Power distribution network source network load storage hierarchical coordination intelligent control method and system

Abstract

The invention relates to the technical field of coordination intelligent control of a power grid, and discloses a power distribution network source network load storage hierarchical coordination intelligent control method and system, and the method comprises the steps: obtaining power grid parameters of intelligent control candidate installation sites; and establishing a source network load storage hierarchical coordination intelligent control model, solving the multi-target problem of the source network load energy storage up-down planning model by using an improved epsilon constraint method, obtaining a Pareto optimal solution set, and performing optimal control on the active power distribution network by using the most preferential solution in the Pareto optimal solution set. The coordinated scheduling strategy provided by the invention achieves remarkable effects in the aspects of improving the new energy utilization rate, optimizing the power resource configuration, reducing pollutant emission and the like. The results not only verify the effectiveness of the coordinated dispatching system, but also provide powerful support and guarantee for the future development of the power industry.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated intelligent control of power grids, and in particular to a method and system for coordinated intelligent control of source, network, load and storage hierarchical layers of a distribution network. Background Art

[0002] At present, the construction of transmission networks and high and medium voltage distribution networks is becoming more and more perfect. However, due to the lag in technology and management, it is difficult to meet the needs of lean management on the grid side, cost reduction and quality improvement on the user side [6]. On the other hand, due to the lack of coordinated operation mode of "source load storage", the new energy absorption pressure is high.

[0003] With the deep integration of advanced information and communication technology, control technology and energy technology, the controllable resources and means of the power grid have become diversified, and new concepts and technologies such as transparent power grid, energy Internet, and Internet of Things technology have been proposed one after another; with the continuous advancement of the power marketization process, new methods such as user-side demand-side response and adjustable load two-way interaction have been gradually promoted and applied, and the characteristics of load flexibility have become more and more obvious.

[0004] The above developments provide new opportunities for the coordinated and optimized operation of the power grid. By using a variety of control methods, Kuhl optimizes the control of controllable units, which can increase the consumption of renewable energy, reduce network losses, improve the power quality of the power grid, increase the service life of equipment, and improve the power experience of users. Therefore, it is of great significance to study the coordinated control method of the power grid that uses the resources of all parties of "source, network, storage and load" in coordination. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention solves the problems that the existing distribution network source network load storage hierarchical coordination intelligent control method has large power consumption, high cost, and cannot achieve optimal coordination.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a distribution network source network load storage hierarchical coordinated intelligent control method, comprising:

[0008] Obtaining grid parameters of candidate installation locations for intelligent control;

[0009] Establish a source-grid-load-storage hierarchical coordinated intelligent control model, including: an upper-level model, which optimizes the configuration of the installed capacity of distributed power sources and energy storage systems at candidate installation sites with the minimum annual comprehensive cost as the coordination control target; a lower-level model, which controls the stable operation of the power system with the minimum network loss as the target through the peak-shaving and valley-filling operation strategy of the energy storage system;

[0010] The improved ε constraint method is used to solve the multi-objective problem of the source-grid-load-energy storage up-and-down planning model, and a Pareto optimal solution set is obtained. The highest priority solution in the Pareto optimal solution set is used to optimally control the active distribution network.

[0011] As a preferred solution of the distribution network source-grid-load-storage hierarchical coordinated intelligent control method described in the present invention, the grid parameters include various electrical parameter information and resource value information during the operation of the grid.

[0012] As a preferred solution of the distribution network source network load storage hierarchical coordinated intelligent control method described in the present invention, wherein: the upper model is specifically an active distribution network upper planning model based on annual comprehensive cost, including:

[0013] Planning is carried out with the minimum annual comprehensive cost as the objective function;

[0014]

[0015] Assume that the distributed generation is DG, the energy storage system is ESS, and the demand response is DR; represents the annual investment cost of DG and ESS; represents the annual maintenance cost of DG and ESS; represents the annual cost of DR; represents the annual network loss cost of DR;

[0016] Annual investment cost It is expressed as:

[0017]

[0018]

[0019]

[0020] in, represents the equivalent annual value conversion coefficient, s is the discount rate, which is 0.08; x represents the life cycle of DG and ESS; , and Represent the annual investment cost per unit capacity of PV, wind power and ESS respectively; , They represent the installed capacity of photovoltaic power at node j and wind power at node i respectively; represents the installed capacity of ESS at node h;

[0021] Annual maintenance cost Including the annual maintenance cost of DG and ESS, expressed as:

[0022]

[0023] In the formula, , , They are the annual maintenance costs per unit capacity for photovoltaic, wind power, and energy storage;

[0024] The annual cost of DR is the compensation cost for interruptible load according to the interruption amount, and its expression is:

[0025]

[0026] in, Indicates the total number of scenes; Indicates the day of the year for each scene; Indicates the number of simulation cycles in a day; is the simulation time interval; Compensation for interruptible load unit electricity charges; Indicate period The load interruption amount under scenario t within;

[0027] The annual network loss cost calculation formula is:

[0028]

[0029] In the formula, It represents the electricity price, which is the cost of electricity stipulated locally; Display period System loss under scenario t within ;

[0030] The constraints of the upper-level planning model are the maximum installed capacity of the selected nodes DG and EES:

[0031]

[0032] in, represents the maximum installed capacity of PV at node j; represents the maximum installed capacity of the ESS at node h; Represents the maximum installed capacity of wind power at node i.

