A power grid peak shaving optimization method based on load power control

Abstract

The application discloses a power grid peak shaving optimization method based on load power control, comprising the following steps: (1) establishing a distribution network peak shaving optimization model based on feeder load power control; (2) solving the distribution network peak shaving optimization model based on feeder load power control; (3) executing the peak shaving optimization model result. The application establishes a distribution network day-ahead peak shaving optimization model considering new energy power fluctuation and feeder load power control to solve the problem that a high proportion of new energy access to the distribution network causes a too large power peak-valley difference of a lower network point, coordinates and optimizes multiple types of reactive voltage regulation devices with different regulation speeds in advance, can coordinate multiple voltage regulation devices to efficiently complete feeder load power control, controls the feeder load power to shave the peak and fill the valley in the system peak power and valley power time period, and keeps the load normal operation in the non-peak-valley power time period, so that the high proportion of new energy distribution network peak shaving optimization is realized.

Description

Technical Field

[0001] This invention belongs to the field of power grid operation optimization technology, specifically relating to a power grid peak shaving optimization method based on load power control. Background Technology

[0002] To promote the "dual carbon" goals and achieve energy structure transformation, new energy sources, represented by photovoltaics and wind power, will be vigorously developed in my country in the long term. In power-loaded areas such as central and southeastern my country, the distributed development and local consumption of new energy sources will be encouraged, with a large amount of new energy being directly installed in the distribution network. In the future, there will be more and more distribution networks with a high proportion of new energy.

[0003] The power generation of new energy sources exhibits significant fluctuations, causing the power output at distribution points in high-proportion new energy distribution networks to vary widely across multiple time periods, thus increasing the peak-to-valley difference in system operation. It is necessary to distribute the peak-shaving pressure from the main grid to the distribution network and optimize peak-shaving within the distribution network to achieve local mitigation of new energy power fluctuations.

[0004] With the continuous improvement of intelligence, the power distribution network is transforming from the traditional "source follows load" to "source interacts with load," and the advantages of load participation in system peak shaving are gradually becoming apparent. For example, Chinese patent CN 113988549 A discloses a real-time optimization method for energy storage based on the anti-peak shaving characteristics of wind power. First, it uses a model combining K-means clustering and kernel principal component analysis (KPCA) with a random forest algorithm to predict load, reducing prediction errors and obtaining a load baseline value to determine the algorithm's iteration step size. Then, using the rated power of the energy storage system as a constraint, it calculates the minimum peak-shifting power value and the maximum valley-filling power value of the system. Finally, it performs charging and discharging actions according to the required power difference in the corresponding time period. For power differences exceeding the range, the actions are performed according to the rated power of the energy storage system, achieving real-time optimization control of the charging and discharging of the energy storage system in each time period. This patent's real-time optimization method for energy storage based on the anti-peak shaving characteristics of wind power can plan the charging and discharging time periods and power of the battery energy storage system participating in peak shaving in real time; it also has a variable power control strategy for power distribution network peak shaving based on the balance of charging and discharging capacity of the energy storage system, which can effectively reduce the peak-valley difference in the power distribution network. However, the optimization method in this patent only addresses the real-time optimization control of the charging and discharging of the grid energy storage system during different time periods when new energy wind power generation is connected to the grid. This optimization method is not applicable to other new energy power generation connected to the grid. Publication number CN 114298366A discloses a robust nonlinear optimization method for energy storage scheduling in microgrid peak shaving. This method represents source-load uncertainty, including renewable energy and user load within the microgrid, as a box-type uncertainty set; and establishes a robust optimization model with optimization objectives including energy storage maintenance cost, renewable energy maintenance cost, total electricity price, and microgrid total power variance, expressed as a nonlinear convex function. Finally, the column and constraint generation algorithm is extended from robust linear optimization to robust nonlinear convex optimization. Without linearizing the optimization objectives, a robust optimal solution for microgrid peak shaving energy storage scheduling considering source-load uncertainty is obtained and applied to the microgrid energy storage system as an energy storage output within a scheduling cycle. However, this patent's optimization method addresses microgrid peak shaving from the perspective of energy storage scheduling and is not applicable to peak shaving when a high proportion of new energy is connected to the distribution network. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a power grid peak-shaving optimization method based on load power control. This method can coordinate multiple voltage regulating devices to efficiently complete feeder load power control, achieving peak-shaving optimization for high-proportion renewable energy distribution networks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A power grid peak-shaving optimization method based on load power control includes the following steps:

[0008] (1) Establish a distribution network peak-shaving optimization model based on feeder load power control;

[0009] (2) Solve the distribution network peak-shaving optimization model based on feeder load power control;

[0010] (3) Execute the peak shaving optimization model results.

