Power distribution network dispatching method, system, equipment and medium

Abstract

The invention relates to the technical field of power distribution network dispatching, and discloses a power distribution network dispatching method, system and device and a medium, and the method comprises the steps: obtaining first regulation and control constraints of a plurality of flexible resources in a target power distribution network in response to the determination of the load characteristics of the target power distribution network, and building a first dispatching model of the target power distribution network, the first scheduling model comprises a first objective function and a first constraint condition set, and performing target power distribution network scheduling based on the first scheduling model. According to the method, regulation and control characteristic differences and load characteristics of different types of flexible resources are comprehensively considered, and comprehensive evaluation and optimal utilization of the regulation and control potential of the flexible resources are realized through detailed regulation and control constraints and a scheduling model.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network dispatching, and in particular to a distribution network dispatching method, system, equipment and medium. Background Art

[0002] At the current stage, electricity demand response (DR) plays a vital role in promoting the energy revolution and promoting clean, low-carbon, safe and efficient energy utilization. By leveraging DR's power transfer and reduction capabilities in time and space, demand-side users can be guided to autonomously adjust their energy consumption behavior, thereby improving the level of refined and intelligent energy management. This adjustment can reduce or increase energy consumption during specific periods, helping to achieve peak shaving and valley filling of electricity demand and promote a balance between energy supply and demand. Therefore, in the context of high penetration of renewable energy, how to fully utilize the regulatory potential of flexible resources to promote the economic, flexible and stable operation of the power grid has become a key issue in the current research field.

[0003] Currently, when evaluating the control potential of flexible resources such as temperature-controlled loads and electric vehicles, relatively few factors are considered, resulting in an incomplete assessment of the control capabilities of different types of flexible resources. In reality, the factors influencing the control capabilities of different types of flexible resources vary significantly and have multidimensional characteristics. Therefore, how to comprehensively consider the differences in the control characteristics of different types of flexible resources while also comprehensively considering load characteristics to more accurately and comprehensively assess the control capabilities of flexible resources has become a pressing issue. Summary of the Invention

[0004] In view of the above existing problems, this application is proposed.

[0005] Therefore, the present application provides a distribution network scheduling method, system, device and medium that can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a distribution network scheduling method, comprising:

[0008] In response to determining the load characteristics of the target distribution network, obtaining first control constraints of a plurality of flexible resources in the target distribution network;

[0009] The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness;

[0010] Establishing a first dispatching model of the target distribution network, wherein the first dispatching model includes a first objective function and a first constraint condition set;

[0011] The constraints in the first constraint condition set include a first control constraint of a flexible resource in the target distribution network, a power flow constraint, a node voltage constraint, a branch active power constraint, and photovoltaic and wind turbine output constraints;

[0012] The flexible resources in the target distribution network include at least one of the following: temperature control loads, electric vehicles;

[0013] The first objective function is to minimize the operating cost of the distribution network;

[0014] The target distribution network is dispatched based on the first dispatching model.

[0015] As a preferred solution of the distribution network dispatching method described in the present application, wherein: the first objective function includes several cost functions established with the minimum daily operating cost of the target distribution network as the optimization goal;

[0016] The plurality of cost functions include flexible resource control costs;

[0017] The plurality of cost functions also include one or more of the following:

[0018] Network loss costs, penalty costs for curtailed solar power, and penalty costs for curtailed wind power.

[0019] As a preferred solution of the distribution network dispatching method described in this application, wherein: the first control constraint of the plurality of flexible resources in the target distribution network includes a flexible load response period, and the flexible load response period includes:

[0020] Determine the operating time periods of several flexible resources;

[0021] Determining a response state of a corresponding flexible resource according to the running time period;

[0022] The response time periods of the plurality of flexible loads are determined according to the operating time period and the response state.

[0023] As a preferred solution of the distribution network dispatching method described in this application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0024] According to the target indoor and outdoor temperatures and air density at any time, the actual operating power of the temperature control load at the corresponding time is obtained;

[0025] According to the actual operating power, combined with the power consumption of the temperature-controlled load corresponding to the indoor temperature range at each response moment in a fixed period, the power increase capability and power decrease capability of the temperature-controlled load at each response moment are obtained.

[0026] As a preferred solution of the distribution network dispatching method described in the present application, wherein: the first control constraint of the plurality of flexible resources in the target distribution network further includes flexible load response capability, and the flexible load response capability includes the response capability of the temperature control load and the response capability of the electric vehicle;

[0027] Obtain the maximum charging and discharging power upper limit of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time;

[0028] According to the maximum charging and discharging power upper limits of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time, combined with the electric quantity of the electric vehicle at the corresponding time, the power increase capability and power decrease capability of the electric vehicle at the corresponding time are obtained.