[0033] As a preferred solution of the distribution network source network load storage hierarchical coordinated intelligent control method described in the present invention, wherein: the lower layer model is specifically an active distribution network low-level planning model based on the peak-shaving and valley-charging energy storage operation strategy;

[0034] In the objective function of network circulation, the minimum load of the nodes connected to the energy storage system is considered. The second-order center distance is expressed as:

[0035]

[0036] in, , , , The time periods Output of loads, photovoltaic, wind power, and energy storage systems; is the charge and discharge state of the energy storage system in time period b, -1 represents the discharge state, 0 represents the floating state, and 1 represents the charge state; represents the average node power, expressed as:

[0037]

[0038] Among them, the constraints include the charging and discharging power, capacity and state constraints of the energy storage system.

[0039] As a preferred solution of the distribution network source network load storage hierarchical coordinated intelligent control method described in the present invention, the constraints on the charging and discharging power of the energy storage system include:

[0040]

[0041] In the formula, Indicates the maximum charge and discharge power calculated by the energy storage system;

[0042] Energy storage system charging and discharging capacity Constraints:

[0043]

[0044] in, , are the minimum state of charge and maximum state of charge of the energy storage system; Planning capacity for energy storage systems;

[0045] Constraints on the charging and discharging state of the energy storage system:

[0046] .

[0047] As a preferred solution of the distribution network source network load storage hierarchical coordinated intelligent control method described in the present invention, wherein: the improved ε constraint method includes converting the multi-objective problem into a series of single-objective problems to solve; selecting one objective as the main optimization objective and converting other objectives into constraints;

[0048] By adjusting the boundary ε value of the constraint condition, different Pareto optimal solutions can be obtained;

[0049] The specific steps of the ε constraint method to solve the multi-objective optimization problem include:

[0050] Step 1: Choose your primary goal:

[0051] Select any one of the multiple objectives as the main optimization objective;

[0052] Step 2: Convert other objectives into constraints:

[0053] For each remaining objective, convert it into a constraint; this is done by setting an ε value;

[0054] Step 3: Solve the single objective optimization problem;

[0055] Standard optimization methods are used to solve the transformed single-objective optimization problem;

[0056] Step 4: Adjust the ε value and repeat the solution:

[0057] By adjusting the ε value of each objective, different Pareto optimal solutions are obtained, which are achieved by gradually relaxing or tightening the constraints; each time the ε value is adjusted, the single-objective optimization problem is recalculated and solved;

[0058] The fifth step is to construct a Pareto optimal solution set:

[0059] By continuously adjusting the ε value and solving the corresponding single-objective optimization problem, a Pareto optimal solution set is obtained. Each solution in the solution set is optimal in one objective and satisfies the constraints of other objectives.

[0060] Step 6: Decision Analysis:

[0061] The decision maker selects one or more solutions from the Pareto optimal solution set as the final decision based on the characteristics and preferences of the problem.

[0062] As a preferred solution of the distribution network source network load storage hierarchical coordinated intelligent control method described in the present invention, the mathematical model of the improved ε constraint method includes:

[0063]

[0064] in, represents the k objectives of the source network and target reservoir stratification coordination control problem, k represents the index of the objective function, y represents the independent variable of the function; K represents the number of objective functions; represents an integer; p represents the index of the objective function that has not been selected; represents the objective function that has not been selected; Represent the constraints of the original model of source-grid-load-storage hierarchical coordinated control; represents the upper bound of the pth objective function of source-grid-load-storage hierarchical coordinated control;

[0065] The feasible domain of the problem also changes when different values ​​are taken; The optimization of the value obtained a series of Pareto optimal solutions;

[0066] For a problem with K competing objective functions, at least the solution range of K-1 competing objective functions is required as a constraint, and the key constraint is:

[0067]

[0068] Calculate the range of the key constraint limit values ​​based on the profit table: Calculate the objective function The optimal solution of ; Calculate the values ​​of other objective functions as row values ​​in the reimbursement table;

[0069] In the reimbursement table, each row represents the optimal solution value obtained by solving an objective function separately during the optimization process, and each column represents an objective function;

[0070] In the profit table, each row represents the specific values ​​of all objective functions when an objective function is optimized to the optimum; each column represents the value comparison of different objective functions in a specific objective function optimization scenario;

[0071] According to the results of the reimbursement table, the objective function of column p , where the maximum and minimum values ​​represent the objective function using the ε constraint method The upper and lower limits of

[0072] The source network load storage hierarchical control model is the main objective function , the objective function of the model is To solve the main objective function The constraint range is Equidistant grid points Divide the solution range of the function into equations of The difference between the right endpoint and the critical constraint Random value decreases with the increase of , which can be specifically expressed as:

[0073]

[0074] The solutions were evaluated using fuzzy decision-making methods;

[0075] Calculate the linear membership function of each objective function for each Pareto solution, which is expressed as:

[0076]

[0077] In the formula, Represents the objective function The linear membership value of the corresponding Pareto solution; Respectively represent the objective function Values ​​at ideal and worst points;

[0078] Calculate the total priority of each solution :

[0079]

[0080] in, Represents membership function and objective function The weight of

[0081] Determine the maximum value of load and storage hierarchical coordination control in the source network , as the result of optimizing the Pareto solution set based on comprehensive consideration of the relative importance of each objective.

