Load control method, device, equipment and storage medium

Abstract

The present application provides a load control method, device, equipment and storage medium. It relates to the field of new energy planning and operation technology. The method includes: taking the economic cost of the distribution network and the system voltage stability as the target, constructing a multi-objective wind, photovoltaic and storage power station planning model; using evaluation indicators to perceive the operating status of the multi-objective wind, photovoltaic and storage power station planning model; wherein the evaluation indicators include control indicators, statistical indicators and constraint indicators; based on the multi-objective wind, photovoltaic and storage power station planning model, constructing a master-slave game load control model; when the multi-objective wind, photovoltaic and storage power station planning model is in different operating states, solving the master-slave game load control model, and outputting load control schemes under different operating states. The method of the present application effectively promotes the absorption of wind power and photovoltaic power, and ensures the economic, stable and efficient operation of the system.

Description

Technical Field

[0001] This application relates to the field of new energy planning and operation technology, and in particular to a load control method, device, equipment and storage medium. Background Technology

[0002] High efficiency, cleanliness, and low carbon emissions are the mainstream directions of energy development worldwide. With the large-scale integration of renewable energy sources, such as wind and solar power, into the grid, higher demands and challenges are placed on the flexibility of system planning schemes, the ability to perceive system operating status, and the ability to control system events. Currently, existing technologies, in the initial stages of new energy planning, only consider the planning of new power systems without considering the perception of operating status. This leaves considerable room for improvement in the economic efficiency and stability of the planned new power systems. Summary of the Invention

[0003] This application provides a load control method, apparatus, equipment, and storage medium to achieve full lifecycle management of the system from planning to operation to control by considering a new integrated strategy for power system planning, operation status perception, and load control. This effectively promotes the absorption of wind and solar power and ensures the economical, stable, and efficient operation of the system.

[0004] In a first aspect, this application provides a load control method, comprising:

[0005] With the goals of distribution network economic cost and system voltage stability, a multi-objective planning model for wind, solar and energy storage power stations is constructed.

[0006] The operational status of the multi-objective wind-solar-storage power station planning model is perceived using evaluation indicators; wherein, the evaluation indicators include control indicators, statistical indicators, and constraint indicators;

[0007] Based on the multi-objective wind-solar-storage power station planning model, a master-slave game load control model is constructed.

[0008] Under the multi-objective planning model of the wind-solar-storage power station, which is in different operating states, the load control model of the master-slave game is solved, and the load control schemes under different operating states are output.

[0009] The controllable indicators include node voltage control deviation, active power control deviation, and active power flexibility. The node voltage control deviation and active power control deviation indicators respectively characterize the voltage and active power control performance of the multi-objective wind-solar-storage power station planning model. Smaller values ​​for these indicators indicate better coordinated control performance. When the active power flexibility indicator value is greater than 1, it indicates that the multi-objective wind-solar-storage power station planning model may not meet the required ramp rate for dispatch instructions.

[0010] The statistical indicators include the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio; wherein, the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio provide information on the renewable energy consumption and power generation of the wind, solar, and energy storage power station within the multi-objective planning model for load control.

[0011] The binding indicators include node voltage limit exceedance indicators and line load factor indicators. When the node voltage limit exceedance indicator is greater than 1, it indicates that there is a voltage limit exceedance problem in the multi-objective wind-solar-storage power station planning model. When the line load factor indicator is greater than 1, it indicates that there is a line power flow limit exceedance problem in the multi-objective wind-solar-storage power station planning model.

[0012] Secondly, this application provides a load control device, comprising:

[0013] The planning model construction module is configured to construct a multi-objective planning model for wind, solar and energy storage power stations, with the objectives of distribution network economic cost and system voltage stability.

[0014] The operation status perception module is configured to perceive the operation status of the multi-objective wind-solar-storage power station planning model using evaluation indicators; wherein, the evaluation indicators include control indicators, statistical indicators, and constraint indicators;

[0015] The controllable indicators include node voltage control deviation, active power control deviation, and active power flexibility. The node voltage control deviation and active power control deviation indicators respectively characterize the voltage and active power control performance of the multi-objective wind-solar-storage power station planning model. Smaller values ​​for these indicators indicate better coordinated control performance. When the active power flexibility indicator value is greater than 1, it indicates that the multi-objective wind-solar-storage power station planning model may not meet the required ramp rate for dispatch instructions.

[0016] The statistical indicators include the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio; wherein, the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio provide information on the renewable energy consumption and power generation of the wind, solar, and energy storage power station within the multi-objective planning model for load control.

[0017] The binding indicators include node voltage limit exceedance indicators and line load factor indicators. When the node voltage limit exceedance indicator is greater than 1, it indicates that there is a voltage limit exceedance problem in the multi-objective wind-solar-storage power station planning model. When the line load factor indicator is greater than 1, it indicates that there is a line power flow limit exceedance problem in the multi-objective wind-solar-storage power station planning model.

[0018] The control model construction module is configured to construct a master-slave game load control model based on the multi-objective wind-solar-storage power station planning model.

[0019] The control model solving module is configured to solve the master-slave game load control model under different operating states of the wind, solar and energy storage power station planning model under the multi-objective conditions, and output the load control scheme under different operating states.

[0020] Thirdly, this application provides an electronic device, comprising: at least one processor and a memory; the memory storing computer-executable instructions; the at least one processor executing the computer-executable instructions stored in the memory, causing the at least one processor to perform the load control method as described in the first aspect above and various possible designs of the first aspect.

[0021] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the load control method described in the first aspect and various possible designs of the first aspect.

[0022] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the load control method described in the first aspect and various possible designs of the first aspect.

