A distributed flexible load dispatching control method and system

Abstract

The present invention relates to the field of power dispatching technology, and in particular to a distributed flexible load dispatching control method and system, including dividing distributed flexible loads into various types of flexible loads; obtaining the load regulation potential of various types of flexible loads in different time periods; using a multi-type flexible load response dispatching model to dispatch various types of flexible loads in different time periods according to the load regulation potential and total load power data of various types of flexible loads in different time periods, and obtaining the day-ahead dispatching data; using a particle swarm optimization algorithm and an artificial bee colony algorithm to solve the distributed flexible load multi-objective dispatching optimization model according to the day-ahead dispatching data, and obtaining the optimal dispatching control strategy to dispatch and control the distributed flexible loads. The present invention realizes efficient and flexible dispatching of distributed flexible loads through modeling and intelligent dispatching of multiple types of flexible loads, and significantly improves the stability of the power grid and the ability to absorb new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a distributed flexible load dispatching control method and system. Background Art

[0002] In the process of the transformation of the power system to a new energy structure, distributed flexible loads, as a key factor in improving the new energy absorption capacity, have become a hot topic in research. However, existing technologies still face many challenges in the field of distributed flexible load dispatching and control. The volatility and uncertainty of new energy power generation have put the stability and reliability of the power grid to a severe test. Especially in the case of large-scale access to distributed new energy, problems such as insufficient regulation capacity of the power grid, reduced power quality, and increased risks of distribution network operation have become increasingly prominent. The emergence of these problems is partly due to the fact that the existing power grid dispatching and control methods cannot effectively respond to the large-scale access and flexible dispatching needs of distributed energy.

[0003] Traditional centralized control methods have obvious limitations when dealing with large-scale distributed flexible loads. Centralized control mainly relies on the central controller for optimization calculations. However, with the increase in the number of distributed flexible loads, the burden on the central controller becomes increasingly heavy when facing large-scale distributed flexible loads, and its performance bottleneck gradually emerges. In addition, under the centralized control mode, the information privacy security of distributed flexible loads is difficult to be effectively guaranteed, and distributed flexible loads cannot be plug-and-play, which makes it difficult to expand applications. In contrast, although traditional distributed control methods have alleviated the above problems to a certain extent, traditional distributed control methods are still difficult to meet multi-modal communication conditions and control requirements when facing large-scale distributed flexible loads. Especially in the context of intelligent scheduling, traditional methods have limitations in dealing with large-scale optimization problems, which limits the improvement of the intelligent scheduling and control capabilities of distributed flexible loads.

[0004] In addition, with the continuous access of new energy power generation equipment, the grid balance demand between distributed flexible loads and new energy power generation equipment has put forward higher requirements on the response speed of distributed controllers. In the new power system with source-load interaction, how to quickly and accurately respond to the real-time changes of the power grid has become an urgent problem to be solved. Traditional distributed control methods still need to be improved in this regard. In summary, the existing distributed flexible load scheduling and control methods have prominent problems such as communication bottlenecks, insufficient computing power, and delayed response speed when facing large-scale, multi-form loads and rapid response requirements. Therefore, in order to improve the intelligent scheduling and control capabilities of distributed flexible loads and solve the problem of grid balance, it is urgent to provide a method that can efficiently, flexibly and intelligently schedule distributed flexible loads, which is of great significance to promoting the stable operation of new power systems and improving the new energy absorption capacity. Summary of the invention

[0005] The object of the present invention is to provide a distributed flexible load scheduling control method and system, which realizes efficient and flexible scheduling of distributed flexible loads through modeling and intelligent scheduling of multiple types of flexible loads.

[0006] In order to solve the above technical problems, the present invention provides a distributed flexible load scheduling control method and system.

[0007] In a first aspect, the present invention provides a distributed flexible load dispatching control method, the method comprising the following steps:

[0008] According to the user's electricity consumption characteristics and user response behavior, the distributed flexible load is divided into various types of flexible loads; various types of flexible loads include transferable flexible loads, translational flexible loads and reducible flexible loads;

[0009] Model the response characteristics of various flexible loads to obtain the load regulation potential of various flexible loads in different periods;

[0010] According to the load regulation potential and total load power data of various flexible loads in different periods, a multi-type flexible load response dispatch model is established;

[0011] Based on the power demand of various flexible loads in different periods, a multi-type flexible load response dispatch model is used to dispatch various flexible loads in different periods on a day-ahead basis to obtain day-ahead dispatch data.

[0012] According to the day-ahead dispatch data, the pre-built distributed flexible load multi-objective dispatch optimization model is solved by using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal dispatch control strategy for the distributed flexible load.

[0013] Distributed flexible loads are dispatched and controlled according to the optimal dispatching and control strategy to adjust the operating state of the power system.

