is the fitness function. When solving the operating cost of a certain population individual, the long-term full-scenario feasible unit combination problem and the corresponding long-term economic dispatch problem are solved;

[0065] S303, using the roulette method to select dominant individuals;

[0066] S304, generating new individuals of the next generation based on the gradient descent method according to the fitness functions and planning capacities of the two selected crossover individuals;

[0067] S305. Randomly generate multiple offspring individuals in the feasible region and put them into the overall population.

[0068] In a second aspect, an embodiment of the present invention provides a wind power energy storage planning system considering hourly robust unit combination problems, including:

[0069] Building modules to establish mathematical models for power system planning problems including wind power and energy storage;

[0070] The calculation module uses a parallel cycle splitting algorithm to solve the obtained mathematical model of the power system planning problem and obtain a specific operation plan of the power system;

[0071] The planning module uses the gradient descent method of the nested genetic algorithm to solve the optimal capacity planning based on the specific operation plan of the power system.

[0072] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned wind power and energy storage planning method considering the hourly robust unit combination problem when executing the computer program.

[0073] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned wind power energy storage planning method considering the hourly robust unit combination problem.

[0074] Compared with the prior art, the present invention has at least the following beneficial effects:

[0075] The wind power and energy storage planning method considering the hourly robust unit combination problem first establishes a mathematical model of the power system planning problem including wind power and energy storage, which is used to scientifically describe the real physical system mathematically, and abstract the various components of the power system into formula expressions, providing a systematic representation method for subsequent steps; the parallel cycle splitting algorithm is used to solve the mathematical model of the power system planning problem to obtain a specific operation plan of the power system. Parallel computing can reduce the required time and improve the solution efficiency; the gradient descent method of the nested genetic algorithm is used to solve the optimal capacity planning. Intelligent algorithms such as genetic algorithms have good effects on multi-parameter optimization problems. Combined with the gradient descent method of traditional power system optimization problems, it is more conducive to efficiently obtaining the global optimal solution for wind power and energy storage planning.

[0076] Furthermore, the mathematical model of the power system planning problem including wind power and energy storage consists of an objective function and constraints. The objective function is to minimize the investment-related costs and operation-related costs, so that the final optimization result has a balanced performance in terms of investment costs and operation costs, and the total cost in the entire planning cycle is the lowest; the investment and construction-related constraints ensure that the planning results can meet the requirements of the physical system during actual operation and avoid the occurrence of infeasible planning results by defining the scope of various investment costs, the upper limit of wind power capacity, and the grid-related network constraints.

[0077] Furthermore, by characterizing the system operation process through the full-scenario feasible transmission safety constrained unit combination and economic dispatch problem, the system operation process can be described when there are actually uncertain loads and new energy sources; the system operation cost is calculated in the process of solving the long-term full-scenario feasible unit combination problem and the corresponding economic dispatch problem, and the cost of system operation in various scenarios on a long-term time scale can be calculated.

[0078] Furthermore, by splitting the time period of the long-period scheduling problem, the original long period is continuously and evenly divided into several short periods. With the splitting of the period, the original problem is also converted into several independent short scheduling period sub-problems. These sub-problems can be solved independently at the same time, and the solving performance of the hardware facilities and the quantity advantage of the hardware facilities can be fully utilized, thereby greatly improving the solution speed of the long-period scheduling problem.

[0079] Furthermore, the gradient descent method of the nested genetic algorithm is used to obtain the optimal planning solution. This step organically combines advanced intelligent algorithms and traditional optimization methods, fully utilizing the advantages of both methods and avoiding the problem that traditional gradient descent algorithms are prone to falling into local optimal solutions. At the same time, it ensures that each step of the algorithm iteration is moving in the direction of the optimal solution, thereby efficiently obtaining high-quality planning solutions.