[0023] The load control method, device, equipment, and storage medium provided in this application aim to achieve both the economic cost of the distribution network and the system voltage stability. They construct a multi-objective planning model for wind, solar, and energy storage power stations, and based on this model, utilize three types of indicators—control indicators, statistical indicators, and constraint indicators—to achieve comprehensive perception of the operational status of the planning model. This leads to load control strategies under normal operation and extreme scenarios, enabling full lifecycle management of the system from planning to operation to control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] Figure 1 Flowchart of the load control method provided in the embodiments of this application Figure 1 ;

[0026] Figure 2 A schematic diagram of a master-slave game model between the power grid and users provided in an embodiment of this application;

[0027] Figure 3 A flowchart illustrating the solution process of the master-slave game model under normal operating conditions, as provided in the embodiments of this application.

[0028] Figure 4 A flowchart illustrating the solution process for the master-slave game model under extreme scenarios provided in this application embodiment;

[0029] Figure 5 This is a schematic diagram of the structure of the load control device provided in the embodiments of this application;

[0030] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0031] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0033] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0034] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0035] This application also provides a load control method. Figure 1 A flowchart illustrating the load control method provided in an embodiment of this application. Figure 1 As shown, the load control method includes:

[0036] S110. With the goals of distribution network economic cost and system voltage stability, construct a multi-objective planning model for wind, solar and energy storage power stations.

[0037] In some embodiments, step S110 includes the following steps:

[0038] S111. Establish the objective function; whereby the objective function includes a first objective function and a second objective function.

[0039] Taking the minimum annual operating cost of the distribution network as the primary objective function, its mathematical expression is:

[0040] ;

[0041] In the formula: min is the minimum value function, f 1 represents the annual operating cost of the power distribution network; C ele,mar To participate in the electricity market costs; C car,mar Costs of participating in the carbon market; C inv The average annual investment and installation cost for wind, solar, and energy storage systems; C was For network loss costs; C a Costs associated with curtailing wind and solar power.

[0042] The formula for calculating the cost of participating in the electricity market is:

[0043] ;

[0044] Where: α ( t )for t Time-of-use pricing in the electricity market; P grid,l ( t ), P load,l ( t ), P was,l ( t ), P wt,l ( t ), P pv,l ( t ), P bat,l ( t )and P g,l ( t ) are respectively l Typical Quarterly Day t The power consumption, load, network loss, wind power absorption, photovoltaic power absorption, energy storage, and gas turbine power of the distribution network during the specified time period; L represents the number of quarters in a year. T l for l The number of days in a quarter; T is the number of time periods in a day.

[0045] The formula for calculating the cost of participating in the carbon market is:

[0046] ;

[0047] Where: λ car , λ ccer These are carbon price and CCER price, respectively. p car Annual carbon allowances collected for power distribution networks; p car,fre Free carbon emission allowances; p ccer,off CCERs are used to offset carbon quotas. p ccer This refers to CCERs (China Certified Emission Reductions) for electricity generated by wind and solar power. CCERs are part of China's carbon trading market.

[0048] Carbon emissions mainly come from gas turbine units and thermal power purchased from the main grid.

[0049] p car , p car,fre , p ccer and p ccer,off The calculation formula is:

[0050] ;

[0051] Where: β g , β d , β g0 , β d0 These are the carbon emission intensity and free carbon emission allowances for gas turbine units and purchased electricity, respectively. β wt , β pv These represent the carbon emission reduction per unit of net on-grid electricity generated by wind and solar power, respectively. This represents the maximum collection ratio for CCER.

[0052] The formula for calculating the average annual investment and installation cost of wind, solar, and energy storage systems is as follows:

[0053] ;

[0054] Where: C wt,inv , C pv,inv , C bat,invThe average annual investment and installation costs for wind, solar, and energy storage are calculated using the following formulas:

[0055] ;

[0056] Where: P wt,cap , P pv,cap These are the installed capacities of wind and solar power, respectively. e wt , e pv , e bat The unit cost for installed capacity of wind, solar, and energy storage, respectively; r 1. r 2. r 3 represents the depreciation rates for wind, solar, and energy storage, respectively; m 1. m 2. m 3 represents the service life of wind, solar, and energy storage, respectively; N wt This refers to the number of wind turbines in a wind power station. N pv This refers to the number of photovoltaic arrays in a photovoltaic power plant; N bat The number of energy storage units; E bat This refers to the installed capacity of the energy storage system.

[0057] The formula for calculating network loss cost is:

[0058] ;

[0059] Where: α was This is the network loss cost coefficient. P was,l ( t )for l Typical Quarterly Day t Network loss in the distribution network during specific time periods.

[0060] The formula for calculating the cost of wind and solar power curtailment is:

[0061] ;

[0062] Where: η a,wt , η a,pv These are the penalty coefficients for wind curtailment and solar curtailment, respectively. P wt,for This represents the total power generation capacity of the wind farm. P pv,for This represents the total power generation capacity of the photovoltaic power station.P wt This represents the actual power generation of the wind farm. P pv This represents the actual power generation of the photovoltaic power station.

[0063] The second objective function is to minimize the standard deviation of the operating voltage amplitude at each node of the distribution network. Its mathematical expression is as follows:

[0064] ;

[0065] Where: f 2 represents the voltage standard deviation of the node with the largest voltage amplitude standard deviation in the distribution network; U i,l ( t )for i node l Quarter t Voltage amplitude over a given period; U i,avg for i The average voltage amplitude at the node is calculated using the following formula. U i,avg :

[0066] .

[0067] S112. Determine the constraints, wherein the constraints include node active power balance constraints, node voltage constraints, energy storage constraints, wind, solar and energy storage unit quantity constraints, gas turbine unit ramping constraints, minimum wind and solar power consumption constraints, and actual carbon quota collection constraints.

