Virtual power plant optimization scheduling method and device based on demand response

Abstract

The invention provides a virtual power plant optimal scheduling method and device based on demand response, and relates to the technical field of virtual power plant scheduling. The method comprises the following steps: monitoring a demand response signal in a virtual power plant; in response to the received demand response signal, acquiring an electricity utilization parameter in the virtual power plant at the current moment and a change range corresponding to the electricity utilization parameter; a plurality of initial position vectors are randomly generated based on the change range of the power utilization parameters, a particle swarm is constructed, and each particle in the particle swarm corresponds to one initial position vector; based on the initial position vector corresponding to each particle, calculating the optimal comprehensive cost corresponding to the particle; and comparing the optimal comprehensive costs corresponding to all the particles, determining a global optimal position vector, and scheduling the power utilization parameters at the current moment based on the global optimal position vector. The scheduling cost of the virtual power plant can be controlled on the basis of demand response, and efficient operation of the virtual power plant is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant scheduling, and particularly to an optimized scheduling method and device for a virtual power plant based on demand response. Background Art

[0002] Under the current development trend of the power field, distributed energy sources such as solar energy and wind energy are widely connected to the power grid due to their green and environmental protection characteristics, and the innovative power management form of virtual power plants has emerged as the times require. As an energy management system, a virtual power plant plays an increasingly crucial role in enhancing the stability and economy of the power system by integrating resources such as distributed power sources, energy storage systems, and controllable loads. Demand response, as an effective demand-side management means, plays a key role in regulating the power supply-demand balance. For example, during the peak electricity consumption period in summer, when the power grid faces huge power supply pressure, by implementing demand response strategies to encourage commercial users to reduce non-essential electricity loads during peak hours, the power supply tension of the local power grid can be alleviated, ensuring the stability of electricity consumption.

[0003] However, in actual operation, how to optimize the scheduling of various resources in a virtual power plant considering demand response remains a highly challenging problem. Traditional scheduling methods are based on fixed rules and preset models, and it is difficult to adapt to the intermittency and volatility of distributed energy generation and the uncertainty of user demand response behaviors. For example, in the face of the power generation power fluctuations of distributed photovoltaic power generation affected by weather and the uncertainty of user demand response behaviors, traditional scheduling methods based on fixed rules are difficult to adjust scheduling strategies in real time and dynamically, thus unable to achieve the optimal scheduling effect. Therefore, there is an urgent need for a more intelligent and efficient optimized scheduling method to meet the increasingly complex operation requirements of the power system. Summary of the Invention

[0004] The present application aims to at least solve one of the technical problems in the related technologies to some extent.

[0005] To this end, the first object of the present application is to propose an optimized scheduling method for a virtual power plant based on demand response to reduce the comprehensive cost required for virtual power plant scheduling when implementing demand response.

[0006] The second object of the present application is to propose an optimized scheduling device for a virtual power plant based on demand response.

[0007] To achieve the above object, the first aspect embodiment of the present application proposes an optimized scheduling method for a virtual power plant based on demand response, including:

[0008] Monitoring the demand response signal in the virtual power plant;

[0009] In response to receiving the demand response signal, obtain the power consumption parameters in the virtual power plant at the current moment and the corresponding change ranges of the power consumption parameters;

[0010] Based on the change ranges of the power consumption parameters, randomly generate a plurality of initial position vectors and construct a particle swarm, where each particle in the particle swarm corresponds to an initial position vector;

[0011] Based on the initial position vectors respectively corresponding to each particle, calculate the optimal comprehensive cost corresponding to the particle;

[0012] Compare the optimal comprehensive costs respectively corresponding to all the particles, determine the global optimal position vector, and schedule the power consumption parameters at the current moment based on the global optimal position vector

[0013] Further, the calculating the optimal comprehensive cost corresponding to the particle based on the initial position vectors respectively corresponding to each particle includes:

[0014] Randomly generate an initial velocity vector for each particle and set the update times associated with the particle to 0, where the dimension of the initial velocity vector is the same as the dimension of the initial position vector corresponding to the particle;

[0015] Based on the randomly generated inertia weight, the initial position vector, the individual optimal position vector corresponding to the current particle, and the global optimal position vector corresponding to all the particles, update the initial velocity vector to obtain an updated first velocity vector, and increment the update times by 1;

[0016] Based on the first velocity vector and the initial position vector, obtain an updated first position vector;

[0017] Based on the variables in the first position vector, determine the comprehensive cost corresponding to the first position vector;

[0018] Compare the comprehensive costs respectively corresponding to the first position vector, the individual optimal position vector, and the global optimal position vector, and update the individual optimal position vector and the global optimal position vector;

[0019] Return to the operation of updating the first velocity vector until the update times and / or the comprehensive cost meet the preset termination conditions, and determine the comprehensive cost corresponding to the individual optimal position vector of each particle as the optimal comprehensive cost corresponding to the particle.

