An active power distribution network flexibility resource coordination optimization method and system

Abstract

The present disclosure provides an active power distribution network flexibility resource coordination optimization method and system, relating to the technical field of power system distribution network planning, including establishing an uncertainty scenario considering comprehensive norm based on historical data of wind and light output, and probability distribution, and formulating a coordinated planning scheme of the distribution network under typical and severe scenarios. The response model of different types of flexibility resources facing flexibility demand is quantified, and the flexibility demand of the distribution network under each scenario is quantified. A target function containing the planning and construction cost of the flexibility resource, the operation cost of the distribution network, and the annual benefit of the distribution network is constructed. The safety constraints of the distribution network, the operation constraints and planning and construction constraints of each flexibility resource are constructed. The column constraint generation algorithm is used for iterative solution. When the difference between the upper and lower bounds is less than the convergence precision, the iteration is ended, and the optimal distribution network planning scheme is output. The flexible and economic operation of the distribution network is realized.

Description

Technical Field

[0001] This disclosure relates to the field of power system distribution network planning technology, specifically to a method and system for coordinating and optimizing the flexibility resources of an active distribution network. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] As the current power distribution network demands an increasing penetration rate of new energy sources, the capacity and demand for wind turbines and photovoltaic power plants to connect to the power distribution network are constantly growing. In order to maximize the access of new energy sources to the power distribution network, it is essential to rationally plan and construct wind power and photovoltaic units.

[0004] Meanwhile, due to the fluctuations and uncertainties in wind and solar power output, the distribution network has a need for flexibility in absorbing wind and solar power. Therefore, it is of great significance to rationally plan and construct the flexibility resources of the distribution network to meet the growing demand for flexibility, and to use new flexible interconnection devices and intelligent energy storage soft switches to achieve flexible power allocation, which is of great significance for the flexible operation of active distribution networks.

[0005] Currently, the planning methods for flexibility resources in distribution networks only focus on the separate planning and construction of flexibility resources such as wind and solar power units or energy storage, but there is a lack of research on the coordinated planning of multiple flexibility resources such as power generation, grid, load and storage. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a method and system for coordinating and optimizing the flexibility resources of an active distribution network. The method employs a sub-Bruker programming model that utilizes scenario probability uncertainty to consider severe scenarios over long timescales required for planning. By optimizing and scheduling, it addresses the supply of flexibility requirements dictated by the uncertainty of wind and solar power fluctuations, thereby achieving flexible and economical operation of the distribution network.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] A method for coordinating and optimizing the flexibility resources of an active distribution network includes:

[0009] Acquire historical data on wind and solar power output of the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data, and carry out coordinated optimization of the distribution network under typical and severe scenarios;

[0010] Quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and establish a sub-Bruker optimization model;

[0011] Construct an objective function with the goal of optimizing the economic indicators of the distribution network;

[0012] The system constructs safety constraints for the distribution network, operational constraints for each flexible resource, and planning and construction constraints, and performs second-order cone relaxation transformation on the constraints. It uses a column constraint generation algorithm for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios. This cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus achieving coordinated optimization of flexible resources.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] An active distribution network flexibility resource coordination and optimization system includes:

[0015] The initialization module is used to acquire historical data on wind and solar power output in the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data, and perform coordinated optimization of the distribution network under typical and severe scenarios.

[0016] The model building module is used to quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and to establish a multi-bar optimization model.

[0017] The objective function construction module is used to construct an objective function that aims to optimize the economic indicators of the distribution network.

[0018] The optimization module is used to construct the distribution network security constraints, the operational constraints of various flexible resources, and the planning and construction constraints, and to perform second-order cone relaxation transformation on the constraints. The column constraint generation algorithm is used for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios, and the cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus realizing the coordinated optimization of flexible resources.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned method for coordinating and optimizing the flexibility resources of an active power distribution network.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the active power distribution network flexibility resource coordination and optimization method.

[0023] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0024] The distributed bar optimization model adopted in this disclosure utilizes the uncertainty of scenario probabilities to consider severe scenarios on the long-term scale required for planning. It realizes the supply of flexibility requirements due to the uncertainty of wind and solar fluctuations from both planning and scheduling perspectives, thereby achieving flexible and economical operation of the distribution network. Attached Figure Description

[0025] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0026] Figure 1 This is a flowchart of an active power distribution network flexibility resource coordination and optimization method according to an embodiment of the present disclosure. Detailed implementation method:

[0027] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0030] Example 1

[0031] One embodiment of this disclosure provides a method for coordinating and optimizing the flexibility resources of an active distribution network, such as... Figure 1 As shown, it includes:

[0032] S1: Obtain historical data on wind and solar power output of the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data on wind and solar power output, and formulate coordinated optimization schemes for the distribution network under typical and severe scenarios.