[0068] The active power balance constraint is expressed as:

[0069] ;

[0070] Where: P bat For the output power of the energy storage power station, P g For the output power of the gas turbine unit, P grid To purchase and sell electricity from the distribution network, P was For network loss power, P load Power consumption of electrical load;

[0071] The node voltage constraint is expressed as:

[0072] ;

[0073] Where: U max , Umin These represent the maximum and minimum voltage amplitudes at the node, respectively.

[0074] The purpose of energy storage constraints is to avoid the damage to the lifespan of energy storage systems caused by overcharging and over-discharging. Therefore, the following constraints are imposed on the power and state of energy storage:

[0075] ;

[0076] Where: P bat,out,max , P bat,in,max These are the maximum discharge and charging power of the energy storage, respectively. S max , S min These represent the maximum and minimum state of charge (SOC) values ​​of the energy storage system, respectively. P bat,out This refers to the discharge power of the energy storage unit. P bat,in Power for charging energy storage units; S ( t ) represents the total state of charge of the energy storage power station during time period t. S 0 represents the initial SOC of the energy storage system. S T The state of charge of the energy storage system is the same at the end of the day, and is equal at the beginning and end. X out and X in These represent the charging and discharging states of the energy storage unit, respectively.

[0077] The quantity constraint of wind, solar and storage turbine units is expressed as:

[0078] ;

[0079] Where: N wt,max , N pv,max , N bat,max These represent the maximum number of wind, solar, and energy storage units, respectively.

[0080] The ramp constraint for a gas turbine unit is expressed as:

[0081] ;

[0082] Where: ΔP h,max This refers to the upper limit of the gas turbine's ramp power. , These represent the output power of the gas turbine unit at time periods t+1 and t, respectively.

[0083] The minimum absorption constraint for wind and solar power is expressed as:

[0084] ;

[0085] Where: , These are the minimum absorption ratios for wind and solar power, respectively.

[0086] The actual carbon allowance payment constraint refers to the practice of the power distribution network combining carbon allowances and CCERs for payment when settling annual carbon allowances with the ecological and environmental authorities. This is represented as:

[0087] ;

[0088] Where: η car , η ccer These are the proportional coefficients for free carbon allowances and free CCERs, used for the collection of carbon allowances and CCERs. p car,grid The distribution network failed to collect the required amount of carbon allowances. p car,grid A positive value indicates that the total CCERs and carbon allowances owned by the power generation group are insufficient to pay the allowances owed. p car,grid A negative value indicates that the power generation group has a surplus after paying off its carbon allowances.

[0089] S120. Use evaluation indicators to perceive the operating status of the multi-objective wind-solar-storage power station planning model; wherein, the evaluation indicators include control indicators, statistical indicators and constraint indicators.

[0090] To comprehensively and objectively assess changes in the operating status of the new power system, and considering the characteristics of uncertainties in new energy sources such as wind power and photovoltaics, as well as load output, an evaluation index for the operating status of the new power system is established from three aspects: control indicators, statistical indicators, and constraint indicators, in order to perceive the system's operating status and assess the operational risks of load users.

[0091] Controllable indicators include node voltage control deviation indicator I1, active power control deviation indicator I2, and active power flexibility indicator I3.

[0092] The node voltage control deviation index characterizes the magnitude of the deviation between the initial node voltage of the power system and the actual steady-state voltage value of each node after the grid connection of new energy sources. The expression is:

[0093] ;

[0094] Where: U X ( t ( ) represents the actual steady-state voltage values ​​at each node after the new energy source is connected to the grid. UC ( t () represents the initial steady-state value of the node voltage in the power system. U N The node's rated voltage, I 1( t )for t The node voltage control deviation index for the time period.

[0095] The system's active power output is the sum of the active power outputs of all power sources in the new power system, that is...

[0096] ;

[0097] Where: P Gn , P windm , P pvk These represent the active power of thermal power unit n, wind farm m, and photovoltaic power station k, respectively. N G , N wind , N pv These represent the number of thermal power units, wind farms, and photovoltaic power stations within the system.

[0098] The active power control deviation index characterizes the magnitude of the deviation between the load control power reduction command value and the actual steady-state value of active power output, and its expression is:

[0099] ;

[0100] Where: I 2( t )for t Active power control deviation index for different time periods; P fc P is the load control power reduction command value issued by the power grid to the system; N This refers to the rated installed capacity of the power supply within the system.

[0101] Based on the ramp rate requirement of the load control power reduction command, a system active power flexibility index can be defined to reflect the ramp characteristics of the system as a whole power source and the risk arising from insufficient ramp resources calculated by the power grid. Therefore, the specific formula for calculating the active power flexibility index I3 is:

[0102] ;

[0103] Where: I 3( t )for t Active power flexibility indicators for different time periods; , These represent the upward and downward adjustment margins for active power, respectively. P fc This refers to the load control power reduction command value issued by the power grid to the system.

[0104] The statistical indicators include the renewable energy output ratio (I4), the renewable energy electricity ratio (I5), and the renewable energy consumption ratio (I6).

[0105] The renewable energy output ratio index I4 represents the real-time output ratio of renewable energy within the new power system, and its calculation formula is as follows:

[0106] ;

[0107] Where: I 4( t )for t The percentage of renewable energy output during a given period; P windm ( t (This refers to the real-time output of wind power.) P pvk ( t (Provides real-time power output for photovoltaics) P f ( t () represents the load power.

[0108] The initial calculation period is denoted as t. start The current time period is denoted as t0, and the system's output power and the output power of the renewable energy power plant are also included. E sys and E re for:

[0109] ;

[0110] The formula for calculating the proportion of renewable energy in electricity is:

[0111] ;

[0112] Where: I 5( t )for t The percentage of renewable energy generated during a given period.