[0080] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0081] In summary, the present invention can effectively solve the problem of wind power and energy storage expansion planning, and provide a method for evaluating system operating costs based on hourly scheduling. The present invention can provide a reference for the formulation of wind power and energy storage planning schemes for power systems by solving long-cycle problems at the hourly level, thereby increasing wind power consumption and energy storage efficiency while reducing economic costs.

[0082] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0084] Figure 1 Plan a flow chart for the present invention;

[0085] Figure 2 This is the flow chart of the gradient descent method of the nested genetic algorithm;

[0086] Figure 3 This is a schematic diagram of the optimal site and expansion plan for the wind farm;

[0087] Figure 4 This is a schematic diagram of the optimal location and expansion plan for the energy storage station;

[0088] Figure 5A schematic diagram of a computer device provided by an embodiment of the present invention;

[0089] Figure 6 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0091] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0092] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0093] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0094] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0095] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0096] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0097] The present invention provides a wind power and energy storage planning method considering the hourly robust unit combination problem. First, a mathematical model description of the power system planning problem including wind power and energy storage is established, including the calculation method of various planning costs and the modeling of the robust transmission safety constrained unit combination problem and the economic dispatch problem; then a parallel cycle splitting method is used to quickly calculate the system operation cost, and further calculate the total system planning cost; finally, the planned capacity is iteratively updated through the gradient descent method of the nested genetic algorithm to solve the optimal wind power and energy storage planning scheme; the wind power and energy storage planning scheme of the power system is obtained efficiently and with high quality.

[0098] See also Figure 1 The present invention provides a wind power energy storage planning method considering hourly robust unit combination problems, comprising the following steps:

[0099] S1. Establish a mathematical model representation of the system;

[0100] The mathematical model characterization of the system mainly includes the objective function and system operation constraints. The description of the objective function includes construction investment cost, fixed operation and maintenance cost, depreciation cost, and system operation cost. The system operation constraints include system load balance constraints, transmission safety constraints, various boundary constraints, unit operation constraints and other auxiliary constraints.

[0101] S101, description of objective function;

[0102] For this type of optimal capacity planning problem, the objective function used in the present invention is to minimize the investment-related costs and operation-related costs. Specifically, the total cost includes investment cost, fixed operating cost, fixed maintenance cost, depreciation cost and operating cost. The objective function expression is as follows:

[0103]

[0104] Among them, cap new represents the expansion planning decision, c pla is the total planning cost, c inv is the investment and construction cost, c fom is the fixed operation and maintenance cost, c sal is the residual value within the planning period.

[0105] The investment cost includes the investment cost of wind power capacity, energy storage equipment charging and discharging power capacity and energy storage capacity, as follows:

[0106]

[0107] where c inv,total is the total investment cost, c inv,total,w is the total investment cost of wind power, c inv,total,sto is the total investment cost of energy storage, K new is the newly added wind power set, k is the wind power index, is the capacity of the kth newly added wind power, is the cost of wind power construction per unit capacity, is the subsidy for wind power construction per unit capacity, J new is the newly added wind power set, j is the wind power index, Add a new upper limit on charging and discharging power for energy storage. The cost of adding a unit of charging and discharging power to energy storage, Subsidy for the additional charging and discharging power per unit capacity of energy storage, Adding a new upper limit to the energy storage capacity, The cost of adding a unit of storage capacity to energy storage, Subsidy for new units of energy storage capacity.

[0108] In order to calculate the investment cost more accurately, the present invention uses the debt repayment fund method, which is a common economic method in investment planning. The borrower is required to pay the loan interest to the lender regularly every period and deposit another amount of money on time to establish a fund. When the loan matures, the amount in the fund is exactly equal to the principal of the loan, and then it is immediately repaid to the lender, as follows:

[0109]

[0110] Among them, P is the loan term, R LE is the borrowing rate, R DE is the interest rate on the debt repayment fund deposit, generally R LE >R DE .