[0033] S2: In the multiple scenarios described in S1, quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and establish a sub-Bruker optimization model.

[0034] S3: Construct an objective function with the goal of optimizing the economic indicators of the distribution network;

[0035] The optimized distribution network economic indicators include the planning and construction costs of flexibility resources, the operating costs of the distribution network, and the annual revenue of the distribution network.

[0036] S4: Construct security constraints, operational constraints of various flexibility resources, and planning and construction constraints for the distribution network, and perform second-order cone relaxation transformation on the constraint conditions;

[0037] S5: The column constraint generation algorithm is used for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios. The cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus realizing flexible resource coordination and optimization.

[0038] As one embodiment, a method for coordinating and optimizing the flexibility resources of wind power, photovoltaic power, energy storage, and smart energy storage soft switching in an active distribution network is implemented as follows:

[0039] In step S1, based on historical data of wind and solar power output, uncertainty scenarios and probability distributions considering the comprehensive norm are established, and coordinated optimization schemes for the distribution network are formulated under typical and severe scenarios.

[0040] S11: Selection of Typical and Severe Scenarios

[0041] The typical scenario is the scenario with the highest probability of occurrence among all power output scenarios in all four seasons, and the severe scenario is the scenario with the highest probability of occurrence among all scenarios that exceed the fluctuation range of the typical scenario.

[0042] The selection method for the typical and severe scenarios is as follows: after obtaining four typical scenarios for the four seasons by k-means clustering based on historical data of wind and solar power output, k-means clustering is performed on the typical scenarios with power output fluctuation range of 5-10% to obtain four severe scenarios with fluctuation range under the typical scenarios, and the probability of occurrence of the corresponding scenarios is calculated based on the ratio of the number of scenario clusters to the total number of scenarios.

[0043] S12: Formulate a framework considering scenarios with uncertainty in the comprehensive norm and probability distribution.

[0044] Based on the initial probability distributions of typical and adverse scenarios formed after k-means clustering, their probability distributions are constrained by the 1-norm and the infinity norm. Therefore, the expression for Φ is:

[0045]

[0046] Where Φ is the set of scene probabilities and constraints, p sLet N be the probability of each scenario occurring, p0 be the probability of the original scenario occurring, and N be the probability of each scenario occurring. s To select the number of scenes, θ1 and θ ∞ These are the allowable limits for probability deviations under the constraints of the 1-norm and the infinite norm.

[0047] The scenario probability satisfies the following confidence constraint:

[0048]

[0049]

[0050] Where Pr represents the scenario probability and M is the number of historical data points.

[0051] Then the confidence level on the right side of the above equation is defined as α1 and α ∞ The following solutions yield θ1 and θ ∞ for:

[0052]

[0053] The above solution yields θ1 and θ ∞ Under the premise that, the above formula can be used to restrict the probability distribution of the scene.

[0054] Furthermore, in step S2, the response models of different types of flexibility resources in the face of flexibility requirements and the requirements in each scenario are quantified.

[0055] S21: For the Flexible Resource Response Model

[0056] Because distribution networks contain various flexibility resources, this disclosure considers gas turbine units, energy storage systems, and demand response loads as flexibility resources in the distribution network. The flexibility resources of the distribution network are mainly used to address the impact of uncertainties in net load power fluctuations on the distribution network. Net load power is defined as the actual power demand of the distribution network after deducting the output of uncontrollable distributed generation sources from the distribution network load power.

[0057] The net load power at time t is calculated as follows:

[0058]

[0059] Simultaneously calculate the flexibility up and down capabilities of the node's flexibility resources at time t:

[0060]

[0061] in, and These represent the upward and downward adjustment flexibility of the gas turbine unit at node i at time t, respectively. and These represent the up-adjustment and down-adjustment flexibility capabilities of the energy storage system at node i at time t; and These represent the flexibility of adjusting the demand response load at node i at time t, both upward and downward. and These represent the ability to increase and decrease the flexibility of node flexibility resources at time t, respectively. NL,t Let be the net load power at time t. t Add a flag to the net load power; it is a 0-1 variable. P L,i,t , and These represent the load power, the predicted output of photovoltaic (PV) power, and the predicted output of wind power, respectively. Ω is the set of all nodes. MTG Ω ESS and Ω DR These are the node sets for gas turbine units, energy storage systems, and demand response loads, respectively.