[0113] The theoretical maximum output power of the renewable energy power plants in the system E re,max for:

[0114] ;

[0115] Where: P windm,ed ,P pvk,ed These are the rated power output values ​​for wind farm m and photovoltaic power station k, respectively.

[0116] The formula for calculating the renewable energy consumption index I6 is:

[0117] ;

[0118] Where, I 6( t )for t Renewable energy consumption indicators for different time periods.

[0119] The binding indicators include node voltage over-limit indicator I7 and line load rate indicator I8.

[0120] Voltage amplitude deviation ΔU at each node in the new power system i for:

[0121] ;

[0122] Where: U i Let be the voltage amplitude at node i. U N This is the node's rated voltage.

[0123] The node voltage over-limit indicators are:

[0124] ;

[0125] Where: I 7( t )for t Node voltage over-limit indicators for the specified time period This is the limit for node voltage deviation.

[0126] The formula for calculating the line load factor index I8 is:

[0127] ;

[0128] Where: I 8( t )for t Line load rate indicators for different time periods P L branch road L Transmitted active power; P Lmax branch road L Active power transmission limit; It is the set of all branches in the system.

[0129] The planning scheme based on the wind-solar-storage power station planning model obtains system operation data through the forward-backward power flow algorithm, and then calculates the system evaluation index value to perceive and judge the system's operating status.

[0130] As shown in Table 1, among the controllable indicators: 1) Indicators I1 and I2 respectively characterize the voltage and active power control performance of the system. The smaller the index value, the better the coordinated control performance of the system; 2) I3 is used to evaluate active power flexibility. When the I3 index value is >1, it indicates that the system is at risk of not meeting the ramp rate required by the scheduling instructions.

[0131] The statistical indicators I4-I6 mainly provide information on the absorption of renewable energy within the system and the power output of power plants for load control.

[0132] Among the constraint indicators, when the value of I7 or I8 is greater than 1, it indicates that the system has voltage over-limit and line power flow over-limit problems, respectively, and timely adjustment is required.

[0133] Table 1 System evaluation indicators and operational status classification

[0134]

[0135] S130. Based on the multi-objective planning model of wind, solar and energy storage power stations, a master-slave game load control model is constructed.

[0136] In some embodiments, step S130 includes the following steps:

[0137] S131. Construct a power grid side model.

[0138] Integrated energy retailers on the grid side formulate pricing strategies based on the power output on the supply side and the load demand on the demand side, aiming to optimize their profits. This can be represented as:

[0139] ;

[0140] In the formula: max is the maximum value function, and T is the number of time periods in a day. F ier To optimize the goal of maximizing revenue on the power grid side; The revenue from energy supply to the energy-consuming side during time period t; The energy purchase cost from the energy supply side during time period t; Let t be the power grid interaction cost during time period t. The penalty cost for heating interruption during time period t.

[0141] The above items can be represented as:

[0142] ;

[0143] Where: The duration; and These represent the electrical and thermal load power on the energy consumption side during time period t; and The electrical and thermal output power of the power supply side during time period t; and These represent the prices of electricity sold to the energy consumer and purchased from the energy supplier during time period t, respectively. and These represent the prices of heat energy sold to the energy consumer and purchased from the energy supplier during time period t, respectively. and These represent the grid connection price and the electricity sales price during time period t, respectively. This is the penalty coefficient for heating interruption.

[0144] To avoid direct transactions between the energy supply side and the energy consumption side and the power grid, the purchase (sell) price of integrated energy sellers should be slightly higher (lower) than the market price, which needs to meet the following constraints:

[0145] ;

[0146] Where: , These are the minimum and maximum price limits for thermal energy, respectively.

[0147] S132. Construct the user-side model.

[0148] The user side includes a new energy operator model on the energy supply side and an electric and heat load aggregator model on the energy consumption side. Based on the electricity and heat prices given by the grid-side model, it optimizes the output of new energy units and gas turbines, as well as the power of electric loads that can be shifted and heat loads that can be reduced.

[0149] For the energy supply-side renewable energy operator model, optimizing the output of wind turbines, photovoltaic power plants, internal combustion generators, and gas boilers in the system to maximize revenue is represented as:

[0150] ;

[0151] Where: F xin To achieve the goal of maximizing the profits of new energy operators on the user side; Fuel cost, determined based on the variable operating efficiency characteristics of internal combustion generators and boilers, can be expressed as follows:

[0152] ;

[0153] Where: and These represent the output electrical power of the internal combustion generator and the output thermal power of the boiler during time period t; a e ,b e ,c e a is the cost coefficient for internal combustion generators; h ,b h ,c h This is the cost coefficient for the boiler.

[0154] The electrical power and thermal power output of the new energy system during time period t are respectively:

[0155] ;

[0156] Where: , The output power of wind power and photovoltaic power during time period t are respectively. They are greatly affected by the environment and have significant fluctuations and uncertainties. To avoid wind and solar curtailment, it is assumed that all wind and solar power generation can be sold. The output thermal power of the waste heat recovery device during time period t is denoted as t.

[0157] In addition, the output of the internal combustion generator and boiler during time period t must meet the following requirements:

[0158] ;

[0159] Where: and These are the rated capacities of the internal combustion generator and the boiler, respectively.

[0160] For the energy-consuming side's electrothermal load aggregation quotient model, optimizing the system's shiftable electrical load and reduceable thermal load power to achieve the goal of maximizing consumer surplus, i.e., the difference between the user's utility function and energy cost, is expressed as:

[0161] ;

[0162] Where: F user To achieve the goal of maximizing consumer surplus on the user side; Let be the user's utility function, representing the user's satisfaction with purchasing electricity and heat. It is typically non-decreasing and convex, and is represented by a quadratic form.