[0111] Fixed operation and maintenance costs are directly related to investment planning decisions. Taking into account the discount rate, the fixed operation and maintenance costs during the planning period are as follows:

[0112]

[0113] Among them, SY and EY represent the index of the first and last year of planning, R DC is the discount rate, and the three parts of the above formula represent the fixed operation and maintenance costs of wind power installed capacity, energy storage charging and discharging capacity, and energy storage installed capacity.

[0114] The residual value of the wind farm and energy storage system is used to describe the depreciation cost, and the total value is equal to the investment cost. The residual value considering the discount rate is expressed as follows:

[0115]

[0116] Among them, R SAL,W ,R SAL,SC ,R SAL,EN It represents the residual value rate of wind power, energy storage charging and discharging power capacity, and energy storage capacity.

[0117] Operation cost is the main factor affecting the planning problem and is also one of the key points of this invention. The operation cost considering the discount rate is as follows:

[0118]

[0119] Among them, c om For operating costs, To plan the cap new The system operating cost for the next year y.

[0120] In order to make the calculation results more consistent with engineering reality and calculate the system operating cost more accurately, the actual operation process of the system should be considered in detail.

[0121] S102, investment and construction related constraints;

[0122] The expansion scale limit of the system needs to be characterized by constraints. Specifically, it is necessary to limit the total investment, the available investment range of wind farms and energy storage stations; it is also necessary to limit the total capacity of wind power generation (i.e., including existing capacity and expansion capacity) to be less than the maximum value of wind power generation to avoid idle wind farms; and connection constraints to limit the total transmission power to the positive direction of transmitting electricity to the main grid. It must be within the transmission power limit range and include all nodes of wind power and energy storage.

[0123] The constraints are as follows:

[0124]

[0125] in, Cinv,total , are the lower and upper limits of the total investment cost, C total,w , is the lower and upper limits of wind power investment costs, C total,sto , are the lower and upper limits of energy storage investment costs, For the existing wind power capacity, The current energy storage charging and discharging power capacity, is the upper limit of naturally available wind, TL , It is the lower and upper limits of the line transmission capacity from the planning node to the main network.

[0126] S103, system operation model;

[0127] When a set of system expansion plans is given, it is necessary to calculate the operation process of the system under this parameter based on this. The detailed model of the actual operation process of the system will be characterized by the transmission security constraint unit combination and economic dispatch problem that is feasible in all scenarios. Accurate operation cost assessment is the premise and key to solving reasonable / economic planning problems. Therefore, it is necessary to consider the actual multi-stage operation process and constraints of the power system to obtain accurate operation cost calculations. In the actual operation of the power system, it is necessary to first solve a robust unit combination problem to obtain the on / off status of the unit, and then solve a corresponding economic dispatch problem to determine the actual unit output power under the actual uncertainty realization value. The optimal power generation cost of the economic dispatch problem is the basis for calculating the actual power generation cost. In the planning problem, it is necessary to solve the long-term uncertain unit dispatch problem. The detailed model is expressed as follows:

[0128] The feasible unit combination model for all scenarios:

[0129]

[0130] The above formula is the objective function, whose goal is to minimize the total operating cost by optimizing the unit on / off state z and the upper and lower bounds of the unit power, where p min ,p max are two auxiliary variables, S,ds represents the scenario set and scenario index, I,di represents the unit set and unit index, T,t represents the time period set and time period index, M,m represents the load set and load index, α s is the weight of scene s, z ,i,t is the on / off status of unit i in period t, which is a 0 / 1 variable, p i,t,s is the power generation of unit i in time period t under scenario s, is the startup cost and fuel cost of unit i, β m,tis the penalty price for load shedding, is the load shedding power of load m in time period t.

[0131]

[0132]

[0133] The above constraints mean that the wind power generation is less than the energy that can be provided by the natural wind volume, and the wind power generation is less than the wind power installed capacity, where is the power generation of wind farm k in time period t under scenario s, It is the energy that can be provided by the natural wind volume of wind farm k in time period t under scenario s.

[0134]

[0135] The above constraints represent load balancing constraints, where the total generated power is equal to the total load power, where d m,t is the actual load power of load m in time period t.

