Wind and light storage resource optimal configuration method and device considering regional mutual aid capability

Abstract

The invention discloses a wind and light storage resource optimal configuration method and device considering regional mutual aid capability. The method comprises the following steps: collecting power market related data and power grid planning related data of each region in the large-scale power system; aiming at the large-scale interconnected power grid formed by the plurality of regions, constructing a wind-light storage resource configuration optimization problem into a wind-light storage resource configuration two-stage optimization model which is formed by a main problem and a sub-problem and takes the regional mutual aid capability into account by utilizing the collected power market related data and power grid planning related data; and adopting a Benders decomposition algorithm to iteratively solve the wind and light storage resource configuration two-stage optimization model considering the regional mutual aid capability, and determining a newly-added wind and light storage resource optimization configuration scheme of each region, the newly-added wind and light storage resource optimization configuration scheme of each region comprising the installed capacity of a wind and light storage power supply in each region. The method can accurately and rapidly plan and configure the wind-solar storage power supply of the power system, and gives consideration to the electricity price income of each region.

Description

Technical Field

[0001] The present invention belongs to the field of power system planning, and particularly relates to a method and device for optimizing the allocation of wind-solar-storage resources considering regional mutual assistance capabilities. Background Art

[0002] At present, the proportion of renewable energy power generation such as wind and solar in the power system is gradually increasing. However, its power generation is intermittent and uncertain, posing challenges to the power balance and stable operation of the power system. Energy storage resources, as a flexible regulating power source, can effectively suppress the power fluctuations of wind and solar power generation and play a key role in addressing the uncertainty of wind and solar power generation.

[0003] Meanwhile, there are obvious differences in the wind-solar resource endowments and load demands of different regions. In some regions, the wind-solar resources are abundant, but the local load demand is limited; while in some regions, the load demand is large, but they face the problem of scarce wind-solar resources.

[0004] With the comprehensive development of various types of power generation resources in the power system, the optimization allocation problem of various resources has become increasingly complex. At present, there are many optimization allocation methods for wind-solar-storage resource allocation, but the optimization allocation methods considering regional mutual assistance capabilities still need to be improved.

[0005] How to achieve the efficient allocation of wind-solar-storage resources on the basis of fully considering regional mutual assistance capabilities has become a key problem to be solved urgently. There is an urgent need to propose new technical solutions to optimize the allocation of wind-solar-storage resources and improve the stability and economy of the power system. Summary of the Invention

[0006] In view of this, the present invention provides a method and device for optimizing the allocation of wind-solar-storage resources considering regional mutual assistance capabilities, aiming to solve the deficiencies in the existing optimization allocation methods considering regional mutual assistance capabilities.

[0007] In a first aspect, the present invention provides a method for optimizing the allocation of wind-solar-storage resources considering regional mutual assistance capabilities, including:

[0008] Collecting power market-related data and grid planning-related data of each region within a large-scale power system; the large-scale power system includes a large interconnected power grid composed of multiple regions;

[0009] For the large interconnected power grid composed of multiple regions, using the collected power market-related data and grid planning-related data, constructing an optimization problem of wind-solar-storage resource allocation into a two-stage optimization model of wind-solar-storage resource allocation considering regional mutual assistance capabilities, which consists of a main problem and a sub-problem;

[0010] The Benders decomposition algorithm is adopted to iteratively solve the two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability, and to determine the optimized allocation schemes for the newly added wind-solar-storage resources in each region. The optimized allocation schemes for the newly added wind-solar-storage resources in each region include the installed capacities of the wind-solar-storage power sources in each region.

[0011] Furthermore, the relevant data of the electricity market includes the electricity market transaction prices in different regions.

[0012] The relevant data of the power grid planning includes the cost coefficients of wind power construction, the cost coefficients of photovoltaic power construction, the cost coefficients of energy storage construction, the newly added annual maximum load power, the newly added effective demand hours of the load, the effective utilization hours of wind power, the effective utilization hours of photovoltaic power, the effective utilization hours of energy storage, and the inter-regional power transmission capacity. Among them, the inter-regional power transmission capacity is used to indicate the regional mutual assistance ability.

[0013] Furthermore, the construction of the optimization problem of wind-solar-storage resource allocation into a two-stage optimization model for wind-solar-storage resource allocation considering the regional mutual assistance ability, which consists of a master problem and a sub-problem, includes:

[0014] Determine the master problem to solve the problem of minimizing the cost of newly added wind-solar-storage in a large interconnected power grid considering the inter-regional mutual assistance ability. The solution of the master problem is the total installed capacity of newly added wind-solar-storage in each region of the large interconnected power grid.

[0015] Determine the sub-problem to solve the problem of maximizing the profit in each region of the large interconnected power grid. The solution of the sub-problem constitutes the optimized allocation scheme for the newly added wind-solar-storage capacity in each region of the large interconnected power grid.