[0082] A distribution network source-network-load-storage hierarchical coordinated intelligent control system using any method described in the present invention, characterized in that:

[0083] A collection unit, which obtains the grid parameters of the candidate installation sites of the intelligent control;

[0084] The analysis unit establishes a source-grid-load-storage hierarchical coordinated intelligent control model, including: an upper-level model, which optimizes the installation capacity of distributed power sources and energy storage systems at candidate installation sites with the minimum annual comprehensive cost as the coordination control target; a lower-level model, which controls the stable operation of the power system with the minimum network loss as the target through the peak-shaving and valley-filling operation strategy of the energy storage system;

[0085] The computing unit uses an improved ε constraint method to solve the multi-objective problem of the source-grid-load-energy-storage up-and-down planning model, obtains a Pareto optimal solution set, and uses the highest priority solution in the Pareto optimal solution set to optimally control the active distribution network.

[0086] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0087] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0088] Beneficial effects of the present invention: The distribution network source network load storage hierarchical coordinated intelligent control method provided by the present invention can effectively improve the utilization rate of new energy, optimize the allocation of power resources, reduce pollutant emissions, and improve the operation efficiency of the power grid. Through the precise upper-level model to optimize the installation capacity of distributed power sources and energy storage systems, combined with the lower-level model to implement the peak shaving and valley filling operation strategy of the energy storage system, to ensure the stable operation of the power system. At the same time, the improved ε constraint method is used to solve multi-objective optimization problems, generate the Pareto optimal solution set, and provide the optimal control strategy. This method has shown remarkable effects in coordinated scheduling, and provides technical support and guarantee for the sustainable development of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0090] Figure 1 An overall flow chart of a distribution network source network load storage hierarchical coordinated intelligent control method provided by the first embodiment of the present invention;

[0091] Figure 2 In a distribution network source network load storage hierarchical coordinated intelligent control method provided by the second embodiment of the present invention, the IEEE33 node system is taken as an example, the network topology structure;

[0092] Figure 3 A distribution network source network load storage hierarchical coordination intelligent control method provided by the second embodiment of the present invention is the daily load of photovoltaic power generation and wind power;

[0093] Figure 4 Comparison results of wind power / photovoltaic consumption before and after coordinated scheduling of a distribution network source-grid-load-storage hierarchical coordinated intelligent control method provided by the second embodiment of the present invention;

[0094] Figure 5 A comparison of the daily peak-to-valley difference rate of a distribution network source-network load-storage hierarchical coordinated intelligent control method before and after coordinated scheduling of conventional thermal power units provided in the second embodiment of the present invention;

[0095] Figure 6 A comparison of pollutant emissions before and after coordinated scheduling of a distribution network source-grid-load-storage hierarchical coordinated intelligent control method provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0096] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0097] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a distribution network source network load storage hierarchical coordinated intelligent control method, comprising:

[0098] It should be noted that the comprehensive interaction and coordination between the source network and the load and storage is an inevitable trend of active distribution network coordinated control. Through various technologies and management means, the dynamic balance ability of the power system can be improved, and resource maximization and economic dispatch mode can be achieved.

[0099] (1) Source layer characteristics

[0100] The power layer is the source of the active distribution network, mainly including dispatching synchronous units, new double-fed induced draft fans, traditional asynchronous induced draft fans and photovoltaic power generation systems with AC and DC conversion. Dispatched synchronous units, including small hydropower units and gas turbines, have synchronous excitation voltage support and fast active power regulation performance, and have negative response characteristics to the frequency change of active power. In the system frequency response, its main adjustment methods are as follows:

[0101] (1)

[0102] in , is the actual output power of the synchronous motor unit and its active power of constant output power; is the frequency characteristic coefficient of synchronous gas turbine; It is the deviation between the actual system frequency and the rated frequency.

[0103] (2) Net layer characteristics

[0104] As a large amount of distributed energy, unstable wind energy and solar energy are connected to the power grid, the steady-state power flow distribution and the uniformity of power transmission in the system have changed, further affecting the safe, stable and reliable operation of the system. and interaction forces The relationship between:

[0105] (2)

[0106] Especially during the period The purchasing status of the interactive power between the microgrid and the large grid and sales status ,generally For one year. Especially the period The purchase price of electricity exchanged between the microgrid and the large grid and sales price ; is the total number of scheduling cycles.

[0107] (3) Characteristics of the charge layer

[0108] The load layer in the active distribution network presents negative feedback characteristics to the voltage and frequency of the system, that is, when the frequency of the power system increases or decreases, the power consumed by the load will increase or decrease synchronously, thereby adjusting the system frequency to prevent it from increasing or decreasing again; if the system voltage increases or decreases, it will cause the power consumed by the load to increase or decrease, thereby causing the voltage drop to increase or decrease, thereby preventing the voltage from increasing or decreasing again. Therefore, more attention should be paid to reality in the optimization strategy of system operation. The static model of the active power characteristics of the above power load is expressed as follows:

[0109] (3)

[0110] in , , Constant impedance, constant current and active power for system load; is the rated power of the system load; , is the real-time voltage and initial voltage of the system; It is the active power change caused by the frequency changing with the system load.