[0136]

[0137] The above constraints represent the line transmission safety constraints. The line power flow should be less than the transmission capacity. l is the line transmission capacity, Γ is the transmission power distribution factor matrix, Γ U ,Γ W ,Γ D It is the matrix part corresponding to the unit, wind power and load.

[0138]

[0139] The above constraints are power generation bound constraints, where P i , is the physical lower and upper bounds of the generating capacity of the unit, p min ,p max are two auxiliary variables, which are the scheduling lower bound and upper bound.

[0140]

[0141]

[0142] The above constraints are the unit climbing constraints. The unit power change between any two time periods cannot exceed the unit's climbing capability limit, and the corresponding power requirements must be met before shutdown and after the startup period.

[0143] Among them, Δ i d is the climbing ability limit, τ is the time period length, which is 1 hour in the present invention, is the startup and shutdown ramp power limit of unit i, is the rated power of unit i.

[0144]

[0145] The above constraints are the minimum on / off time constraints of the unit, that is, the unit must be on / off for at least a certain number of periods, where G i is the number of time periods that unit i needs to keep running at the beginning of scheduling, is the minimum number of startup periods of unit i, O i is the number of time periods that unit i needs to remain shut down at the beginning of scheduling, τ i is the minimum number of shutdown periods for unit i.

[0146] By solving the above full-scenario feasible unit combination problem, the on / off state z of the unit and the unit output power range [p *min ,p *max ], but the specific unit power generation power still needs to wait for the intraday scheduling stage, and then re-solve after obtaining the uncertainty value. The present invention solves this problem by using a full-scenario feasible economic scheduling method, which is specifically expressed as follows:

[0147]

[0148] The above formula is the objective function, whose goal is to minimize the total operating cost by optimizing the power generation of the unit, where p is the power generation of the unit.

[0149]

[0150] The above constraints are the basic boundary constraints of various types, ensuring that the unit power generation is within the output power range obtained by the unit combination problem [p *min ,p *max ] to ensure that the wind power and energy storage charging and discharging power are within the actual equipment limits.

[0151] in, is the charging power of energy storage j in time period t under scenario s, is the discharge power of energy storage j in time period t under scenario s.

[0152]

[0153] The above constraints describe the operation mechanism of the energy storage system. The energy storage capacity of the energy storage device must be within the upper and lower limits. The difference in energy storage between two time periods is the charge / discharge power of this period multiplied by the period length (1 hour), and the energy storage capacity of the initial period should be equal to the energy storage capacity of the final period. μ j , is the lower and upper limits of the energy storage capacity of energy storage j, e j,t,s is the energy storage capacity of energy storage j in time period t under scenario s, η ch is the charging power coefficient, η dis is the discharge power coefficient.

[0154]

[0155] The above constraints represent load balance constraints, and the total power generation power of thermal power units, wind power units, and energy storage is equal to the total load power.

[0156]

[0157] The above constraints are the transmission security constraints of the line.

[0158] S2, using a parallel cycle splitting algorithm to solve the system operation problem;

[0159] Although step S103 lists the feasible unit combination and economic dispatch problems in the entire scenario, it is impossible to solve the above problems directly because the time period involved is too long. Therefore, a parallel cycle splitting algorithm is needed to solve the specific operation plan of the system.

[0160] The time period of the original long-period scheduling problem is [1, T], which is continuously and evenly divided into K short periods, that is, the following series of periods:

[0161] [1,t1],[t1+1,t2],[t2+1,t3],1 / 4,[t K-1 +1,t K ]

[0162] With the splitting of the cycle, the original problem is converted into several independent short-cycle sub-problems. The time coupling constraints between these sub-problems are temporarily relaxed, so they can be solved separately at the same time. Each sub-problem is a mixed integer linear programming problem of limited scale, which can be solved directly using solver tools such as COPT and Gurobi. During the solution process, it is necessary to set up parallel solution, which can greatly improve the solution speed.