[0163] ;

[0164] Where: v e , , v h , These represent preference coefficients for using electrical energy and thermal energy, respectively, which can reflect users' energy demand preferences and influence the magnitude of load control.

[0165] User electrical load includes fixed electrical load and movable electrical load, which can be represented as:

[0166] ;

[0167] In the formula For fixed loads during time period t, high reliability requirements are needed; the electricity consumption time is fixed, and it is used for normal production and daily life. This indicates a load that can be shifted, allowing users to adjust their power consumption and time based on electricity pricing information, but the following constraints must be met:

[0168] ;

[0169] Where: This represents the upper limit of the transferable load during time period t; W sel This represents the total amount of load that can be moved within T time periods, meaning that the total amount of load that can be moved must remain unchanged before and after load control.

[0170] Meanwhile, the heat load also consists of two parts: fixed heat load and reducible heat load, as shown below:

[0171] ;

[0172] Where: The fixed heat load for time period t refers to the user's minimum requirement for heating; while This indicates that the heat load can be reduced, and a certain percentage can be reduced based on user comfort and energy supply adequacy, subject to the following constraints:

[0173] ;

[0174] Where: The upper limit of heat load reduction for time period t.

[0175] S133. Construct a master-slave game model between the power grid and users.

[0176] like Figure 2 As shown, this master-slave game model includes an upper-level model and a lower-level model.

[0177] Upper-level model: Using electricity and heat prices as decision variables, it sets purchase and sale prices for electricity and heat based on supply and demand, electricity consumption, and market information to maximize its own profits.

[0178] The lower-level model uses wind power, photovoltaic power, gas turbine power, and load participation power as decision variables. On the energy supply side, new energy operators determine the optimal output based on price signals, while on the energy demand side, electricity and heat load aggregators optimize load demand based on upper-level prices.

[0179] In the master-slave game model between the power grid and users, the lower-level renewable energy operators and electricity and heat load aggregators will make decisions based on the bids of the upper-level operators. Simultaneously, their optimization strategies will also influence the bids of the integrated energy seller, thus realizing a hierarchical dynamic game in energy trading. Therefore, by treating the integrated energy seller as the leader and the renewable energy operators and load aggregators as different followers, a master-slave game model can be established, which can be represented as:

[0180] ;

[0181] The master-slave game model G includes three elements: participants, strategies, and payoffs, specifically represented as follows:

[0182] 1) Participants: The grid-side model, the renewable energy operator model on the energy supply side, and the load aggregator model on the energy consumption side are the three participants in the master-slave game model. The set of participants is represented as follows: N ={ier, xin, user}, where ier, xin, and user represent the master-slave game model of the grid-side model, the new energy operator model on the energy supply side, and the load aggregator model on the energy consumption side, respectively.

[0183] 2) Strategy: The strategy of the grid-side model is the 24-hour purchase and sale price of electricity and heat, which can be represented in vector form as follows: The strategy of the renewable energy operator model on the energy supply side is the output power in each time period, represented as a vector. The strategy of the load aggregator model on the energy side is the power of the electrical load that can be shifted and the heat load that can be reduced in each time period, represented as a vector. .

[0184] 3) Profits: The profits of each participant are the objective functions of the grid side and the user side, which are calculated using the relevant objective function formulas given above.

[0185] S140. Under the multi-objective planning model of wind, solar and energy storage power stations, solve the load control model of master-slave game under different operating states, and output the load control scheme under different operating states.

[0186] In competitive electricity markets, there are numerous participants with opaque information, necessitating individual optimization. Therefore, this embodiment employs a distributed equilibrium solution method combining genetic algorithm and quadratic programming (GA-QP). The specific solution process for the master-slave game model under normal operating conditions is as follows: Figure 3 As shown, it includes the following steps:

[0187] S301, Input data and parameters.

[0188] S302. Initialize the electricity price and heat price populations in the grid-side model.

[0189] S303, the new energy operator model, and the load aggregator model are all subject to secondary planning based on pricing.

[0190] S304. Calculate the revenue of integrated energy sellers based on supply and demand.

[0191] S305, selection, crossover, mutation.

[0192] S306. Determine whether load balance has been achieved. If not, return to step S303. If yes, output the result of the optimal load control scheme.

[0193] The grid-side model (integrated energy distributor), as the leader, faces a large-scale nonlinear optimization problem in its electricity and heat price decisions. Genetic algorithms can improve the optimization capability and reduce the complexity of the solution process. Since the models established by the supply and consumption sides, as followers, have quadratic function objectives, quadratic programming can improve the solution speed and accuracy. Combining quadratic programming with genetic algorithms, during the iterative solution process, the supply and consumption sides receive price signals from the integrated energy distributor and feed back their respective power signals, effectively preventing information leakage and protecting the privacy of all parties. When all participants obtain the same optimal strategy in two consecutive iterations, the strategy combination is considered to have converged to an equilibrium point. At this point, no participant can unilaterally change its strategy to gain more profit.