[0111] (4) Reservoir characteristics

[0112] Energy storage on the power supply side can reduce the impact of uncertainty in renewable energy, which is beneficial to the consumption of renewable energy and reduces operating costs; on the load side, it can reduce peak load, fill valleys, reduce the capacity of standby units, and improve the flexibility of power supply. The model is expressed as:

[0113] (4)

[0114] in, , For time period arrive Energy storage capacity, is the energy storage self-discharge rate, For energy storage charging efficiency, is the energy storage discharge efficiency, , are the charge and discharge power respectively.

[0115] S1: Obtain grid parameters of candidate installation locations for intelligent control.

[0116] S2: Establish a source-grid-load-storage layered coordinated intelligent control model, including: an upper-level model, which takes the minimum annual comprehensive cost as the coordinated control target and optimizes the installation capacity of the distributed power source and energy storage system at the candidate installation site; a lower-level model, which controls the stable operation of the power system with the goal of minimizing network losses through the peak shaving and valley filling operation strategy of the energy storage system.

[0117] The upper-level model is specifically an active distribution network upper-level planning model based on annual comprehensive costs, including:

[0118] The power grid, distributed generation (DG), energy storage system (ESS) and demand response (DR) belong to different stakeholders. Considering that it is difficult to maximize the value of each resource by focusing on the interests of a single or several entities in distribution network planning

[19] , the upper-level planning model in this paper starts from the perspective of the power grid as a comprehensive energy service provider and plans with the minimum annual comprehensive cost as the objective function.

[0119]

[0120] Assume that the distributed generation is DG, the energy storage system is ESS, and the demand response is DR; represents the annual investment cost of DG and ESS; represents the annual maintenance cost of DG and ESS; represents the annual cost of DR; Represents the annual network loss cost of DR.

[0121] Annual investment costs It is expressed as:

[0122]

[0123]

[0124]

[0125] in, represents the equivalent annual value conversion coefficient, s is the discount rate, which is 0.08; x represents the life cycle of DG and ESS; , and Represent the annual investment cost per unit capacity of PV, wind power and ESS respectively; , They represent the installed capacity of photovoltaic power at node j and wind power at node i respectively; represents the installed capacity of the ESS at node h.

[0126] Annual maintenance cost Including the annual maintenance cost of DG and ESS, expressed as:

[0127]

[0128] In the formula, , , They are the annual maintenance cost per unit capacity for photovoltaic, wind power and energy storage respectively.

[0129] The annual cost of DR is the compensation cost for interruptible load according to the interruption amount, and its expression is:

[0130]

[0131] in, Indicates the total number of scenes; Indicates the day of the year for each scene; Indicates the number of simulation cycles in a day; is the simulation time interval; Compensation for interruptible load unit electricity charges; Display period The load interruption amount under scenario t.

[0132] The annual network loss cost calculation formula is:

[0133]

[0134] In the formula, It represents the electricity price, which is the cost of electricity stipulated locally; Display period System loss under scenario t.

[0135] The constraints of the upper-level planning model are the maximum installed capacity of the selected nodes DG and EES:

[0136]

[0137] in, represents the maximum installed capacity of PV at node j; represents the maximum installed capacity of the ESS at node h; Represents the maximum installed capacity of wind power at node i.

[0138] The lower-level model is specifically an active distribution network low-level planning model based on the peak-shaving and valley-charging energy storage operation strategy; due to the intermittent output of distributed power sources in the active distribution network connected to wind turbines and photovoltaics, the power fluctuation of the system will be aggravated. The energy storage system has the characteristics of integrated charging and discharging and rapid response, and has a strong peak-shaving and valley-charging capability. Before the site selection and capacity determination of the distributed power supply energy storage system, an operating strategy that meets the peak-shaving and valley-charging capabilities of the energy storage system is proposed, and the subsequent site selection and capacity determination work is completed on the basis of this operating strategy.

[0139] The ability of the energy storage system to cut peaks and valleys is reflected in its ability to compensate for load fluctuations. In the objective function of network circulation, the minimum load of the nodes connected to the energy storage system is considered. The second-order center distance is expressed as:

[0140]

[0141] in, , , , The time periods Output of loads, photovoltaic, wind power, and energy storage systems; is the charge and discharge state of the energy storage system in time period b, -1 represents the discharge state, 0 represents the floating state, and 1 represents the charge state; represents the average node power, expressed as:

[0142]

[0143] Among them, the constraints include the charging and discharging power, capacity and state constraints of the energy storage system.

[0144] The constraints on the charging and discharging power of the energy storage system include:

[0145]

[0146] In the formula, Represents the charging and discharging power calculated by the energy storage system.

[0147] Energy storage system charging and discharging capacity Constraints:

[0148]

[0149] in, , are the minimum state of charge and maximum state of charge of the energy storage system; Planning capacity for energy storage systems.

[0150] Constraints on the charging and discharging state of the energy storage system:

[0151]

[0152] The constraint uses a set of integers to represent the three states of the energy storage system: discharging, floating charge, and charging.