[0011] Furthermore, in step (1), the distribution network peak-shaving optimization model takes the source and load day-ahead forecast information, load regulation characteristics and system parameters as inputs to optimize the node voltage, load power and downstream network power.

[0012] Furthermore, the objective function of the power distribution network peak-shaving optimization model is as shown in equation (1):

[0013]

[0014] in, This represents the peak power of the offline network within a day. The off-peak power of the network outlet within a day. The active power of the offline network point during the time period t; Let i be the reactive power of the new energy source at node i during time period t; V0 represents the switching state of the capacitor at node i during time interval t; V0 is the rated voltage (1.0 pu). Let be the output voltage of the voltage regulator at node i during time interval t; β1, β2, β3, and β4 are weighting coefficients; T is the time set; N RG N is a set of nodes containing new energy sources; CB N is the set of nodes containing capacitors. VT It is a set of nodes containing voltage regulators.

[0015] Furthermore, the constraints of the objective function of the distribution network peak-shaving optimization model include power flow constraints, node voltage constraints, capacitor equipment operation constraints, photovoltaic equipment operation constraints, wind power equipment operation constraints, voltage regulator equipment operation constraints, and load regulation characteristic constraints.

[0016] Furthermore, the conditions for the power flow constraint are shown in equations (2) and (3):

[0017]

[0018]

[0019] in, and These are the active power and reactive power of the distribution network's downstream point during time period t, respectively. This variable is ignored when the node is not a downstream point. and These are the active power and reactive power of the renewable energy source at node i during time period t, respectively. This variable is ignored when there is no renewable energy source at the node. Let be the reactive power of the capacitor at node i during time period t. This variable is ignored when there is no capacitor at the node. and These represent the active and reactive power of the load at node i during time period t; e i,t with f i,t These represent the real and imaginary parts of the node voltage at node i during time interval t, respectively; G i,j With B i,j Let N and T be the real and imaginary parts of the line admittance between node i and node j, respectively; N is the set of nodes; and T is the time set.

[0020] The conditions for the node voltage constraint are shown in equation (4):

[0021]

[0022] Among them, V i,t V is the voltage at node i during time interval t. max With V min These represent the maximum and minimum node voltages, respectively; according to national standards, V in medium-voltage distribution networks max With V min The values ​​are 0.93 pu and 1.07 pu, respectively; N is the set of nodes; T is the set of times.

[0023] The operating constraints of the capacitor device are shown in equations (5) and (6):

[0024]

[0025] in, and These represent the reactive power and switching status of the capacitor at node i during time period t; The rated reactive power of the capacitor at node i; N is the threshold for the maximum number of daily switching changes for capacitors; CB Let T be the set of capacitor nodes; T be the time set.

[0026] The operating constraints of the photovoltaic equipment are shown in equations (7)-(10):

[0027]

[0028] in, and These represent the active power and reactive power of the photovoltaic system at node i during time period t, respectively. and These represent the maximum and minimum values ​​of the photovoltaic reactive power at node i, respectively. V represents the rated photovoltaic capacity of node i; i,t V is the voltage at the photovoltaic grid connection point of node i; PV,max With V PV,min These represent the maximum and minimum voltage values ​​at the photovoltaic grid connection point, respectively. Let N be the predicted active power of the photovoltaic system at node i during time period t; PV Let N be the set of photovoltaic nodes; T be the set of nodes; and T be the set of time periods.