[0016] When solving the sub-problem, the solution of the master problem is used as a known quantity to provide equality constraint conditions for the sub-problem.

[0017] When solving the master problem, the solution of the sub-problem is used to update the objective function of the master problem.

[0018] Furthermore, the master problem is an optimization problem for the newly added installed power generation capacity in each region with constraints. Its optimization variables include the total installed capacity of newly added wind-solar-storage power generation in each region. Its optimization objective function is to minimize the total cost of the configuration of various types of power sources newly added in each region of the large interconnected power grid. Its constraint conditions include the regional mutual assistance ability constraint.

[0019] Furthermore, the sub-problem is an optimization problem for the newly added wind-solar-storage in each region considering the electricity revenue with constraints. Its optimization variables are the newly added installed capacities of wind-solar-storage in each region. Its optimization objective function is to maximize the total profit after the allocation of wind-solar-storage resources in each region. The constraint conditions of its sub-problem include the regional total configuration capacity constraint and the electricity reserve constraint.

[0020] Further, the Benders decomposition algorithm is adopted to iteratively solve the two-stage optimization model for the configuration of wind-solar-storage resources considering the regional mutual assistance ability, and determine the optimal configuration scheme for the newly added wind-solar-storage resources in each region, including:

[0021] During the iterative solution process, the global optimal solution is gradually approximated through the interaction between the master problem and the sub-problem;

[0022] First, a random initial feasible solution is selected as the current solution of the master problem, and the sub-problem is solved based on the current solution of the master problem to determine whether the sub-problem is feasible;

[0023] If the sub-problem is feasible, the optimal cutting plane is generated using its dual solution, and the upper bound of the solution space of the master problem is updated using the optimal cutting plane;

[0024] If the sub-problem is infeasible, the feasibility cutting plane is generated, and the lower bound of the solution space of the master problem is updated using the feasibility cutting plane; the generated feasibility cutting plane is re-incorporated into the master problem to update the master problem model.

[0025] Second, the present invention provides a device for optimizing the configuration of wind-solar-storage resources considering the regional mutual assistance ability, including:

[0026] A data collection module, configured to collect power market-related data of each region and grid planning-related data of each region in a large-scale power system; the large-scale power system includes a large interconnected power grid composed of multiple regions;

[0027] A two-stage optimization model construction module, configured to construct the optimization problem of wind-solar-storage resource configuration into a two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, which is composed of a master problem and a sub-problem, for the large interconnected power grid composed of multiple regions, by using the collected power market-related data and grid planning-related data;

[0028] An optimal configuration scheme generation module, configured to adopt the Benders decomposition algorithm to iteratively solve the two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, and determine the optimal configuration scheme for the newly added wind-solar-storage resources in each region, where the optimal configuration scheme for the newly added wind-solar-storage resources in each region includes the installed capacity of wind-solar-storage power sources in each region.

[0029] Further, the two-stage optimization model construction module constructs the optimization problem of wind-solar-storage resource configuration into a two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, which is composed of a master problem and a sub-problem, including:

[0030] Determine that the master problem is used to solve the problem of minimizing the cost of newly added wind-solar-storage in a large interconnected power grid considering the mutual assistance ability between regions, and the solution of the master problem is the total capacity of newly added wind-solar-storage in each region of the large interconnected power grid;

[0031] Determine sub - problems to solve the profit - maximization problem of each region within a large - scale interconnected power grid. The solutions of the sub - problems form an optimal allocation scheme for the newly added wind, light, and energy storage capacities in each region within the large - scale interconnected power grid;

[0032] When solving the sub - problems, the solutions of the main problem are used as known quantities to provide equality constraint conditions for the sub - problems;

[0033] When solving the main problem, the solutions of the sub - problems are used to update the objective function of the main problem.

[0034] In a third aspect, the present invention provides a terminal, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method described in the first aspect.

[0035] In a fourth aspect, the present invention provides a computer storage medium storing computer - executable instructions for executing the method described in the first aspect.

[0036] Additional aspects and advantages of the present invention will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not intended to limit the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0038] Figure 1 is a schematic flowchart of the method for optimizing the allocation of wind, light, and energy storage resources considering the regional mutual - assistance ability according to an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of the composition of the device for optimizing the allocation of wind, light, and energy storage resources considering the regional mutual - assistance ability according to an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of the structure of an interconnected power grid composed of three regions according to an embodiment of the present invention;

[0041] Figure 4 is a schematic diagram of the composition of the terminal applying the embodiment of the present invention;

[0042] Figure 5 is a schematic diagram of the program product applying the method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, the embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0044] Referring to the foregoing description, there are obvious differences in the endowment of wind and light resources and load demands in different regions. In some regions, the wind and light resources are rich, but the local load demand is limited; while in some other regions, the load demand is large, but they face the problem of scarce wind and light resources. This imbalance between regions provides an opportunity for regional mutual assistance.