[0062] S22: Definition of flexibility requirements margin and deficit

[0063] When the distribution network generates flexibility demand, if the flexibility resource adjustment capacity proposed in this application still has a surplus after meeting the flexibility demand in the direction of net load power fluctuation, it is defined as the distribution network flexibility margin, and the definition formula is as follows:

[0064]

[0065] In the formula: and These represent the upward and downward adjustment flexibility margins of the distribution network at time t, respectively.

[0066] If the upward and downward adjustment of the distribution network's flexibility margin cannot meet the net load power demand, a negative flexibility margin will occur, indicating that the distribution network is experiencing insufficient flexibility. The resulting flexibility deficit is defined as follows:

[0067]

[0068] In the formula: and These represent the upward and downward adjustment flexibility deficits of the distribution network at time t.

[0069] In step S3, the overall objective function is to optimize the economic indicators of the distribution network. These optimized economic indicators include the planning and construction costs of flexibility resources, the operating costs of the distribution network, and the annual revenue of the distribution network. The planning and construction costs of flexibility resources include wind power investment costs, photovoltaic investment costs, controllable gas turbine investment costs, energy storage system investment costs, and ESOP investment costs. The operating costs of the distribution network include the main grid power purchase costs, energy storage operating costs, wind and solar curtailment costs, gas turbine costs, and network loss costs. The revenue of the distribution network includes revenue from selling electricity to users and revenue from the distribution network's flexibility margin.

[0070] The overall objective function is to optimize the economic performance of the distribution network, and the constructed objective function is shown in the following formula:

[0071] minf = f inv +f ope -f inc

[0072] In the formula, f inv For planning and construction costs; f ope For the operating cost of the distribution network, f inc For the annual revenue of the distribution network.

[0073] The objective functions for each component, including the planning and construction costs of flexible resources, the operating costs of the distribution network, and the annual revenue of the distribution network, are as follows:

[0074] S31: Flexible Resource Planning and Construction Costs

[0075] Annual investment and construction costs f inv Including wind power investment cost C WT Photovoltaic investment cost C PV Investment cost of controllable gas turbine units C MTG Energy storage system investment cost C ESS And ESOP investment cost C ESOP The specific formula is as follows:

[0076] f inv =C WT +C PV +C MTG +C ESS +C ESOP

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula: r is the discount rate, T WTG T PVG T MTG T ESS and T ESOP These refer to the service life of ESOP, wind turbines, photovoltaic systems, gas turbines, and energy storage systems, respectively. WTG I PVG I MTG I ESS and I SOP,C I SOP,CELL I SOP,DCDC These are the standard operating procedures (SOPs) for wind turbines, photovoltaic systems, gas turbine units, and energy storage systems, as well as the unit capacity construction costs of energy storage systems and DC-DC converters within the ESOP, N. WTG,i N PVG,i N MTG,i N ESS,i and S SOP,i,C S SOP,i,CELL S SOP,i,DCDC Let d and y represent the construction capacity of wind turbines, photovoltaics, gas turbines, and energy storage systems, respectively, and the construction capacity of the Standard Operating Procedure (SOP), energy storage system, and DC-DC converter within the ESOP. Let d and y represent the ESOP discount rate and service life, respectively. η WTG Set as the ESOP annual maintenance cost factor.

[0083] S32: Distribution Network Operating Costs

[0084] The operating costs of a power distribution network include the cost of purchasing electricity from the main grid, the operating costs of energy storage, the costs of curtailing wind and solar power, the costs of gas turbine units, and the costs of network losses.

[0085]

[0086] Where: N s and p s c represents the number of scenes and the probability of the s-th scene occurring, respectively. p,t c ESS c PV c WT c MTG and c CL This includes time-of-use pricing, energy storage operating costs, solar curtailment penalty costs, wind curtailment penalty costs, gas turbine units, interruptible load costs, and flexibility adjustment revenue coefficients. g,i,t P is the active power input to the main network at time t for node i. ESS,i,t P represents the charging and discharging power of the energy storage system at time t at node i. PV,i,t and P WT,i,t P represents the actual wind power and solar power output at time t, respectively, at node i.WTG,i,t Let r be the output of the gas turbine unit at node i at time t. ij and I ij These represent the resistance and current on branch ij, respectively. Let t be the loss of the nth ESOP branch at time t.

