Provincial and regional power grid day-ahead orderly electrical load control method considering power grid operation risk

Abstract

The invention discloses a provincial and regional power grid day-ahead orderly electrical load control method considering power grid operation risks. The method comprises the following steps: constructing a provincial and regional load control model; through real-time calculation and monitoring of regional loads, initial constraint conditions are constructed for day-ahead loads; establishing a provincial and regional load cooperative control architecture, configuring the capacity of an energy storage system on the basis of considering the real-time total load and controllable capacity of each region, and decomposing and coordinating the total cut-off load capacity to each region by a provincial dispatcher; after safety verification, automatic interaction of control instructions is completed, smooth loads are optimized, a two-stage particle swarm optimization algorithm is adopted, the loads tend to be stable, and batch load cooperative closed-loop rapid control is finally achieved between provinces and regions; and in combination with the optimized constraint condition and the scheduling strategy, considering the control success rate and the evaluation of the regulation and control capability, so that the power supply and demand are balanced. According to the invention, the orderly power utilization adjustment of the adjustable load is promoted, and the overall safety and reliability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of orderly power consumption, and in particular relates to a method for controlling the orderly power consumption load of a provincial or local power grid in the day ahead, taking into account the power grid operation risk. Background Art

[0002] In recent years, with the development of the economy and the continuous improvement of people's living standards, the balance of electricity supply and demand can be achieved through reasonable regulation of power load, the efficiency and stability of power grid operation can be improved, and the reliability of power supply can be guaranteed. People's demand for load control and optimization management is increasing, which has become an important research direction in my country.

[0003] At present, there have been many studies on the optimization of load control of provincial and local power grids. The existing load control methods have ignored the impact on the stability calculation of their systems. In order to achieve sustainable development, the number of electric vehicles in the community has increased, and the charging period control strategy is adopted, which is too mechanical and lacks flexibility. It has an important impact on the stability and reliability of the system. Taking into account the optimization and evaluation of response scheduling, load forecasting, and evaluation will not only lead to reduced efficiency, but also affect the operation of the entire system. At the same time, in traditional scheduling schemes, there is often a shortage of power supply during peak hours, which affects the overall stability of the system. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method for controlling the orderly power load of provincial and local power grids in the day ahead, taking into account the operation risks of the power grid, aiming to overcome the limitations of the prior art, improve the stability of the system, and control the load.

[0005] The present invention adopts the following technical solution:

[0006] The method for controlling the day-ahead load of provincial and local power grids considering operation risks includes the following steps:

[0007] Construct inter-provincial load control model based on wide-area information interaction;

[0008] Through real-time calculation and monitoring of regional loads, initial constraints are established for day-ahead loads;

[0009] Establish a provincial and local load coordination control framework. Based on the total real-time load and controllable capacity of each region, configure the capacity of the energy storage system. The provincial dispatch will decompose and coordinate the total load removal to each region.

[0010] After safety verification, the control instructions are automatically interacted, the load is optimized and smoothed, and the load is stabilized by using a two-stage particle swarm optimization algorithm, ultimately achieving rapid closed-loop control of batch loads between provinces and regions;

[0011] Combined with the optimized constraints, the evaluation of control capabilities balances power supply and demand and improves the operation mode.

[0012] The load control model of provincial and local coordination is further constructed, and its expression is as follows:

[0013]

[0014] In the formula: In the formula: M is the number of areas to be monitored; P j is the power of the jth region; P ref is the reference power; γ j is the control coefficient.

[0015] The initial constraint condition is further constructed based on the real-time controllable dimension of the region, and its expression is as follows:

[0016]

[0017] Where P i is the power restriction capacity shared by the ith local dispatching station within the power restriction implementation range; P1 is the maximum removal target of the local dispatching station according to the general accident level of the power safety regulations; P t To control the total capacity; P is the real-time total controllable capacity within the power restriction implementation range; ctrl,i It is the real-time controllable capacity of the ith local dispatching station within the scope of power restriction implementation.