[0163] After obtaining the optimal solution to each short-period sub-problem, they are arranged in the original time order, and based on the requirements of the unit operation constraints, the scheduling decision is adjusted for several time periods of each two adjacent sub-problems. This connection process is repeated K-1 times to obtain a good feasible solution to the original problem. When adjusting the start and stop status and power generation of the unit, it is only necessary to solve a series of short-term unit combination problems.

[0164] S3, using the gradient descent method of the nested genetic algorithm to solve the optimal capacity planning;

[0165] According to the description of step S1 and step S2, it is necessary to solve a series of scheduling problems to obtain the operating cost with process planning capabilities, and the entire solution of the planning problem is an iterative process. Intelligent algorithms such as genetic algorithms are suitable for solving this multi-parameter optimization problem. Genetic algorithms generally include population initialization, fitness function, selection, crossover and mutation. The termination mark of the algorithm is population convergence, that is, no offspring significantly different from the previous generation is produced.

[0166] In addition, for the planning problem considered by the present invention, when only one planning node is available, as the planning capacity increases, the total planning cost first decreases and then increases, which is caused by the wind power output characteristics and the capacity of the power system. Therefore, in order to make full use of this characteristic, the present invention establishes a genetic algorithm nested gradient descent method to update the experimental planning decision and accelerate iterative convergence. It is also because of this feature that the present invention directly uses plain code instead of binary code in the genetic algorithm nested gradient descent method, thereby effectively solving the capacity expansion planning problem.

[0167] The process of the gradient descent method of the nested genetic algorithm is shown in the attached Figure 2 , the specific steps are summarized as follows:

[0168] S301, population initialization

[0169] Considering the impact of a single planning position on the total planning cost, i.e., the total planning cost first decreases and then increases, the initial trial planning decisions are selected proportionally within the capacity range limited by the constraints in step S1.

[0170] S302, fitness function

[0171] The total planning cost under the experimental plan is the fitness function, which has a specific expression in step S101. It should be noted that when solving the operating cost of a population individual (i.e., the experimental plan), it is necessary to solve the long-term full-scenario feasible unit combination problem and the corresponding long-term economic scheduling problem.

[0172] S303, Select

[0173] The fitness of each individual can be solved according to the fitness function, so individuals with higher fitness (ie, smaller total planning cost) have more chances to be selected for the next reproduction. In the present invention, the roulette method is used to select dominant individuals.

[0174] S304, Cross

[0175] On the basis of selecting the dominant individuals, the gradient descent method is used to nest and generate new offspring. That is to say, according to the fitness function and planning capacity of the two selected crossover individuals, the new individuals of the next generation are generated based on the gradient descent method.

[0176] S305, mutation

[0177] Multiple offspring individuals are randomly generated in the feasible region and placed in the overall population to prevent the algorithm from falling into a local optimal solution.

[0178] Combined with the parallel cycle splitting algorithm in step S2, the capacity expansion planning problem of wind power and energy storage systems considering the actual multi-stage operation of the power system can not only be solved but also solved effectively.

[0179] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0180] In another embodiment of the present invention, a wind power energy storage planning system considering the hourly robust unit combination problem is provided. The system can be used to implement the above-mentioned wind power energy storage planning method considering the hourly robust unit combination problem. Specifically, the wind power energy storage planning system considering the hourly robust unit combination problem includes a construction module, a calculation module and a planning module.

[0181] Among them, the construction module establishes a mathematical model for the power system planning problem including wind power and energy storage;

[0182] The calculation module uses a parallel cycle splitting algorithm to solve the obtained mathematical model of the power system planning problem and obtain a specific operation plan of the power system;

[0183] The planning module uses the gradient descent method of the nested genetic algorithm to solve the optimal capacity planning based on the specific operation plan of the power system.