[0029] The operating constraints of the wind power equipment are shown in equations (11)-(16):

[0030]

[0031] in, and These refer to the input and output voltages at the grid connection point of voltage regulators such as dynamic voltage restorers; V represents the tap position of the transformer at node i within the time period t; tap This refers to the voltage change corresponding to each tap of the transformer. and These are the upper and lower limits of the tap position for the transformer at node i; This is the threshold for the maximum number of tap changes per day for the transformer tap changer. Let i be the output voltage of the intelligent transformer at node i during the time period t. The compensation voltage for voltage regulators such as the i-node dynamic voltage restorer during the time period t; and These represent the maximum and minimum compensation voltages of voltage regulators such as dynamic voltage restorers. OLTC For a traditional set of transformer nodes with taps; N ST N represents the set of intelligent transformer nodes. DVR T represents the set of nodes for voltage regulators such as dynamic voltage restorers; T represents the time set.

[0032] The conditions for the load regulation characteristic constraint are shown in equations (17) and (18):

[0033]

[0034] in, and These represent the active power and reactive power of the load at node i during time period t, respectively. and These represent the initial active and reactive power of the load at node i during time period t. Let i be the initial voltage of node i during time interval t; and Let N be the active power-voltage characteristic coefficient and reactive power-voltage characteristic coefficient of the load at node i within the time period t, respectively; N is the set of nodes; and T is the set of time periods.

[0035] Furthermore, the specific method for solving the distribution network peak-shaving optimization model based on feeder load power control in step (2) is as follows: linearization techniques such as linearized power flow model and McCormick envelope are used to linearize the day-ahead peak-shaving optimization model, and the solver is used to solve the optimization model to determine the voltage of each node, the action of reactive power regulating equipment and the adjustment of feeder load power in each time period of the next day.

[0036] Furthermore, the specific method for executing the peak shaving optimization model results in step (3) is as follows: every 15 minutes, the operators adjust the operating settings of the system's reactive power voltage regulation equipment according to the previous day's optimization results, and control the power of the feeder load through voltage regulation to achieve peak shaving and valley filling at the distribution network points.

[0037] my country's power grid generally faces significant peak-shaving pressure. Under the large power grid platform, utilizing high-quality power sources to coordinate load differences among multiple provincial power grids plays a crucial role in alleviating this pressure. Current inter-grid coordination models require that the regulated surplus load not only reduce the peak-to-valley difference but also maintain a relatively stable surplus load curve to ensure the safe and efficient operation of the power system. However, while existing peak-shaving optimization methods can reduce the peak-to-valley difference to some extent when distributing load across multiple power grids, the surplus load curve is prone to fluctuations in localized periods, especially when peak-shaving capacity is severely insufficient. Chinese patent CN 111384728 A discloses a method and system for coordinating and optimizing power grid peak-shaving resources. The method includes: obtaining load reduction quotation packages from demand suppliers; performing optimization calculations based on the quotation packages and a pre-established multi-source peak-shaving coordination optimization model to obtain the flexible load adjustment amount and power grid equipment output for each time period within the scheduling cycle; performing power grid peak-shaving scheduling based on the flexible load adjustment amount and power grid equipment output for each time period; and formulating the multi-source peak-shaving coordination optimization model based on the maximum absorption of new energy and minimizing the sum of the economic costs of operating various power sources within the scheduling cycle. This method and system consider the flexible load peak-shaving characteristics and electricity price response characteristics under market mechanisms, and, based on the premise of maximum new energy absorption, can guide the optimized operation of the power grid where the grid, sources, and loads jointly participate in peak-shaving under the current high-penetration new energy access. While this patent can reduce the peak-valley difference of the power grid surplus load to a certain extent due to the high proportion of new energy access, it cannot guarantee the normal operation of the power grid load during non-peak-valley power periods. Chinese patent CN 115173453 A discloses an optimized configuration method for energy storage-assisted grid peak shaving. This method includes: obtaining daily load curves, renewable energy generation curves, and the maximum peak-shaving output of traditional thermal and hydropower units; determining the daily net load curve based on the daily load curve and renewable energy generation curve; constructing a multi-objective optimization model based on the daily net load curve, with the economic objective of energy storage-assisted grid peak shaving as the first objective function and the peak-shaving and valley-filling effect objective as the second objective function; transforming the first and second objective functions into a single-objective optimization model containing only a third objective function using a linear weighted summation method based on the multi-objective optimization model; and using an immune particle swarm optimization algorithm to solve the single-objective optimization model based on the constructed third objective function, obtaining the optimal configuration power and capacity for energy storage-assisted grid peak shaving. While this patent can improve the economic benefits and auxiliary peak-shaving effect of energy storage-assisted grid peak shaving, it cannot coordinate multiple voltage regulating devices to achieve optimal grid peak shaving by controlling the feeder load power.