[0029] As a preferred solution of the distribution network dispatching method described in this application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0030] Establish a response willingness acquisition model;

[0031] The steps of establishing the response willingness acquisition model include:

[0032] Determine several factors influencing the response willingness under several flexible resources, define three fuzzy subsets of high, medium and low for each influencing factor, and use trapezoidal function curve for the membership degree of each fuzzy subset;

[0033] Determine the membership value of flexible resources according to fuzzy rules;

[0034] The response willingness of flexible resources under a single rule is determined through fuzzy reasoning.

[0035] As a preferred solution of the distribution network dispatching method described in this application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0036] Calculate the weighted mean of the response willingness of flexible resources under each fuzzy rule to obtain the final response willingness of several flexible resources:

[0037] During the flexible resource response period, the sum of the product of the response capability and response willingness of each type of flexible resource is the regulation capability of the flexible resource at each moment.

[0038] The control capability includes the ability to adjust upward and downward flexible resources at each moment.

[0039] As a beneficial effect of this preferred solution, the first control constraint enables more precise scheduling based on the actual conditions of flexible resources, improving scheduling efficiency and accuracy. By comprehensively considering the response willingness and response capability of flexible resources, the characteristics and limitations of flexible resources can be fully considered during the scheduling process, avoiding over-scheduling or under-scheduling.

[0040] In a second aspect, the present application provides a distribution network dispatching system, comprising:

[0041] a first constraint determination module, configured to obtain first control constraints of a plurality of flexible resources in the target distribution network in response to determination of the load characteristics of the target distribution network;

[0042] The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness;

[0043] A model building module, configured to establish a first dispatching model of a target distribution network, wherein the first dispatching model includes a first objective function and a first set of constraints;

[0044] The constraints in the first constraint condition set include a first control constraint of a flexible resource in the target distribution network, a power flow constraint, a node voltage constraint, a branch active power constraint, and photovoltaic and wind turbine output constraints;

[0045] The flexible resources in the target distribution network include at least one of the following: temperature control loads, electric vehicles;

[0046] The first objective function is to minimize the operating cost of the distribution network;

[0047] A scheduling module is used to perform target distribution network scheduling based on the first scheduling model.

[0048] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0050] Compared with the prior art, the beneficial effects of the present application are as follows: the present application proposes a distribution network dispatching method, which obtains the first control constraints of several flexible resources in the target distribution network in response to the determination of the load characteristics of the target distribution network, and establishes a first dispatching model of the target distribution network. The first dispatching model includes a first objective function and a first constraint condition set, and the target distribution network is dispatched based on the first dispatching model. The present application comprehensively considers the differences in control characteristics and load characteristics of different types of flexible resources, and realizes a comprehensive evaluation and optimal utilization of the control potential of flexible resources through detailed control constraints and dispatching models. This can not only improve the economy and flexibility of the power grid, but also help promote the balance between energy supply and demand, and improve the level of refined and intelligent energy management.

[0051] Specifically, in response to the determination of the target distribution network's load characteristics, the first control constraints for several flexible resources in the target distribution network are obtained. This allows for refined management and optimization of the control of flexible resources based on the actual operating conditions and needs of the target distribution network. The first constraint determination module accurately captures and responds to changes in the target distribution network's load characteristics, including the flexible load's response period, response capability, and response willingness, thereby providing key information for subsequent scheduling decisions. Based on these constraints, the model establishment module combines the actual operating parameters and constraints of the power grid, such as power flow constraints and node voltage constraints, to establish a comprehensive first scheduling model. This model aims to minimize the operating costs of the distribution network and comprehensively considers multiple aspects such as network loss costs, penalty costs for curtailed solar power, and penalty costs for curtailed wind power to ensure economical and efficient scheduling. Based on this model, the scheduling module performs real-time distribution network scheduling and achieves stable operation and cost minimization of the power grid by optimizing the control strategy of flexible resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 A flow chart of a distribution network scheduling method provided in one embodiment of the present application.

[0054] Figure 2 An internal structural diagram of an electronic device for a distribution network scheduling method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0055] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0056] Example 1, reference Figure 1-Figure 2 , which is the first embodiment of the present application, provides a distribution network scheduling method, including:

[0057] There are some problems in the existing related technologies.

[0058] For example, the assessment of the regulatory capacity of flexible resources is not comprehensive enough, and fails to fully consider the differences in regulatory characteristics and load characteristics of different types of flexible resources.

[0059] This application provides a method that can effectively solve the above-mentioned problems. Next, we will explain in detail how to implement the distribution network scheduling method in combination with multiple embodiments;

[0060] Figure 1 A method flow chart showing a distribution network scheduling method includes:

[0061] S101, in response to determining the load characteristics of the target distribution network, obtaining first control constraints of a plurality of flexible resources in the target distribution network;

[0062] In the embodiment of the present application, the target distribution network load characteristics include a flexible load response period, a flexible load response capability, and a flexible load response willingness.