[0014] In a second aspect, the present invention provides a distributed flexible load dispatching control system, the system comprising:

[0015] The load classification module is used to classify the distributed flexible load into various types of flexible loads according to the user's power consumption characteristics and user response behavior; the various types of flexible loads include transferable flexible loads, shiftable flexible loads and reducible flexible loads;

[0016] Load analysis module, used to model the response characteristics of various flexible loads and obtain the load regulation potential of various flexible loads in different time periods;

[0017] The response dispatch module is used to establish a multi-type flexible load response dispatch model based on the load regulation potential and total load power data of various flexible loads in different time periods;

[0018] The day-ahead dispatching module is used to dispatch various flexible loads in different time periods based on the power demand of various flexible loads in different time periods by using a multi-type flexible load response dispatching model to obtain day-ahead dispatching data;

[0019] The model solving module is used to solve the pre-built distributed flexible load multi-objective scheduling optimization model based on the day-ahead scheduling data using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal scheduling control strategy for the distributed flexible load;

[0020] The dispatching control module is used to dispatch and control the distributed flexible load according to the optimal dispatching control strategy and adjust the operating state of the power system.

[0021] The present invention provides a distributed flexible load dispatching control method and system, wherein the method divides the distributed flexible load into various types of flexible loads; performs response characteristic modeling on various types of flexible loads to obtain the load regulation potential of various types of flexible loads in different time periods; establishes a multi-type flexible load response dispatching model according to the load regulation potential and total load power data of various types of flexible loads in different time periods; performs day-ahead dispatching on various types of flexible loads in different time periods based on the power demand of various types of flexible loads in different time periods using the multi-type flexible load response dispatching model to obtain day-ahead dispatching data; solves the pre-built distributed flexible load multi-objective dispatching optimization model using a particle swarm optimization algorithm and an artificial bee colony algorithm according to the day-ahead dispatching data to obtain the optimal dispatching control strategy of the distributed flexible load; dispatches and controls the distributed flexible load according to the optimal dispatching control strategy to adjust the operation state of the power system. Compared with the prior art, the method realizes efficient and flexible dispatching of large-scale distributed flexible loads by classifying and responding to dispatching modeling of distributed flexible loads, and solves the optimal dispatching control strategy using a particle swarm optimization algorithm and an artificial bee colony algorithm, thereby improving the regulation capability and operation efficiency of the power system, and at the same time improving the dispatching flexibility and response speed, and can adapt to the communication and control requirements of large-scale distributed flexible loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the flow of a distributed flexible load dispatching control method provided by an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of the output power of a device under normal operating conditions provided by an embodiment of the present invention;

[0024] Figure 3It is a schematic diagram of equipment output power under distributed flexible load dispatching control provided by an embodiment of the present invention;

[0025] Figure 4 It is a block diagram of a distributed flexible load dispatching control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0027] refer to Figure 1 , the embodiment of the present invention provides a distributed flexible load dispatching control method, such as Figure 1 As shown, the method comprises the following steps:

[0028] S1. Distributed flexible loads are divided into various types of flexible loads according to the user's electricity consumption characteristics and user response behavior; various types of flexible loads include transferable flexible loads, shiftable flexible loads and reducible flexible loads.

[0029] This embodiment classifies distributed flexible loads into three types of flexible loads: transferable flexible loads, shiftable flexible loads, and reducible flexible loads according to user power consumption characteristic data (such as power consumption type, power consumption period, and power consumption, etc.) and user response behavior. Among them, the characteristic of the transferable flexible load is that the total power consumption remains unchanged within a scheduling cycle, but the power consumption in each period can be flexibly adjusted. The transferable flexible load can be one or more of electric vehicle battery swap stations, ice storage systems, energy storage equipment, and partial loads of industrial and commercial users; the characteristic of the shiftable flexible load is that it is constrained by the production process and can only shift the power consumption curve between different time periods, such as large industrial users; the reducible flexible load can reduce power consumption according to demand, such as air conditioning, lighting, etc. This embodiment realizes the refined management of transferable, shiftable and reducible loads by classifying distributed flexible loads, and improves the flexibility and accuracy of load regulation.

[0030] S2. Model the response characteristics of various flexible loads to obtain the load regulation potential of various flexible loads in different time periods.

[0031] In this embodiment, the step of modeling the response characteristics of various flexible loads to obtain the load regulation potential of various flexible loads in different time periods includes:

[0032] The response characteristic model of the transferable load is performed according to the mutual elasticity vector, transfer rate, base load and time period demand difference vector of each time period, and the transferable load response amount of each time period is calculated by the transferable load response characteristic model; wherein, this embodiment accurately reflects the load transfer amount by considering the sensitivity of the load to the demand change, and the transferable load response characteristic model is specifically:

[0033]

[0034] In the formula, is the transferable load response in period t; is the base load during period t; is the mutual elasticity vector in period t; is the time period demand difference vector; is the transfer rate in period t;

[0035] The response characteristic model of the shiftable load is constructed according to the initial power consumption curve of each period and the load shift period caused by the change of power demand in the period, and the shiftable load response amount of each period is calculated by the shiftable load response characteristic model; wherein the shiftable load response characteristic model is specifically:

[0036]

[0037] In the formula, is the shiftable load response in period t; is the initial power consumption curve; The electricity demand changes during the time period; is the load shift period caused by the change in electricity demand during the period; the first integral represents the period before the load shift period begins, that is, the time period The first integral represents the consumption before the load shift. The second integral represents the consumption after the load shift period begins, that is, the time period from t to The integral of the power consumption curve in represents the consumption after the shift. In this embodiment, the load change caused by the load shift caused by the change in user demand is obtained through the above-mentioned shiftable load response characteristic model;