[0038] With the deepening of economic transformation, heavy industry and energy-intensive industries are declining, people's living standards are improving, and electricity consumption in the tertiary sector, including residential, commercial, and service industries, is increasing. This has led to a widening peak-valley difference and a deterioration in load characteristics, placing higher demands on the system's peak-shaving capabilities. On the other hand, the proportion of new loads with dual power supply and load characteristics, such as electric vehicles and energy storage systems, which have bidirectional interaction capabilities with the grid, is continuously increasing. Some traditional loads can also adjust their own electricity demand based on incentives or electricity prices, making it feasible for loads to participate in system regulation. Therefore, under the new circumstances, the high proportion of new energy access, the increasing proportion of flexible loads in the system, and their application have increased the uncertainty and difficulty of grid operation. There is an urgent need to establish a power system peak-shaving resource coordination and optimization method that considers the peak-shaving capabilities of flexible loads, guiding the coordinated and optimized operation of flexible loads participating in system peak-shaving under conditions of high new energy access.

[0039] Compared with the prior art, the positive and beneficial effects of this invention are as follows:

[0040] (1) This invention addresses the problem of excessive peak-valley power difference at downstream distribution network points caused by the high proportion of new energy access to the distribution network. It establishes a day-ahead peak-shaving optimization model for the distribution network that considers the power fluctuation of new energy and the power control of feeder loads. It coordinates and optimizes various types of reactive power voltage regulation equipment with different adjustment speeds during the day-ahead period, thereby constructing a peak-shaving optimization method based on feeder load power control. This method can coordinate various voltage regulation equipment to efficiently complete feeder load power control, control feeder load power to shave peaks and fill valleys during the peak and valley power periods of the system, and maintain normal load operation during non-peak and valley power periods.

[0041] (2) The power grid peak shaving optimization method in this invention belongs to the day-ahead optimization strategy. It optimizes the feeder load power from the perspective of all-day operation. On the one hand, it reduces the peak-valley difference by source-load power complementarity during peak-valley power periods. On the other hand, it restricts the power grid company's use of feeder load power control during non-peak-valley periods, reducing the potential impact of long-term and large-scale voltage control on users' electricity consumption. At the same time, by coordinating various series and parallel devices, it achieves deeper voltage optimization and better feeder load power control, thereby obtaining a better peak shaving and valley filling effect. Attached Figure Description

[0042] Figure 1 This is a flowchart of the power grid peak shaving optimization method based on load power control according to the present invention. Detailed Implementation

[0043] To make the objectives and technical solutions of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings.

[0044] See appendix Figure 1A power grid peak-shaving optimization method based on load power control includes the following steps:

[0045] (1) Establish a distribution network peak-shaving optimization model based on feeder load power control. The optimization model takes the source and load day-ahead forecast information, load regulation characteristics and system parameters as inputs to optimize node voltage, load power and downstream power. The optimization interval is 15 minutes. The decision variables are the capacitor switching status, transformer tap, dynamic voltage restorer and smart transformer output voltage, photovoltaic inverter reactive power and wind power inverter reactive power in each time period of the next day. The optimization objective is to minimize the peak-valley difference of downstream power and the regulation amount of voltage regulating equipment.