[0194] The specific solution process for the master-slave game model in extreme scenarios is as follows: Figure 4As shown. Extreme weather is characterized by low probability but high impact. With increasingly accurate weather forecasts, the prediction of extreme events is becoming more precise. By sensing the operational status of the new power system, system operational status information can be provided, and the operational risks to load users can be assessed. When an extreme event is highly likely to occur, the system status will be reflected intuitively and effectively, thus effectively determining the type of extreme weather. Load control enters the extreme scenario mode. Integrated energy sellers will add incentive-based response prices on top of the original electricity and heat prices to reduce load levels as much as possible and encourage load participation in extreme scenario control. New energy operators generate electricity based on specific extreme weather conditions to ensure normal power supply or complete sales of generated electricity. Users will receive subsidies. Load aggregators will determine whether the subsidy level meets their expectations, thus obtaining the load participation control amount under the influence of economic factors. The energy supply side and the energy consumption side feed back their respective power control signals and calculate the integrated energy seller's revenue under the extreme scenario mode based on the incentive price. Through the selection, crossover, and mutation process of a genetic algorithm, the solution is iterated to finally determine whether an equilibrium state has been reached and output the load control scheme for the extreme scenario.

[0195] Figure 5 This is a schematic diagram of the structure of a load control device provided in an embodiment of this application. An embodiment of this application also provides a load control device. Figure 5 As shown, the load control device includes:

[0196] The planning model construction module 501 is configured to construct a multi-objective planning model for wind, solar and energy storage power stations with the objectives of distribution network economic cost and system voltage stability.

[0197] The operation status perception module 502 is configured to perceive the operation status of the multi-objective wind-solar-storage power station planning model using evaluation indicators; wherein, the evaluation indicators include control indicators, statistical indicators and constraint indicators;

[0198] The controllable indicators include node voltage control deviation, active power control deviation, and active power flexibility. The node voltage control deviation and active power control deviation indicators respectively characterize the voltage and active power control performance of the multi-objective wind-solar-storage power station planning model. Smaller values ​​for these indicators indicate better coordinated control performance. When the active power flexibility indicator value is greater than 1, it indicates that the multi-objective wind-solar-storage power station planning model may not meet the required ramp rate for dispatch instructions.

[0199] The statistical indicators include the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio; wherein, the renewable energy output ratio, renewable energy power ratio, and renewable energy consumption ratio provide information on the renewable energy consumption and power generation of the wind, solar, and energy storage power station within the multi-objective planning model for load control.

[0200] The binding indicators include node voltage limit exceedance indicators and line load factor indicators. When the node voltage limit exceedance indicator is greater than 1, it indicates that there is a voltage limit exceedance problem in the multi-objective wind-solar-storage power station planning model. When the line load factor indicator is greater than 1, it indicates that there is a line power flow limit exceedance problem in the multi-objective wind-solar-storage power station planning model.

[0201] The control model construction module 503 is configured to construct a master-slave game load control model based on the multi-objective wind-solar-storage power station planning model.

[0202] The control model solving module 504 is configured to solve the master-slave game load control model under different operating states of the wind, solar and energy storage power station planning model under the multi-objective conditions, and output the load control scheme under different operating states.

[0203] The load control device provided in this application embodiment can be used to execute the technical solution of the load control method in the above embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0204] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device may include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can communicate; for example, the processor 61 and the memory 62 communicate via a communication bus 63.

[0205] Processor 61 executes computer execution instructions stored in memory 62, causing processor 61 to perform the scheme in the above embodiments. Processor 61 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0206] The communication bus 63 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. System buses can be divided into address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0207] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0208] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the load control method described in the above embodiments.

[0209] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the load control method in the above embodiments.

[0210] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0211] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0212] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0213] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0214] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0215] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0216] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0217] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0218] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0219] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A load control method, characterized in that: include: Taking the economic cost of distribution network and system voltage stability as the objectives, a multi-objective planning model for wind, solar and energy storage power stations is constructed. Using evaluation indicators to perceive the operating status of the wind, solar and energy storage power station planning model under the multi-objectives; wherein the evaluation indicators include control indicators, statistical indicators and constraint indicators; Based on the multi-objective wind-solar-storage power station planning model, a master-slave game load control model is constructed; When the wind, solar and energy storage power station planning model under the multi-objective is in different operating states, solving the master-slave game load control model, and outputting the load control schemes under different operating states; The controllable indicators include a node voltage control deviation indicator, an active power control deviation indicator and an active flexibility indicator; wherein the node voltage control deviation indicator and the active power control deviation indicator respectively characterize the voltage and active control performance of the wind-solar-storage power station planning model under multiple objectives, and the smaller the indicator values ​​of the node voltage control deviation indicator and the active power control deviation indicator, the better the coordinated control performance; when the indicator value of the active flexibility indicator is greater than 1, it indicates that the wind-solar-storage power station planning model under multiple objectives has the risk of not meeting the climbing rate required by the dispatching instruction; The statistical indicators include renewable energy output ratio index, renewable energy electricity ratio index and renewable energy consumption index; wherein, the renewable energy output ratio index, renewable energy electricity ratio index and renewable energy consumption index provide load control with information related to the consumption of renewable energy and site power in the wind, solar and storage power station planning model under multiple objectives; The constraint indicators include a node voltage over-limit indicator and a line load rate indicator; when the node voltage over-limit indicator is greater than 1, it indicates that the multi-objective wind, solar and storage power station planning model has a voltage over-limit problem; when the line load rate indicator is greater than 1, it indicates that the multi-objective wind, solar and storage power station planning model has a line flow over-limit problem.