[0063] It should be noted that the target distribution network load characteristics are key factors influencing the regulation of flexible resources. Together, they determine the availability and flexibility of flexible resources in distribution network scheduling. The flexible load response period refers to the time range within which flexible resources can respond to scheduling instructions. This is generally related to the user's electricity usage habits, the operating characteristics of the equipment, and the grid's demand response strategy. Flexible load response capability reflects the ability of flexible resources to adjust their power consumption within a specific time period, which depends on the physical characteristics and operating status of the equipment, as well as the user's scheduling preferences. Flexible load response willingness reflects the user's enthusiasm for participating in grid scheduling and is influenced by multiple factors such as economic incentives, policy guidance, and the user's own needs.

[0064] In an optional embodiment, the target distribution network load characteristics may also include the type of flexible load, the geographical distribution of the flexible load, and the historical power consumption data of the flexible load. This information helps to more accurately characterize the control characteristics of flexible resources and provide a more comprehensive basis for subsequent scheduling decisions.

[0065] Specifically, for example, the type of flexible load (such as temperature-controlled load, electric vehicles, etc.) determines its regulation method and potential; the geographical distribution of flexible loads affects their position in the power grid and the electrical connection between them, which in turn affects the formulation of scheduling strategies; the historical electricity consumption data of flexible loads provides valuable information on user electricity consumption behavior, equipment operating status, etc., which helps to predict future electricity consumption trends and formulate more accurate scheduling plans.

[0066] It should be noted that this application only considers flexible load response period, flexible load response capability and flexible load response willingness as target distribution network load characteristics because these three characteristics are key factors affecting the regulation potential of flexible resources and are easy to obtain and quantify in practical applications. The flexible load response period reflects the time dispatchability of flexible resources and is the basis for formulating scheduling plans. The flexible load response capability directly determines the power adjustment range of flexible resources during the scheduling process and is the core indicator for evaluating regulation potential. The flexible load response willingness reflects the user's enthusiasm and degree of cooperation in participating in scheduling, and is the key to ensuring that the scheduling strategy is effectively implemented. Taking these three characteristics into consideration, the regulation potential of flexible resources can be evaluated more comprehensively and accurately, providing strong support for formulating effective scheduling strategies.

[0067] In an optional embodiment, the flexible load response period can be predicted by analyzing historical electricity usage data, combining user electricity usage habits and equipment operating patterns, to predict the likely response period of the flexible resource. Furthermore, advanced sensors and monitoring technologies can be used to monitor the operating status and power consumption of the flexible resource in real time, thereby dynamically determining its response period.

[0068] While these methods can flexibly reflect the actual control potential of flexible resources, they cannot finely divide specific corresponding time periods, which may lead to inaccurate and inefficient scheduling strategies. The method provided in the embodiments of the present application can accurately determine the response period based on the operating period and response status of flexible resources, thereby providing a more reliable and practical basis for subsequent scheduling decisions.

[0069] In an embodiment of the present application, the first control constraint of the plurality of flexible resources in the target distribution network includes a flexible load response period, and the flexible load response period includes:

[0070] Determine the operating time periods of several flexible resources;

[0071] Determine the response status of the corresponding flexible resource according to the runtime period;

[0072] The response time periods of several flexible loads are determined according to the operating time period and the response status.

[0073] Specifically, the response period of temperature control load and electric vehicle is related to factors such as user type and operation status. Assume that the operation period set of the r-th type of flexible resource is Right now:

[0074]

[0075] Where: is the sth operating period of the rth flexible resource; is the response state of the rth type of flexible resource in time period s, 1 represents response and 0 represents no response; r = 1 and r = 2 represent temperature control load and electric vehicle, respectively.

[0076] It should be noted that obtaining the flexible load response period through the above steps allows for more precise scheduling and management of flexible loads in the distribution network. This not only helps optimize grid efficiency and reduce energy waste, but also better meets user electricity needs and improves power supply quality. Furthermore, by accurately understanding the flexible load response period, power system operators can more flexibly adjust the power supply and demand balance, enhancing the stability and reliability of the grid.

[0077] In an optional embodiment, the responsiveness of temperature-controlled loads can dynamically adjust their operating state based on changes in indoor and outdoor temperature and humidity to better adapt to the grid's dispatch requirements. Specifically, when the outdoor temperature is too high or too low, and the indoor humidity is high, temperature-controlled loads such as air conditioners or dehumidifiers will tend to increase their operating power to meet user comfort requirements for the indoor environment. However, during peak grid load periods, temperature-controlled loads can appropriately adjust their operating state, such as reducing operating power or temporarily shutting down, to respond to grid dispatch instructions and provide the grid with necessary regulatory capabilities.