[0038] The curtailable load response characteristic model is constructed according to the load demand change rate, curtailment rate and load self-elasticity coefficient in each period, and the curtailable load response amount in each period is calculated by the curtailable load response characteristic model; wherein, the curtailable load response characteristic model is constructed based on the relationship between the demand change amount and the load curtailment amount, and the curtailable load response characteristic model is specifically:

[0039]

[0040] In the formula, is the load response amount that can be reduced in period t; is the self-elastic coefficient of the load during period t; is the load demand change rate in period t; is the reduction rate in period t, which is related to the elastic coefficient Together they determine the degree of load shedding response;

[0041] The transferable load response, shiftable load response and curtailable load response in each period are analyzed to obtain the time series characteristics of load response behavior of various flexible loads.

[0042] According to the time series characteristics of load response behavior of various flexible loads, the load regulation potential of various flexible loads in each time period is evaluated.

[0043] Specifically, this embodiment models the response characteristics of the transferable load according to the mutual elasticity vector, transfer rate, base load and time period demand difference vector of each time period, and calculates the transferable load response amount of each time period; calculates the shiftable load response amount of each time period by integrating the load shift amount on the time axis; models the response characteristics of the curtailable load according to the load demand change rate, curtailment rate and load self-elasticity coefficient of each time period, and calculates the curtailable load response amount of each time period; uses time series analysis methods or other algorithms to analyze the response amount change of the transferable load response amount, the shiftable load response amount and the curtailable load response amount of each time period, so as to analyze their behavior characteristics and obtain the load response behavior time series characteristics of various types of flexible loads, such as response speed, response amplitude, periodic change, etc.; Based on the load response behavior time series characteristics of various types of flexible loads, this embodiment can use machine learning algorithms such as random forest regression to identify the load response behavior time series characteristics, so as to predict and obtain various types of flexible loads in each time period. The load regulation potential of flexible loads can be considered. For example, for transferable flexible loads, this embodiment can consider the size and rate of their transfer amount, as well as their contribution to the balance of supply and demand of the power grid after the transfer; for shiftable flexible loads, this embodiment can consider the size of their shift amount and the selection of shift period, as well as the improvement effect on the load curve of the power grid after the shift; for curtailable loads, this embodiment can consider the size and rate of their curtailment amount, as well as the impact on the safe and stable operation of the power grid after curtailment, so as to quantitatively evaluate and obtain the regulation potential of various types of flexible loads in each period. This embodiment can not only predict the load regulation potential in different periods by establishing response characteristic models of various types of flexible loads, but also provide a scientific basis for scheduling decisions. In addition, this embodiment adopts a distributed control method, and handles load scheduling problems in a decentralized manner through a multi-type flexible load response scheduling model, which can adapt to the communication and control requirements of large-scale distributed flexible loads, reduce the burden on the central controller, avoid performance bottlenecks, and improve scheduling flexibility and response speed.

[0044] S3. According to the load regulation potential and total load power data of various types of flexible loads in different time periods, a multi-type flexible load response dispatch model is established.

[0045] In this embodiment, the step of establishing a multi-type flexible load response dispatch model according to the load regulation potential and total load power data of various types of flexible loads in different time periods includes:

[0046] According to the power consumption data of each device of each flexible load in different time periods, the total load power curve of each flexible load in different time periods is obtained;

[0047] Identify power consumption events of various flexible loads from total load power curves of various flexible loads at different time periods, and calculate the prominence of power consumption events of various flexible loads according to the degree of deviation between power consumption events of various flexible loads at different time periods and the benchmark value and the load regulation potential;

[0048] According to the distribution and aggregation degree of power consumption events of various flexible loads in time, the load data distribution weight of each flexible load in various flexible loads is calculated;

[0049] According to the prominence of power consumption events of various flexible loads in different time periods and the load data distribution weights, the response dispatch coefficients of various flexible loads are obtained;

[0050] According to the response dispatch coefficients of various types of flexible loads, a multi-type flexible load response dispatch model is constructed; wherein the construction process of the multi-type flexible load response dispatch model includes:

[0051] According to the total amount of transferable flexible load and the response dispatch coefficient, the transfer-in and transfer-out power of the transferable flexible load is obtained;

[0052] According to the reference power of the translatable flexible load and the response dispatch coefficient, the response dispatch power of the translatable flexible load is obtained;

[0053] According to the maximum reduction amount of the flexible load that can be reduced and the response dispatch coefficient, the reduction amount of the flexible load that can be reduced is obtained;

[0054] According to the transfer-in and transfer-out power of the transferable flexible load, the response dispatch power of the shiftable flexible load and the reduction amount of the reducible flexible load, a multi-type flexible load response dispatch model is established.