[0020] Further, the randomly generated inertia weight is determined based on the following formula:

[0021]

[0022] where w represents the inertial weight, σ is a preset fixed value, N(0,1) represents a random number of the standard normal distribution, μ represents the weight generation factor, μ min represents the minimum value of the weight generation factor, μ max represents the maximum value of the weight generation factor, and rand(0,1) represents a random number between 0 and 1.

[0023] Further, the comprehensive cost is determined by the following formula:

[0024] F = C op + C dr + C grid-int + C banlance ,

[0025] where F represents the comprehensive cost, C op represents the operating cost of the virtual power plant, C dr represents the demand response cost, C grid-int represents the grid interaction power cost of the virtual power plant, C banlance represents the power supply - demand balance cost of the virtual power plant.

[0026] Further, the position vector includes the power generation power of distributed power sources and the charge - discharge power of energy storage systems in the virtual power plant, and the operating cost is determined by the following formula:

[0027]

[0028] where n represents the number of distributed power sources in the virtual power plant, c gi represents the power generation cost coefficient of distributed power source i in the virtual power plant, i is a positive integer less than or equal to n, P gi represents the power generation power of distributed power source i, m represents the number of energy storage systems in the virtual power plant, c sj represents the charge - discharge loss cost coefficient of energy storage system j, P sj represents the charge - discharge power of energy storage system j, C grid represents the price of purchasing electricity from the grid, P grid represents the power of purchasing electricity from the grid by the virtual power plant.

[0029] Further, the position vector includes the power adjustment amount of controllable loads, and the demand response cost is determined by the following formula:

[0030]

[0031] where r represents the total number of users in the demand response signal, c lkdenotes the incentive cost coefficient of user k in the demand response signal, where k is a positive integer less than or equal to r, P lk denotes the power regulation amount of user k, S lk denotes the user satisfaction of user k.

[0032] Further, the user satisfaction of user k is determined by the following formula:

[0033]

[0034] where sn denotes the number of user satisfaction indicators of user k, w a denotes the weight of the a-th user satisfaction indicator of user k, s a denotes the score of the a-th user satisfaction indicator of user k.

[0035] Further, the power supply-demand balance cost is determined by the following formula:

[0036] C banlance = λ2·|ΔP|,

[0037] where λ2 denotes the penalty coefficient for power supply-demand balance corresponding to the virtual power plant, and ΔP denotes the power supply-demand imbalance amount corresponding to the virtual power plant.

[0038] Further, after monitoring the demand response signal in the monitoring virtual power plant, it further includes:

[0039] When the demand response signal is not received and the difference between the current moment and the historical scheduling moment reaches the threshold, it is determined that the demand response cost is zero, where the historical scheduling moment is the time of the last scheduling of the electricity consumption parameters.

[0040] To achieve the above object, an embodiment of the second aspect of the present application proposes a virtual power plant optimal scheduling device based on demand response, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements the virtual power plant optimal scheduling method based on demand response as described in the embodiment of the first aspect of the present application.

[0041] The virtual power plant optimal scheduling method and device provided by the present application, after receiving the demand response signal, combine the particle swarm algorithm to determine a scheduling plan that can make the comprehensive cost of the virtual power plant optimal while satisfying the change range of electricity consumption parameters and demand response, so as to optimize the electricity consumption parameters of the virtual power plant, thereby being able to control the comprehensive cost of the virtual power plant as much as possible on the basis of demand response, improve the reliability of optimal scheduling, and ensure the efficient operation of the virtual power plant.

[0042] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Brief Description of the Drawings

[0043] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where:

[0044] Figure 1 is a schematic flowchart of a method for optimizing the scheduling of a virtual power plant based on demand response provided by an embodiment of the present application;

[0045] Figure 2 is a schematic flowchart of another method for optimizing the scheduling of a virtual power plant based on demand response provided by an embodiment of the present application;

[0046] Figure 3 is a schematic flowchart of the calculation process of a particle swarm optimization algorithm provided by an embodiment of the present application. And Detailed Description of the Embodiments

[0047] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0048] The method and device for optimizing the scheduling of a virtual power plant based on demand response according to embodiments of the present application will be described below with reference to the accompanying drawings.

