Distributed energy storage main and distribution transformer equipment load rate control method and system for supply protection

Abstract

The invention relates to the technical field of power distribution network control, in particular to a load rate control method and system of distributed energy storage main and distribution transformer equipment for supply protection. The scheme comprises the steps of collecting power grid operation data in real time through a sensor, wherein the data comprises load demand and photovoltaic generating capacity; detailed parameters of the photovoltaic system and the energy storage system are counted, and constraint conditions of the energy storage system are loaded; constructing an objective function of a multi-objective optimization problem by taking minimization of the power consumption cost and the line electric energy loss cost of a power distribution network user as an objective; setting power balance, voltage stability and equipment capacity limitation; obtaining an optimal distributed energy storage power distribution scheme on line; and generating a control instruction according to the distributed energy storage power distribution scheme, and issuing the control instruction to each energy storage unit through the communication network. According to the scheme, load requirements and collected data of each node of the power distribution network are collected in real time, load rate control of main and distribution transformer equipment is carried out based on distributed energy storage, system changes are dynamically coped with, and system overload is reduced.

Description

Technical Field

[0001] The present invention relates to the field of distribution network control technology, and more specifically, to a method and system for controlling the load rate of a distributed energy storage main distribution transformer for ensuring power supply. Background Art

[0002] In the field of distribution network control, the research on the load rate control method and system of main distribution transformer equipment based on distributed energy storage is of great significance. With the large-scale access of renewable energy and the continuous growth of electricity demand, the distribution network faces many challenges, such as the increase in load peak-to-valley difference and voltage fluctuation. By introducing distributed energy storage technology, it is possible to accurately control the load rate of the main distribution transformer equipment, effectively balance the load, optimize the power quality, and improve the stability and reliability of the distribution network. This technology can not only reduce the operating cost of the distribution network, but also promote the consumption and utilization of renewable energy, which plays an important role in promoting energy transformation and achieving sustainable development.

[0003] Before the technology of the present invention was proposed, the existing load rate control methods for main distribution transformers based on distributed energy storage mainly adopted centralized scheduling and simple load response strategies. These methods usually rely on the direct management of the energy storage unit by the main control system, and perform unified scheduling and control by collecting the status information of each energy storage unit. However, this method has some obvious limitations, such as information transmission delay, high system complexity and difficulty in coping with rapidly changing load demands. In addition, due to the lack of effective optimization algorithm support, traditional methods often find it difficult to achieve the comprehensive optimization of economic cost and power quality. Therefore, it is particularly important to develop a more flexible, efficient and adaptable to complex power grid environment based on distributed energy storage. Main distribution transformer load rate control method. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method and system for controlling the load rate of distributed energy storage main distribution transformer equipment for ensuring supply. By collecting the load demand and data of each node in the distribution network in real time, the load rate of the main distribution transformer equipment is controlled based on distributed energy storage, dynamically responding to system changes and reducing system overload.

[0005] According to a first aspect of an embodiment of the present invention, a method for controlling a load rate of a distributed energy storage main distribution transformer for ensuring supply is provided.

[0006] In one or more embodiments, preferably, the method for controlling the load rate of a distributed energy storage main distribution transformer device for supply guarantee includes:

[0007] The sensors and data acquisition modules installed at each node of the distribution network collect the grid operation data in real time, including load demand and photovoltaic power generation;

[0008] Collect detailed parameters of the photovoltaic system and energy storage system, and load the constraints of the energy storage system;

[0009] With the goal of minimizing the electricity cost of distribution network users and the power loss cost of lines, an objective function of a multi-objective optimization problem is constructed;

[0010] Set power balancing, voltage stability, and equipment capacity limits;

[0011] Obtain the optimal distributed energy storage power allocation solution online;

[0012] Control instructions are generated according to the distributed energy storage power allocation plan and sent to each energy storage unit through the communication network.

[0013] In one or more embodiments, preferably, the sensors and data acquisition modules installed at each node of the distribution network are used to collect grid operation data in real time, and the grid operation data includes load demand and photovoltaic power generation, specifically including:

[0014] Install sensors and data acquisition modules at each node of the distribution network to monitor grid operation data in real time, including load demand and photovoltaic power generation;

[0015] Through the sensors and data acquisition modules installed at each node of the distribution network, the grid operation data is collected in real time and transmitted to the central processing system or data center;

[0016] The collected data is analyzed using a central processing system or data center to form grid operation data for generating control instructions.

[0017] In one or more embodiments, preferably, the statistical detailed parameters of the photovoltaic system and the energy storage system and the loading of the constraints of the energy storage system specifically include:

[0018] Collect detailed parameters of the PV system, including the specifications, quantity, installation angle and geographical location of the PV modules;

[0019] Collect detailed parameters of the energy storage system, including battery type, capacity, charge and discharge rate, and cycle life;

[0020] Based on the collected PV and energy storage system parameters, a mathematical model including these parameters is established to describe the power generation of the PV system under different conditions and the performance of the energy storage system under different charging and discharging states;

[0021] The constraints of the energy storage system are added to the model, including maximum charging and discharging power, battery capacity limitations and cycle life requirements.