[0055] In this embodiment, the multi-type flexible load response scheduling model is specifically:

[0056]

[0057] in,

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] In the formula, is the total dispatch power; is the base load power excluding flexible load; is the output power of the jth transferable flexible load; is the output power of the jth transferable flexible load; is the response dispatch power of the mth translatable flexible load; is the reduction amount of the lth reducible flexible load in period t; is the total amount of the jth transferable flexible load; is the transfer load factor of the jth transferable flexible load; is the response dispatch coefficient of the transferable flexible load; is the transfer-out load factor of the jth transferable flexible load; is the reference power of the mth translatable flexible load; is the translation state of the mth translatable flexible load; is the response dispatch coefficient of the translatable flexible load; is the allowed translation period, which is the active period during which the load can be translated; is the maximum reduction amount of the lth flexible load that can be reduced; is the reduction ratio in period t, and its value is between 0 and 1; is the response dispatch coefficient of the flexible load that can be reduced; is the response dispatch coefficient of various flexible loads; is the load data distribution weight of the i-th type of flexible load; is the prominence degree of the power consumption event of the i-th type of flexible load; is the power consumption event weight of the i-th type of flexible load, which can be determined according to factors such as the urgency and impact scope of the power consumption event in this embodiment; is the benchmark value of the i-th type of flexible load, which represents the expected or standard power consumption level for a certain type of flexible load under specific conditions. For example, the benchmark value is the average power consumption of a certain type of flexible load over a period of time. is the degree of deviation between the power consumption event of the i-th type of flexible load in period t and the benchmark value, which reflects the difference between the current load state and the expected state; is the load regulation potential of the i-th flexible load in period t; J is the number of transferable flexible loads; M is the number of translational flexible loads; and L is the number of reducible flexible loads.

[0066] Specifically, this embodiment obtains power consumption events of various flexible loads (such as power-on, power-off, power mutation and other power consumption events) according to the power consumption data of each device of each flexible load in different time periods, and calculates the prominence of each power consumption event, for example, using statistics such as rate of change and standard deviation to measure, and at the same time analyzes the distribution and aggregation degree of power consumption events of various flexible loads in time, and calculates the load data distribution weight of each flexible load according to the distribution and aggregation degree. The load data distribution weight reflects the importance or dispatchability of the load in different time periods. The higher the weight, the greater the impact of the load on the total load change. Then, this embodiment can combine the load data distribution weight and the prominence degree of power consumption events to calculate the response scheduling coefficient of each flexible load, thereby constructing a multi-type flexible load response scheduling model. This embodiment realizes the optimized scheduling of loads in different time periods and improves the operation efficiency of the power system by constructing a multi-type flexible load response scheduling model.

[0067] S4. Based on the power demand of various flexible loads in different time periods, a multi-type flexible load response scheduling model is used to schedule various flexible loads in different time periods on the day before, and the day-ahead scheduling data is obtained.

[0068] Considering the transferable load and adjustable load of the flexible load in the day-ahead demand response, the transferable load usually has the characteristics of large power, constant power consumption, and loose time requirements. Therefore, adjustment through day-ahead scheduling can not only meet the actual demand, but also reduce the data processing volume of intra-day scheduling and improve the efficiency of intra-day scheduling. The characteristics of the adjustable load are similar to those of the transferable load. The scheduling plan can be sent to the user in advance through day-ahead scheduling, so that the user can adjust the load power according to their own needs. Specifically, this embodiment solves the multi-type flexible load response scheduling model to obtain the total scheduling power of each time period, and determines the scheduling time period priority of each type of flexible load according to the dispatchability, response speed and duration of each type of flexible load. Then, according to the calculated total scheduling power, combined with the power demand and scheduling time period priority of each flexible load, the total scheduling power is reasonably allocated to various types of flexible loads in different time periods using heuristic rules, and the day-ahead scheduling data containing the scheduling power of various types of flexible loads in different time periods is generated. The day-ahead scheduling data at least includes relevant data of the day-ahead scheduling load, such as the type and quantity of the day-ahead scheduling load, the time period of the day-ahead scheduling load, and the response status of the day-ahead scheduling load.

[0069] S5. According to the day-ahead dispatch data, the pre-built distributed flexible load multi-objective dispatch optimization model is solved using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal dispatch control strategy for the distributed flexible load.

[0070] In this embodiment, the minimization of the total dispatching cost and the minimization of the load fluctuation variance are taken as optimization objectives to establish a distributed flexible load multi-objective dispatching optimization model. In this embodiment, the objective function of the distributed flexible load multi-objective dispatching optimization model is specifically:

[0071]

[0072] in,

[0073]

[0074]

[0075]

[0076] In the formula, is the total dispatch cost; is the load fluctuation variance; , is the adjustment coefficient; D is the flexible load dispatching cost; is the unit regulation cost of the day-ahead dispatch load n; is the regulation amount of the day-ahead dispatching load n in time period t; is the unit imbalance cost of the day-ahead dispatch load n; is the power demand of the day-ahead dispatch load n; is the actual load of the day-ahead dispatched load n in time period t; N is the number of day-ahead dispatched loads; is the transfer amount of the jth transferable flexible load in period t; is the unit transfer cost of the jth transferable flexible load; T is the total dispatch period; J is the number of transferable flexible loads; is the unit translation cost of the mth translatable flexible load; is the translation amount of the mth translatable flexible load in time period t; M is the number of translatable flexible loads; For the Unit cost reduction that can reduce flexible loads; For the The reduction amount of flexible loads that can be reduced in time period t; L is the number of flexible loads that can be reduced; is the interaction power between the system and the grid during period t; is the mean value of interaction power in all time periods.