[0153] Observe the operation strategy model of the energy storage system in the planning area , , , Before configuring the energy storage system, there are variables in the model. The change of will make the constraint space in the inequality constraint constantly change, making the position of the global optimal solution constantly change, making it difficult to search. Similarly, the end points of the left and right intervals of the formula also contain variables Due to the uncertainty of the optimization range, the optimization problem is difficult to solve within a limited time. In order to determine the optimization range of the model and simplify the model solution process, the inequality constraint (15) is relaxed and the maximum charge and discharge power calculated by the energy storage system in the operation strategy proposed in this paper is used to replace the right end point of the interval. , the expression is as follows:

[0154]

[0155] The maximum charge and discharge power is used instead of the rated power of the energy storage system to determine the optimization range of the original changes. At the same time, these constraints also minimize the energy storage capacity and fill the valley , to compensate for power fluctuations. After solving the model, the maximum value of the energy storage system charging and discharging power can be used as the solution . Used to configure the rated power of the energy storage system.

[0156] S3: Use the improved ε constraint method to solve the multi-objective problem of the source-grid-load-energy-storage up-and-down planning model, obtain a Pareto optimal solution set, and use the highest priority solution in the Pareto optimal solution set to optimally control the active distribution network.

[0157] There is no unique global optimal solution for multi-objective optimization problems. Usually, multiple Pareto optimal solutions that do not dominate each other can be obtained to form a Pareto optimal solution set. This paper uses the ε constraint method to solve the multi-objective problem of the upper and lower planning model of source network load storage. The ε constraint method is an effective method for dealing with multi-objective optimization problems. In multi-objective optimization problems, there is usually no solution that can optimize all objectives at the same time due to possible conflicts between objectives. Instead, we get a Pareto optimal solution set, in which each solution is optimal in a certain objective.

[0158] The basic idea of ​​the ε constraint method is to transform the multi-objective problem into a series of single-objective problems to solve. Specifically, one objective is selected as the main optimization objective, and the other objectives are transformed into constraints. By adjusting the boundaries of these constraints (i.e., the ε value), different Pareto optimal solutions can be obtained. The specific steps of the ε constraint method to solve multi-objective optimization problems are as follows:

[0159] The specific steps of the ε constraint method to solve the multi-objective optimization problem include:

[0160] Step 1: Choose your primary goal:

[0161] Select any one of the multiple objectives as the main optimization goal.

[0162] Step 2: Convert other objectives into constraints:

[0163] For each remaining objective, convert it to a constraint; do this by setting a value for ε.

[0164] Step 3: Solve the single-objective optimization problem: Use standard optimization methods to solve the transformed single-objective optimization problem.

[0165] Step 4: Adjust the ε value and repeat the solution:

[0166] By adjusting the ε value of each objective, different Pareto optimal solutions are obtained, which are achieved by gradually relaxing or tightening the constraints; each time the ε value is adjusted, the single-objective optimization problem is recalculated and solved.

[0167] The fifth step is to construct a Pareto optimal solution set:

[0168] By continuously adjusting the ε value and solving the corresponding single-objective optimization problem, a Pareto optimal solution set is obtained. Each solution in the solution set is optimal in one objective and satisfies the constraints of other objectives.

[0169] Step 6: Decision Analysis:

[0170] The decision maker selects one or more solutions from the Pareto optimal solution set as the final decision based on the characteristics and preferences of the problem.

[0171] The mathematical model of the improved ε constraint method includes:

[0172]

[0173] in, represents the k objectives of the source network and target reservoir stratification coordination control problem, k represents the index of the objective function, y represents the independent variable of the function; K represents the number of objective functions; represents an integer; p represents the index of the objective function that has not been selected; represents the objective function that has not been selected; Represent the constraints of the original model of source-grid-load-storage hierarchical coordinated control; It represents the upper bound of the pth objective function of source-grid-load-storage hierarchical coordinated control.

[0174] The feasible domain of the problem also changes when different values ​​are taken; The optimization of the value of , obtained a series of Pareto optimal solutions. For a problem with K competitive objective functions, at least the solution range of K-1 competitive objective functions is required as a constraint, and the key constraint is:

[0175]

[0176] Calculate the range of the key constraint limit values ​​based on the profit table: Calculate the objective function The optimal solution of ; Calculate the values ​​of other objective functions as row values ​​in the reimbursement table (each row in the reimbursement table represents the optimal solution value of an objective function. These values ​​are obtained when solving each objective function separately during the optimization process);

[0177] The "reimbursement table" is also a tool used in multi-objective optimization problems. It is used to determine the relationship between different objective functions and help calculate the limit values ​​of key constraints. In multi-objective optimization problems, it is usually necessary to balance multiple conflicting objectives, so a method is needed to determine the optimal solution range for these objectives. In the reimbursement table, each row represents the optimal solution value obtained by solving an objective function separately during the optimization process, and each column represents an objective function.

[0178] The "profit table" is a tool for analyzing multi-objective optimization problems. It is mainly used to determine the relationship between different objective functions and how to set the boundary values ​​of constraints (i.e., ε values) during the optimization process. In multi-objective optimization problems, there is usually no solution that can optimize all objectives at the same time, so some method is needed to balance these objectives in order to find a set of non-inferior solutions (Pareto optimal solutions). In the profit table, each row represents the specific values ​​of all objective functions when an objective function is optimized to the optimal value; each column represents the comparison of the values ​​of different objective functions in a specific objective function optimization scenario.