[0078] Furthermore, the responsiveness of the temperature-controlled load is affected by its physical characteristics and user settings. For example, different models and brands of air conditioners or dehumidifiers may have different maximum operating power and adjustment ranges. Furthermore, the user's comfort requirements for the indoor environment can also affect the responsiveness of the temperature-controlled load. If the user has very strict indoor temperature requirements, the responsiveness of the temperature-controlled load may be limited to ensure a stable indoor temperature.

[0079] Based on comprehensive consideration of the above factors, the embodiment of the present application provides a more accurate and practical method. In the embodiment of the present application, the first control constraint of the multiple flexible resources in the target distribution network also includes the flexible load response capability, which includes the response capability of the temperature control load and the response capability of the electric vehicle;

[0080] According to the target indoor and outdoor temperatures and air density at any time, the actual operating power of the temperature control load at the corresponding time is obtained;

[0081] According to the actual operating power and the power consumption of the temperature-controlled load corresponding to the indoor temperature range at each response moment in a fixed period, the power increase and power decrease capabilities of the temperature-controlled load at each response moment are obtained.

[0082] Specifically, the operating power of the temperature control load is closely related to the indoor and outdoor temperatures, including:

[0083]

[0084] Where: P t TCL is the actual operating power of the temperature control load at time t; ρ A is the air density; C A is the specific heat capacity of air; V A is the indoor air volume; is the indoor temperature at time t; is the rated indoor temperature at time t, and Δt is the time interval.

[0085] Therefore, the power increase and power decrease capabilities of the temperature control load at each response time are:

[0086]

[0087] Where: ΔP t TCL,up and ΔP t TCL,down are the power up-regulation capability and power down-regulation capability of the temperature-controlled load at time t, respectively; and are the power consumption of the temperature control load at the highest (lowest) and lowest (highest) allowable indoor temperature during heating (cooling) at time t; P t TCL is the actual operating power of the temperature control load at time t.

[0088] In the embodiment of the present application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0089] Obtain the maximum charging and discharging power upper limit of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time;

[0090] According to the maximum charging and discharging power upper limit of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time, combined with the electric power of the electric vehicle at the corresponding time, the power increase capability and power decrease capability of the electric vehicle at the corresponding time are obtained.

[0091] Specifically, electric vehicles can achieve upward and downward regulation of load power through charging and discharging. Their power adjustment capabilities and power reduction capabilities during the response period are:

[0092]

[0093] Where: ΔP t EV,up and ΔP t EV,down are the power up-regulation capability and power down-regulation capability of the electric vehicle at time t, respectively; and are the maximum charging and discharging power limits of electric vehicles; P t EV,ch 、P t EV,dis are the actual charging and discharging power of the electric vehicle at time t; E EV,t and E EV,t-1 are the electric power of the electric vehicle at time t and time t-1 respectively.

[0094] It should be noted that the benefit of obtaining the responsiveness of flexible resources is that it allows for a more accurate assessment of their availability and flexibility in distribution network scheduling. By analyzing the flexible load response period, responsiveness, and willingness to respond, power system operators can develop more reasonable and efficient scheduling strategies.

[0095] In the embodiment of the present application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0096] Establish a response willingness acquisition model;

[0097] The steps to establish the response intention acquisition model include:

[0098] Determine several factors influencing the response willingness under several flexible resources, define three fuzzy subsets of high, medium and low for each influencing factor, and use trapezoidal function curve for the membership degree of each fuzzy subset;

[0099] Determine the membership value of flexible resources according to fuzzy rules;

[0100] The response willingness of flexible resources under a single rule is determined through fuzzy reasoning.

[0101] Specifically, based on the demand response principle and consumer psychology model, a response willingness model of temperature control equipment and electric vehicles is established that considers the combination of incentive electricity prices and energy comfort, and the response degree of each type of flexible resources at each response time is obtained.

[0102] First, three fuzzy subsets of high, medium, and low are defined for each influencing factor, and the membership of each fuzzy subset is calculated using a trapezoidal function curve. Second, the membership value of the flexible resource is determined based on the fuzzy rules. Finally, the response willingness of the flexible resource under a single rule is determined through fuzzy reasoning. The fuzzy rules used are:

[0103]

[0104] Where: y i are the i-th influencing factors; i=1,2,...,n are the fuzzy subsets corresponding to the input variables of the q-th rule; g q (y) is the function value corresponding to the qth fuzzy rule; is the fixed parameter of the qth rule of the i-th influencing factor; b q is the fixed parameter of the qth rule; Q is the number of fuzzy rules.