[0087] S33: Annual revenue of distribution network

[0088] Distribution network revenue includes revenue from selling electricity to users and revenue from distribution network flexibility margin. The price and volume of electricity sold are based on the real-time price after the fluctuation of renewable energy output and the active power demand of user load after demand response. The distribution network flexibility margin revenue includes the penalty cost for insufficient flexibility of the distribution network.

[0089]

[0090]

[0091] In the formula: c p,t For the real-time electricity price of the distribution network, C fle For the benefits of distribution network flexibility, c fle c is the flexibility resource margin benefit coefficient. pun The penalty coefficient for insufficient flexibility resources.

[0092] Furthermore, in step S4, the distribution network security constraints, operational constraints of various flexibility resources, and planning and construction constraints are constructed, and the constraints are transformed into second-order cone relaxation conditions, including:

[0093] S41: Distribution Network Operation and Safety Constraints

[0094]

[0095]

[0096]

[0097]

[0098] In the formula: and P is the sum of the squares of the voltage amplitude and upper and lower limits at node i at time t, and the squares of the current amplitude and maximum value at branch ij. ij,t and Q ij,t P represents the active and reactive power transmitted on branch ij at time t. jk,t and Q jk,t Let P be the active and reactive power transmitted on the jk branch connected to the k node adjacent to j, respectively. i,t and Q i,tω represents the active and reactive power injected into node i at time t. j Let r be the set of branches connected to node j. ij and x ij These are the resistance and reactance of branch ij, respectively.

[0099] S42: ESOP Operational Constraints

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] In the formula: P SOP,n,i,t P SOP,n,j,t and Let A represent the active power injected and active power loss at ports i and j of the nth ESOP at time t, respectively; SOP,i and A SOP,j Q represents the active power loss coefficients at ports i and j of the ESOP, respectively. SOP,n,i,t and Q SOP,n,j,t Let Q be the reactive power injected into ports i and j of the nth ESOP at time t; SOP,i,min Q SOP,i,max Q SOP,j,min and Q SOP,j,max These are the upper and lower limits of reactive power injected into ports i and j of the ESOP, respectively. SOP,n,C The investment and construction capacity for the nth ESOP. Let be the total active power loss of the nth ESOP at time t.

[0106] S43: Flexible Resource Operation Constraints

[0107] The operating constraints of the energy storage system are:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] E ESS,i,t,min ≤EESS,i,t ≤E ESS,i,t,max

[0114] In the formula: E ESS,i,t E ESS,i,t,min and E ESS,i,t,max Let η be the energy level and upper / lower limit of the energy storage system at node i at time t. ci and η di These represent the charging and discharging efficiencies of the energy storage system at node i.

[0115] The operating constraints of the gas turbine unit are:

[0116]

[0117] P MTG,i,min ≤P MTG,i,t ≤P MTG,i,max

[0118] Q MTG,i,min ≤Q MTG,i,t ≤Q MTG,i,max

[0119] In the formula: Q MTG,i,t Let Q be the reactive power output of the gas turbine unit at node i at time t. MTG,i,min and Q MTG,i,max These are the upper and lower limits of reactive power output of the gas turbine unit at node i.

[0120] The operating constraints of the wind and solar turbine units are as follows:

[0121]

[0122]

[0123]

[0124]

[0125] In the formula: and These are the operating power factor angles for wind power and photovoltaic power, respectively.

[0126] In step S5, a column constraint generation algorithm is used for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios. This cost is then added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus achieving flexible resource coordination and optimization.

[0127] Specifically, a column constraint generation algorithm is used for iterative solution. Upper and lower bounds are set for the iteration. The entire method is divided into two stages: the first stage is the planning and construction stage, which involves planning and constructing the distribution network's flexibility resources; the second stage is the operation stage, where an operation plan is designed based on the planning scheme of the first stage, aiming for optimal comprehensive benefits. The two stages are further divided into a main problem and subproblems in the Bruker programming model. First, the main problem is solved to obtain the optimal planning and construction results and the annual comprehensive benefits of the distribution network. The planning and construction scheme of the main problem is input into the subproblems, and the objective function is output as the lower bound. Second, the subproblems, under the planning and construction scheme of the main problem, solve for the optimal annual operating cost of the distribution network under the probability distribution of the worst-case wind and solar power output scenario, and add this cost to the planning and construction cost of the first stage as the upper bound output. The iteration ends when the upper and lower bounds are less than the convergence accuracy ε0. At this point, the optimal result for the annual comprehensive benefits of the distribution network and the optimal planning and construction scheme are obtained.