[0077] The constraints of the distributed flexible load multi-objective scheduling optimization model include system operation power balance constraints, transferable load power constraints, shiftable load power constraints and reducible load power constraints, wherein the system operation power balance constraints are specifically:

[0078]

[0079] In the formula, is the power generation of generator q in time period t; is the actual load of the day-ahead dispatched load n in time period t; is the interaction power between the system and the grid during period t; is the system loss in period t; is the number of generators.

[0080] The transferable load power constraints are as follows:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] In the formula, is the output power of the jth transferable flexible load; is the output power of the jth transferable flexible load; is the lower limit of the transfer power of the jth transferable flexible load; is the upper limit of the transfer power of the jth transferable flexible load; is the lower limit of the transfer power of the jth transferable flexible load; is the upper limit of the transfer power of the jth transferable flexible load; is the lower limit of the transfer amount of the jth transferable flexible load in time period t; is the upper limit of the transfer amount of the jth transferable flexible load in period t; is the total load of the jth transferable flexible load in the entire scheduling period.

[0087] The specific power constraints of the movable load are:

[0088]

[0089] In the formula, is the total load of the mth translatable flexible load in the entire scheduling period.

[0090] The load power constraints that can be reduced are as follows:

[0091]

[0092] In the formula, is the upper limit of the reduction amount of the lth flexible load that can be reduced.

[0093] In this embodiment, the step of solving the pre-built distributed flexible load multi-objective scheduling optimization model using the particle swarm optimization algorithm and the artificial bee colony algorithm according to the day-ahead scheduling data to obtain the optimal scheduling control strategy for the distributed flexible load includes:

[0094] According to the day-ahead scheduling data, a number of particle populations are randomly generated;

[0095] The particle swarm optimization algorithm is used to update the speed and position of each particle and select the local optimal solution from each particle population;

[0096] Using several local optimal solutions as the initial nectar source of the artificial bee colony algorithm, performing iterative calculations of the artificial bee colony algorithm on the local optimal solutions to obtain the latest local optimal solution;

[0097] The latest local optimal solution is used as the global optimal solution of the particle swarm optimization algorithm for the next round of iterative calculation until the preset convergence condition is met to obtain the optimal frontier solution set;

[0098] The optimal dispatching control strategy of distributed flexible loads in each time period is determined according to the optimal frontier solution set.

[0099] Specifically, this embodiment randomly generates particle positions of several groups according to the day-ahead scheduling data so that the algorithm converges to the optimal solution faster, and randomly assigns a speed to each particle, which is used to represent the changing trend of the scheduling scheme. Then, based on the particle swarm optimization algorithm, the distributed flexible load multi-objective scheduling optimization model is used to calculate the fitness of each particle. For each particle, according to the speed update and position update formulas of the particle swarm algorithm, the position of each particle is adjusted to find a better solution. If the current position is better than the previously recorded local optimal position, its local optimal position is updated, the optimal solution is found among all particles, the local optimal solution is updated, and then the local optimal solution obtained by the particle swarm algorithm is used as the initial nectar source position of the artificial bee colony algorithm, and the iterative calculation of the artificial bee colony algorithm is started. Specifically, the employed bees explore according to the position of the nectar source and generate a new position through certain strategies (such as random perturbations); the observation bees evaluate the fitness of the new position and compare it with the original nectar source, and select a better position as the new nectar source; for the nectar source that cannot be improved, the detection bees The bee will randomly generate a new position and try to find a better solution, so that the improved honey source position obtained by the artificial bee colony algorithm is used as the global optimal solution of the particle swarm algorithm to guide the next round of iteration of the particle swarm algorithm, update the state of the particle swarm, and after multiple iterations, until the preset convergence condition is met, the optimal scheduling control strategy for various types of flexible loads in each time period is obtained, so that the scheduling system can perform scheduling control. In this embodiment, the preset convergence condition can be reaching the maximum number of iterations or fitness convergence. Through this method, this embodiment can make full use of the global search capability of the particle swarm optimization algorithm and the local search capability of the artificial bee colony algorithm, effectively solve the distributed flexible load multi-objective scheduling optimization model, and obtain the optimal distributed flexible load scheduling control strategy. Especially when dealing with large-scale, multi-objective distributed flexible load optimization problems, this hybrid algorithm can strike a balance between global search and local search, accelerate the convergence speed of the algorithm, converge to a high-quality solution more quickly, and improve the global search capability of the algorithm.

[0100] S6. Dispatching and controlling the distributed flexible loads according to the optimal dispatching and control strategy to adjust the operating state of the power system.