[0105] Finally, fuzzy reasoning is performed. The weighted mean of the response willingness of flexible resources under each fuzzy rule is solved to obtain the final response willingness of flexible resources:

[0106]

[0107] Where: α is the flexible resource response willingness value; is the membership function corresponding to the fuzzy subset i under the qth rule.

[0108] In an optional embodiment, the response willingness acquisition model can also incorporate historical data to learn and predict the response willingness of flexible resources, thereby improving the model's accuracy and applicability. Specifically, machine learning algorithms, such as support vector machines and neural networks, can be used to model and predict the response willingness of flexible resources. By training and learning from historical data, the model can gradually grasp the response patterns of flexible resources, thereby providing a more reliable basis for future scheduling decisions. Furthermore, consideration can be given to incorporating factors such as economic incentives and policy guidance into the response willingness acquisition model to further improve its practicality and accuracy.

[0109] In the embodiment of the present application, the first control constraint of the plurality of flexible resources in the target distribution network further includes:

[0110] Calculate the weighted mean of the response willingness of flexible resources under each fuzzy rule to obtain the final response willingness of several flexible resources:

[0111] During the flexible resource response period, the sum of the product of the response capability and response willingness of each type of flexible resource is the regulation capability of the flexible resource at each moment.

[0112] Regulation and control capabilities include the ability to adjust flexible resources upward and downward at each moment.

[0113] It should be noted that, in response to the determination of the load characteristics of the target distribution network, the first control constraints of several flexible resources in the target distribution network are obtained, which provides an essential first control constraint for the following steps, and helps to achieve the optimal scheduling of the distribution network. In the embodiment of the present application, the determination of the first control constraint not only takes into account the response period and response capability of the flexible resources, but also comprehensively considers the response willingness, so as to more comprehensively reflect the actual control potential of the flexible resources. Through the refined analysis of these control constraints, a more reasonable and efficient scheduling strategy can be formulated to achieve the economic, safe and reliable operation of the distribution network. At the same time, the method provided in the embodiment of the present application can also be flexibly adjusted according to actual needs to adapt to the scheduling needs of distribution networks of different scales and types, and has high practicality and adaptability.

[0114] S102, establishing a first dispatching model of the target distribution network, where the first dispatching model includes a first objective function and a first constraint condition set;

[0115] In an optional embodiment, the first scheduling model can be constructed using a linear programming model or a mixed integer linear programming model. The linear programming model is suitable for processing optimization problems involving continuous variables and can efficiently solve economic scheduling problems in distribution network scheduling. The mixed integer linear programming model is suitable for optimization problems involving discrete variables and can process discrete decision variables such as the switching state of flexible resources. By selecting an appropriate modeling method, it can be ensured that the first scheduling model can accurately reflect the actual operating conditions and needs of the target distribution network, providing strong support for subsequent optimization scheduling.

[0116] In an optional embodiment, the first dispatch model may also employ convolutional neural networks for feature extraction and pattern recognition to improve the accuracy and robustness of the dispatch model. As a deep learning method, convolutional neural networks possess powerful feature learning and generalization capabilities. They can extract key features relevant to the target distribution network dispatch from large amounts of historical and real-time data, thereby enabling accurate assessment of the flexible resource regulation potential and optimizing the dispatch strategy.

[0117] By applying convolutional neural networks to the first dispatch model, we can further explore and analyze the load characteristics of flexible resources in the target distribution network. For example, we can use convolutional neural networks to learn from historical electricity consumption data of flexible loads, extract patterns and trends in load changes, and thus provide more accurate predictions and basis for future dispatch decisions. Furthermore, convolutional neural networks can also model and analyze characteristics such as the response period, response capability, and response willingness of flexible resources, further refining the formulation and implementation of dispatch strategies.

[0118] In an optional embodiment, the first scheduling model can also use a machine learning algorithm to predict and evaluate the regulation potential of flexible resources. The machine learning algorithm can learn the regulation laws of flexible resources from a large amount of historical data. By analyzing and utilizing these laws, it can more accurately predict the regulation potential of flexible resources in the future, thereby providing a more scientific basis for scheduling decisions. In specific implementation, appropriate machine learning algorithms, such as support vector machines, random forests, neural networks, etc., can be selected to perform modeling and training based on the actual situation and needs of the target distribution network. Through training and verification of the model, a machine learning model with high prediction accuracy can be obtained, which is used to predict and evaluate the regulation potential of flexible resources in real time, further improving the accuracy and efficiency of distribution network scheduling.

[0119] It should be noted that while convolutional neural networks and machine learning have shown great potential in distribution network scheduling, their application also faces certain challenges. For example, how to accurately extract key scheduling-related features from large amounts of data and how to ensure the robustness and generalization ability of prediction models are issues that require in-depth research and resolution. To address these issues, this application chooses to use a linear programming model or a mixed integer linear programming model for construction.