[0022] In one or more embodiments, preferably, the objective function of a multi-objective optimization problem is constructed with the goal of minimizing the electricity cost of distribution network users and the line power loss cost, specifically including:

[0023] Calculate the total user electricity cost using the first calculation formula;

[0024] Calculate the line power loss cost using the second calculation formula;

[0025] Setting the third calculation formula as the objective function of the multi-objective optimization problem;

[0026] The first calculation formula is:

[0027]

[0028] Where Cuser is the total user electricity cost, ci is the unit electricity price of user i, i is the user number, n is the total number of users, P i,t is the electricity consumption of the i-th user at time t;

[0029] The second calculation formula is calculated as:

[0030]

[0031] Among them, Ploss is the line power loss cost, Ii is the current flowing through node i;

[0032] The third calculation formula is:

[0033] MB=min(w1×Cuser+w2×Ploss)

[0034] Among them, the calculated target value of the objective function of the MB multi-objective optimization problem, w1 and w2 are the first weight coefficient and the second weight coefficient respectively.

[0035] In one or more embodiments, preferably, the setting of power balance, voltage stability and equipment capacity limitation specifically includes:

[0036] The first constraint is set to ensure that the power input to each node is equal to the power output plus the loss;

[0037] The second constraint condition is set to ensure that the voltage amplitude is within a preset safety range;

[0038] The third constraint condition is set to ensure that the equipment operates within the maximum active and reactive power generation capacity;

[0039] The first constraint condition is:

[0040] Pi,t+Qi,t=Pi,gen,t+Qi,gen,t-Ploss,i,t-Qloss,i,t

[0041] Where Pi,gen,t and Qi,gen,t are the active and reactive power generation of node i at time t, Ploss,i,t and Qloss,i,t are the active and reactive losses of node i at time t, Pi,t is the active and reactive power of node i at time t, Qi,t is the active and reactive power of node i at time t, respectively;

[0042] The second constraint is:

[0043] Vmin≤Vi,t≤Vmax

[0044] Among them, Vi,t is the real-time voltage, Vmax is the maximum allowable voltage, and Vmin is the minimum operating voltage;

[0045] The third constraint condition is:

[0046] Pi,gen,t≤Pmax,i

[0047] Qi,gen,t≤Qmax,iPi

[0048] Among them, Pmax,i and Qmax,i are the maximum active and reactive power generation capabilities of the equipment respectively.

[0049] In one or more embodiments, preferably, the online acquisition of the optimal distributed energy storage power allocation solution specifically includes:

[0050] The output power of each energy storage is sorted from large to small according to the maximum output power upper limit at the current moment to form an energy storage output capacity sequence;

[0051] Calculate the energy storage output power using the fourth calculation formula;

[0052] Within the preset constraint range, the energy storage power source is automatically selected from large to small according to the energy storage output capacity sequence, so that the energy storage output power can be achieved within each preset area. After all energy storage output distribution is completed, the power flow calculation is performed according to this energy storage output distribution;

[0053] In power flow calculation, the magnitude of power exchange between different areas is analyzed;

[0054] When the power exchange between the two areas satisfies the fifth calculation formula, the distribution of the overall energy storage power output power within the two areas is adjusted so that the external exchange power of the two areas satisfies the sixth calculation formula;

[0055] The fourth calculation formula is:

[0056] CDS=P MINLOSS

[0057] Among them, CDS is the energy storage output power, P MINLOSS The power output matched when the line loss is the lowest;

[0058] The fifth calculation formula is:

[0059] JHp>JY

[0060] Among them, JHp is the total power exchange between the two regions, and JY is the interaction comparison threshold;

[0061] The sixth calculation formula is:

[0062] MTW <y1

[0063] MT <y2

[0064] Among them, MTW is the maximum value of the external interaction power of two regions, MT is the inter-regional interaction power of two regions, y1 is the external interaction margin, and y2 is the mutual interaction margin.

[0065] In one or more embodiments, preferably, the generating of control instructions according to the distributed energy storage power allocation scheme and issuing the control instructions to each energy storage unit through a communication network specifically includes:

[0066] For each energy storage unit, generate corresponding control instructions;

[0067] Using communication protocols to send control instructions to each energy storage unit;

[0068] Implement a communication module to package and send control instructions to the designated energy storage unit;

[0069] Each energy storage unit is equipped with a communication interface for receiving control instructions from the central controller;

[0070] After receiving the command, the energy storage unit analyzes the command content and adjusts its working state according to the command;

[0071] After execution, the energy storage unit sends status feedback to the central controller to confirm that the command has been successfully executed;

[0072] The central controller continuously monitors the status of each energy storage unit to ensure that they are working as expected.

[0073] According to a second aspect of an embodiment of the present invention, a distributed energy storage main distribution transformer load rate control system for supply assurance is provided.