[0046] The objective function of the distribution network peak-shaving optimization model is shown in equation (1):

[0047]

[0048] in, This represents the peak power of the offline network within a day. The off-peak power of the network outlet within a day. The active power of the offline network point during the time period t; Let i be the reactive power of the new energy source at node i during time period t; V0 represents the switching state of the capacitor at node i during time interval t; V0 is the rated voltage (1.0 pu). Let be the output voltage of the voltage regulator at node i during time interval t; β1, β2, β3, and β4 are weighting coefficients; T is the time set; N RG N is a set of nodes containing new energy sources; CB N is the set of nodes containing capacitors. VT It is a set of nodes containing voltage regulators.

[0049] The first term of the objective function of the distribution network peak-shaving optimization model is related to reducing the peak-valley difference. It uses feeder load power control to minimize the peak power and maximize the valley power of the downstream network point. The latter three terms of the objective function are related to reducing the impact on load power consumption. That is, voltage optimization related to feeder load power control is not performed during non-peak and valley periods, the use of feeder load power control is restricted, and the impact of long-term feeder load power control on users' normal power consumption is reduced.

[0050] The constraints of the objective function of the distribution network peak-shaving optimization model include power flow constraints, node voltage constraints, capacitor bank operation constraints, photovoltaic power plant operation constraints, wind power plant operation constraints, voltage regulator operation constraints, and load regulation characteristic constraints.

[0051] The conditions for power flow constraints are shown in equations (2) and (3):

[0052]

[0053]

[0054] in, and These are the active power and reactive power of the distribution network's downstream point during time period t, respectively. This variable is ignored when the node is not a downstream point. and These are the active power and reactive power of the renewable energy source at node i during time period t, respectively. This variable is ignored when there is no renewable energy source at the node. Let be the reactive power of the capacitor at node i during time period t. This variable is ignored when there is no capacitor at the node. and These represent the active and reactive power of the load at node i during time period t; e i,t with f i,t These represent the real and imaginary parts of the node voltage at node i during time interval t, respectively; G i,j With B i,j Let N and T be the real and imaginary parts of the line admittance between node i and node j, respectively; N is the set of nodes; and T is the time set.

[0055] The conditions for node voltage constraints are shown in equation (4):

[0056]

[0057] Among them, V i,t V represents the voltage at node i during time interval t. max With V min These represent the maximum and minimum node voltages, respectively; according to national standards, V in medium-voltage distribution networks max With V min The values ​​are 0.93 pu and 1.07 pu, respectively; N is the set of nodes; T is the set of times.

[0058] The operating constraints of the capacitor bank are shown in equations (5) and (6):

[0059]

[0060] in, and These represent the reactive power and switching status of the capacitor at node i during time period t; The rated reactive power of the capacitor at node i; N is the threshold for the maximum number of daily switching changes for capacitors; CB Let T be the set of capacitor nodes; T be the time set.

[0061] The operating constraints of photovoltaic equipment are shown in equations (7)-(10):

[0062]

[0063] in, and These represent the active power and reactive power of the photovoltaic system at node i during time period t, respectively. and These represent the maximum and minimum values ​​of the photovoltaic reactive power at node i, respectively. V represents the rated photovoltaic capacity of node i; i,t V is the voltage at the photovoltaic grid connection point of node i; PV,max With V PV,min These represent the maximum and minimum voltage values ​​at the photovoltaic grid connection point, respectively. Let N be the predicted active power of the photovoltaic system at node i during time period t; PV Let N be the set of photovoltaic nodes; T be the set of nodes; and T be the set of time periods.

[0064] The operating constraints of wind power equipment are shown in equations (11)-(16):

[0065]

[0066] in, and These refer to the input and output voltages at the grid connection point of voltage regulators such as dynamic voltage restorers; V represents the tap position of the transformer at node i within the time period t; tap This refers to the voltage change corresponding to each tap of the transformer. and These are the upper and lower limits of the tap position for the transformer at node i; This is the threshold for the maximum number of tap changes per day for the transformer tap changer. Let i be the output voltage of the intelligent transformer at node i during the time period t. The compensation voltage for voltage regulators such as the i-node dynamic voltage restorer during the time period t; and These represent the maximum and minimum compensation voltages of voltage regulators such as dynamic voltage restorers. OLTC For a traditional set of transformer nodes with taps; N ST N represents the set of intelligent transformer nodes. DVR T represents the set of nodes for voltage regulators such as dynamic voltage restorers; T represents the time set.