[0045] Taking into account the power generation characteristics and operating characteristics of the resources themselves, as well as the transmission capacity of the inter-regional tie lines, realizing the optimal allocation of wind-solar-storage resources is an important issue that urgently needs to be solved in the current field of power system planning and construction.

[0046] As mentioned in the foregoing description, the allocation of wind-solar-storage resources not only requires considering the power generation characteristics and operating characteristics of the resources themselves, but also must focus on the coordinated allocation and comprehensive optimization of multiple resources. Considering that the insufficient inter-regional power mutual assistance ability is an important factor restricting the cross-regional consumption of new energy power, through reasonable resource allocation and scheduling, realizing the complementarity and sharing of power among regions under the current limitation of tie line transmission capacity is conducive to improving the operating efficiency of the entire power system. In this way, through regional mutual assistance, the optimal allocation of resources can be achieved, the energy utilization efficiency can be improved, and the energy supply cost can be reduced.

[0047] The present invention provides a method and device for optimizing the allocation of wind-solar-storage resources considering the regional mutual assistance ability. In the power system planning stage, by considering the power and energy balance and the regional mutual assistance ability, a two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability is constructed. Using this two-stage optimization model, the inter-regional power mutual assistance ability can be fully considered, the flexible and reasonable allocation of wind-solar-storage resources in a large-scale power system can be realized, the effective consumption of new energy power generation can be promoted, and the flexible supply of electricity demand can be guaranteed.

[0048] It should be understood that the method and device for optimizing the allocation of wind-solar-storage resources considering the regional mutual assistance ability provided by the present invention are applied to the scenario where the original installed capacity (conventional energy or wind-solar-storage) in each region within a large-scale power system meets the existing load, and for the newly added load demand, it is balanced by the newly added wind-solar-storage power sources.

[0049] As Figure 1As shown in the figure, the method for optimizing the allocation of wind-solar-storage resources considering the regional mutual assistance ability in the embodiments of the present invention includes the following steps S100, S200, and S300.

[0050] Step S100: Collect the power market-related data of each region and the grid planning-related data of each region in the large-scale power system.

[0051] Specifically, the large-scale power system is a large interconnected power grid composed of multiple regions. The power market-related data of each region includes the power market trading electricity prices in different regions. The grid planning-related data of each region includes the wind power construction cost coefficient, the photovoltaic construction cost coefficient, the energy storage construction cost coefficient, the newly added annual maximum load power, the newly added load effective demand hours, the wind power effective utilization hours, the photovoltaic effective utilization hours, the energy storage effective utilization hours, and the inter-regional power transmission capacity (such as the transmission capacity of the tie line, such as the line capacity of a single circuit line). Among them, the inter-regional power transmission capacity is used to indicate the regional mutual assistance ability.

[0052] Step S200: For the large interconnected power grid composed of multiple regions, construct a two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability.

[0053] The optimization of the allocation of wind-solar-storage resources belongs to a large-scale mixed-integer programming problem. To solve the problem of solving large-scale mixed-integer programming problems, the optimization problem of the allocation of wind-solar-storage resources in a large interconnected power grid considering the regional mutual assistance ability is constructed as a two-stage optimization model composed of a master problem and a sub-problem, that is, construct a two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability.

[0054] Specifically, the master problem is used to solve the problem of minimizing the cost of newly added wind-solar-storage in a large interconnected power grid considering the inter-regional mutual assistance ability. The solution of the master problem (that is, the first set of optimization variables) is the total capacity of newly added wind-solar-storage in each region of the large interconnected power grid. When there are N regions in the large interconnected power grid, the solution of the master problem is a vector including N elements. The sub-problem is used to solve the problem of maximizing the profit of each region in the large interconnected power grid. The solution of the sub-problem (that is, the second set of optimization variables) constitutes the optimized allocation scheme of the newly added wind-solar-storage capacity in each region of the large interconnected power grid. When there are N regions in the large interconnected power grid, the solution of the sub-problem is a matrix or array including N×3 elements. Naturally, the profit is the difference between the newly added electricity sales revenue and the newly added wind-solar-storage construction cost in each region, usually a non-negative value.

[0055] When solving the sub-problem, the solution of the master problem, that is, the current value of the first set of optimization variables, that is, the total capacity of newly added wind-solar-storage in each region of the large interconnected power grid, is used as a known quantity to provide one of the equality constraint conditions for the sub-problem.

[0056] When solving the master problem, the solution of the sub-problem, that is, the second set of optimization variables, that is, the optimal configuration plan of the newly added wind, light, and storage capacity in each region within the large interconnected power grid, is used to update the objective function of the master problem, that is, to calculate the total cost of the newly added load in each region. In this way, the master problem and the sub-problem are interdependent, and the optimal solution is achieved through repeated joint iteration.