[0074] In one or more embodiments, preferably, the distributed energy storage main distribution transformer load rate control system for supply guarantee includes:

[0075] A data collection and prediction module is used to collect grid operation data in real time through sensors and data acquisition modules installed at each node of the distribution network. The grid operation data includes load demand and photovoltaic power generation;

[0076] Parameter statistics and modeling module, used to count the detailed parameters of the photovoltaic system and energy storage system, and load the constraints of the energy storage system;

[0077] An objective function building module is used to build an objective function of a multi-objective optimization problem with the goal of minimizing the electricity cost of distribution network users and the line power loss cost;

[0078] Constraint setting module, used to set power balance, voltage stability and equipment capacity limits;

[0079] Relaxation and solution module, used to obtain the optimal distributed energy storage power allocation solution online;

[0080] The control strategy execution module is used to generate control instructions according to the distributed energy storage power allocation plan and send them to each energy storage unit through the communication network.

[0081] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0082] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0083] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0084] In the solution of the present invention, with the goal of minimizing user electricity costs and line power losses, an objective function of a multi-objective optimization problem is constructed, specific control instructions are generated through online optimization, and are sent to each energy storage unit through a communication network.

[0085] In the solution of the present invention, an advanced optimization algorithm is used to relax the objective function of complex constraints and solve it efficiently. Through efficient computing tools, the optimal distributed energy storage power allocation solution is quickly obtained to reduce system overload and improve the system's response speed and decision-making efficiency.

[0086] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0087] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0089] Figure 1 It is a flow chart of a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0090] Figure 2 It is a flow chart of a method for controlling the load rate of a distributed energy storage main distribution transformer for ensuring supply in an embodiment of the present invention, in which sensors and data acquisition modules installed at each node of the distribution network are used to collect grid operation data in real time, wherein the grid operation data includes load demand and photovoltaic power generation.

[0091] Figure 3 It is a flowchart of counting detailed parameters of a photovoltaic system and an energy storage system and loading constraints of an energy storage system in a method for controlling a load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0092] Figure 4 It is a flow chart of an objective function of a multi-objective optimization problem constructed with the goal of minimizing the electricity cost of distribution network users and line power loss cost in a method for controlling the load rate of a distributed energy storage main distribution transformer for ensuring supply in an embodiment of the present invention.

[0093] Figure 5 It is a flow chart of setting power balance, voltage stability and equipment capacity limitation in a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0094] Figure 6 It is a flow chart of online acquisition of the optimal distributed energy storage power allocation scheme in a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0095] Figure 7It is a flow chart of a method for controlling the load rate of a distributed energy storage main distribution transformer for ensuring supply according to an embodiment of the present invention, in which a control instruction is generated according to a distributed energy storage power allocation scheme and is sent to each energy storage unit through a communication network.

[0096] Figure 8 It is a structural diagram of a load rate control system of a distributed energy storage main distribution transformer equipment for supply guarantee according to an embodiment of the present invention.

[0097] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0098] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0099] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0100] In the field of distribution network control, the research on the load rate control method and system of main distribution transformer equipment based on distributed energy storage is of great significance. With the large-scale access of renewable energy and the continuous growth of electricity demand, the distribution network faces many challenges, such as the increase in load peak-to-valley difference and voltage fluctuation. By introducing distributed energy storage technology, it is possible to accurately control the load rate of the main distribution transformer equipment, effectively balance the load, optimize the power quality, and improve the stability and reliability of the distribution network. This technology can not only reduce the operating cost of the distribution network, but also promote the consumption and utilization of renewable energy, which plays an important role in promoting energy transformation and achieving sustainable development.

[0101] Before the technology of the present invention was proposed, the existing load rate control methods for main distribution transformers based on distributed energy storage mainly adopted centralized scheduling and simple load response strategies. These methods usually rely on the direct management of the energy storage unit by the main control system, and perform unified scheduling and control by collecting the status information of each energy storage unit. However, this method has some obvious limitations, such as information transmission delay, high system complexity and difficulty in coping with rapidly changing load demands. In addition, due to the lack of effective optimization algorithm support, traditional methods often find it difficult to achieve the comprehensive optimization of economic cost and power quality. Therefore, it is particularly important to develop a more flexible, efficient and adaptable to complex power grid environment based on distributed energy storage. Main distribution transformer load rate control method.

[0102] In an embodiment of the present invention, a method and system for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee is provided. The solution collects the load demand and data of each node of the distribution network in real time, controls the load rate of the main distribution transformer based on distributed energy storage, dynamically responds to system changes, and reduces system overload.

[0103] According to a first aspect of an embodiment of the present invention, a method for controlling a load rate of a distributed energy storage main distribution transformer for ensuring supply is provided.

[0104] Figure 1 It is a flow chart of a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0105] In one or more embodiments, preferably, the method for controlling the load rate of a distributed energy storage main distribution transformer device for supply guarantee includes:

[0106] S101, collecting grid operation data in real time through sensors and data acquisition modules installed at each node of the distribution network, wherein the grid operation data includes load demand and photovoltaic power generation;

[0107] S102, counting detailed parameters of the photovoltaic system and the energy storage system, and loading constraints of the energy storage system;

[0108] S103, constructing an objective function of a multi-objective optimization problem with the goal of minimizing the electricity cost of distribution network users and the line power loss cost;

[0109] S104, setting power balance, voltage stability and equipment capacity limit;

[0110] S105, obtaining an optimal distributed energy storage power allocation solution online;

[0111] S106. Generate control instructions according to the distributed energy storage power allocation plan, and send them to each energy storage unit through the communication network.