[0049] Figure 1 is a schematic flowchart of a method for optimizing the scheduling of a virtual power plant based on demand response provided by an embodiment of the present application.

[0050] Step 101, monitor the demand response signal in the virtual power plant.

[0051] Among them, the demand response signal is an information carrier for the virtual power plant to transmit scheduling instructions and response requirements. The demand response signal can, based on the real-time power supply and demand situation of the power grid, electricity price fluctuations, or specific incentive measures, guide controllable loads, energy storage and other resources in the virtual power plant to adjust their electricity consumption or discharge behavior to respond to the needs of the power grid.

[0052] In the embodiments of the present application, devices such as sensors and smart meters can be used to monitor the operation data of various resources in the virtual power plant in real time to identify demand response signals in the virtual power plant, such as electricity price adjustment signals, signals for directly controlling load increase or decrease, or incentive response signals. The demand response signals may include the duration of the demand response, the amount of load increase or reduction required by the demand response, the user identification of the participants in the demand response, and so on.

[0053] Step 102, in response to receiving the demand response signal, obtain the electricity consumption parameters in the virtual power plant at the current moment and the corresponding change ranges of the electricity consumption parameters.

[0054] Among them, the electricity consumption parameters of the virtual power plant may include at least one of the power generation power of distributed power sources, the state of charge or charge and discharge power of energy storage systems, and the power demand of controllable loads.

[0055] In the embodiments of the present application, the current power generation power of distributed power sources (such as solar photovoltaic power plants, wind farms, etc.) in the virtual power plant can be collected from the system of the virtual power plant, the state of charge or charge and discharge power of the energy storage system can be obtained according to the performance parameters of the system, and the power demand of each controllable load can be determined according to the demand reports of different users in the virtual power plant.

[0056] It should be noted that in order to ensure the accuracy of the data and improve the reliability of the optimal dispatching of the virtual power plant, after collecting the electricity consumption parameters in the virtual power plant at the current moment, the collected data can also be cleaned, such as removing outliers and noise data.

[0057] In the embodiments of the present application, in addition to collecting the electricity consumption parameters in the virtual power plant at the current moment, detailed information such as the electricity consumption preference habits of users participating in the demand response (such as total electricity consumption, peak electricity consumption, electricity consumption time distribution, and maximum power), adjustable electricity consumption periods, power adjustment ranges, and historical electricity consumption data can also be obtained.

[0058] In the embodiments of the present application, when the electricity consumption parameters include parameters in different dimensions, the corresponding change ranges of each parameter can be determined respectively to ensure the effectiveness and safety of the dispatching strategy for the electricity consumption parameters, and prevent equipment overload, damage or safety accidents caused by improper parameter settings during the dispatching process. For example, the change range corresponding to the power generation power of the distributed power source can be determined according to its installed capacity and real-time power generation capacity limit; the change range corresponding to the charge and discharge power (i.e., the state of charge) of the energy storage system can be determined according to its capacity and charge and discharge rate limit; the power adjustment amount of the controllable load user can be determined according to the adjustable range of the user.

[0059] In the embodiment of the present application, after collecting the power consumption parameters in the virtual power plant at the current moment, the change range corresponding to each parameter in the power consumption parameters can be determined according to the power generation power of the distributed power sources, the state of charge of the energy storage system, and the real-time power demand of the controllable load in the current power consumption parameters, that is, within the duration corresponding to the demand response signal, on the premise of meeting the operation constraints of each device and the upper and lower limit ranges of the adjustable amount of the controllable load, the power adjustment amount of the energy storage system and the load adjustment amount of the controllable load should be equal to the actual load increase amount or reduction amount required by the demand response.

[0060] Step 103: Based on the change range of the power consumption parameters, randomly generate a plurality of initial position vectors to obtain a particle swarm.

[0061] Among them, each particle in the particle swarm corresponds to an initial position vector.

[0062] In the embodiment of the present application, particle coding and position vector initialization can be performed first. When performing particle coding, each particle is represented as a multi-dimensional vector, and its dimension corresponds to the variables to be scheduled in the virtual power plant.

[0063] For example, if the virtual power plant includes n distributed power sources, m energy storage systems, and k controllable load users, the position vector generated for each can be expressed by the following formula (1).