[0128] This disclosure decomposes the above mathematical framework into a main problem MP and a subproblem SP, and solves them iteratively based on a column constraint generation algorithm.

[0129] The main problem MP is to find the optimal coordination planning scheme under the initial clustering probability distribution, and the probability distribution p is obtained by solving the subproblem SP. s The optimal solution that maximizes the annual comprehensive benefit is found. The planning and construction scheme under this solution is the optimal construction scheme. The objective function is used as the lower bound value, and its compact framework is expressed as follows:

[0130]

[0131]

[0132] After solving for the first-stage variables using MP, SP solves for the worst-case probability distribution of the second stage based on the first-stage programming result x*, and uses the objective function as the upper bound. Its compact framework is expressed as:

[0133]

[0134] The decomposed MP and SP can be solved iteratively using a column constraint generation algorithm. The iteration ends when the gap ε between the upper and lower bounds is less than a certain convergence accuracy threshold ε0.

[0135] ε=UBd-LBd

[0136] In the formula: UB d LB d Let represent the upper and lower bounds in the d-th iteration, respectively.

[0137] Example 2

[0138] One embodiment of this disclosure provides an active distribution network flexibility resource coordination and optimization system, including:

[0139] The initialization module is used to acquire historical data on wind and solar power output in the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data, and perform coordinated optimization of the distribution network under typical and severe scenarios.

[0140] The model building module is used to quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and to establish a multi-bar optimization model.

[0141] The objective function construction module is used to construct an objective function that aims to optimize the economic indicators of the distribution network.

[0142] The optimization module is used to construct the distribution network security constraints, the operational constraints of various flexible resources, and the planning and construction constraints, and to perform second-order cone relaxation transformation on the constraints. The column constraint generation algorithm is used for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios, and the cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus realizing the coordinated optimization of flexible resources.

[0143] Example 3

[0144] One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the active power distribution network flexibility resource coordination and optimization method.

[0145] Example 4

[0146] One embodiment of this disclosure provides an electronic device, characterized in that it includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the steps of the active power distribution network flexibility resource coordination and optimization method.

[0147] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for coordinating and optimizing the flexibility resources of an active distribution network, characterized in that, include: Acquire historical data on wind and solar power output of the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data, and carry out coordinated optimization of the distribution network under typical and severe scenarios; Quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and establish a sub-Bruker optimization model; Construct an objective function with the goal of optimizing the economic indicators of the distribution network; The system constructs safety constraints for the distribution network, operational constraints for each flexible resource, and planning and construction constraints, and performs second-order cone relaxation transformation on the constraints. It uses a column constraint generation algorithm for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios. This cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus achieving coordinated optimization of flexible resources. The optimized distribution network economic indicators include the planning and construction cost of flexibility resources, the operation cost of the distribution network, and the annual revenue of the distribution network. The planning and construction cost of flexibility resources includes wind power investment cost, photovoltaic investment cost, controllable gas turbine investment cost, energy storage system investment cost, and ESOP investment cost. The operation cost of the distribution network includes the main grid power purchase cost, energy storage operation cost, wind and solar curtailment cost, gas turbine cost, and network loss cost. The distribution network revenue includes the revenue from selling electricity to users and the distribution network flexibility margin revenue. When using the column constraint generation algorithm for iterative solution, upper and lower limits are set for iteration, and the bibliometric optimization model is divided into a main problem and subproblems. The main problem is solved first to obtain the optimal construction cost and the annual comprehensive revenue of the distribution network. The optimal solution under the optimal result of the main problem is input into the subproblem, and the objective function is output as the lower bound. The optimal annual operation cost of the distribution network under the probability distribution of severe wind and solar power output scenarios under the optimization solution of the main problem is solved, and added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the upper and lower bounds are less than the convergence accuracy.

2. The method for coordinating and optimizing the flexibility resources of an active distribution network as described in claim 1, characterized in that, The selection method for the typical and severe scenarios is as follows: k-means clustering is performed based on historical data of wind and solar power output to obtain four typical scenarios for the four seasons. k-means clustering is then performed on the typical scenarios with power output fluctuations of 5-10% to obtain four severe scenarios with fluctuations under the typical scenarios, and the corresponding probability of occurrence of the scenarios is obtained.