[0112] In the embodiment of the present invention, by real-time data collection and load forecasting, combined with parameter statistics and modeling, the objective function of multi-objective optimization is constructed, and clear constraints are set to ensure safety. On this basis, algorithm optimization is used for relaxation and solution, and finally the control strategy is executed to implement instruction issuance and dynamic adjustment, so as to achieve the goal of minimizing costs while ensuring safety.

[0113] Figure 2 It is a flow chart of a method for controlling the load rate of a distributed energy storage main distribution transformer for ensuring supply in an embodiment of the present invention, in which sensors and data acquisition modules installed at each node of the distribution network are used to collect grid operation data in real time, wherein the grid operation data includes load demand and photovoltaic power generation.

[0114] like Figure 2 As shown, in one or more embodiments, preferably, the sensors and data acquisition modules installed at each node of the distribution network are used to collect grid operation data in real time, and the grid operation data includes load demand and photovoltaic power generation, specifically including:

[0115] S201. Install sensors and data acquisition modules at each node of the distribution network to monitor the grid operation data in real time, including load demand and photovoltaic power generation;

[0116] S202, collecting grid operation data in real time through sensors and data acquisition modules installed at each node of the distribution network, and transmitting the data to a central processing system or data center;

[0117] S203: Analyze the collected data using a central processing system or a data center to generate power grid operation data for generating control instructions.

[0118] In an embodiment of the present invention, by installing sensors and data acquisition modules at each node of the distribution network, the grid operation data, including key parameters such as load demand and photovoltaic power generation, are monitored in real time. These sensors and data acquisition modules can accurately capture the dynamic changes in the grid and transmit the collected data to the central processing system or data center in real time. For example, when the load demand of a node suddenly increases, the corresponding sensor can immediately detect this change and quickly transmit the information to the central processing system. After receiving the data from each node, the central processing system or data center analyzes the data in detail to form basic grid operation data for generating control instructions. This process ensures the accuracy and real-time performance of the control strategy. Through in-depth analysis of historical data and real-time data, the central processing system can predict future load demand and photovoltaic power generation, thereby providing a scientific basis for subsequent control strategies. Finally, specific control instructions are generated based on the analysis results and sent to each energy storage unit through the communication network. Each energy storage unit adjusts its own charge and discharge state according to the received control instructions to achieve effective control of the load rate of the main distribution transformer. At the same time, the system continuously monitors the state of the grid and dynamically adjusts the control strategy according to the actual situation to cope with possible emergencies or unplanned changes. For example, if a certain area suddenly experiences a large demand for electricity, the system can quickly adjust the output power of nearby energy storage units to meet the electricity demand in that area, thereby avoiding grid overload and improving grid stability and reliability.

[0119] Figure 3 It is a flowchart of counting detailed parameters of a photovoltaic system and an energy storage system and loading constraints of an energy storage system in a method for controlling a load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0120] like Figure 3 As shown, in one or more embodiments, preferably, the statistical detailed parameters of the photovoltaic system and the energy storage system and the constraints of the energy storage system are loaded, specifically including:

[0121] S301, collecting detailed parameters of the photovoltaic system, including specifications, quantity, installation angle and geographical location of photovoltaic modules;

[0122] S302, collecting detailed parameters of the energy storage system, including battery type, capacity, charge and discharge rate, and cycle life;

[0123] S303, based on the collected photovoltaic and energy storage system parameters, establish a mathematical model including these parameters to describe the power generation of the photovoltaic system under different conditions and the performance of the energy storage system under different charging and discharging states;

[0124] S304. Add constraints of the energy storage system to the model, including maximum charge and discharge power, battery capacity limit and cycle life requirement.

[0125] In an embodiment of the present invention, in order to realize the load rate control method of the main distribution transformer based on distributed energy storage, it is necessary to construct an objective function of a multi-objective optimization problem. The objective function aims to minimize the electricity cost of users of the distribution network and the line power loss cost. The specific steps are as follows: Calculate the total user electricity cost using the first calculation formula. The formula is Cuser=∑i=ni=1ci×Pi,t, where Cuser represents the total user electricity cost, ci represents the unit electricity price of user i, i represents the user number, n represents the total number of users, and Pi,t represents the power consumption of the i-th user at time t. Through this formula, the total electricity cost of all users within a certain period of time can be accurately calculated. The line power loss cost is calculated using the second calculation formula. The formula is Ploss=R×∑i=ni=1Ii2, where Ploss represents the line power loss cost, Ii represents the current flowing through node i, and R represents the line resistance. This formula can reflect the energy loss caused by the current flowing through the line, thereby providing a basis for optimization. Set the third calculation formula as the objective function of the multi-objective optimization problem. The objective function is MB = min (w1 × Cuser + w2 × Ploss), where MB represents the calculated target value of the multi-objective optimization problem, and w1 and w2 represent the first weight coefficient and the second weight coefficient, respectively. By adjusting the values ​​of w1 and w2, a balance can be achieved between economy and power quality to achieve a global optimal solution. For example, if more attention is paid to reducing user electricity costs, the weight of w1 can be increased; if more attention is paid to reducing line power losses, the weight of w2 can be increased.