[0064] X = [P g1 , P g2 , …, P gn , P s1 , P s2 , …, P sm , P l1 , P l2 , … P lk (1)

[0065] Among them, P gi represents the power generation power of the i-th distributed power source, i ∈ {1, …, n}, P sj represents the charge and discharge power of the j-th energy storage system (positive value for charging, negative value for discharging), j ∈ {1, …, m}, P lr represents the power adjustment amount of the r-th controllable load user, r ∈ {1, …, k}.

[0066] In the embodiment of the present application, according to the actual scheduling accuracy requirements, the number of particles to be generated can be determined, and then the corresponding number of particles can be randomly generated to form an initial particle swarm. The initial position vector corresponding to each particle in the particle swarm (i.e., the encoded position vector X) is randomly initialized within its corresponding variable value range (i.e., the change range of the power consumption parameters).

[0067] That is to say, the power generation power of distributed power sources can be initialized according to their installed capacity and real-time power generation capacity limits, the charge and discharge power of energy storage systems can be initialized according to their capacity and charge and discharge rate limits, and the power adjustment amount of controllable load users can be initialized according to the adjustable range of users.

[0068] Step 104: Calculate the optimal comprehensive cost corresponding to each particle based on the initial position vector corresponding to each particle.

[0069] In the embodiment of the present application, based on the particle swarm algorithm, the initial position vector corresponding to each particle can be iterated, and for the position vector obtained after each iteration, the fitness function constructed can be used to evaluate the pros and cons of each particle. The variable values in the position vector of the particle can be substituted into the fitness function to calculate the fitness value, and thus the comprehensive cost corresponding to the particle can be obtained. The smaller the comprehensive cost, the better the power consumption parameters reflected by the corresponding particle. Therefore, the comprehensive costs corresponding to the position vectors of each particle after each iteration can be compared, and the minimum comprehensive cost in the iteration process can be determined as the optimal comprehensive cost corresponding to the particle.

[0070] In the embodiment of the present application, the comprehensive cost can include multiple aspects of costs such as the operating cost of the virtual power plant, the implementation effect of demand response (i.e., demand response cost), the grid interaction power cost, and the power supply and demand balance cost, etc., so as to improve the rationality of dispatching cost control.

[0071] Step 105: Compare the optimal comprehensive costs corresponding to all particles in the particle swarm, determine the global optimal position vector, and based on the global optimal position vector, dispatch the power consumption parameters at the current moment.

[0072] In the embodiment of the present application, the optimal comprehensive costs corresponding to all particles in the particle swarm can be compared, and the position vector of the particle corresponding to the minimum value in the optimal comprehensive costs is selected and determined as the global optimal position vector. Then it can be explained that by using the values of the elements in the global optimal position vector to regulate the corresponding power consumption parameters in the virtual power plant, not only the requirements of demand response can be met, but also the dispatching cost can be minimized to the greatest extent. Therefore, based on the values corresponding to the power consumption parameters in the global optimal position vector, the power consumption parameters at the current moment can be dispatched to obtain the optimal dispatching scheme of the virtual power plant at the demand response moment.

[0073] In this embodiment, after receiving the demand response signal, combined with the particle swarm algorithm, a dispatching scheme that can optimize the comprehensive cost of the virtual power plant while satisfying the power consumption parameter change range and demand response is determined, so as to optimize the power consumption parameters of the virtual power plant, thereby being able to control the comprehensive cost of the virtual power plant as much as possible on the basis of demand response, improve the reliability of optimal dispatching, and ensure the efficient operation of the virtual power plant.

[0074] It should be noted that in the embodiments of the present application, the particle swarm algorithm is used to iterate the position vector corresponding to each particle to determine the particle value that can minimize the comprehensive cost required for the virtual power plant scheduling, that is, the values of the electricity consumption parameters corresponding to the position vector. The following will be combined with Figure 2 to illustrate this process. Figure 2 is a schematic flowchart of another virtual power plant optimal scheduling method based on demand response provided by the embodiments of the present application.

[0075] As Figure 2 shown, the virtual power plant optimal scheduling method based on demand response may include the following steps:

[0076] Step 201, monitor the demand response signal in the virtual power plant.

[0077] Step 202, in response to receiving the demand response signal, obtain the electricity consumption parameters and the corresponding change ranges of the electricity consumption parameters in the virtual power plant at the current moment.

[0078] Step 203, based on the change ranges of the electricity consumption parameters, randomly generate a plurality of initial position vectors to construct a particle swarm.