[0101] This embodiment is based on the day-ahead dispatch strategy of the multi-type flexible load response dispatch model, and combines the particle swarm optimization algorithm and the artificial bee colony algorithm to solve the optimal dispatch control strategy, thereby realizing efficient and flexible dispatch of large-scale distributed flexible loads, and supporting plug-and-play of distributed flexible loads, significantly improving the response speed and intelligent dispatching capability of distributed controllers, and realizing more flexible grid regulation. Figure 2This is a schematic diagram of the power changes of photovoltaic, air conditioning, energy storage and electric vehicles in the microgrid at different times of the day under normal operation. Figure 3 The figure is a schematic diagram of power changes of each device in a microgrid after adopting the distributed flexible load dispatching control method proposed in this embodiment.

[0102] An embodiment of the present invention provides a distributed flexible load scheduling and control method, which divides distributed flexible loads into various types of flexible loads; models the response characteristics of various types of flexible loads to obtain the load regulation potential of various types of flexible loads in different time periods; establishes a multi-type flexible load response scheduling model based on the load regulation potential and total load power data of various types of flexible loads in different time periods; based on the power demand of various types of flexible loads in different time periods, uses the multi-type flexible load response scheduling model to schedule various types of flexible loads in different time periods to obtain day-ahead scheduling data; based on the day-ahead scheduling data, uses the particle swarm optimization algorithm and the artificial bee colony algorithm to solve the pre-constructed distributed flexible load multi-objective scheduling optimization model to obtain the optimal scheduling control strategy of the distributed flexible load; dispatches and controls the distributed flexible loads according to the optimal scheduling control strategy to adjust the operating state of the power system. Compared with the prior art, this embodiment adopts a distributed control strategy, and handles load scheduling problems in a decentralized manner through modeling and scheduling of multiple types of flexible loads. At the same time, it combines the particle swarm optimization algorithm and the artificial bee colony algorithm to quickly solve the optimal scheduling control strategy, thereby achieving efficient and flexible scheduling of large-scale distributed flexible loads, being able to adapt to the communication and control requirements of large-scale distributed flexible loads, improving the flexibility and response speed of scheduling, and achieving more flexible grid regulation.

[0103] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0104] In one embodiment, Figure 4 As shown, an embodiment of the present invention provides a distributed flexible load dispatching control system, the system comprising:

[0105] The load classification module 101 is used to classify the distributed flexible load into various types of flexible loads according to the user's power consumption characteristics and the user's response behavior; the various types of flexible loads include transferable flexible loads, shiftable flexible loads and reducible flexible loads;

[0106] The load analysis module 102 is used to model the response characteristics of various flexible loads and obtain the load regulation potential of various flexible loads in different time periods;

[0107] The response scheduling module 103 is used to establish a multi-type flexible load response scheduling model according to the load regulation potential and total load power data of various types of flexible loads in different time periods;

[0108] The day-ahead scheduling module 104 is used to perform day-ahead scheduling on various flexible loads in different time periods based on the power demands of various flexible loads in different time periods by using a multi-type flexible load response scheduling model to obtain day-ahead scheduling data;

[0109] The model solving module 105 is used to solve the pre-built distributed flexible load multi-objective scheduling optimization model according to the day-ahead scheduling data using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal scheduling control strategy of the distributed flexible load;

[0110] The dispatching control module 106 is used to perform dispatching control on the distributed flexible loads according to the optimal dispatching control strategy to adjust the operating state of the power system.

[0111] In this embodiment, the load analysis module 102 is specifically used for:

[0112] The response characteristics of the transferable load are modeled according to the mutual elasticity vector, transfer rate, base load and time period demand difference vector of each time period, and the response amount of the transferable load in each time period is calculated through the transferable load response characteristic model;

[0113] Model the response characteristics of the shiftable load according to the initial power consumption curve of each period and the load shift period caused by the change of power demand in the period, and calculate the response amount of the shiftable load in each period through the shiftable load response characteristic model;

[0114] Model the response characteristics of the curtailable load according to the load demand change rate, curtailment rate and load self-elasticity coefficient in each period, and calculate the curtailable load response amount in each period through the curtailable load response characteristic model;

[0115] The transferable load response, shiftable load response and curtailable load response in each period are analyzed to obtain the time series characteristics of load response behavior of various flexible loads.

[0116] According to the time series characteristics of load response behavior of various flexible loads, the load regulation potential of various flexible loads in each time period is evaluated.

[0117] In this embodiment, the response scheduling module 103 is specifically used for:

[0118] According to the power consumption data of each device of each flexible load in different time periods, the total load power curve of each flexible load in different time periods is obtained;

[0119] Identify power consumption events of various flexible loads from total load power curves of various flexible loads at different time periods, and calculate the prominence of power consumption events of various flexible loads according to the degree of deviation between power consumption events of various flexible loads at different time periods and the benchmark value and the load regulation potential;

[0120] According to the distribution and aggregation degree of power consumption events of various flexible loads in time, the load data distribution weight of each flexible load in various flexible loads is calculated;

[0121] According to the prominence of power consumption events of various flexible loads in different time periods and the load data distribution weights, the response dispatch coefficients of various flexible loads are obtained;

[0122] According to the response dispatch coefficients of various flexible loads, a multi-type flexible load response dispatch model is constructed.