[0126] Figure 4 It is a flow chart of an objective function of a multi-objective optimization problem constructed with the goal of minimizing the electricity cost of distribution network users and line power loss cost in a method for controlling the load rate of a distributed energy storage main distribution transformer for ensuring supply in an embodiment of the present invention.

[0127] like Figure 4 As shown, in one or more embodiments, preferably, the objective function of a multi-objective optimization problem is constructed with the goal of minimizing the electricity cost of distribution network users and the line power loss cost, specifically including:

[0128] S401, calculating the total user electricity cost using a first calculation formula;

[0129] S402, calculating the line power loss cost using a second calculation formula;

[0130] S403, setting the third calculation formula as the objective function of the multi-objective optimization problem;

[0131] The first calculation formula is:

[0132]

[0133] Where Cuser is the total user electricity cost, ci is the unit electricity price of user i, i is the user number, n is the total number of users, P i,t is the electricity consumption of the i-th user at time t;

[0134] The second calculation formula is calculated as:

[0135]

[0136] Among them, Ploss is the line power loss cost, Ii is the current flowing through node i;

[0137] The third calculation formula is:

[0138] MB=min(w1×Cuser+w2×Ploss)

[0139] Among them, the calculated target value of the objective function of the MB multi-objective optimization problem, w1 and w2 are the first weight coefficient and the second weight coefficient respectively.

[0140] In an embodiment of the present invention, in order to implement a method for controlling the load rate of a main distribution transformer based on distributed energy storage, it is necessary to construct an objective function of a multi-objective optimization problem. The objective function aims to minimize the electricity cost of distribution network users and the line power loss cost. The specific steps are as follows: Calculate the total user electricity cost using the first calculation formula. The formula is Cuser=∑i=ni=1ci×Pi,t, where Cuser represents the total user electricity cost, ci represents the unit electricity price of user i, i represents the user number, n represents the total number of users, and Pi,t represents the power consumption of the i-th user at time t. Through this formula, the total electricity cost of all users within a certain period of time can be accurately calculated. The line power loss is calculated using the second calculation formula.

[0141] Figure 5 It is a flow chart of setting power balance, voltage stability and equipment capacity limitation in a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0142] like Figure 5 As shown, in one or more embodiments, preferably, the setting of power balance, voltage stability and equipment capacity limitation specifically includes:

[0143] S501, setting a first constraint condition to ensure that the power input of each node is equal to the power output plus the loss;

[0144] S502, setting a second constraint condition to ensure that the voltage amplitude is within a preset safety range;

[0145] S503, setting a third constraint condition to ensure that the equipment operates within the maximum active and reactive power generation capacity range;

[0146] The first constraint condition is:

[0147] Pi,t+Qi,t=Pi,gen,t+Qi,gen,t-Ploss,i,t-Qloss,i,t

[0148] Where Pi,gen,t and Qi,gen,t are the active and reactive power generation of node i at time t, Ploss,i,t and Qloss,i,t are the active and reactive losses of node i at time t, Pi,t is the active and reactive power of node i at time t, Qi,t is the active and reactive power of node i at time t, respectively;

[0149] The second constraint is:

[0150] Vmin≤Vi,t≤Vmax

[0151] Among them, Vi,t is the real-time voltage, Vmax is the maximum allowable voltage, and Vmin is the minimum operating voltage;

[0152] The third constraint condition is:

[0153] Pi,gen,t≤Pmax,i

[0154] Qi,gen,t≤Qmax,iPi

[0155] Among them, Pmax,i and Qmax,i are the maximum active and reactive power generation capabilities of the equipment respectively.

[0156] In an embodiment of the present invention, in order to ensure the implementation effect of the load rate control method of the main distribution transformer equipment based on distributed energy storage, it is necessary to clarify the various constraints on the operation of the distribution network, photovoltaic system and energy storage system. The specific steps are as follows: Set the first constraint to ensure that the power input of each node is equal to the power output plus the loss. This condition can be expressed as Pi,t+Qi,t=Pi,gen,t+Qi,gen,t-Ploss,i,t-Qloss,i,t, where Pi,t and Qi,t are the active and reactive load requirements of node i at time t, respectively, Pi,gen,t and Qi,gen,t are the active and reactive power generation of node i at time t, respectively, and Ploss,i,t and Qloss,i,t are the active and reactive losses of node i at time t, respectively. Through this constraint, the power balance in the power grid can be ensured to avoid equipment damage or power supply interruption caused by power mismatch. Set the second constraint to ensure that the voltage amplitude is within a preset safety range. This condition can be expressed as Vmin≤Vi,t≤Vmax, where Vi,t is the real-time voltage, Vmax is the maximum allowable voltage, and Vmin is the minimum operating voltage. This constraint can ensure the stability of the power grid and prevent safety accidents caused by excessively high or low voltage. The third constraint is set to ensure that the equipment operates within the maximum active and reactive power generation capacity. This condition can be expressed as Pi,gen,t≤Pmax,i and Qi,gen,t≤Qmax,i, where Pmax,i and Qmax,i are the maximum active and reactive power generation capacities of the equipment, respectively. This constraint can ensure the safe and stable operation of the equipment and avoid equipment failure or shortened life due to overload operation. By strictly defining and executing the above constraints, not only can the formulated control strategy be ensured to be both safe and feasible, but also the stability and reliability of the distribution network can be effectively improved.