[0184] In another embodiment of the present invention, a terminal device is provided, 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 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, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the wind power energy storage planning method considering the hourly robust unit combination problem, including:

[0185] A mathematical model for the power system planning problem including wind power and energy storage is established; a parallel cycle splitting algorithm is used to solve the mathematical model of the power system planning problem to obtain a specific operation plan of the power system; based on the specific operation plan of the power system, the gradient descent method of the nested genetic algorithm is used to solve and obtain the optimal capacity planning.

[0186] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which 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, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0187] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0188] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0189] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the wind power energy storage planning method considering the hourly robust unit combination problem in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0190] A mathematical model for the power system planning problem including wind power and energy storage is established; a parallel cycle splitting algorithm is used to solve the mathematical model of the power system planning problem to obtain a specific operation plan of the power system; based on the specific operation plan of the power system, the gradient descent method of the nested genetic algorithm is used to solve and obtain the optimal capacity planning.

[0191] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the wind power energy storage planning method considering the hourly robust unit combination problem in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the wind power energy storage planning system considering the hourly robust unit combination problem in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0192] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0193] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics 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, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0194] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0195] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0196] Any reference to memory, database or other medium used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0197] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0198] See also Figure 6 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0199] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0200] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0201] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0202] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0203] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0204] 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, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in 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.

[0205] An embodiment of the present invention is provided below.

[0206] Numerical tests were conducted on an IEEE 118-node system, which includes 8 energy storage stations and 8 wind farms. To achieve energy conservation and emission reduction, it is necessary to expand the capacity of existing energy storage stations and wind farms or add new stations. There are 9 new wind farm sites and 13 energy storage sites to choose from.

[0207] The environmental penalty cost of thermal power units is set at 3.5$ / MWh, and the load shedding cost is set at 300$ / MWh. The minimum total investment cost during the planning period is 8.5×10 7 $, the maximum value is 2.4×10 10 $. Other cost parameters for wind power and energy storage planning are shown in Table 1:

[0208] Table 1 Basic planning cost information

[0209]

[0210] After using the capacity planning method of the present invention, the optimal locations and expansion plans of the wind farm and energy storage station obtained for the above system parameters are as shown in the attached Figure 3 and attached Figure 4 The following is an analysis of the main factors that affect the planning results.

[0211] According to the attached Figure 3 and attached Figure 4 , all candidate nodes of wind farms and energy storage stations are used to develop new wind power generation and energy storage systems. By comparing the planned capacity of wind farms and energy storage stations at the same planning node, and comparing the planned capacity at planning nodes with similar wind volume, it can be found that in this case, there is no obvious coupling relationship between the planned capacity of wind farms and the planning of energy storage capacity. This is because there are many important factors that affect the planning results, which are summarized as follows:

[0212] (1) Allows planning of node net load power;

[0213] (2) Natural wind power output capacity;

[0214] (3) Network topology of the power grid;

[0215] (4) Capacity configuration of wind farms and energy storage stations;

[0216] (5) Construction costs of planning nodes are allowed.

[0217] In essence, net load and natural wind output capacity directly affect the available capacity level of wind power installed at a certain planning node. Network topology and capacity configuration affect the planned capacity by affecting the system tolerance of wind power. From the perspective of investment cost, the limit of investment and construction cost directly affects the upper limit of planned capacity. It can be found that since energy storage systems can only transfer electricity but not generate electricity, the capacity level of wind power planning determines the economic benefits of the planned capacity. Therefore, short-term multi-stage scheduling is crucial to accurately evaluate the economic benefits of renewable energy consumption and planned capacity, which also reflects the significance of the present invention.

[0218] In summary, the present invention, a wind power and energy storage planning method and system that considers hourly robust unit combinations, solves the problem that the actual multi-stage operation process of the power system is not considered in the prior art. The present invention can obtain more accurate operating costs and more reasonable planning results through the precise calculation of capacity planning costs; by using a parallel cycle splitting algorithm, the time required for solving is greatly reduced; and by iterating the solution through the gradient descent method of the nested genetic algorithm, the quality of the solution is guaranteed. The present invention can meet the capacity expansion planning needs of my country's existing power system for wind power and energy storage.