[0057] Specifically, the master problem is an optimization problem for the newly added installed power generation capacity in each region with constraints. Its optimization variables include the total installed capacity of the newly added wind, light, and storage power generation in each region. The optimization objective function is listed as in Equation (1), which is used to minimize the total cost of the newly added multiple types of power source configurations in each region within the large interconnected power grid, that is, to minimize the total cost of the newly added wind, light, and storage capacity configuration:

[0058]

[0059] Among them, N is the number of regions in the power system, c min =min(a, b, c) represents the optimal cost coefficient of the newly added power source configuration in region i. a i , b i , c i are the cost coefficients of the newly added wind power, photovoltaic power, and energy storage in region i respectively. P Wi , P PVi , P Bi are the installed capacities of wind power, photovoltaic power, and energy storage in region i respectively. P i represents the total newly added power source capacity in region i;

[0060] θ is an auxiliary variable or a slack variable, which is used to represent the additional information provided by the sub-problem. Referring to the following description, θ is the cost correction amount generated after the solution of the sub-problem is determined.

[0061] Specifically, the constraint conditions of the master problem include: the regional mutual assistance ability constraint. Specifically, considering that power is transmitted between adjacent regions through tie lines, and the tie lines have a capacity limit, which is a key limiting factor for the power transmission power. Therefore, based on the capacity limit of the tie lines, the regional mutual assistance ability constraint is constructed. The regional mutual assistance ability constraint is listed as in Equation (2):

[0062] -P ijmax ≤T ij =L i -P i ≤P ijmax (2)

[0063] Among them, T ij represents the power transmission power from region j to region i, L i is the newly added annual maximum load power in region i, P i represents the total newly added power source capacity in region i, P ijmaxis the maximum transmission capacity of the tie line between two adjacent regions, namely region j and region i. The "-" sign indicates the reverse transmission power from region j to region i, that is, the power is transmitted from region i to region j.

[0064] Specifically, the sub-problem is an optimization configuration problem of newly added wind, light, and energy storage in each region considering the electricity revenue with constraints. Its optimization variable is the installed capacity of newly added wind, light, and energy storage in each region. Its optimization objective function is listed in Equation (3), which is used to maximize the total profit of the wind, light, and energy storage resource allocation in each region, that is, to maximize the revenue of the newly added wind, light, and energy storage resource allocation:

[0065]

[0066] where N is the number of regions in the power system, C i is the electricity price of region i, h Wi , h PVi , h Bi are the effective utilization hours of wind power, photovoltaic power, and energy storage in region i respectively. P Wi , P PVi , P Bi are the installed capacities of wind power, photovoltaic power, and energy storage in region i respectively. a i , b i , c i are the cost coefficients of newly added wind power, photovoltaic power, and energy storage in region i respectively, such as the unit capacity installation construction cost coefficients described later.

[0067] Specifically, the constraint conditions of the sub-problem include the total regional configuration capacity constraint, the electricity reserve constraint, and the upper and lower limits of the output of common wind, light, and energy storage power sources, etc.

[0068] Specifically, the total regional configuration capacity constraint is listed in Equation (4), which is used to constrain the total capacity of various types of wind, light, and energy storage power sources according to the solution results of the main problem, that is, on the basis of the solution results of the main problem (such as the N installed capacities of N regions), to limit the total capacity of wind, light, and energy storage power sources:

[0069] P wi +P PVi +P Bi =P i (4)

[0070] where P i represents the total capacity of the newly added power sources in region i, and P Wi , P PVi , P Bi are the installed capacities of wind power, photovoltaic power, and energy storage in region i respectively.

[0071] For a large interconnected power grid composed of multiple regions, in order to ensure normal power supply on the load side, the installed power generation capacity must meet or exceed the electricity demand to guarantee power supply. Specifically, the electricity reserve constraint is listed as in Equation (5):

[0072]

[0073] where h Li is the effective demand hours of load in Region i, N is the number of regions in the power system, and L i is the newly added annual maximum load power in Region i.

[0074] Step S300: Use the Benders decomposition algorithm to iteratively solve the two-stage optimization model of wind-solar-storage resource allocation considering the regional mutual assistance ability, and determine the optimized allocation scheme of newly added wind-solar-storage resources in each region.

[0075] Specifically, in the iterative solution process, the global optimal solution is gradually approximated through the interaction between the master problem and the sub-problem. First, randomly select an initial feasible solution (such as the total newly added power generation capacity in each region) as the current solution of the master problem. Solve the sub-problem based on the current solution of the master problem, and generate Benders cuts based on the current solution of the sub-problem

[0076] If the sub-problem is feasible, generate optimality cuts using its dual solution π (the solution of the dual problem of the sub-problem), and update the upper bound of the master problem solution space using the optimality cuts. The optimality cuts are listed as in Equation (5):

[0077]

[0078] where h * is a constant vector, H is a matrix that maps the current solution of the master problem to the constraints of the sub-problem, is the current solution of the sub-problem; the optimality cut θ is the aforementioned auxiliary variable or slack variable, which is used to interactively provide additional information to the master problem.