[0067] The conditions for the load regulation characteristic constraints are shown in equations (17) and (18):

[0068]

[0069] in, and These represent the active power and reactive power of the load at node i during time period t, respectively. and These represent the initial active and reactive power of the load at node i during time period t. Let i be the initial voltage of node i during time interval t; and Let N be the active power-voltage characteristic coefficient and reactive power-voltage characteristic coefficient of the load at node i within the time period t, respectively; N is the set of nodes; and T is the set of time periods.

[0070] (2) Solve the peak-shaving optimization model of the distribution network based on feeder load power control. The established peak-shaving optimization model involves a variety of reactive power regulation devices. The product of voltage and power will introduce bilinear nonlinear terms. The extremum function, absolute value function of the objective function, and power flow calculation are all nonlinear. This model is a mixed integer nonlinear programming problem, which is an NP-hard problem. There is no mature solution method at present, and the model needs to be simplified.

[0071] The calculation of power flow constraints can be simplified using linearized power flow models of distribution networks such as Distflow; the bilinear nonlinear terms can be linearly relaxed using the McCormick envelope.

[0072] Absolute value calculation items, such as the absolute value of photovoltaic reactive power. The following linearization process can be used:

[0073]

[0074]

[0075] in, These are auxiliary decision-making variables.

[0076] Extreme value calculation items, such as the maximum power of the lower grid point. Maximum power of the lower grid point Linearization can be achieved using two full-time inequality constraints:

[0077]

[0078] Through the above simplification and relaxation, the original mixed-integer nonlinear optimization model can be transformed into a mixed-integer linearized problem, which can be solved directly and quickly using existing commercial solvers, such as the CPLEX solver, to determine the voltage at each node, the operation of reactive power regulation equipment, and the power adjustment of feeder loads at each time period within the next day.

[0079] (3) Implement the peak shaving optimization model results. Operators adjust the operating settings of the system's reactive power voltage regulation equipment according to the previous optimization results, and control the power of the feeder load through voltage regulation to achieve peak shaving and valley filling at the distribution network points.

[0080] This invention controls the power of the feeder load by regulating voltage, increasing the load power during periods of high renewable energy generation and decreasing the load power during periods of low renewable energy generation, thereby reducing the peak-valley difference in the distribution network through source-load power complementarity. At the same time, this invention optimizes the feeder load power control status in each time period from the perspective of all-day operation, achieving maximum voltage control and obtaining better peak shaving and valley filling effects.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power grid peak-shaving optimization method based on load power control, characterized in that, Includes the following steps: (1) Establish a distribution network peak-shaving optimization model based on feeder load power control; (2) Solve the distribution network peak-shaving optimization model based on feeder load power control; (3) Execute the results of the peak shaving optimization model; In step (1), the distribution network peak-shaving optimization model takes the source and load day-ahead forecast information, load regulation characteristics and system parameters as inputs to optimize the node voltage, load power and downstream network power. The objective function of the distribution network peak-shaving optimization model is shown in equation (1): in, This represents the peak power of the offline network within a day. The off-peak power of the network outlet within a day. The active power of the offline network point during the time period t; Let i be the reactive power of the new energy source at node i during time period t; V represents the switching state of the capacitor at node i during time period t; V0 is the rated voltage. Let be the output voltage of the voltage regulator at node i during time interval t; β1, β2, β3, and β4 are weighting coefficients; T is the time set; N RG N is a set of nodes containing new energy sources; CB N is the set of nodes containing capacitors. VT It is a set of nodes containing voltage regulators.

2. The power grid peak-shaving optimization method based on load power control according to claim 1, characterized in that, The constraints of the objective function of the distribution network peak-shaving optimization model include power flow constraints, node voltage constraints, capacitor equipment operation constraints, photovoltaic equipment operation constraints, wind power equipment operation constraints, voltage regulator equipment operation constraints, and load regulation characteristic constraints.