[0219] The technicians in the relevant field 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 practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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.

[0220] 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.

[0221] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention 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 the present invention.

[0222] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0223] The units described 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.

[0224] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0225] If the integrated module / 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0226] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 generate 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 flowchart and / or block diagram. 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.

[0227] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0228] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0229] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A wind power and energy storage planning method considering hourly robust unit combination, characterized in that: The following steps are involved: S1. Establish a mathematical model for power system planning problems including wind power and energy storage; S2, using a parallel cycle splitting algorithm to solve the mathematical model of the power system planning problem obtained in step S1 to obtain a specific operation plan of the power system; S3. Based on the specific operation plan of the power system, the optimal capacity planning is solved using the gradient descent method of the nested genetic algorithm.

2. The wind power and energy storage planning method considering hourly robust unit combination according to claim 1 is characterized in that: Step S1 is specifically as follows: Constructing the objective function with the goal of minimizing investment-related costs and operation-related costs; Determine investment and construction-related constraints, including limiting the total investment, the available investment range for wind farms and energy storage stations; limiting the total capacity of wind power generation to be less than the maximum value of wind power generation; and limiting the total transmission power to within the transmission power limit when the connection constraint is to transmit power to the main grid as the positive direction, including all nodes of wind power and energy storage; The system operation process is characterized by the full-scenario feasible transmission safety constraint unit combination and economic dispatch problem, and the system operation cost is calculated in the process of solving the long-term full-scenario feasible unit combination problem and the corresponding economic dispatch problem.

3. The wind power and energy storage planning method considering hourly robust unit combination according to claim 2 is characterized in that: The objective function is: Among them, cap new represents the expansion planning decision, c pla is the total planning cost, c inv is the investment and construction cost, c fom is the fixed operation and maintenance cost, c sal is the residual value within the planning period, c om For running costs.

4. The wind power and energy storage planning method considering hourly robust unit combination according to claim 2 is characterized in that: The constraints related to investment and construction are as follows: in, C inv,total , are the lower and upper limits of the total investment cost, C total,w , is the lower and upper limits of wind power investment costs, C total,sto , are the lower and upper limits of energy storage investment costs, For the existing wind power capacity, The current energy storage charging and discharging power capacity, is the upper limit of naturally available wind, TL , K is the lower and upper limits of the line transmission capacity from the planning node to the main network, new is the newly added wind power set, k is the wind power index, J new is the newly added wind power set, j is the wind power index, N is the planning-related node set, n is the node index, is the existing energy storage charging and discharging power capacity of node n, Add energy storage charging and discharging power capacity for node n, is the existing wind power capacity of node n, Add new wind power capacity to node n, is the existing energy storage charging and discharging power capacity of node j, Add energy storage charging and discharging power capacity for node j, They are the unit investment and construction costs of wind power, energy storage charging and discharging power, and energy storage capacity.

5. The wind power and energy storage planning method considering hourly robust unit combination according to claim 2 is characterized in that: In the day-ahead stage, the objective function of the unit commitment model that is feasible in all scenarios is: Among them, p min ,p max are two auxiliary variables, S,ds represents the scenario set and scenario index, I,di represents the unit set and unit index, T,t represents the time period set and time period index, M,m represents the load set and load index, α s is the weight of scene s, z ,i,t is the on / off status of unit i in period t, p i,t,s is the power generation of unit i in time period t under scenario s, is the startup cost and fuel cost of unit i, β m,t is the penalty price for load shedding, is the load shedding power of load m in time period t; In the intraday scheduling stage, the objective function of the unit commitment model that is feasible in all scenarios is: Where p is the generating power of the unit, represents the objective function of the economic dispatch problem, and p represents the power generation vector of the firing unit.