[0018] Further considering the relationship between the total real-time load and controllable capacity of each region, the specific formula is as follows:

[0019]

[0020] Where T is the current time, The sum of the maximum power loads during the period of 08:00-11:00 on the previous working day within the scope of power restriction implementation; P max1,i The real-time power load capacity of the ith local dispatching station during the period 08:00-11:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 13:00 to 17:00 of the previous working day within the scope of power restriction implementation; P max2,i The real-time power load capacity of the ith local dispatching station during the period of 13:00-17:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 19:00 to 22:00 on the previous working day within the scope of power restriction implementation; P max3,i It is the real-time power load capacity of the ith local dispatching station during the period of 19:00-22:00 on the previous working day within the scope of power restriction implementation. P is the real-time total power load capacity within the power restriction implementation range; real,iIt is the real-time power load capacity of the ith local dispatching station within the scope of power restriction implementation.

[0021] Further coordination and optimization of the load control model can make the load tend to be stable. The specific expression is:

[0022]

[0023] P t =P load,t -P PV,t -P wind,t +P storage,t

[0024]

[0025] Where P t is the active load of the system at time t; P load,t , P PV,t , P wind,t , P storage,t are the active powers of the load, photovoltaic power, wind power and energy storage at the time t of the system. The active power of energy storage is defined as charging is positive and discharging is negative; P average is the average active load within the duration T.

[0026] Further optimize the smoothing load, the specific expression is:

[0027]

[0028] Where P t is the active load of the system at time t; P t-1 is the active load of the system at time t; T is the duration.

[0029] Further configuration of the energy storage system capacity and energy storage power constraints are expressed as follows:

[0030] -P max ≤P storage ≤P max

[0031] Where P max is the maximum charging and discharging power of energy storage;

[0032] The energy storage operation constraints are expressed as follows:

[0033]

[0034] In the formula, t a , t b That is the upper and lower limits of the charge and discharge time; P ch , P dis are the power of charging and discharging in the time period respectively; ε is the charge and discharge balance indicator;

[0035] The energy storage flow balance constraint and voltage constraint conditions are as follows:

[0036]

[0037] V min ≤V it ≤V max

[0038] Where P i , Q i Represent the active and reactive values ​​of node i respectively; e and f are the real and imaginary components of the node voltage, G ij and B ij are the real and imaginary components of the branch admittance matrix; V min and V max is the system voltage limit.

[0039] To further stabilize the load, the specific expression is as follows:

[0040]

[0041] Where P1, P2, ... P T is the active power at the corresponding moment; P average is the average active load within the duration T.

[0042] The control ability is further evaluated, and the calculation formula is:

[0043]

[0044]

[0045] Where V 413 To control the delay rate; ∑c 413 (i) is the number of times the load control delay exceeds the allowable range; N is the total number of load controls; Δt is the load control delay; and t is the allowable load control delay.

[0046] The two-stage particle swarm optimization algorithm (PSO) is further used to solve the load control model, and the process of generating the Pareto front solution set is as follows:

[0047] 1) Generate a particle swarm, each particle represents a load control strategy, randomly initialize the particle speed and position, and calculate the objective function value of each particle, including grid operation risk and load stability.

[0048] 2) Perform non-dominated sorting according to the objective function value of the particles to obtain solution sets of different levels to screen out a better load control strategy.

[0049] 3) Calculate the crowding distance for particles at each level to measure the diversity of solutions and ensure that the optimal solution is evenly distributed among the objectives.

[0050] 4) Sort the particle sets at each level according to the crowding distance, and select the first N individuals as parents to ensure the diversity of solutions. N is the preset population size.

[0051] 5) According to the update rule of particle swarm optimization, the particle speed and position are adjusted through inertia weight and learning factor to converge to the individual optimal and global optimal directions.

[0052] 6) Generate descendant particle swarms through crossover and mutation operations to expand the search scope and improve the diversity of solutions.

[0053] 7) Merge the parent and child generations into a new population, and perform non-dominated sorting and crowding calculation again to ensure continuous optimization of the solution set.

[0054] 8) Repeat steps 2) to 7) until a preset stopping condition is reached, and finally a Pareto optimal solution set that meets the requirements is output. The preset stopping condition refers to reaching the maximum number of iterations or the convergence of the objective function.