[0157] Figure 6 It is a flow chart of online acquisition of the optimal distributed energy storage power allocation scheme in a method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to an embodiment of the present invention.

[0158] like Figure 6 As shown, in one or more embodiments, preferably, the online acquisition of the optimal distributed energy storage power allocation scheme specifically includes:

[0159] S601, sorting the output power of each energy storage from large to small according to the maximum output power upper limit at the current moment to form an energy storage output capacity sequence;

[0160] S602, calculating the energy storage output power using a fourth calculation formula;

[0161] S603, within the preset constraint range, automatically select energy storage power sources according to the energy storage output capacity sequence from large to small, so that the energy storage output power can be achieved in each preset area, and after all energy storage output distribution is completed, perform power flow calculation according to this energy storage output distribution;

[0162] S604. In the power flow calculation, the magnitude of power exchange between different areas is analyzed;

[0163] S605: When the power exchange between the two areas satisfies the fifth calculation formula, the distribution of the overall energy storage power output power within the two areas is adjusted so that the external exchange power of the two areas satisfies the sixth calculation formula;

[0164] The fourth calculation formula is:

[0165] CDS=P MINLOSS

[0166] Among them, CDS is the energy storage output power, P MINLOSS The power output matched when the line loss is the lowest;

[0167] The fifth calculation formula is:

[0168] JHp>JY

[0169] Among them, JHp is the total power exchange between the two regions, and JY is the interaction comparison threshold;

[0170] The sixth calculation formula is:

[0171] MTW <y1

[0172] MT <y2

[0173] Among them, MTW is the maximum value of the external interaction power of two regions, MT is the inter-regional interaction power of two regions, y1 is the external interaction margin, and y2 is the mutual interaction margin.

[0174] In an embodiment of the present invention, the output powers of each energy storage unit are sorted according to the upper limit of the maximum output power at the current moment to form an energy storage output capacity sequence. This step ensures that when allocating power, the energy storage units that can provide a larger power output are given priority, thereby improving the overall efficiency and response speed of the system. The energy storage output power is calculated using the fourth calculation formula. The formula is CDS = PMINLOSS, where CDS represents the energy storage output power and PMINLOSS represents the power output matched when the line loss is the lowest. Through this formula, the power output values that each energy storage unit should provide while minimizing the line loss can be calculated. Within the preset constraint range, the energy storage power sources are automatically selected from largest to smallest according to the energy storage output capacity sequence, so that the energy storage output power can be achieved within each preset area. When all the energy storage output allocations are completed, a power flow calculation is performed based on this energy storage output allocation. Power flow calculation is a method used in power systems to analyze the current and voltage distributions in the power grid. Through it, the power exchange situations between different regions can be understood. In the power flow calculation, the magnitudes of the power exchanges between different regions are analyzed. When the power exchange between two regions satisfies the fifth calculation formula, that is, JHp > JY (where JHp is the total power exchange between the two regions and JY is the interaction comparison threshold), the output powers of the energy storage power sources within these two regions are overall adjusted so that the external exchange powers of the two regions satisfy the sixth calculation formulas MTW < y1 and MT < y2 (where MTW is the maximum external interaction power between the two regions, MT is the inter-regional interaction power between the two regions, y1 is the external interaction margin, and y2 is the mutual interaction margin). Through these steps, an optimal distributed energy storage power allocation scheme can be obtained online, thereby effectively improving the stability and economy of the distribution network.

[0175] Figure 7 It is a flowchart of generating a control instruction according to a distributed energy storage power allocation scheme and sending it to each energy storage unit in a method for controlling the load rate of a distributed energy storage main and distribution transformer device for power supply guarantee in an embodiment of the present invention.

[0176] As Figure 7 shown, in one or more embodiments, preferably, generating a control instruction according to a distributed energy storage power allocation scheme and sending it to each energy storage unit specifically includes:

[0177] S701. Generate corresponding control instructions for each energy storage unit;

[0178] S702. Send the control instructions to each energy storage unit using a communication protocol;

[0179] S703. Implement a communication module responsible for packing the control instructions and sending them to the specified energy storage unit;

[0180] S704, each energy storage unit is equipped with a communication interface for receiving control instructions from the central controller;

[0181] S705, after receiving the instruction, the energy storage unit analyzes the instruction content and adjusts its working state according to the instruction;

[0182] S706: After the execution is completed, the energy storage unit sends status feedback to the central controller to confirm that the command has been successfully executed;

[0183] S707. The central controller continuously monitors the status of each energy storage unit to ensure that they work as expected.