[0120] In an optional embodiment, the objective function and constraints in the first scheduling model can be designed according to actual conditions. In this application, in order to achieve scheduling, minimizing the operating cost of the distribution network is selected as the objective function. Relevant technical personnel can select other objective functions according to actual conditions, such as improving the reliability of the power grid or reducing carbon emissions.

[0121] It should be noted that the present application is different from other methods using objective functions and constraints in that a set of objective functions and constraints for flexible resources is designed.

[0122] In an embodiment of the present application, the constraints in the first constraint condition set include the first regulation constraint of a flexible resource in the target distribution network, flow constraint, node voltage constraint, branch active power constraint, photovoltaic and wind turbine output constraint.

[0123] In an embodiment of the present application, the flexible resources in the target distribution network include at least one of the following: temperature-controlled loads and electric vehicles.

[0124] In an embodiment of the present application, the first objective function is to minimize the operating cost of the distribution network.

[0125] Specifically, the first objective function includes several cost functions established with the minimum daily operating cost of the target distribution network as the optimization goal;

[0126] Several cost functions include flexible resource regulation costs;

[0127] Several cost functions also include one or more of the following:

[0128] Network loss costs, penalty costs for curtailed solar power, and penalty costs for curtailed wind power.

[0129] For example, a distribution network optimization scheduling model considering flexible resource regulation is established, with the flexible resource power control amount at each moment as the decision variable and the minimization of the distribution network daily operating cost (consisting of network loss cost, flexible resource control cost, solar curtailment penalty cost, and wind curtailment penalty cost) as the optimization objective. The first objective function of the model is shown in Equation (7).

[0130]

[0131] Where: Loss ,ξ FR ,ξ PV and ξ WT are the network loss cost, flexible resource regulation cost, solar curtailment penalty, and wind curtailment penalty at time t; c Loss is the unit cost coefficient of distribution network loss; P t Loss is the sum of losses of each branch at time t; is the regulation unit price of the rth type of flexible resources; is the control power of the rth flexible resource at time t; c PV is the penalty cost per unit abandoned optical power; c WT P is the penalty cost per unit wind power curtailment; t PV，0 and P t PV are the active power that can be generated by photovoltaic power generation and the active power actually utilized at time t; P t WT，0 and P t WT is the active power that the wind turbine can generate and the active power actually utilized at time t.

[0132] The first set of constraints for the model is as follows:

[0133] (1) Power flow constraints

[0134]

[0135] Where: P it and Q it is the active power and reactive power of node i at time t; U it is the voltage amplitude of node i at time t; U jt is the voltage amplitude of node j at time t; G ij 、B ij Corresponding to the conductance and susceptance parameters of the branch respectively; θ ij is the power angle difference between nodes i and j.

[0136] (2) Node voltage constraints:

[0137] U min ≤U it ≤U max (9)

[0138] Where: U max 、U min are the upper and lower limits of the voltage amplitude of the node, respectively.

[0139] (3) Branch active power constraints:

[0140] P min ≤P ijt ≤P max (10)

[0141] Where: P max 、P min are the upper and lower limits of branch active power transmission respectively; P ijt is the active power of the branch between nodes i and j.

[0142] (4) Photovoltaic and wind turbine output constraints: Photovoltaic and wind turbine outputs should be less than their predicted values.

[0143]

[0144] (5) Flexible resource control power constraints:

[0145]

[0146] Where: and are the power increase and power decrease of flexible resources at each moment, t a and t b They are the upward adjustment period and the downward adjustment period respectively; They are the upward adjustment capacity and downward adjustment capacity of flexible resources at each moment.

[0147] It should be noted that establishing a first dispatching model of the target distribution network, wherein the first dispatching model includes a first objective function and a first constraint condition set, can provide clear optimization objectives and constraint conditions for subsequent dispatching decisions.

[0148] Specifically, the first objective function defines the optimization direction: minimizing the operating costs of the distribution network. This helps ensure safe and reliable operation of the power grid while achieving economic benefits. The first set of constraints, on the other hand, specifies the rules and restrictions that must be followed during the dispatch process, such as power flow constraints, node voltage constraints, and branch active power constraints. These constraints ensure the feasibility and safety of the dispatch scheme. By comprehensively considering the objective function and constraints, a dispatch strategy that is both economical and safe can be developed, achieving optimal allocation and efficient utilization of distribution network resources. Furthermore, this dispatch model is highly flexible and scalable, allowing for adjustment and optimization based on actual conditions and needs to accommodate the dispatching needs of distribution networks of varying sizes and types.

[0149] S103: Perform target distribution network scheduling based on the first scheduling model.