[0123] For the specific definition of a distributed flexible load dispatching and control system, please refer to the above-mentioned definition of a distributed flexible load dispatching and control method, which will not be repeated here. A person of ordinary skill in the art can appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0124] The embodiment of the present invention provides a distributed flexible load dispatching and control system, wherein the system divides the distributed flexible load into various types of flexible loads through a load classification module; models the response characteristics of various types of flexible loads through a load analysis module to obtain the load regulation potential of various types of flexible loads in different time periods; establishes a multi-type flexible load response dispatching model through a response dispatching module; dispatches various types of flexible loads in different time periods through a day-ahead dispatching module to obtain day-ahead dispatching data; solves a pre-built distributed flexible load multi-objective dispatching optimization model through a model solving module to obtain an optimal dispatching and control strategy for distributed flexible loads; dispatches and controls distributed flexible loads according to the optimal dispatching and control strategy through a dispatching and control module to adjust the operation state of the power system. Compared with the prior art, the present embodiment adopts a distributed control strategy, and processes load dispatching problems in a decentralized manner through modeling and dispatching of multiple types of flexible loads, and combines a particle swarm optimization algorithm and an artificial bee colony algorithm to quickly solve the optimal dispatching and control strategy, thereby realizing efficient and flexible dispatching of large-scale distributed flexible loads, being able to adapt to the communication and control requirements of large-scale distributed flexible loads, improving the dispatching flexibility and response speed, and realizing more flexible power grid regulation.

[0125] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A distributed flexible load dispatching control method, characterized in that: The following steps are involved: According to the user's power consumption characteristics and user response behavior, the distributed flexible loads are divided into various types of flexible loads; Various types of flexible loads include transferable flexible loads, translatable flexible loads and reducible flexible loads; Model the response characteristics of various flexible loads to obtain the load regulation potential of various flexible loads in different periods; According to the load regulation potential and total load power data of various flexible loads in different periods, a multi-type flexible load response dispatch model is established; Based on the power demand of various flexible loads in different periods, a multi-type flexible load response dispatch model is used to dispatch various flexible loads in different periods on a day-ahead basis to obtain day-ahead dispatch data. According to the day-ahead dispatch data, the pre-built distributed flexible load multi-objective dispatch optimization model is solved by using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal dispatch control strategy for the distributed flexible load. Dispatching and controlling the distributed flexible loads according to the optimal dispatching and control strategy to adjust the operating state of the power system; The step of modeling the response characteristics of various flexible loads to obtain the load regulation potential of various flexible loads in different time periods includes: The response characteristics of the transferable load are modeled according to the mutual elasticity vector, transfer rate, base load and time period demand difference vector of each time period, and the response amount of the transferable load in each time period is calculated through the transferable load response characteristic model; Model the response characteristics of the shiftable load according to the initial power consumption curve of each period and the load shift period caused by the change of power demand in the period, and calculate the response amount of the shiftable load in each period through the shiftable load response characteristic model; Model the response characteristics of the curtailable load according to the load demand change rate, curtailment rate and load self-elasticity coefficient in each period, and calculate the curtailable load response amount in each period through the curtailable load response characteristic model; The transferable load response, shiftable load response and curtailable load response in each period are analyzed to obtain the time series characteristics of load response behavior of various flexible loads. According to the time series characteristics of the load response behavior of various flexible loads, the load regulation potential of various flexible loads in each period is evaluated; The step of establishing a multi-type flexible load response dispatch model according to the load regulation potential and total load power data of various flexible loads in different time periods includes: According to the power consumption data of each device of each flexible load in different time periods, the total load power curve of each flexible load in different time periods is obtained; Identify power consumption events of various flexible loads from total load power curves of various flexible loads at different time periods, and calculate the prominence of power consumption events of various flexible loads according to the degree of deviation between power consumption events of various flexible loads at different time periods and the benchmark value and the load regulation potential; According to the distribution and aggregation degree of power consumption events of various flexible loads in time, the load data distribution weight of each flexible load in various flexible loads is calculated; According to the prominence of power consumption events of various flexible loads in different time periods and the load data distribution weights, the response dispatch coefficients of various flexible loads are obtained; According to the response dispatch coefficients of various flexible loads, a multi-type flexible load response dispatch model is constructed; The construction process of the multi-type flexible load response scheduling model includes: According to the total amount of transferable flexible load and the response dispatch coefficient, the transfer-in and transfer-out power of the transferable flexible load is obtained; According to the reference power of the translatable flexible load and the response dispatch coefficient, the response dispatch power of the translatable flexible load is obtained; According to the maximum reduction amount of the flexible load that can be reduced and the response dispatch coefficient, the reduction amount of the flexible load that can be reduced is obtained; According to the transfer-in and transfer-out power of the transferable flexible load, the response dispatch power of the shiftable flexible load and the reduction amount of the reducible flexible load, a multi-type flexible load response dispatch model is established.