[0055] The beneficial effects of the present invention are as follows: compared with the prior art, the method of the present invention takes into account the optimization of the load control of the provincial and local power grids on the day before, and fully considers the influence of the uncertainty factors of the load control in different time periods on the system calculation; the control effect of the load is considered by establishing the evaluation capability, and the uncertainty and instability are reduced; the load is optimized by the energy storage system, and the constraints in the system model are optimized to combat uncertainty and reduce risks; at the same time, a scheduling strategy is formulated, and compared with the traditional scheduling strategy, a more adaptable and globally convergent two-stage particle swarm optimization algorithm (PSO) is used to solve the model, so that the power supply is smoother, and the safe and stable operation of the power grid is guaranteed; the economy and safety and reliability of the overall system are improved, and the load control of the provincial and local power grid is further promoted to develop in the direction of intelligence, efficiency and sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is an overall flow chart of the day-ahead orderly power load control strategy considering the power grid operation risk of the present invention;

[0057] Figure 2 It is a schematic diagram of the overall framework of the collaborative closed-loop control of the present invention;

[0058] Figure 3 It is a schematic diagram of the configuration of the stable energy storage capacity of the maintenance system of the present invention;

[0059] Figure 4A schematic diagram of a two-stage PSO algorithm flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0061] In this example, according to Figure 1 , the campus power distribution system is selected, which is a typical radial distribution, including photovoltaic system, electric vehicle and energy storage system. The power conversion equipment is connected to 300kWp photovoltaic, 100 electric vehicles and the energy storage system to be configured. By acquiring and analyzing historical data, the more typical conventional load curve and photovoltaic output curve are selected as the initial input of the system, and the attendance habits of faculty and staff are obtained according to the questionnaire, and the electric vehicle load curve is predicted. In the optimization algorithm, c1 and c2 are both 1.49, ω start ,ω end They are set to 0.9 and 0.4 respectively, and the calculation step is 1kW. In addition, the conversion formula parameters α1 and α2 are set to 0.0709 and 0.0605, respectively.

[0062] This example includes:

[0063] Step 1: Construct a load control model between provinces and regions based on wide-area information interaction

[0064] Taking the comprehensive load online modeling based on wide-area information as an example, this method can use widely distributed real-time information to quickly and accurately model the load of the substation by establishing a high-precision load forecasting model. In practical applications, this method can provide an accurate reference for operation and provide basic technical support for the optimization and upgrading of the power system. Wide-area information interaction can also be applied to the construction of inter-provincial load control models to achieve cross-regional load forecasting and control.

[0065] Step 2: Control the day-ahead load and construct initial constraints through real-time calculation and monitoring of regional loads;

[0066] according to Figure 2 The provincial and local load control information model simultaneously realizes the provincial dispatching's control over the local dispatching's controllable resource information, providing a basic model for the provincial dispatching's monitoring of the local dispatching's controllable resource information and the provincial and local coordinated control linkage. Combining the two calculation dimensions of regional controllable load capacity and power load capacity, the load required by each region is calculated and allocated in a coordinated manner to ensure the control effect and power grid safety.

[0067] The initial constraint conditions for constructing the real-time controllable dimensions of the region are expressed as follows:

[0068]

[0069] Where P i is the power restriction capacity shared by the ith local dispatching station within the power restriction implementation range; P1 is the maximum removal target of the local dispatching station according to the general accident level of the power safety regulations; P t To control the total capacity; is the real-time total controllable capacity within the power restriction implementation range; P ctrl,i It is the real-time controllable capacity of the ith local dispatching station within the scope of power restriction implementation.

[0070] The power load is combined with the maximum power load of the region in the corresponding period of the previous working day to calculate the allocation capacity strategy. The specific formula is as follows:

[0071]

[0072] In the formula, T is the current time, The sum of the maximum power loads during the period of 08:00-11:00 on the previous working day within the scope of power restriction implementation; P max1,i The real-time power load capacity of the ith local dispatching station during the period 08:00-11:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 13:00 to 17:00 of the previous working day within the scope of power restriction implementation; P max2,i The real-time power load capacity of the ith local dispatching station during the period of 13:00-17:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 19:00 to 22:00 on the previous working day within the scope of power restriction implementation; P max3,i It is the real-time power load capacity of the ith local dispatching station during the period of 19:00-22:00 on the previous working day within the scope of power restriction implementation. P is the real-time total power load capacity within the power restriction implementation range; real,i It is the real-time power load capacity of the ith local dispatching station within the scope of power restriction implementation.