[0150] In an optional embodiment, when the target distribution network is dispatched based on the first dispatch model, a heuristic algorithm, an intelligent optimization algorithm or a hybrid algorithm can be used to solve the problem. Heuristic algorithms such as genetic algorithms and particle swarm algorithms can search for optimal solutions or approximate optimal solutions by simulating natural processes, and are suitable for processing complex nonlinear optimization problems. Intelligent optimization algorithms such as simulated annealing algorithms and taboo search algorithms can avoid falling into local optimal solutions during the search process, thereby improving the quality and efficiency of the solution. Hybrid algorithms combine the advantages of multiple algorithms, combine and optimize them according to the specific characteristics of the problem, and achieve better solution results.

[0151] During implementation, an appropriate solution algorithm can be selected based on the actual conditions and needs of the target distribution network, and algorithm parameters can be adjusted and optimized to ensure the accuracy and reliability of the solution. By solving the first dispatch model, the optimal dispatch plan for the target distribution network can be obtained, including key information such as the control power of each flexible resource and the dispatch period, providing strong support for subsequent actual dispatch operations.

[0152] In summary, the present application proposes a distribution network dispatching method, system, equipment and medium. In response to the determination of the load characteristics of the target distribution network, the first control constraints of several flexible resources in the target distribution network are obtained, and a first dispatching model of the target distribution network is established. The first dispatching model includes a first objective function and a first constraint condition set, and the target distribution network is dispatched based on the first dispatching model. The present application comprehensively considers the differences in control characteristics and load characteristics of different types of flexible resources, and realizes a comprehensive evaluation and optimal utilization of the control potential of flexible resources through detailed control constraints and dispatching models. This can not only improve the economy and flexibility of the power grid, but also help promote the balance between energy supply and demand, and improve the level of refined and intelligent energy management.

[0153] Example 2: In a preferred embodiment, in order to verify the effectiveness of the distribution network flexible scheduling model considering the flexible resource load characteristics proposed in this paper, a simulation analysis of the IEEE 33-node distribution network system was performed, and the following two comparison schemes were set:

[0154] Option 1: Distribution network day-ahead optimal dispatch model without considering flexible resource regulation

[0155] Option 2: Day-ahead Optimal Scheduling Model for Distribution Networks Considering Flexible Resource Control

[0156] The distribution network operation costs obtained by solving the above two solutions are shown in Table 1.

[0157] Table 1 Operating costs of distribution networks under different schemes

[0158] Option 1 Option 2 Network loss cost / yuan 143 123 Flexible resource control cost / yuan / 645 Abandoned light penalty cost / yuan 680 278 Wind curtailment penalty cost / yuan 755 254 Total cost / yuan 1578 1300

[0159] As can be seen from Table 1, Scheme 2 effectively reduces the network loss cost by 20 yuan, the solar curtailment penalty by 402 yuan, and the wind curtailment penalty by 501 yuan at a flexible resource control cost of 645 yuan compared with Scheme 1. The operating cost of Scheme 2 is reduced by 17.62% compared with Scheme 1. Therefore, the proposed economic and flexible dispatching method for distribution networks considering the load characteristics of flexible resources significantly reduces the operating cost of the distribution network.

[0160] Embodiment 3: This embodiment further provides a distribution network dispatching system, including:

[0161] a first constraint determination module, configured to obtain first control constraints of a plurality of flexible resources in the target distribution network in response to determination of the load characteristics of the target distribution network;

[0162] The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness;

[0163] A model building module, configured to establish a first dispatching model of a target distribution network, wherein the first dispatching model includes a first objective function and a first set of constraints;

[0164] The constraints in the first constraint condition set include the first control constraint of a flexible resource in the target distribution network, power flow constraint, node voltage constraint, branch active power constraint, photovoltaic and wind turbine output constraints;

[0165] The flexible resources in the target distribution network include at least one of the following: temperature-controlled loads, electric vehicles;

[0166] The first objective function is to minimize the operating cost of the distribution network;

[0167] The scheduling module is used to perform target distribution network scheduling based on the first scheduling model.

[0168] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0169] This embodiment also provides an electronic device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a distribution network scheduling method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0170] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0171] In response to determining the load characteristics of the target distribution network, obtaining first control constraints of a plurality of flexible resources in the target distribution network;

[0172] The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness;

[0173] Establishing a first dispatching model of the target distribution network, the first dispatching model including a first objective function and a first constraint condition set;

[0174] The constraints in the first constraint condition set include the first control constraint of a flexible resource in the target distribution network, power flow constraint, node voltage constraint, branch active power constraint, photovoltaic and wind turbine output constraints;

[0175] The flexible resources in the target distribution network include at least one of the following: temperature-controlled loads, electric vehicles;

[0176] The first objective function is to minimize the operating cost of the distribution network;

[0177] The target distribution network is dispatched based on the first dispatch model.