[0179] According to the results of the reimbursement table, the objective function of column p , where the maximum and minimum values ​​represent the objective function using the ε constraint method The upper and lower limits of

[0180] The source network load storage hierarchical control model is the main objective function , the objective function of the model is To solve the main objective function The constraint range is Equidistant grid points Divide the solution range of the function into equations of The difference between the two adjacent points is calculated. In the solution range of a function, some uniformly distributed points (i.e., equidistant grid points) are used to divide the range, and then the difference between each two adjacent points is calculated. It can be understood as: using some uniformly distributed points to divide all possible solutions of a function into many small segments, and then see how big the gap is between each two adjacent points.

[0181] m means dividing the solution range of the function into m intervals of equal length. For example, if the solution range is from 0 to 100, and m=5, then:

[0182] It is necessary to place m−1=4 grid points at 20, 40, 60, and 80. This divides the solution range into five intervals of equal length: [0, 20], [20, 40], [40, 60], [60, 80], [80, 100].

[0183] In the key constraint, the limit value of the right endpoint Random value decreases with the increase of , which can be specifically expressed as:

[0184]

[0185] The solutions were evaluated using fuzzy decision making method.

[0186] Calculate the linear membership function of each objective function for each Pareto solution, which is expressed as:

[0187]

[0188] In the formula, Represents the objective function The linear membership value of the corresponding Pareto solution; Respectively represent the objective function Values ​​at the ideal point and the worst point.

[0189] Calculate the total priority of each solution :

[0190]

[0191] in, Represents membership function and objective function The weight of

[0192] Determine the maximum value of load and storage hierarchical coordination control in the source network , as the result of optimizing the Pareto solution set based on comprehensive consideration of the relative importance of each objective.

[0193] On the other hand, this embodiment also provides a distribution network source network load storage hierarchical coordination intelligent control system, which includes:

[0194] The acquisition unit obtains the grid parameters of the candidate installation locations of the intelligent control.

[0195] The analysis unit establishes a source-grid-load-storage layered coordinated intelligent control model, including: an upper-level model, which takes the minimum annual comprehensive cost as the coordinated control target and optimizes the installation capacity of the distributed power source and energy storage system at the candidate installation site; a lower-level model, which controls the stable operation of the power system with the goal of minimizing network losses through the peak-shaving and valley-filling operation strategy of the energy storage system.

[0196] The computing unit uses an improved ε constraint method to solve the multi-objective problem of the source-grid-load-energy-storage up-and-down planning model, obtains a Pareto optimal solution set, and uses the highest priority solution in the Pareto optimal solution set to optimally control the active distribution network.

[0197] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0198] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0199] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0200] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0201] Example 2, reference Figure 2-Figure 6 , which is an embodiment of the present invention, provides a distribution network source network load storage hierarchical coordinated intelligent control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0202] The overall architecture of the source network load and storage hierarchical coordination intelligent control system based on active distribution network is designed. The coordinated control system mainly consists of four parts: power supply, power grid, energy storage equipment and load. Hierarchical coordination and control between source network, load and storage are realized.

[0203] In order to verify the effectiveness of the source network load and storage hierarchical coordinated intelligent control method of the active distribution network, this paper conducted an experiment. The probability distribution of daily consumption production was directly simulated, and the uncertainty of these quantities was described by probability density function. By defining fuzzy variables and fuzzy rules, a fuzzy model of consumption production was established. The simulation cycle of the calculation test scenario was 1 year. This paper takes the IEEE33 node system as an example, and the network topology is as follows Figure 2 The daily output and daily load of photovoltaic power generation and wind power are shown in Table 1 and Figure 3 , the settings of various economic indicators are shown in Table 2.

[0204] Table 1 Daily output details of photovoltaic and wind power

[0205] Table 2 Economic parameters and technical indicators

[0206] The relevant investment and operation and maintenance costs of distributed photovoltaic power sources are shown in Table 3. The capacity of each node connected to the wind turbine and photovoltaic power source shall not exceed 30% of the annual maximum load power value of the node. When calculating the equivalent annual value, the investment period of the equipment is 15 years and the discount rate is 6%. The energy storage system uses sodium-sulfur batteries, and the relevant calculation parameters are shown in Table 4.

[0207] Table 3 Cost of wind turbines and photovoltaic power sources

[0208] Table 4 Battery energy storage system parameters

[0209] Testing of the methods used in this article:

[0210] (1) Upper control effect test:

[0211] When this method is applied, the intelligent planning scheme of the distributed active distribution network (DG and ESS, DR location and interruption) is shown in Table 5, and the cost before and after the use of the active distribution network is shown in Table 6.

[0212] Table 5 Detailed information of planning plan

[0213] Table 6 Various costs and expenses of active distribution network operation before and after using this method in this paper

[0214] It can be seen from Table 6 that after adopting this method, DG and ESS The annual investment costs of DG and ESS are reduced by 71141.96$ and 332.23$ respectively. Because this method needs to rely more on ESS, the investment cost of ESS is higher than that of DR. Adding DR can reduce the installation capacity of ESS and further reduce the annual investment cost. Therefore, the annual cost of DR is Higher. Annual network loss costs The annual cost increased by 307.25$ and 12,895.90$ respectively. However, comprehensive analysis shows that the annual cost A decrease of $57990.53. In summary, this method can make full use of the adjustment functions of DG, ESS and DR, reduce unnecessary investment on the basis of meeting safe and economical operation, and is more economical.