[0073] Combining the optimized constraints and the dispatching strategy, the power supply and demand are balanced, the operation mode is improved to establish an energy storage optimization configuration model, the energy storage constraints are constructed, the optimization trend of load characteristics under energy storage power constraints and the optimal configuration change trend of energy storage capacity are obtained, which provides an effective reference for the configuration of the energy storage system.

[0074] Coordinated optimization of load control, specific expression:

[0075]

[0076] P t =P load,t -P PV,t -P wind,t +P storage,t

[0077]

[0078] Where P t is the active load of the system at time t; P load,t , P PV,t , P wind,t , P storage,t are the active powers of the load, photovoltaic power, wind power and energy storage at the time t of the system. The active power of energy storage is defined as charging is positive and discharging is negative; P average is the average active load within the duration T.

[0079] Optimize smooth load, specific expression:

[0080]

[0081] Where P t is the active load of the system at time t; P t-1 is the active load of the system at time t; T is the duration.

[0082] The capacity configuration of the energy storage system and the energy storage power constraint are expressed as follows:

[0083] -P max ≤P storage ≤P max

[0084] Where P max is the maximum charging and discharging power of energy storage;

[0085] The energy storage operation constraints are expressed as follows:

[0086]

[0087] In the formula, t a , t b That is the upper and lower limits of the charge and discharge time; P ch , P dis are the power of charging and discharging in the time period respectively; ε is the charge and discharge balance indicator;

[0088] The energy storage flow balance constraint and voltage constraint conditions are as follows:

[0089]

[0090] V min ≤V it ≤V max

[0091] Where P i , Q iRepresent the active and reactive values ​​of node i respectively; e and f are the real and imaginary components of the node voltage, G ij and B ij are the real and imaginary components of the branch admittance matrix; V min and V max is the system voltage limit.

[0092] Make the load tend to be stable, the specific expression is as follows:

[0093]

[0094] Where P1, P2, ... P T is the active power at the corresponding moment; P average is the average active load within the duration T.

[0095] The calculation formula for the minimum load change rate is:

[0096]

[0097] Where P t is the active load of the system at time t; P t-1 is the active load of the system at moment t; Δt is the change time.

[0098] Step 3: According to Figure 4 , evaluate the load control capability, and use a two-stage particle swarm optimization algorithm (PSO) to solve the load control model to avoid the optimization process falling into the local optimal solution. At the same time, the original particle swarm algorithm is extended to a two-stage algorithm. In the first stage, the particle swarm optimizes the load control strategy, and in the second stage, the particle swarm optimizes the energy storage configuration strategy.

[0099] The load control model is solved using a two-stage particle swarm optimization algorithm (PSO) to generate a Pareto front solution set:

[0100] The first step is to optimize the efficiency and safety of load reduction; the second step is to optimize and integrate the load regulation results. This method can achieve real-time and multi-objective optimization effects under complex power grid conditions. The two-stage particle swarm optimization algorithm (PSO) used in this paper is implemented as follows:

[0101] 1) Initialize the population: Generate a particle swarm, each particle represents a load control strategy, randomly initialize the particle speed and position, and calculate the objective function value of each particle, including grid operation risk and load stability.

[0102] 2) Non-dominated sorting: Non-dominated sorting is performed according to the objective function value of the particles to obtain solution sets at different levels to screen out a better load control strategy.

[0103] 3) Crowding calculation: Calculate the crowding distance for particles at each level to measure the diversity of solutions and ensure that the optimal solution is evenly distributed among the targets.

[0104] 4) Parent selection: Sort the particle set at each level according to the crowding distance, and select the first N individuals as the parents to ensure the diversity of solutions. N is the preset population size.

[0105] 5) Update speed and position: According to the update rule of particle swarm optimization, the particle speed and position are adjusted through inertia weight and learning factor to converge to the individual optimal and global optimal directions.

[0106] 6) Generate offspring: Generate offspring particle swarms through crossover and mutation operations to expand the search scope and improve the diversity of solutions.

[0107] 7) Population merging: Merge the parent and child generations into a new population, and perform non-dominated sorting and crowding calculation again to ensure continuous optimization of the solution set.

[0108] 8) Convergence judgment: Repeat steps 2 to 7 until the preset stopping condition is reached (such as reaching the maximum number of iterations or the objective function converges), and finally output a Pareto optimal solution set that meets the requirements.