3. The power grid peak-shaving optimization method based on load power control according to claim 2, characterized in that, The conditions for the power flow constraints are shown in equations (2) and (3): in, and These are the active power and reactive power of the distribution network's downstream point during time period t, respectively. This variable is ignored when the node is not a downstream point. and These are the active power and reactive power of the renewable energy source at node i during time period t, respectively. This variable is ignored when there is no renewable energy source at the node. Let be the reactive power of the capacitor at node i during time period t. This variable is ignored when there is no capacitor at the node. and These represent the active and reactive power of the load at node i during time period t; e i,t with f i,t These represent the real and imaginary parts of the node voltage at node i during time interval t, respectively; G i,j With B i,j Let N and T be the real and imaginary parts of the line admittance between node i and node j, respectively; N is the set of nodes; and T is the time set. The conditions for the node voltage constraint are shown in equation (4): Among them, V i,t V represents the voltage at node i during time interval t. max With V min These represent the maximum and minimum node voltages, respectively; according to national standards, V in medium-voltage distribution networks max With V min The values ​​are 0.93 pu and 1.07 pu, respectively; N is the set of nodes; T is the set of times. The operating constraints of the capacitor device are shown in equations (5) and (6): in, and These represent the reactive power and switching status of the capacitor at node i during time period t; The rated reactive power of the capacitor at node i; N is the threshold for the maximum number of daily switching changes for capacitors; CB Let T be the set of capacitor nodes; T be the time set. The operating constraints of the photovoltaic equipment are shown in equations (7)-(10): in, and These represent the active power and reactive power of the photovoltaic system at node i during time period t, respectively. and These represent the maximum and minimum values ​​of the photovoltaic reactive power at node i, respectively. V represents the rated photovoltaic capacity of node i; i,t V is the voltage at the photovoltaic grid connection point of node i; PV,max With V PV,min These represent the maximum and minimum voltage values ​​at the photovoltaic grid connection point, respectively. Let N be the predicted active power of the photovoltaic system at node i during time period t; PV Let N be the set of photovoltaic nodes; T be the set of nodes; and T be the set of time periods. The operating constraints of the wind power equipment are shown in equations (11)-(16): in, and These refer to the input and output voltages at the grid connection point of voltage regulators such as dynamic voltage restorers; V represents the tap position of the transformer at node i within the time period t; tap This refers to the voltage change corresponding to each tap of the transformer. and These are the upper and lower limits of the tap position for the transformer at node i; This is the threshold for the maximum number of tap changes per day for a transformer. The output voltage of the intelligent transformer at node i during time period t; The compensation voltage for voltage regulators such as the i-node dynamic voltage restorer during the time period t; and These represent the maximum and minimum compensation voltages of voltage regulators such as dynamic voltage restorers; N OLTC For a traditional set of transformer nodes with taps; N ST N represents the set of intelligent transformer nodes. DVR T represents the set of nodes for voltage regulators such as dynamic voltage restorers; T represents the time set. The conditions for the load regulation characteristic constraint are shown in equations (17) and (18): in, and These represent the active power and reactive power of the load at node i during time period t, respectively. and These represent the initial active and reactive power of the load at node i during time period t. Let i be the initial voltage of node i during time interval t; and Let N be the active power-voltage characteristic coefficient and reactive power-voltage characteristic coefficient of the load at node i within the time period t, respectively; N is the set of nodes; and T is the set of time periods.

4. The power grid peak-shaving optimization method based on load power control according to claim 3, characterized in that, The specific method for solving the distribution network peak-shaving optimization model based on feeder load power control in step (2) is as follows: linearization techniques such as linearized power flow model and McCormick envelope are used to linearize the day-ahead peak-shaving optimization model, and the solver is used to solve the optimization model to determine the voltage of each node, the action of reactive power regulating equipment and the adjustment of feeder load power in each time period of the next day.

5. The power grid peak-shaving optimization method based on load power control according to claim 1, characterized in that, The specific method for executing the peak shaving optimization model results in step (3) is as follows: Every 15 minutes, the operators adjust the operating settings of the system's reactive power regulating equipment according to the previous day's optimization results, and control the power of the feeder load through voltage regulation to achieve peak shaving and valley filling at the distribution network points.

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