2. The method according to claim 1, characterized in that: Taking the economic cost of distribution network and system voltage stability as the objectives, a multi-objective planning model of wind, solar and energy storage power station is constructed, including: Constructing an objective function; wherein the objective function includes a first objective function and a second objective function; The first objective function aims to minimize the annual operating cost of the distribution network, and the expression is: ； Where: min is the minimum function, f 1 is the annual operating cost of the distribution network; C ele,mar Cost of participating in the electricity market; C car,mar Costs of participating in carbon markets; C inv The average annual investment and installation cost of wind, solar and storage; C was is the network loss cost; C a Cost of curtailing wind and solar power; The cost of participating in the electricity market is calculated using the following formula: ； Where: α ( t )for t Time-of-use electricity prices in the electricity market; P grid,l ( t ), P load,l ( t ), P was,l ( t ), P wt,l ( t ), P pv,l ( t ), P bat,l ( t )and P g,l ( t ) are respectively l Typical day of the quarter t The power purchase and sale, power load, network loss, wind power consumption, photovoltaic power consumption, energy storage and gas-fired unit power of the distribution network during the time period; L is the number of quarters in a year; T l for l The number of days in a quarter; T is the number of time periods in a day; The cost of participating in the carbon market is calculated using the following formula: ； Where: λ car , λ ccer They are carbon price and CCER price respectively; p car Annual carbon quotas cleared for the distribution network; p car,fre For free carbon emission quotas; p ccer,off CCER to offset carbon quota; p ccer CCER for wind and solar power consumption; The average annual investment and installation cost of wind, solar and energy storage is calculated by the following formula: ； Where: C wt,inv , C pv,inv , C bat,inv The annual average investment and installation costs of wind, solar and storage are calculated using the following formula: ； Where: P wt,cap , P pv,cap are the installed capacity of wind and solar power respectively; e wt , e pv , e bat The unit cost of installed capacity of wind, solar and storage respectively; r 1. r 2. r 3 are the depreciation rates of wind, solar and storage respectively; m 1. m 2. m 3 are the service life of wind, solar and storage respectively; N wt is the number of wind turbines in the wind power station; N pv is the number of PV arrays in the PV power station; N bat is the number of energy storage units; E bat is the installed capacity of the energy storage system; The network loss cost is calculated by the following formula: ； Where: α was is the network loss cost coefficient, P was,l ( t )for l Typical day of the quarter t Network loss of distribution network during the period; The cost of curtailing wind and solar power is calculated using the following formula: ； Where: η a,wt , η a,pv are the penalty coefficients for wind and solar abandonment, P wt,for is the total power generation of the wind farm, P pv,for is the total power generation of the photovoltaic power station, P wt is the actual power generation of the wind farm, P pv is the actual power generation of the photovoltaic power station; The second objective function aims to minimize the standard deviation of the operating voltage amplitude of each node in the distribution network, and the expression is: ； Where: f 2 is the voltage standard deviation of the node with the largest voltage amplitude standard deviation in the distribution network; U i,l ( t )for i node l Quarter t Voltage amplitude during the time period; U i,avg for i The average voltage amplitude of the node is calculated by the following formula U i,avg : ； Determine the constraints; wherein the constraints include node active power balance constraints, node voltage constraints, energy storage constraints, wind, solar and energy storage unit quantity constraints, gas turbine unit ramp constraints, wind and solar minimum consumption constraints, and carbon quota actual clearance constraints; The active power balance constraint is expressed as: ； Where: P bat Output power for energy storage power station, P g is the output power of the gas unit, P grid For the purchase and sale of electricity from the distribution network, P was is the network loss power, P load is the power of the electrical load; The node voltage constraint is expressed as: ； Where: U max , U min are the maximum and minimum voltage amplitudes of the nodes respectively; The energy storage constraint is expressed as: ； Where: P bat,out,max , P bat,in,max They are the maximum discharge and charge power of energy storage respectively; S max , S min are the maximum and minimum state of charge of the energy storage system respectively; P bat,out is the discharge power of the energy storage unit, P bat,in Charging power for energy storage unit; S ( t ) is the total state of charge of the energy storage power station during period t, S 0 is the initial charge state of the energy storage system, S T The state of charge of the energy storage system at the end of the day is the same at the beginning and the end; X out and X in They are respectively the charging state and discharging state of the energy storage unit; The wind, solar and storage unit quantity constraint is expressed as: ； Where: N wt,max , N pv,max , N bat,max are the maximum number of wind, solar and storage units respectively; The gas turbine unit ramp constraint is expressed as: ； Where: ΔP h,max is the upper limit of the gas turbine climbing power; , are the output power of the gas generator set at time periods t+1 and t respectively; The minimum wind and solar power consumption constraint is expressed as: ； Where: , are the minimum absorption ratios of wind and light, respectively; The actual settlement constraint of the carbon quota is expressed as: ； Where: η car , η ccer They are the ratio coefficients of free carbon quota and free CCER, which are used to clear carbon quota and CCER; p car,grid The distribution network has not fully paid the carbon quota. p car,grid A positive value indicates that the total CCER and carbon quota owned by the power generation group is not enough to pay the payable quota. p car,grid A negative value indicates that the power generation group has a surplus after paying its carbon quota.