[0109] This paper proposes a strategy for controlling the orderly power load of provincial and local power grids in the day ahead, which comprehensively considers the risks of power grid operation, and conducts simulation analysis based on the relationship between energy storage power limit, energy storage capacity change, and load change square. Figure 3 As shown, the following conclusions can be drawn.

[0110] As the energy storage power limit gradually increases, the energy storage capacity demand shows a significant nonlinear growth trend. When the energy storage power is low, the energy storage capacity demand increases rapidly, but when the energy storage power limit reaches a certain level, the capacity growth gradually tends to saturation. This shows that when planning the energy storage system, it is necessary to fully consider the role of energy storage power limit on the optimal configuration of energy storage capacity to balance investment cost and operation efficiency. The simulation results show that the square of the load change shows a downward trend with the increase of energy storage power limit, indicating that the energy storage system plays an important role in suppressing load fluctuations. Especially at low energy storage power limit, the load fluctuation is more severe, but with the increase of energy storage power, the load fluctuation gradually decreases until it stabilizes. This further verifies the importance of energy storage power limit optimization in ensuring the smooth operation of the power grid. The proposed strategy not only effectively improves the stability of power grid operation, but also significantly reduces the risk of power grid operation. Within the reasonable range of energy storage power limit, load fluctuations are fully suppressed, energy storage investment costs are optimized, and the economy and safety of the system are guaranteed. In addition, through the coordinated optimization of energy storage configuration and load control, flexible response to different load scenarios can be achieved, providing a reference for actual engineering. It provides theoretical support for energy storage system planning and grid load regulation. Research shows that reasonable energy storage power limit can not only significantly reduce energy storage capacity demand, but also significantly reduce grid operation risks. Especially in the context of increasing penetration of renewable energy, this strategy has important practical significance.

[0111] The orderly power load control method proposed in the present invention realizes peak load shaving and valley filling and supply-demand balance of the power grid load by coordinating multiple types of adjustable load resources. It can optimize the stability of power grid operation while meeting the power demand of users. This scheme verifies the feasibility and effectiveness of coordinated control of renewable energy and flexible loads, and provides a reference for the low-carbon and efficient operation of the power grid. Through the analysis of the model, the power grid load coordinated control strategy proposed in the present invention reduces the risk level in the event of an accident and effectively maintains the operational stability of the power system. Figure 3 It can be seen that the configuration of energy storage capacity has played a role in optimizing the configuration of the target and the response result of energy storage capacity, and the required energy storage power capacity and energy capacity configuration have been obtained, which has high practical value. The orderly power load control strategy proposed in this patent effectively takes into account the needs of energy storage investment, load fluctuation smoothing and safe operation of the power grid. It has high theoretical value and practical significance, and provides a reliable technical path for promoting the sustainable development of modern power grids.

[0112] The above technical solutions are only used to illustrate the specific implementation process of the present invention, rather than to limit the scope of the present invention. It should be understood by those skilled in the art that the technical solutions described in the embodiments can still be modified or replaced by equivalents, as long as these modifications or replacements do not deviate from the essence and scope of the present invention, they should be included in the protection scope of the present invention.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the embodiments of the present invention have 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 of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the orderly power load of a provincial and local power grid in the day ahead, taking into account the risk of power grid operation, characterized in that: include: Construct inter-provincial load control model based on wide-area information interaction; Through real-time calculation and monitoring of regional loads, initial constraints are established for day-ahead loads; Establish a provincial and local load coordination control framework. Based on the total real-time load and controllable capacity of each region, configure the capacity of the energy storage system. The provincial dispatch will decompose and coordinate the total load removal to each region. After safety verification, the control instructions are automatically interacted, the load is optimized and smoothed, and the load is stabilized by using a two-stage particle swarm optimization algorithm, ultimately achieving rapid closed-loop control of batch loads between provinces and regions; Combined with the optimized constraints, the control capacity is evaluated to balance the power supply and demand and improve the operation mode.

2. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: A load control model for provincial and local coordination is constructed, and its expression is as follows: In the formula: In the formula: M is the number of areas to be monitored; P j is the power of the jth region; P ref is the reference power; γ j is the control coefficient.

3. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: The initial constraint conditions are constructed based on the real-time controllable dimensions of the region, and the expressions are as follows: Where P i is the power restriction capacity shared by the ith local dispatching station within the power restriction implementation range; P1 is the maximum removal target of the local dispatching station according to the general accident level of the power safety regulations; P t To control the total capacity; P is the real-time total controllable capacity within the power restriction implementation range; ctrl,i It is the real-time controllable capacity of the ith local dispatching station within the scope of power restriction implementation.

4. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: Considering the relationship between the total real-time load and controllable capacity of each region, the specific formula is as follows: In the formula, T is the current time, The sum of the maximum power loads during the period of 08:00-11:00 on the previous working day within the scope of power restriction implementation; P max1,i The real-time power load capacity of the ith local dispatching station during the period 08:00-11:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 13:00 to 17:00 of the previous working day within the scope of power restriction implementation; P max2,i The real-time power load capacity of the ith local dispatching station during the period from 13:00 to 17:00 on the previous working day within the scope of power restriction implementation; The sum of the maximum power loads during the period from 19:00 to 22:00 on the previous working day within the scope of power restriction implementation; P max3,i It is the real-time power load capacity of the ith local dispatching station during the period of 19:00-22:00 on the previous working day within the scope of power restriction implementation. P is the real-time total power load capacity within the power restriction implementation range; real,i It is the real-time power load capacity of the ith local dispatching station within the scope of power restriction implementation.

5. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that The coordinated optimization of the load control model makes the load tend to be stable. The specific expression is: P t =P load,t -P PV,t -P wind,t +P storage,t Where P t is the active load of the system at time t; P load,t , P PV,t , P wind,t , P storage,t are the active powers of the load, photovoltaic power, wind power and energy storage at the time t of the system, respectively. The active power of energy storage is defined as charging is positive and discharging is negative; P average is the average active load within the duration T.

6. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: The optimized smoothing load is specifically expressed as: Where P t is the active load of the system at time t; P t-1 is the active load of the system at time t; T is the duration.

7. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: Capacity configuration of the energy storage system; The energy storage power constraint condition is expressed as follows: -P max ≤P storage ≤P max Where P max is the maximum charging and discharging power of energy storage; The energy storage operation constraints are expressed as follows: In the formula, t a , t b That is the upper and lower limits of the charge and discharge time; P ch , P dis are the power of charging and discharging in the time period respectively; ε is the charge and discharge balance indicator; The energy storage flow balance constraint and voltage constraint conditions are as follows: In min ≤V it ≤V max Where P i , Q i Represent the active and reactive values ​​of node i respectively; e and f are the real and imaginary components of the node voltage, G ij and B ij are the real and imaginary components of the branch admittance matrix; V min and V max is the system voltage limit.

8. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: The specific expression for making the load stable is as follows: Where P1, P2, ... P T is the active power at the corresponding moment; P average is the average active load within the duration T.

9. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1 is characterized in that: The control ability evaluation is calculated as follows: Where V 413 To control the delay rate; ∑c 413 (i) is the number of times the load control delay exceeds the allowable range; N is the total number of load controls; Δt is the load control delay; and t is the allowable load control delay.

10. The method for controlling the orderly power load of provincial and local power grids in the day ahead considering the power grid operation risk according to claim 1, characterized in that: The two-stage particle swarm optimization algorithm (PSO) is used to solve the load control model and generate the Pareto front solution set as follows: 1) Generate a particle swarm, each particle represents a load control strategy, randomly initialize the particle speed and position, and calculate the objective function value of each particle, including grid operation risk and load stability. 2) Perform non-dominated sorting according to the objective function value of the particles to obtain solution sets of different levels to screen out a better load control strategy. 3) Calculate the crowding distance for particles at each level to measure the diversity of solutions and ensure that the optimal solution is evenly distributed among the objectives. 4) Sort the particle sets at each level according to the crowding distance, and select the first N individuals as parents to ensure the diversity of solutions. N is the preset population size. 5) According to the update rule of particle swarm optimization, the particle speed and position are adjusted through inertia weight and learning factor to converge to the individual optimal and global optimal directions. 6) Generate descendant particle swarms through crossover and mutation operations to expand the search scope and improve the diversity of solutions. 7) Merge the parent and child generations into a new population, and perform non-dominated sorting and crowding calculation again to ensure continuous optimization of the solution set. 8) Repeat steps 2) to 7) until a preset stopping condition is reached, and finally a Pareto optimal solution set that meets the requirements is output. The preset stopping condition refers to reaching the maximum number of iterations or the convergence of the objective function.

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