[0079] If the sub-problem is infeasible, generate feasibility cuts to exclude those solutions of the master problem in the master problem solution space that will cause the sub-problem to be infeasible, and update the current lower bound of the master problem accordingly. Then, incorporate the generated feasibility cuts back into the master problem to update the master problem model, such as the objective function or the current solution of the master problem. Subsequently, solve the master problem to obtain a new solution.

[0080] ​Thus, in each iteration, the upper or lower bound of the master problem is updated, and the convergence condition is checked. If the convergence condition is not met, the above interactive iteration steps are repeated. The iteration ends until the gap between the upper and lower bounds is small enough. After the convergence condition is satisfied, the final decision-making scheme is output. The final decision-making scheme includes the installed capacity of wind-solar-storage power sources in each region.

[0081] In the above iterative solution process, the convergence condition is repeatedly checked, gradually approaching the global optimal solution. When the iteration ends, the gap between the upper and lower bounds is small enough, restricting the solution of the master problem to a very small range with high accuracy.

[0082] Thus, the wind-solar-storage resource optimal allocation method considering regional mutual assistance ability in the embodiment of the present invention constructs a two-stage optimization model for wind-solar-storage resource allocation considering regional mutual assistance ability by decomposing and relaxing the large-scale mixed integer programming problem. The constructed two-stage model can reduce the solution difficulty and accurately and quickly generate the optimal allocation scheme of the installed capacity of wind-solar-storage in each region. On the premise of meeting the power transmission constraints between regions, considering the improvement of power generation profit after configuration, the installed capacity of various types of power sources is further rationally configured to improve the economic benefits of the power grid and promote the effective consumption of new energy.

[0083] Thus, the wind-solar-storage resource optimal allocation method considering regional mutual assistance ability in the embodiment of the present invention can not only realize the power source planning and allocation of the wind-solar-storage power system, but also take into account the power mutual assistance between regions and the power quantity and price benefits of each region.

[0084] Further, using the aforementioned wind-solar-storage resource optimal allocation method considering regional mutual assistance ability, based on the capacity upper limit of each tie line, a two-stage optimization model for the Figure 3 shown interconnected power system is constructed, and it is optimized and solved to generate Figure 3 the installed capacity of the wind-solar-storage power sources in each of the 3 regions after optimal allocation within the shown interconnected power system.

[0085] Figure 3 The shown interconnected power system composed of three regions requires the addition of installed capacity of three types of wind, light, and storage in each region. Each region is interconnected through tie lines (such as T 13 , T 12 , T 23 ) to achieve power mutual transmission. In the case of regional power shortage, the tie line can obtain sufficient power from the outside in time; on the contrary, when the regional power is surplus, the excess power can be transmitted to other regions through the tie line for consumption. Therefore, the capacity upper limit of the tie line can be used to measure the power mutual assistance ability between regions. The following specifically describes the steps.

[0086] Step 1: Collect data related to the power market and grid planning. Among them, the representation symbols and typical values of some data are listed in Table 1.

[0087] Table 1 Representation symbols and typical values of some grid-related data

[0088] Transaction electricity price Wind power cost coefficient Photovoltaic cost coefficient Energy storage cost coefficient Single-circuit line capacity <![CDATA[C i (yuan / kWh)]]> a (ten thousand yuan / MW) b (ten thousand yuan / MW) c (ten thousand yuan / MW) <![CDATA[P max (MW)]]> 0.142 400-600 300-500 1000 400

[0089] Step 2: Construct a two-stage optimization model for the configuration of wind-solar-storage resources considering the regional mutual assistance ability. The main problem is the problem of minimizing the cost considering the regional mutual assistance ability, while the sub-problem is the problem of maximizing the profit considering the economic benefits. The solution of the main problem is the total capacity of the power sources in each region. These capacities, as known quantities, provide one of the equality constraint conditions for the sub-problem. The solution of the sub-problem can be used to correct the cost calculation in the objective function of the main problem. The main problem and the sub-problem are interdependent and need to be jointly iteratively optimized and solved.