3. The method according to claim 1, characterized in that: Based on the multi-objective wind-solar-storage power station planning model, a master-slave game load control model is constructed, including: Construct a grid-side model; wherein the grid-side model takes achieving maximum benefit as the optimization goal, which is expressed as: ； Where: max is the maximum value function, T is the number of time periods in a day, F ier To achieve the maximum benefit optimization goal for the grid side; is the energy supply income to the energy user during period t; is the energy purchase cost from the energy supply side during period t; is the grid interaction cost during period t, is the penalty cost of heating interruption during period t; , , , Respectively expressed as: ； Where: is the length of time; and They are the electric and thermal load powers on the energy consumption side during period t respectively; and is the electrical and thermal output power of the energy supply side during period t; and are the prices of electricity sold to the energy user and purchased from the energy supplier during period t; and are the prices of heat energy sold to the energy user and purchased from the energy supplier during period t; and are the grid-connected and electricity-selling prices in period t respectively; is the penalty coefficient for heat interruption; The grid-side model satisfies the following constraints: ； Where: , They are the minimum and maximum price limits for heat energy; Constructing a user-side model, wherein the user-side model includes a new energy operator model on the energy supply side and an electric and thermal load aggregator model on the energy consumption side; The new energy operator model on the energy supply side is expressed as: ； Where: F xin To achieve the goal of maximizing the profits of new energy operators on the user side; For fuel costs; The relationship between fuel cost and output power is expressed as: ； Where: and are the output electrical power of the internal combustion generator and the output thermal power of the boiler during period t; a e ,b e ,c e is the cost coefficient of the internal combustion generator; a h ,b h ,c h is the cost coefficient of the boiler; The output electric power and thermal power in the new energy system during period t are: ； Where: , are the output power of wind power and photovoltaic power in period t respectively; is the output thermal power of the waste heat recovery device during period t; The output of the internal combustion generator and boiler during period t satisfies: ； Where: and are the rated capacities of the internal combustion generator and boiler respectively; The electric and thermal load aggregation model on the energy consumption side is expressed as: ； Where: F user To achieve the goal of maximizing consumer surplus on the user side; is the user's utility function, which indicates the user's satisfaction with the purchase of electricity and heat energy. It is expressed in quadratic form. : ； Where: v e , , v h , They represent the preference coefficients for the use of electrical energy and thermal energy, respectively, reflecting the user's preference for energy demand and affecting the size of load control; The user's electric load includes fixed electric load and movable electric load, which can be expressed as: ； In the formula is the fixed load during period t; represents a translatable load, satisfying the following constraints: ； Where: is the upper limit of the load that can be translated during period t; W sel is the total amount of load that can be translated in T time periods; The heat load also includes fixed heat load and reducible heat load, which can be expressed as: ； Where: is the fixed heat load during period t; Indicates that the heat load can be reduced, satisfying the following constraints: ； Where: is the upper limit of heat load reduction that can be achieved during period t; Construct a master-slave game model between the power grid and the user; wherein the master-slave game model between the power grid and the user includes an upper model and a lower model, wherein the upper model uses electricity price and heat price as decision variables, and formulates purchase and sale electricity price and heat price according to the supply and demand relationship, electricity consumption and market information to maximize its own benefits; the lower model uses wind power, photovoltaic power, gas turbine and load participation power as decision variables, the new energy operator model on the energy supply side determines the optimal output according to the price signal, and the electric and thermal load aggregator model on the energy consumption side optimizes the load demand according to the upper price; The master-slave game model is expressed as: ； Where: G represents the master-slave game model, N represents the participants of the master-slave game model, including the grid side model, the new energy operator model on the energy supply side, and the electric and thermal load aggregator model on the energy consumption side; , , They respectively represent the strategies of the grid-side model, the strategies of the new energy operator model on the energy supply side, and the strategies of the electric and thermal load aggregator model on the energy consumption side. The strategy of the grid-side model is the 24-hour purchased and sold electricity price and heat price, the strategy of the new energy operator model on the energy supply side is the output power in each time period, and the strategy of the electric and thermal load aggregator model is the power that can shift the electric load and reduce the thermal load in each time period.

4. The method according to claim 3, characterized in that When the wind, solar and energy storage power station planning model under the multi-objective is in normal operation, the master-slave game load control model is solved by a distributed equilibrium solution method of genetic algorithm combined with quadratic programming to obtain the optimal load control solution.

5. The method according to claim 3, characterized in that: When the wind, solar and energy storage power station planning model under the multi-objective is in an abnormal operating state, the energy supply side and the energy consumption side feed back the power signals involved in the control, and calculate the grid side benefits under the abnormal operating state in combination with the incentive price. The solution is iterated through the selection, crossover and mutation process of the genetic algorithm. When the equilibrium state is reached, the load control plan for the extreme scenario is output.

6. A load control device, characterized in that: include: The planning model building module is configured to build a multi-objective wind, solar and storage power station planning model based on the economic cost of the distribution network and the system voltage stability; An operation status perception module is configured to perceive the operation status of the wind-solar-storage power station planning model under the multi-objective by using evaluation indicators; wherein the evaluation indicators include control indicators, statistical indicators and constraint indicators; The controllable indicators include a node voltage control deviation indicator, an active power control deviation indicator and an active flexibility indicator; wherein the node voltage control deviation indicator and the active power control deviation indicator respectively characterize the voltage and active control performance of the wind-solar-storage power station planning model under multiple objectives, and the smaller the indicator values ​​of the node voltage control deviation indicator and the active power control deviation indicator, the better the coordinated control performance; when the indicator value of the active flexibility indicator is greater than 1, it indicates that the wind-solar-storage power station planning model under multiple objectives has the risk of not meeting the climbing rate required by the dispatching instruction; The statistical indicators include renewable energy output ratio index, renewable energy electricity ratio index and renewable energy consumption index; wherein, the renewable energy output ratio index, renewable energy electricity ratio index and renewable energy consumption index provide load control with information related to the consumption of renewable energy and site power in the wind, solar and storage power station planning model under multiple objectives; The constraint indicators include node voltage over-limit indicators and line load rate indicators; when the node voltage over-limit indicator is greater than 1, it indicates that the multi-objective wind-solar-storage power station planning model has a voltage over-limit problem; when the line load rate indicator is greater than 1, it indicates that the multi-objective wind-solar-storage power station planning model has a line flow over-limit problem; A control model building module is configured to build a master-slave game load control model based on the multi-objective wind-solar-storage power station planning model; The control model solving module is configured to solve the master-slave game load control model when the wind, solar and energy storage power station planning model is in different operating states under the multi-objective, and output the load control schemes under different operating states.

7. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

9. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when being executed by a processor.

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