[0079] For the detailed description of the above steps 201 to 203, reference may be made to the above embodiments of the present application, which will not be elaborated herein.

[0080] Step 204, randomly generate an initial velocity vector for each particle in the particle swarm, and set the update times associated with the particle to 0.

[0081] Among them, the dimension of the initial velocity vector is the same as that of the initial position vector corresponding to the particle.

[0082] In the embodiments of the present application, an initial velocity vector V can be randomly assigned to each particle, and the dimension of the velocity vector is the same as that of the particle position vector. Moreover, in order to facilitate the determination of the iterative update times of the particle velocity and position, a update times parameter can be associated with each initialized particle, and the initial value of the update times is set to 0.

[0083] Step 205, based on the randomly generated inertia weight, initial position vector, the individual optimal position vector corresponding to the current particle, and the global optimal position vector corresponding to all particles, update the initial velocity vector to obtain the updated first velocity vector, and increment the update times by 1.

[0084] Among them, the individual optimal position vector refers to the position vector corresponding to the least comprehensive cost among all updated position vectors corresponding to a single particle during the iteration process. The global optimal position vector refers to the position vector corresponding to the least comprehensive cost among all updated position vectors corresponding to all particles in the particle swarm during the iteration process.

[0085] In the embodiments of the present application, the velocity update formula shown in the following formula (2) can be used to update the initial velocity vector corresponding to each particle to obtain the updated first velocity vector, and each time the velocity update is completed, the associated update count of the particle is incremented by 1.

[0086] V i (t + 1) = w·V i (t) + c1·r1·(P best,i - X i (t)) + c2·r2·(G best - X i (t)) (2)

[0087] Wherein, V i (t) is the velocity of particle i at time t, where time t refers to the update time and the value of t is equal to the update count. w is the inertia weight, c1 and c2 are different learning factors, r1 and r2 are random numbers in the range [0, 1], P best,i is the optimal position experienced by particle i, that is, the individual optimal position vector, G best is the optimal position found by the entire particle swarm so far, that is, the global optimal position vector, X i (t) is the position of particle i at time t.

[0088] In the embodiments of the present application, in order to make the influence of the particle's historical velocity on the current velocity random, the inertia weight w is not a fixed value but is randomly generated at each iteration. For example, the inertia weight can be randomly generated within a pre-set value range.

[0089] Optionally, the randomly generated inertia weight can also be determined based on the following formula (3),

[0090]

[0091] Wherein, w represents the inertia weight, σ is a preset fixed value, N(0, 1) represents a random number of the standard normal distribution, μ represents the weight generation factor, μ min represents the minimum value of the weight generation factor, μ max represents the maximum value of the weight generation factor, rand(0, 1) represents a random number between 0 and 1.

[0092] Step 206, based on the first velocity vector and the initial position vector, obtain the updated first position vector.

[0093] In the embodiments of the present application, the position update formula shown in the following formula (4) can be used to determine the updated first position vector.

[0094] Xi (t + 1)= X i (t)+V i (t + 1)(4)

[0095] In the embodiments of the present application, it should be ensured that the values of the variables in the updated position vector are within the value range that meets the requirements of the demand response signal. If the value of any variable exceeds the range, the value of the variable can be adjusted to the boundary value of the value range.

[0096] Step 207: Determine the comprehensive cost corresponding to the first position vector based on each variable in the first position vector.

[0097] In the embodiments of the present application, the values of the variables in the position vector of the particle can be substituted into the preset fitness function to calculate the fitness value, and thus the comprehensive cost corresponding to the particle can be obtained. To ensure the reliability of the comprehensive cost calculation result, the costs of different dimensions can be calculated using the values of different variables in the position vector of the particle, and the costs of these dimensions can be added up to obtain the comprehensive cost.

[0098] It should be noted that in the present application, after receiving the demand response signal, the construction framework of the comprehensive cost of the virtual power plant can be changed, and the demand response cost can be added to the comprehensive cost framework, making the optimal scheduling of the virtual power plant more intelligent and efficient.

[0099] In the embodiments of the present application, the comprehensive cost can be calculated using the following formula (5):

[0100] F = C op + C dr + C grid-int + C banlance (5)

[0101] Where F represents the comprehensive cost, C op represents the operating cost of the virtual power plant, C dr represents the demand response cost, C grid-int represents the grid interaction power cost of the virtual power plant, C banlance represents the power supply - demand balance cost of the virtual power plant.