[0090] The main problem is the optimization problem of the total installed capacity of power generation in each region under the influence of the regional mutual assistance ability. Its optimization variable is the total installed capacity of power generation in each region, and the optimization objective is to minimize the total cost of configuring various types of power sources in each region:

[0091]

[0092] Among them, N is the number of regions in the system, Figure 3 In the embodiment of, N = 3. c min = min(a, b, c) represents the minimum cost coefficient of all power source configurations in region i. a, b, and c are the unit capacity installation cost coefficients of wind power, photovoltaic power, and energy storage respectively, and are taken as 4 million yuan / MW, 3 million yuan / MW, and 10 million yuan / MW respectively. P i represents the total installed capacity of all power sources in region i. θ is an auxiliary variable used to represent the additional information provided by the sub-problem, which can be understood as the cost correction amount after the solution of the sub-problem is determined in this problem.

[0093] The constraint conditions of the main problem include: the upper limit constraint of regional mutual assistance ability. The upper limit constraint of regional mutual assistance ability takes into account that power is transmitted between adjacent regions through tie lines, and the tie lines have a capacity upper limit. This capacity upper limit is a key limiting factor for the power transmission power. Therefore, based on the capacity limit of the tie lines, the upper limit constraint of regional mutual assistance ability is constructed, which can be expressed as:

[0094] -P max ≤T ij = L i -P i ≤P max (8)

[0095] Among them, T ijIndicates the amount of power transmitted from region j to region i, L i Is the annual maximum load of region i, P max Is the maximum capacity of the tie line between the two adjacent regions. The line capacity of a single circuit is 400MW, and the regions can be connected by two or three circuits.

[0096] The sub-problem is the optimization problem of the specific configuration scheme of wind-solar-storage resources in each region considering the economic benefits of electricity. Its optimization goal is to maximize the total economic profit after the configuration of wind-solar-storage resources in each region, which can be expressed as:

[0097]

[0098] Among them, C i Is the electricity price of region i. In this embodiment, the electricity prices of the 3 regions are equal, which is 0.142 yuan / kWh, h Wi 、h PVi 、h Bi Are the effective utilization hours of wind energy, photovoltaic energy and energy storage in region i respectively, which are different in each region, P Wi 、P PVi 、P Bi Are the installed capacities of wind power, photovoltaic power and energy storage in region i respectively.

[0099] The constraint conditions of the sub-problem include the total configuration capacity constraint, the power reserve constraint and the upper and lower limits of the output of common wind-solar-storage power sources. The total configuration capacity constraint is that on the basis of the solution result of the main problem, the total capacity of various wind-solar-storage power sources is fixed:

[0100] P wi +P PVi +P Bi =P i (10)

[0101] The power reserve constraint is to ensure the normal power supply on the load side. For a large integrated power grid composed of multiple regions, the installed power generation capacity must meet or exceed the power consumption demand to ensure that the power demand of users is met.

[0102]

[0103] Among them, h Li Are the effective demand hours of the load in region i respectively.

[0104] Step 3: Use the Benders decomposition algorithm to iteratively solve the optimization model. This process gradually approaches the global optimal solution through the interaction between the master problem and the sub-problem. First, randomly select an initial feasible solution as the solution of the master problem. Subsequently, start the iterative process, solve the master problem to obtain a new solution. Solve the sub-problem based on the current solution, and generate Benders cuts based on the solution of the sub-problem. If the sub-problem is feasible, use its dual solution π to generate an optimality cut to update the current upper bound of the optimization problem. The optimality cut can be expressed as the following formula:

[0105]

[0106] where h is a constant vector, H is a matrix that maps the solution of the master problem to the constraints of the sub-problem, is the current solution vector of the optimization variables of the sub-problem.

[0107] If the sub-problem is infeasible, generate a feasibility cut to exclude the infeasible solution region in the master problem and update the lower bound accordingly. Then, incorporate the generated cut back into the master problem to update the master problem model. Finally, check the convergence condition (for example, the gap between the upper and lower bounds is small enough, that is, the solution is restricted to a very small range). If the convergence condition is not met, repeat the above steps. Once the convergence condition is satisfied, output the final decision-making plan.

[0108] The optimization configuration decision-making plan generated by this embodiment includes the installed capacities of wind-solar-storage power sources in 3 regions. Since the economic benefits after planning configuration are considered, the access ratio of new energy can be increased compared with the traditional planning problem that only considers cost, promoting the consumption and utilization of new energy. At the same time, considering the restrictive effect of the inter-regional mutual assistance ability, the safe and stable operation of the system can be ensured.

[0109] As Figure 2 shown, the wind-solar-storage resource optimization configuration device considering the inter-regional mutual assistance ability in the embodiment of the present invention is used to execute the wind-solar-storage resource optimization configuration method considering the inter-regional mutual assistance ability described above, and includes:

[0110] A data collection module 10, configured to collect power market-related data and grid planning-related data of each region in a large-scale power system; the large-scale power system includes a large interconnected power grid composed of multiple regions;

[0111] A two-stage optimization model construction module 20, configured to construct a two-stage optimization model for wind-solar-storage resource configuration considering the inter-regional mutual assistance ability for the large interconnected power grid composed of multiple regions by using the collected power market-related data and grid planning-related data;

[0112] The optimized configuration scheme generation module 30 is used to iteratively solve the two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability by using the Benders decomposition algorithm, and determine the optimized configuration scheme for the newly added wind-solar-storage resources in each region. The optimized configuration scheme for the newly added wind-solar-storage resources in each region includes the installed capacities of the wind-solar-storage power sources in each region.