[0215] (2) Lower control effect test:

[0216] After adopting the method in this paper, taking the system terminal node 21 as an example, the load power change before and after the configuration of the energy storage system is shown in Table 7. The results show that under the control of this method, the fluctuation of the load power of the energy storage system is significantly reduced, and the power change range is [290kW, 292kW].

[0217] Table 7 Node power changes before and after connecting to the energy storage system

[0218] (3) Benefit evaluation of collaborative scheduling:

[0219] Figure 4 This is the comparison result of wind power / PV consumption before and after coordinated dispatch. Figure 3 The analysis shows that after the implementation of the coordinated dispatch strategy, both wind and photovoltaic power generation have increased significantly. The power generation of these two renewable energy sources remained at 1800MW·h, but after optimal dispatch, their power generation has jumped to 3200MW·h. This significant increase not only shows that the coordinated dispatch system has a positive effect on improving the utilization rate of new energy, but also proves that the system plays an important role in transforming the energy structure and increasing the proportion of renewable energy. The increase in renewable energy power generation will help reduce dependence on fossil energy and reduce greenhouse gas emissions, thereby promoting the sustainable development of the power industry.

[0220] Figure 5 This is a comparison of the daily peak-to-valley difference rate before and after the coordinated dispatch of conventional thermal power units. Figure 4From the analysis, it can be seen that after adopting the coordinated dispatch strategy, the peak-to-valley difference of the system load has been effectively reduced. This change further affects the peak-to-valley difference of the traditional thermal power unit, which also shows a downward trend. This change is very important for the stable operation of the power system, because reducing the peak-to-valley difference can reduce the peak-shaving pressure of the power grid and improve the reliability and economy of power supply. In addition, it also helps to optimize the configuration of power resources and improve the overall efficiency of the power system.

[0221] Figure 6 To coordinate the comparison of pollutant emissions before and after scheduling. Figure 5 From the analysis, it can be seen that after coordinated dispatch, the pollutant emissions of the system have been significantly reduced. This achievement was achieved thanks to the increase in new energy power generation and the reduction in the peak-to-valley difference of traditional thermal power units. By reducing dependence on fossil fuels and reducing pollutants generated by combustion, the environmental performance of the power system in the power generation process has been significantly improved. This not only helps to improve air quality and protect the ecological environment, but also meets the requirements of today's society for green, low-carbon and sustainable development.

[0222] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network source network load storage hierarchical coordination intelligent control method, characterized in that: include: Obtaining grid parameters of candidate installation locations for intelligent control; Establish a hierarchical coordinated intelligent control model for source, grid, load and storage, including: an upper-level model that optimizes the installation capacity of distributed power sources and energy storage systems at candidate installation sites with the minimum annual comprehensive cost as the coordination control target; The lower model controls the stable operation of the power system with the goal of minimizing network losses through the peak-shaving and valley-filling operation strategy of the energy storage system; The improved ε constraint method is used to solve the multi-objective problem of the source-grid-load-energy storage up-and-down planning model, and a Pareto optimal solution set is obtained. The highest priority solution in the Pareto optimal solution set is used to optimally control the active distribution network.

2. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 1, characterized in that: The grid parameters include various electrical parameter information and resource value information during the operation of the grid.

3. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 2, characterized in that: The upper-level model is specifically an active distribution network upper-level planning model based on annual comprehensive costs, including: Planning is carried out with the minimum annual comprehensive cost as the objective function; ， Assume that the distributed generation is DG, the energy storage system is ESS, and the demand response is DR; represents the annual investment cost of DG and ESS; represents the annual maintenance cost of DG and ESS; represents the annual cost of DR; represents the annual network loss cost of DR; Annual investment cost It is expressed as: ， ， ， in, represents the equivalent annual value conversion coefficient, s is the discount rate, which is 0.08; x represents the life cycle of DG and ESS; , and Represent the annual investment cost per unit capacity of PV, wind power and ESS respectively; , They represent the installed capacity of photovoltaic power at node j and wind power at node i respectively; represents the installed capacity of ESS at node h; Annual maintenance cost Including the annual maintenance cost of DG and ESS, expressed as: ， In the formula, , , They are the annual maintenance costs per unit capacity for photovoltaic, wind power, and energy storage; The annual cost of DR is the compensation cost for interruptible load according to the interruption amount, and its expression is: ， in, Indicates the total number of scenes; Indicates the day of the year for each scene; Indicates the number of simulation cycles in a day; is the simulation time interval; Compensation for interruptible load unit electricity charges; Indicate period The load interruption amount under scenario t within; The annual network loss cost calculation formula is: ， In the formula, It represents the electricity price, which is the cost of electricity stipulated locally; Display period System loss under scenario t within ; The constraints of the upper-level planning model are the maximum installed capacity of the selected nodes DG and EES: ， in, represents the maximum installed capacity of PV at node j; represents the maximum installed capacity of the ESS at node h; Represents the maximum installed capacity of wind power at node i.

4. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 3, characterized in that: The lower-level model is specifically an active distribution network low-level planning model based on peak-shaving and valley-charging energy storage operation strategy; In the objective function of network circulation, the minimum load of the nodes connected to the energy storage system is considered. The second-order center distance is expressed as: ， in, , , , The time periods Output of loads, photovoltaic, wind power, and energy storage systems; is the charge and discharge state of the energy storage system in time period b, -1 represents the discharge state, 0 represents the floating state, and 1 represents the charge state; represents the average node power, expressed as: ， Among them, the constraints include the charging and discharging power, capacity and state constraints of the energy storage system.

5. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 4, characterized in that: The constraints on the charging and discharging power of the energy storage system include: ， In the formula, Indicates the maximum charge and discharge power calculated by the energy storage system; Energy storage system charging and discharging capacity Constraints: ， in, , are the minimum state of charge and maximum state of charge of the energy storage system; Planning capacity for energy storage systems; Constraints on the charging and discharging state of the energy storage system: 。 6. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 5, characterized in that: The improved ε constraint method includes converting the multi-objective problem into a series of single-objective problems to solve; selecting one objective as the main optimization objective and converting other objectives into constraints; By adjusting the boundary ε value of the constraint condition, different Pareto optimal solutions can be obtained; The specific steps of the ε constraint method to solve the multi-objective optimization problem include: Step 1: Choose your primary goal: Select any one of the multiple objectives as the main optimization objective; Step 2: Convert other objectives into constraints: For each remaining objective, convert it into a constraint; this is done by setting an ε value; Step 3: Solve the single objective optimization problem; Standard optimization methods are used to solve the transformed single-objective optimization problem; Step 4: Adjust the ε value and repeat the solution: By adjusting the ε value of each objective, different Pareto optimal solutions are obtained, which are achieved by gradually relaxing or tightening the constraints; each time the ε value is adjusted, the single-objective optimization problem is recalculated and solved; The fifth step is to construct a Pareto optimal solution set: By continuously adjusting the ε value and solving the corresponding single-objective optimization problem, a Pareto optimal solution set is obtained. Each solution in the solution set is optimal in one objective and satisfies the constraints of other objectives. Step 6: Decision Analysis: The decision maker selects one or more solutions from the Pareto optimal solution set as the final decision based on the characteristics and preferences of the problem.

7. The distribution network source network load storage hierarchical coordinated intelligent control method according to claim 6, characterized in that: The mathematical model of the improved ε constraint method includes: ， in, represents the k objectives of the source network and target reservoir stratification coordination control problem, k represents the index of the objective function, y represents the independent variable of the function; K represents the number of objective functions; represents an integer; p represents the index of the objective function that has not been selected; represents the objective function that has not been selected; Represent the constraints of the original model of source-grid-load-storage hierarchical coordinated control; represents the upper bound of the pth objective function of source-grid-load-storage hierarchical coordinated control; The feasible domain of the problem also changes when different values ​​are taken; The optimization of the value obtained a series of Pareto optimal solutions; For a problem with K competing objective functions, at least the solution range of K-1 competing objective functions is required as a constraint, and the key constraint is: ， Calculate the range of the key constraint limit values ​​based on the profit table: Calculate the objective function The optimal solution of ; Calculate the values ​​of other objective functions as row values ​​in the reimbursement table; In the reimbursement table, each row represents the optimal solution value obtained by solving an objective function separately during the optimization process, and each column represents an objective function; In the profit table, each row represents the specific values ​​of all objective functions when an objective function is optimized to the optimum; each column represents the value comparison of different objective functions in a specific objective function optimization scenario; According to the results of the reimbursement table, the objective function of column p , where the maximum and minimum values ​​represent the objective function using the ε constraint method The upper and lower limits of The source network load storage hierarchical control model is the main objective function , the objective function of the model is To solve the main objective function The constraint range is Equidistant grid points Divide the solution range of the function into equations of The difference between the right endpoint and the critical constraint Random value decreases with the increase of , which can be specifically expressed as: ， The solutions were evaluated using fuzzy decision-making methods; Calculate the linear membership function of each objective function for each Pareto solution, which is expressed as: ， In the formula, Represents the objective function The linear membership value of the corresponding Pareto solution; Respectively represent the objective function Values ​​at ideal and worst points; Calculate the total priority of each solution : ， in, Represents membership function and objective function The weight of Determine the maximum value of load and storage hierarchical coordination control in the source network , as the result of optimizing the Pareto solution set based on comprehensive consideration of the relative importance of each objective.

8. A distribution network source-network-load-storage hierarchical coordinated intelligent control system using the method according to any one of claims 1 to 7, characterized in that: A collection unit, which obtains the grid parameters of the candidate installation sites of the intelligent control; The analysis unit establishes a source-grid-load-storage hierarchical coordinated intelligent control model, including: an upper-level model, which optimizes the installation capacity of distributed power sources and energy storage systems at candidate installation sites with the minimum annual comprehensive cost as the coordination control target; a lower-level model, which controls the stable operation of the power system with the minimum network loss as the target through the peak-shaving and valley-filling operation strategy of the energy storage system; The computing unit uses an improved ε constraint method to solve the multi-objective problem of the source-grid-load-energy-storage up-and-down planning model, obtains a Pareto optimal solution set, and uses the highest priority solution in the Pareto optimal solution set to optimally control the active distribution network.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any method as claimed in claim 1 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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