6. The wind power and energy storage planning method considering hourly robust unit combination according to claim 5 is characterized in that: The constraints in the day-ahead phase are as follows: The amount of wind power generated is less than the energy that can be provided by natural wind volume, and the amount of wind power generated is less than the installed capacity of wind power: in, is the power generation of wind farm k in time period t under scenario s, is the energy that can be provided by the natural wind volume of wind farm k in time period t under scenario s; Load balancing constraints: Among them, d m,t is the actual load power of load m in time period t; Line transmission security constraints: Among them, F l is the line transmission capacity, Γ is the transmission power distribution factor matrix, Γ U ,Γ W ,Γ D The matrix part corresponding to the unit, wind power and load; Power generation limit constraints in, P i , is the physical lower and upper bounds of the generating capacity of the unit, p min ,p max are two auxiliary variables, which are the scheduling lower and upper bounds; Unit climbing constraints: in, is the climbing ability limit, τ is the time period, which is 1 hour in the present invention, and P i u , P i d is the startup and shutdown ramp power limit of unit i, is the rated power of unit i; Minimum on / off time constraints for the unit: Among them, G i is the number of time periods that unit i needs to keep running at the beginning of scheduling, is the minimum number of startup periods of unit i, O i is the number of time periods that unit i needs to remain shut down at the beginning of scheduling, τ i is the minimum number of shutdown periods for unit i.

7. The wind power and energy storage planning method considering hourly robust unit commitment according to claim 5 is characterized in that: The constraints of the intraday scheduling phase are as follows: Various basic bounds: in, is the charging power of energy storage j in time period t under scenario s, is the discharge power of energy storage j in time period t under scenario s; The operation mechanism of energy storage system: in, μ j , is the lower and upper limits of the energy storage capacity of energy storage j, e j,t,s is the energy storage capacity of energy storage j in time period t under scenario s, η ch is the charging power coefficient, η dis is the discharge power coefficient; Load balancing constraint: the total power generation of thermal power units, wind power units, and energy storage is equal to the total load power: Transmission security constraints on lines in, It is the part corresponding to the energy storage node in the transmission power distribution factor matrix.

8. The wind power and energy storage planning method considering hourly robust unit commitment according to claim 1 is characterized in that: In step S2, the time period of the long-term scheduling problem is [1, T], which is continuously and evenly divided into K short periods, that is, the following series of periods: [1, t1], [t1+1, t2], [t2+1, t3], …, [t K-1 +1,t K ]; With the splitting of the cycle, the original problem is converted into several independent short scheduling cycle sub-problems. The short scheduling cycle sub-problems are solved separately at the same time. After the optimal solution of each short cycle sub-problem is obtained, they are arranged in the original time sequence, and based on the requirements of the unit operation constraints, the scheduling decision is adjusted for several time periods of every two adjacent sub-problems. This is repeated K-1 times in total to obtain a feasible solution to the original problem.

9. The wind power and energy storage planning method considering hourly robust unit commitment according to claim 1, characterized in that: Step S3 is specifically as follows: S301, considering the impact of a single planning location on the total planning cost, select the initial trial planning decision in proportion within the capacity range limited by the constraint conditions in step S1; S302. The total planning cost under the experimental plan is the fitness function. When solving the operating cost of a certain population individual, the long-term full-scenario feasible unit combination problem and the corresponding long-term economic dispatch problem are solved; S303, using the roulette method to select dominant individuals; S304, generating new individuals of the next generation based on the gradient descent method according to the fitness functions and planning capacities of the two selected crossover individuals; S305. Randomly generate multiple offspring individuals in the feasible region and put them into the overall population.

10. A wind power and energy storage planning system considering hourly robust unit combination, characterized in that: include: Building modules to establish mathematical models for power system planning problems including wind power and energy storage; The calculation module uses a parallel cycle splitting algorithm to solve the obtained mathematical model of the power system planning problem and obtain a specific operation plan of the power system; The planning module uses the gradient descent method of the nested genetic algorithm to solve the optimal capacity planning based on the specific operation plan of the power system.

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