[0113] Furthermore, the two-stage optimization model construction module 20 constructs the wind-solar-storage resource allocation optimization problem into a two-stage optimization model for the allocation of wind-solar-storage resources considering the regional mutual assistance ability, which consists of a master problem and a sub-problem, including:

[0114] Determine the master problem to solve the problem of minimizing the cost of newly added wind-solar-storage in a large interconnected power grid considering the mutual assistance ability between regions. The solution of the master problem is the total capacity of newly added wind-solar-storage in each region within the large interconnected power grid;

[0115] Determine the sub-problem to solve the problem of maximizing the profit in each region within the large interconnected power grid. The solution of the sub-problem constitutes the optimized configuration scheme for the newly added wind-solar-storage capacity in each region within the large interconnected power grid;

[0116] When solving the sub-problem, the solution of the master problem is used as a known quantity to provide equality constraint conditions for the sub-problem;

[0117] When solving the master problem, the solution of the sub-problem is used to update the objective function of the master problem.

[0118] The embodiment of the present invention also provides a terminal to execute the method. Please refer to Figure 4 It shows a schematic diagram of a terminal provided by some embodiments of the present invention. Refer to Figure 4 As shown, the terminal 8 includes: a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected through the bus 802; a computer program that can run on the processor 800 is stored in the memory 801, and when the processor 800 runs the computer program, it executes the method provided by any one of the embodiments of the present invention.

[0119] Among them, the memory 801 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 803 (which can be wired or wireless), a communication connection is realized between this device network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0120] The bus 802 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 801 is used to store a program. After receiving an execution instruction, the processor 800 executes the program. Any implementation manner of the method disclosed in the embodiments of the present invention can be applied to or implemented by the processor 800.

[0121] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method can be completed by the integrated logic circuit in the hardware of the processor 800 or the instructions in the form of software. The processor 800 can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed by the hardware decoding processor, or executed by the combination of the hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and combines its hardware to complete the steps of the method.

[0122] The terminal provided in the embodiments of the present invention and the method in the embodiments of the present invention are based on the same inventive concept and have the same beneficial effects as the method adopted, run, or implemented by it.

[0123] As Figure 5 shown, the embodiments of the present invention also provide a computer-readable storage medium corresponding to the method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., program product 900) is stored. When the computer program is run by a processor, it will execute the method provided in any of the foregoing embodiments.

[0124] It should be noted that examples of the computer-readable storage medium may further include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other optical and magnetic storage media, which will not be elaborated here one by one.

[0125] The computer-readable storage medium provided by the embodiments of the present invention is based on the same inventive concept as the method of the embodiments of the present invention, and has the same beneficial effects as the methods adopted, run, or implemented by the application programs stored therein.

[0126] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if the various adjustments and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the allocation of wind-solar-storage resources considering the regional mutual assistance ability, characterized in that Including: Collecting power market - related data and grid - planning - related data of each region within a large - scale power system; The large - scale power system includes a large interconnected power grid composed of multiple regions; For the large interconnected power grid composed of multiple regions, using the collected power market - related data and grid - planning - related data, constructing the problem of optimizing the allocation of wind - solar - storage resources into a two - stage optimization model of wind - solar - storage resource allocation considering regional mutual - aid ability, which consists of a master problem and a sub - problem; Adopting the Benders decomposition algorithm to iteratively solve the two - stage optimization model of wind - solar - storage resource allocation considering regional mutual - aid ability, and determining the optimized allocation scheme of newly added wind - solar - storage resources in each region. The optimized allocation scheme of newly added wind - solar - storage resources in each region includes the installed capacity of wind - solar - storage power sources in each region.

2. The method for optimizing the allocation of wind - solar - storage resources considering regional mutual - aid ability according to claim 1, characterized in that The power market - related data includes the electricity trading prices in different regions; The grid - planning - related data includes the wind power construction cost coefficient, the photovoltaic power construction cost coefficient, the energy storage construction cost coefficient, the newly added annual maximum load power, the newly added load effective demand hours, the effective utilization hours of wind power, the effective utilization hours of photovoltaic power, the effective utilization hours of energy storage, and the inter - regional power transmission capacity. Among them, the inter - regional power transmission capacity is used to indicate the regional mutual - aid ability.