2. A distributed flexible load dispatching control method as claimed in claim 1, characterized in that: The objective function of the distributed flexible load multi-objective scheduling optimization model takes minimization of total scheduling cost and minimization of load fluctuation variance as optimization objectives. The objective function of the distributed flexible load multi-objective scheduling optimization model is specifically: in, In the formula, is the total dispatch cost; is the load fluctuation variance; , is the adjustment coefficient; D is the flexible load dispatching cost; is the unit regulation cost of the day-ahead dispatch load n; is the regulation amount of the day-ahead dispatching load n in time period t; is the unit imbalance cost of the day-ahead dispatch load n; is the power demand of the day-ahead dispatch load n; is the actual load of the day-ahead dispatched load n in time period t; N is the number of day-ahead dispatched loads; T is the total dispatching period.

3. A distributed flexible load dispatching control method as claimed in claim 2, characterized in that: The constraints of the distributed flexible load multi-objective scheduling optimization model include system operation power balance constraints, transferable load power constraints, shiftable load power constraints and reducible load power constraints.

4. A distributed flexible load dispatching control method as claimed in claim 1, characterized in that: The step of solving the pre-built distributed flexible load multi-objective scheduling optimization model by using the particle swarm optimization algorithm and the artificial bee colony algorithm according to the day-ahead scheduling data to obtain the optimal scheduling control strategy of the distributed flexible load includes: According to the day-ahead scheduling data, a number of particle populations are randomly generated; The particle swarm optimization algorithm is used to update the speed and position of each particle and select the local optimal solution from each particle population; Using several local optimal solutions as the initial nectar source of the artificial bee colony algorithm, performing iterative calculations of the artificial bee colony algorithm on the local optimal solutions to obtain the latest local optimal solution; The latest local optimal solution is used as the global optimal solution of the particle swarm optimization algorithm for the next round of iterative calculation until the preset convergence condition is met to obtain the optimal frontier solution set; The optimal dispatching control strategy of distributed flexible loads in each time period is determined according to the optimal frontier solution set.

5. A distributed flexible load dispatching control system, characterized in that: The system comprises: The load classification module is used to classify the distributed flexible load into various types of flexible loads according to the user's power consumption characteristics and user response behavior; the various types of flexible loads include transferable flexible loads, shiftable flexible loads and reducible flexible loads; Load analysis module, used to model the response characteristics of various flexible loads and obtain the load regulation potential of various flexible loads in different time periods; The response dispatch module is used to establish a multi-type flexible load response dispatch model based on the load regulation potential and total load power data of various flexible loads in different time periods; The day-ahead dispatching module is used to dispatch various flexible loads in different time periods based on the power demand of various flexible loads in different time periods by using a multi-type flexible load response dispatching model to obtain day-ahead dispatching data; The model solving module is used to solve the pre-built distributed flexible load multi-objective scheduling optimization model based on the day-ahead scheduling data using the particle swarm optimization algorithm and the artificial bee colony algorithm to obtain the optimal scheduling control strategy for the distributed flexible load; A dispatching control module, used to perform dispatching control on distributed flexible loads according to the optimal dispatching control strategy and adjust the operating state of the power system; The load analysis module is specifically used for: The response characteristics of the transferable load are modeled according to the mutual elasticity vector, transfer rate, base load and time period demand difference vector of each time period, and the response amount of the transferable load in each time period is calculated through the transferable load response characteristic model; Model the response characteristics of the shiftable load according to the initial power consumption curve of each period and the load shift period caused by the change of power demand in the period, and calculate the response amount of the shiftable load in each period through the shiftable load response characteristic model; Model the response characteristics of the curtailable load according to the load demand change rate, curtailment rate and load self-elasticity coefficient in each period, and calculate the curtailable load response amount in each period through the curtailable load response characteristic model; The transferable load response, shiftable load response and curtailable load response in each period are analyzed to obtain the time series characteristics of load response behavior of various flexible loads. According to the time series characteristics of the load response behavior of various flexible loads, the load regulation potential of various flexible loads in each period is evaluated; The response scheduling module is specifically used for: According to the power consumption data of each device of each flexible load in different time periods, the total load power curve of each flexible load in different time periods is obtained; Identify power consumption events of various flexible loads from total load power curves of various flexible loads at different time periods, and calculate the prominence of power consumption events of various flexible loads according to the degree of deviation between power consumption events of various flexible loads at different time periods and the benchmark value and the load regulation potential; According to the distribution and aggregation degree of power consumption events of various flexible loads in time, the load data distribution weight of each flexible load in various flexible loads is calculated; According to the prominence of power consumption events of various flexible loads in different time periods and the load data distribution weights, the response dispatch coefficients of various flexible loads are obtained; According to the response dispatch coefficients of various flexible loads, a multi-type flexible load response dispatch model is constructed; The construction process of the multi-type flexible load response scheduling model includes: According to the total amount of transferable flexible load and the response dispatch coefficient, the transfer-in and transfer-out power of the transferable flexible load is obtained; According to the reference power of the translatable flexible load and the response dispatch coefficient, the response dispatch power of the translatable flexible load is obtained; According to the maximum reduction amount of the flexible load that can be reduced and the response dispatch coefficient, the reduction amount of the flexible load that can be reduced is obtained; According to the transfer-in and transfer-out power of the transferable flexible load, the response dispatch power of the shiftable flexible load and the reduction amount of the reducible flexible load, a multi-type flexible load response dispatch model is established.

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