[0184] In an embodiment of the present invention, in order to implement a load rate control method for a main distribution transformer based on distributed energy storage, it is necessary to generate control instructions according to a distributed energy storage power allocation scheme and send them to each energy storage unit through a communication network. The specific steps are as follows: For each energy storage unit, a corresponding control instruction is generated. These control instructions are formulated according to the optimal distributed energy storage power allocation scheme mentioned above, and are intended to guide each energy storage unit on how to adjust its output power to optimize the performance of the entire distribution network. The control instructions are sent to each energy storage unit using a communication protocol. Existing mature communication protocols such as Modbus, DNP3, etc. can be used here to ensure that the control instructions can be accurately conveyed to each energy storage unit. A communication module is implemented to package and send the control instructions to a specified energy storage unit. The communication module should have efficient data processing capabilities and stable transmission performance to ensure the timeliness and reliability of the control instructions. Each energy storage unit is equipped with a communication interface for receiving control instructions from a central controller. These communication interfaces can be wireless or wired, depending on the requirements of the actual application scenario. After receiving the instruction, the energy storage unit parses the instruction content and adjusts its working state according to the instruction. This may involve operations such as changing the charging and discharging mode of the energy storage unit and adjusting the output power. After execution, the energy storage unit sends status feedback to the central controller to confirm that the instruction has been successfully executed. This link is crucial to ensuring the closed-loop control of the system. It allows the central controller to understand the actual operation of each energy storage unit and make adjustments when necessary. The central controller continuously monitors the status of each energy storage unit to ensure that they work as expected. By collecting and analyzing the status information of each energy storage unit in real time, the central controller can promptly detect and handle any abnormal situations to ensure the stable operation of the entire system. Through the above steps, precise control of the distributed energy storage system can be achieved, thereby effectively improving the stability and economy of the distribution network.

[0185] According to a second aspect of an embodiment of the present invention, a distributed energy storage main distribution transformer load rate control system for supply assurance is provided.

[0186] Figure 8 It is a structural diagram of a load rate control system of a distributed energy storage main distribution transformer equipment for supply guarantee according to an embodiment of the present invention.

[0187] In one or more embodiments, preferably, the distributed energy storage main distribution transformer load rate control system for supply guarantee includes:

[0188] The data collection and prediction module 801 is used to collect the grid operation data in real time through the sensors and data acquisition modules installed at each node of the distribution network, and the grid operation data includes load demand and photovoltaic power generation;

[0189] The parameter statistics and modeling module 802 is used to count the detailed parameters of the photovoltaic system and the energy storage system and load the constraints of the energy storage system;

[0190] An objective function construction module 803 is used to construct an objective function of a multi-objective optimization problem with the goal of minimizing the electricity cost of distribution network users and the line power loss cost;

[0191] Constraint setting module 804, used to set power balance, voltage stability and equipment capacity constraints;

[0192] The relaxation and solution module 805 is used to obtain the optimal distributed energy storage power allocation solution online;

[0193] The control strategy execution module 806 is used to generate control instructions according to the distributed energy storage power allocation plan and send them to each energy storage unit through the communication network.

[0194] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0195] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0196] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9The electronic device shown is a general main distribution transformer load rate control device based on distributed energy storage, which includes a general computer hardware structure, which at least includes a processor 901 and a memory 902. The processor 901 and the memory 902 are connected via a bus 903. The memory 902 is suitable for storing instructions or programs executable by the processor 901. The processor 901 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 901 executes the instructions stored in the memory 902 to execute the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 903 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 904 and the display device and the input / output (I / O) device 905. The input / output (I / O) device 905 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 905 is connected to the system through an input / output (I / O) controller 906.

[0197] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0198] In the solution of the present invention, with the goal of minimizing user electricity costs and line power losses, an objective function of a multi-objective optimization problem is constructed, specific control instructions are generated through online optimization, and are sent to each energy storage unit through a communication network.

[0199] In the solution of the present invention, an advanced optimization algorithm is used to relax the objective function of complex constraints and solve it efficiently. Through efficient computing tools, the optimal distributed energy storage power allocation solution is quickly obtained, thereby improving the response speed and decision-making efficiency of the system.

[0200] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0201] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0202] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0204] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee, characterized in that: The method includes: The sensors and data acquisition modules installed at each node of the distribution network collect the grid operation data in real time, including load demand and photovoltaic power generation; Collect detailed parameters of the photovoltaic system and energy storage system, and load the constraints of the energy storage system; With the goal of minimizing the electricity cost of distribution network users and the power loss cost of lines, an objective function of a multi-objective optimization problem is constructed; Set power balancing, voltage stability, and equipment capacity limits; Obtain the optimal distributed energy storage power allocation solution online; Control instructions are generated according to the distributed energy storage power allocation plan and sent to each energy storage unit through the communication network.

2. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The sensors and data acquisition modules installed at each node of the distribution network collect grid operation data in real time. The grid operation data includes load demand and photovoltaic power generation, specifically including: Install sensors and data acquisition modules at each node of the distribution network to monitor grid operation data in real time, including load demand and photovoltaic power generation; Through the sensors and data acquisition modules installed at each node of the distribution network, the grid operation data is collected in real time and transmitted to the central processing system or data center; The collected data is analyzed using a central processing system or data center to form grid operation data for generating control instructions.

3. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The detailed parameters of the photovoltaic system and the energy storage system are counted and the constraints of the energy storage system are loaded, specifically including: Collect detailed parameters of the PV system, including the specifications, quantity, installation angle and geographical location of the PV modules; Collect detailed parameters of the energy storage system, including battery type, capacity, charge and discharge rate, and cycle life; Based on the collected PV and energy storage system parameters, a mathematical model including these parameters is established to describe the power generation of the PV system under different conditions and the performance of the energy storage system under different charging and discharging states; The constraints of the energy storage system are added to the model, including maximum charging and discharging power, battery capacity limitations and cycle life requirements.

4. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The objective function of a multi-objective optimization problem is constructed with the goal of minimizing the electricity cost of distribution network users and the line power loss cost, specifically including: Calculate the total user electricity cost using the first calculation formula; Calculate the line power loss cost using the second calculation formula; Setting the third calculation formula as the objective function of the multi-objective optimization problem; The first calculation formula is: Where Cuser is the total user electricity cost, ci is the unit electricity price of user i, i is the user number, n is the total number of users, P i,t is the electricity consumption of the i-th user at time t; The second calculation formula is calculated as: Among them, Ploss is the line power loss cost, Ii is the current flowing through node i; The third calculation formula is: MB=min(w1×Cuser+w2×Ploss) Among them, the calculated target value of the objective function of the MB multi-objective optimization problem, w1 and w2 are the first weight coefficient and the second weight coefficient respectively.

5. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The setting of power balance, voltage stability and equipment capacity limitation specifically includes: The first constraint is set to ensure that the power input to each node is equal to the power output plus the loss; The second constraint condition is set to ensure that the voltage amplitude is within a preset safety range; The third constraint condition is set to ensure that the equipment operates within the maximum active and reactive power generation capacity; The first constraint condition is: Pi,t+Qi,t=Pi,gen,t+Qi,gen,t-Ploss,i,t-Qloss,i,t Where Pi,gen,t and Qi,gen,t are the active and reactive power generation of node i at time t, Ploss,i,t and Qloss,i,t are the active and reactive losses of node i at time t, Pi,t is the active and reactive power of node i at time t, Qi,t is the active and reactive power of node i at time t, respectively; The second constraint is: Vmin≤Vi,t≤Vmax Among them, Vi,t is the real-time voltage, Vmax is the maximum allowable voltage, and Vmin is the minimum operating voltage; The third constraint condition is: Pi,gen,t≤Pmax,i Qi,gen,t≤Qmax,iPi Among them, Pmax,i and Qmax,i are the maximum active and reactive power generation capabilities of the equipment respectively.

6. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The online acquisition of the optimal distributed energy storage power allocation solution specifically includes: The output power of each energy storage is sorted from large to small according to the maximum output power upper limit at the current moment to form an energy storage output capacity sequence; Calculate the energy storage output power using the fourth calculation formula; Within the preset constraint range, the energy storage power source is automatically selected from large to small according to the energy storage output capacity sequence, so that the energy storage output power can be achieved within each preset area. After all energy storage output distribution is completed, the power flow calculation is performed according to this energy storage output distribution; In power flow calculations, the magnitude of power exchange between different areas is analyzed; When the power exchange between the two areas satisfies the fifth calculation formula, the distribution of the overall energy storage power output power within the two areas is adjusted so that the external exchange power of the two areas satisfies the sixth calculation formula; The fourth calculation formula is: CDS=P MINLOSS Among them, CDS is the energy storage output power, P MINLOSS The power output matched when the line loss is the lowest; The fifth calculation formula is: JHp>JY Among them, JHp is the total power exchange between the two regions, and JY is the interaction comparison threshold; The sixth calculation formula is: MTW <y1 MT <y2 Among them, MTW is the maximum value of the external interaction power of two regions, MT is the inter-regional interaction power of two regions, y1 is the external interaction margin, and y2 is the mutual interaction margin.

7. The method for controlling the load rate of a distributed energy storage main distribution transformer for supply guarantee according to claim 1, characterized in that: The generating of control instructions according to the distributed energy storage power allocation scheme and sending the control instructions to each energy storage unit through the communication network specifically includes: For each energy storage unit, generate corresponding control instructions; Using communication protocols to send control instructions to each energy storage unit; Implement a communication module to package and send control instructions to the designated energy storage unit; Each energy storage unit is equipped with a communication interface for receiving control instructions from the central controller; After receiving the command, the energy storage unit analyzes the command content and adjusts its working state according to the command; After execution, the energy storage unit sends status feedback to the central controller to confirm that the command has been successfully executed; The central controller continuously monitors the status of each energy storage unit to ensure that they are working as expected.

8. A distributed energy storage main distribution transformer load rate control system for supply guarantee, characterized in that: The system is used to implement the method according to any one of claims 1 to 7, and the system comprises: A data collection and prediction module is used to collect grid operation data in real time through sensors and data acquisition modules installed at each node of the distribution network. The grid operation data includes load demand and photovoltaic power generation; Parameter statistics and modeling module, used to count the detailed parameters of the photovoltaic system and energy storage system, and load the constraints of the energy storage system; An objective function building module is used to build an objective function of a multi-objective optimization problem with the goal of minimizing the electricity cost of distribution network users and the line power loss cost; Constraint setting module, used to set power balance, voltage stability and equipment capacity limits; Relaxation and solution module, used to obtain the optimal distributed energy storage power allocation solution online; The control strategy execution module is used to generate control instructions according to the distributed energy storage power allocation plan and send them to each energy storage unit through the communication network.

9. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-7.

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