3. The method for optimizing the allocation of wind-solar-storage resources considering regional mutual assistance capabilities according to claim 2, characterized in that, The construction of the problem of optimizing the allocation of wind - solar - storage resources into a two - stage optimization model of wind - solar - storage resource allocation considering regional mutual - aid ability, which consists of a master problem and a sub - problem, includes: Determining that the master problem is used to solve the problem of minimizing the cost of newly added wind - solar - storage in a large interconnected power grid considering inter - regional mutual - aid ability. The solution of the master problem is the total capacity of newly added wind - solar - storage in each region within the large interconnected power grid; Determining that the sub - problem is used to solve the problem of maximizing the profit in each region within the large interconnected power grid. The solution of the sub - problem constitutes the optimized allocation scheme of the newly added wind - solar - storage capacity in each region within the large interconnected power grid; When solving the sub - problem, the solution of the master problem is used as a known quantity to provide equality constraint conditions for the sub - problem; When solving the master problem, the solution of the sub - problem is used to update the objective function of the master problem.

4. The method for optimizing the allocation of wind - solar - storage resources considering regional mutual - aid ability according to claim 3, characterized in that The master problem is an optimization problem of the newly added installed power generation capacity in each region with constraints. Its optimization variables include the total installed capacity of newly added wind - solar - storage power generation in each region. Its optimization objective function is to minimize the total cost of the configuration of various types of power sources newly added in each region within the large interconnected power grid. Its constraint conditions include the regional mutual - aid ability constraint.

5. The method for optimizing the allocation of wind - solar - storage resources considering regional mutual - aid ability according to claim 4, characterized in that The sub - problem is an optimization problem of the newly added wind - solar - storage in each region with constraints considering the electricity revenue. Its optimization variables are the newly added wind - solar - storage installed capacity in each region. Its optimization objective function is to maximize the total profit after the allocation of wind - solar - storage resources in each region. The constraint conditions of the sub - problem include the total regional allocation capacity constraint and the electricity reserve constraint.

6. The method for optimizing the allocation of wind-solar-storage resources considering the regional mutual assistance ability according to claim 5, wherein Adopt the Benders decomposition algorithm to iteratively solve the two-stage optimization model for the configuration of wind-solar-storage resources considering the regional mutual assistance ability, and determine the optimal configuration plan for the newly added wind-solar-storage resources in each region, including: During the iterative solution process, gradually approach the global optimal solution through the interaction between the master problem and the sub-problem; First, randomly select an initial feasible solution as the current solution of the master problem, solve the sub-problem based on the current solution of the master problem, and determine whether the sub-problem is feasible; If the sub-problem is feasible, generate an optimality cut using its dual solution, and update the upper bound of the solution space of the master problem using the optimality cut; If the sub-problem is infeasible, generate a feasibility cut, and update the lower bound of the solution space of the master problem using the feasibility cut; incorporate the generated feasibility cut back into the master problem to update the master problem model.

7. An optimization configuration device for wind-solar-storage resources considering regional mutual assistance ability, characterized in that, Including: A data collection module for collecting relevant data on the electricity market in each region and relevant data on the grid planning in each region within a large-scale power system; The large-scale power system includes a large interconnected power grid composed of multiple regions; A two-stage optimization model construction module for constructing the optimization problem of wind-solar-storage resource configuration into a two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, which consists of a master problem and a sub-problem, for the large interconnected power grid composed of multiple regions, using the collected relevant data on the electricity market and grid planning; An optimal configuration plan generation module for adopting the Benders decomposition algorithm to iteratively solve the two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, and determine the optimal configuration plan for the newly added wind-solar-storage resources in each region. The optimal configuration plan for the newly added wind-solar-storage resources in each region includes the installed capacity of wind-solar-storage power sources in each region.

8. The optimized allocation device for wind-solar-storage resources considering regional mutual assistance ability according to claim 7, wherein The two-stage optimization model construction module constructs the optimization problem of wind-solar-storage resource configuration into a two-stage optimization model for wind-solar-storage resource configuration considering the regional mutual assistance ability, which consists of a master problem and a sub-problem, including: Determine that the master problem is used to solve the problem of minimizing the cost of newly added wind-solar-storage in a large interconnected power grid considering the mutual assistance ability between regions. The solution of the master problem is the total capacity of newly added wind-solar-storage in each region within the large interconnected power grid; Determine that the sub-problem is used to solve the problem of maximizing the profit in each region within the large interconnected power grid. The solution of the sub-problem constitutes the optimal configuration plan for the newly added wind-solar-storage capacity in each region within the large interconnected power grid; When solving the sub-problem, the solution of the master problem is used as a known quantity to provide equality constraint conditions for the sub-problem; When solving the master problem, the solution of the sub-problem is used to update the objective function of the master problem.

9. A terminal, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

10. A computer storage medium, characterized in that, Store computer controllable instructions for executing the method according to any one of claims 1 to 6.

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