[0178] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application 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 application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and all of these should be included in the scope of the claims of the present application.

[0179] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0180] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0183] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0184] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A distribution network dispatching method, characterized in that: include: In response to determining the load characteristics of the target distribution network, obtaining first control constraints of a plurality of flexible resources in the target distribution network; The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness; Establishing a first dispatching model of the target distribution network, wherein the first dispatching model includes a first objective function and a first constraint condition set; The constraints in the first constraint condition set include a first control constraint of a flexible resource in the target distribution network, a power flow constraint, a node voltage constraint, a branch active power constraint, and photovoltaic and wind turbine output constraints; The flexible resources in the target distribution network include at least one of the following: temperature control loads, electric vehicles; The first objective function is to minimize the operating cost of the distribution network; The target distribution network is dispatched based on the first dispatching model.

2. A distribution network dispatching method according to claim 1, characterized in that: The first objective function includes several cost functions established with the minimum daily operating cost of the target distribution network as the optimization goal; The plurality of cost functions include flexible resource control costs; The plurality of cost functions also include one or more of the following: Network loss costs, penalty costs for curtailed solar power, and penalty costs for curtailed wind power.

3. A distribution network dispatching method according to claim 2, characterized in that: The first control constraint of the plurality of flexible resources in the target distribution network includes a flexible load response period, and the flexible load response period includes: Determine the operating time periods of several flexible resources; Determining a response state of a corresponding flexible resource according to the running time period; The response time periods of the plurality of flexible loads are determined according to the operating time period and the response state.

4. A distribution network dispatching method according to claim 3, characterized in that: The first control constraint of the plurality of flexible resources in the target distribution network further includes a flexible load response capability, wherein the flexible load response capability includes a response capability of a temperature control load and a response capability of an electric vehicle; According to the target indoor and outdoor temperatures and air density at any time, the actual operating power of the temperature control load at the corresponding time is obtained; According to the actual operating power, combined with the power consumption of the temperature-controlled load corresponding to the indoor temperature range at each response moment in a fixed period, the power increase capability and power decrease capability of the temperature-controlled load at each response moment are obtained.

5. A distribution network dispatching method according to claim 4, characterized in that: The first control constraints of the plurality of flexible resources in the target distribution network further include: Obtain the maximum charging and discharging power upper limit of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time; According to the maximum charging and discharging power upper limits of the electric vehicle and the actual charging and discharging power of the electric vehicle at any time, combined with the electric quantity of the electric vehicle at the corresponding time, the power increase capability and power decrease capability of the electric vehicle at the corresponding time are obtained.

6. A distribution network dispatching method according to claim 5, characterized in that: The first control constraints of the plurality of flexible resources in the target distribution network further include: Establish a response willingness acquisition model; The steps of establishing the response willingness acquisition model include: Determine several factors influencing the response willingness under several flexible resources, define three fuzzy subsets of high, medium and low for each influencing factor, and use trapezoidal function curve for the membership degree of each fuzzy subset; Determine the membership value of flexible resources according to fuzzy rules; The response willingness of flexible resources under a single rule is determined through fuzzy reasoning.

7. A distribution network dispatching method according to claim 6, characterized in that: The first control constraints of the plurality of flexible resources in the target distribution network further include: Calculate the weighted mean of the response willingness of flexible resources under each fuzzy rule to obtain the final response willingness of several flexible resources: During the flexible resource response period, the sum of the product of the response capability and response willingness of each type of flexible resource is the regulation capability of the flexible resource at each moment. The control capability includes the ability to adjust upward and downward flexible resources at each moment.

8. A distribution network dispatching system, applying the method according to any one of claims 1 to 7, characterized in that: include: a first constraint determination module, configured to obtain first control constraints of a plurality of flexible resources in the target distribution network in response to determination of the load characteristics of the target distribution network; The target distribution network load characteristics include flexible load response period, flexible load response capability and flexible load response willingness; A model building module, configured to establish a first dispatching model of a target distribution network, wherein the first dispatching model includes a first objective function and a first constraint condition set; The constraints in the first constraint condition set include a first control constraint of a flexible resource in the target distribution network, a power flow constraint, a node voltage constraint, a branch active power constraint, and photovoltaic and wind turbine output constraints; The flexible resources in the target distribution network include at least one of the following: temperature control loads, electric vehicles; The first objective function is to minimize the operating cost of the distribution network; A scheduling module is used to perform target distribution network scheduling based on the first scheduling model.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a distribution network scheduling method according to any one of claims 1 to 7 are implemented.

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 a distribution network scheduling method according to any one of claims 1 to 7 are implemented.

Images