3. The method for coordinating and optimizing the flexibility resources of an active distribution network as described in claim 1, characterized in that, The quantitative response models of different types of flexible resources in various scenarios and the requirements in each scenario include: considering gas turbine units, energy storage systems and demand response loads as flexible resources in the distribution network. The flexible resources are used to cope with the impact of the uncertainty of net load power fluctuation on the distribution network. The net load power is the actual power demand of the distribution network after subtracting the output of uncontrollable distributed power sources from the load power of the distribution network.

4. The method for coordinating and optimizing the flexibility resources of an active distribution network as described in claim 1, characterized in that, The overall objective function is to optimize the economic indicators of the distribution network. The objective function is constructed as follows: in, f inv For planning and construction costs; For the operating costs of the distribution network, For the annual revenue of the distribution network.

5. An active power distribution network flexibility resource coordination and optimization system, characterized in that, include: The initialization module is used to acquire historical data on wind and solar power output in the distribution network, establish uncertainty scenarios and probability distributions considering the comprehensive norm based on the historical data, and perform coordinated optimization of the distribution network under typical and severe scenarios. The model building module is used to quantify the response models of different types of flexible resources to demands in multiple scenarios and the demands in each scenario, and to establish a multi-bar optimization model. The objective function construction module is used to construct an objective function that aims to optimize the economic indicators of the distribution network. The optimization module is used to construct the distribution network security constraints, the operation constraints of various flexible resources, and the planning and construction constraints, and to perform second-order cone relaxation transformation on the constraint conditions. The column constraint generation algorithm is used for iterative solution to obtain the optimal annual operating cost of the distribution network under the probability distribution of wind and solar power output scenarios, and the cost is added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the difference between the upper and lower bounds is less than the convergence accuracy, thus realizing the coordinated optimization of flexible resources. The optimized distribution network economic indicators include the planning and construction cost of flexibility resources, the operation cost of the distribution network, and the annual revenue of the distribution network. The planning and construction cost of flexibility resources includes wind power investment cost, photovoltaic investment cost, controllable gas turbine investment cost, energy storage system investment cost, and ESOP investment cost. The operation cost of the distribution network includes the main grid power purchase cost, energy storage operation cost, wind and solar curtailment cost, gas turbine cost, and network loss cost. The distribution network revenue includes the revenue from selling electricity to users and the distribution network flexibility margin revenue. When using the column constraint generation algorithm for iterative solution, upper and lower limits are set for iteration, and the bibliometric optimization model is divided into a main problem and subproblems. The main problem is solved first to obtain the optimal construction cost and the annual comprehensive revenue of the distribution network. The optimal solution under the optimal result of the main problem is input into the subproblem, and the objective function is output as the lower bound. The optimal annual operation cost of the distribution network under the probability distribution of severe wind and solar power output scenarios under the optimization solution of the main problem is solved, and added to the first-stage planning and construction cost as the upper bound output. The iteration ends when the upper and lower bounds are less than the convergence accuracy.

6. The active distribution network flexibility resource coordination and optimization system as described in claim 5, characterized in that, The selection method for the typical and severe scenarios is as follows: k-means clustering is performed based on historical data of wind and solar power output to obtain four typical scenarios for the four seasons. k-means clustering is then performed on the typical scenarios with power output fluctuations of 5-10% to obtain four severe scenarios with fluctuations under the typical scenarios, and the corresponding probability of occurrence of the scenarios is obtained.

7. The active distribution network flexibility resource coordination and optimization system as described in claim 5, characterized in that, The quantitative response models of different types of flexible resources in various scenarios and the requirements in each scenario include: considering gas turbine units, energy storage systems and demand response loads as flexible resources in the distribution network. The flexible resources are used to cope with the impact of the uncertainty of net load power fluctuation on the distribution network. The net load power is the actual power demand of the distribution network after subtracting the output of uncontrollable distributed power sources from the load power of the distribution network.

8. The active distribution network flexibility resource coordination and optimization system as described in claim 5, characterized in that, The overall objective function is to optimize the economic indicators of the distribution network. The objective function is constructed as follows: in, f inv For planning and construction costs; For the operating costs of the distribution network, For the annual revenue of the distribution network.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement an active power distribution network flexibility resource coordination and optimization method as described in any one of claims 1-4.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the active power distribution network flexibility resource coordination and optimization method as described in any one of claims 1-4.

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