[0102] Optionally, the operating cost of the virtual power plant may include the power generation cost of distributed power sources (such as fuel cost, equipment maintenance cost, etc.), the charge - discharge loss cost of the energy storage system, and the cost of purchasing electricity from the grid. Therefore, the calculation formula of the comprehensive cost can be shown by the following formula (6):

[0103]

[0104] Where n represents the number of distributed power sources in the virtual power plant, c girepresents the power generation cost coefficient of distributed power source i in the virtual power plant, where i is a positive integer less than or equal to n, and P gi represents the power generation power of distributed power source i, m represents the number of energy storage systems in the virtual power plant, and c sj represents the charge-discharge loss cost coefficient of energy storage system j, and P sj represents the charge-discharge power of energy storage system j, and C grid represents the price of purchasing electricity from the power grid, and P grid represents the power of purchasing electricity from the power grid by the virtual power plant.

[0105] In the embodiments of the present application, the numbers of distributed power sources and energy storage systems in the virtual power plant can be determined by the attribute parameters of the virtual power plant. The power generation cost coefficient of each distributed power source is determined by the performance of the distributed power source, and the power generation cost coefficients of different distributed power sources may be the same or different. The charge-discharge loss cost coefficient of each energy storage system can be determined by the performance of the energy storage system, and the charge-discharge loss cost coefficients of different energy storage systems may be the same or different. The values of each power generation power or charge-discharge power can be obtained at the corresponding positions in the position vector. The price of purchasing electricity from the power grid and the power of purchasing electricity from the power grid by the virtual power plant can be determined according to the actual situation, and the present application does not limit this.

[0106] Optionally, the demand response cost can be calculated by the following formula (7),

[0107]

[0108] where r represents the total number of users in the demand response signal, and c lk represents the incentive cost coefficient of user k in the demand response signal, where k is a positive integer less than or equal to r, and P lk represents the power adjustment amount of user k, and S lk represents the user satisfaction of user k.

[0109] In the embodiments of the present application, the controllable load refers to the load in the power grid that can be interrupted or the electricity consumption can be changed. The controllable load users can include factories, shopping malls, office buildings, and households, etc. For different types of controllable load users, the adjustable power size, incentive cost coefficient, etc. may be different.

[0110] Optionally, the user satisfaction of user k can be calculated according to the user's electricity consumption preference and actual adjustment situation, and its calculation formula can be as shown in the following formula (7),

[0111]

[0112] where sn represents the number of user satisfaction indexes of user k, and w a represents the weight of the a-th user satisfaction index of user k, and sa Represents the score of the a-th user satisfaction index of user k.

[0113] In the embodiments of the present application, the user satisfaction index, the number of user satisfaction indexes, and the weights of each user satisfaction index can all be set according to actual needs. For example, the user satisfaction index can include at least one of the load reduction completion rate, response timeliness, and compensation amount rationality, etc. The present application does not make any limitations in this regard.

[0114] In the embodiments of the present application, the sum of the weights of all user satisfaction indexes is 1. The scores of each user satisfaction index of each user are determined by the user's feedback and scoring situation, and the value range is 0 - 100.

[0115] Optionally, to avoid additional costs and risks caused by excessive interaction between the virtual power plant and the power grid, in the present application, the interaction power cost with the power grid can be considered when calculating the comprehensive cost. The calculation formula of the interaction power cost can be as shown in the following formula (8),

[0116] C grid-int =λ1·|P grid | (8)

[0117] Among them, C grid-int represents the interaction power cost, λ1 is the penalty coefficient of the power grid interaction power, and P grid is the interaction power.

[0118] In the embodiments of the present application, the interaction power between the virtual power plant and the power grid and the penalty coefficient of the interaction power can be obtained according to actual situations. The present application does not make any limitations in this regard.

[0119] Optionally, the power supply - demand imbalance in the virtual power plant refers to the difference between power supply and power demand during the operation of the virtual power plant, which is used to ensure the power supply - demand balance inside the virtual power plant. The power supply - demand balance cost can be calculated by the following formula (9),

[0120] C banlance =λ2·|ΔP| (9)

[0121] Among them, λ2 represents the penalty coefficient of the power supply - demand balance corresponding to the virtual power plant, and ΔP represents the power supply - demand imbalance corresponding to the virtual power plant.

[0122] In the embodiments of the present application, the penalty coefficient of the power supply - demand balance of the virtual power plant and the power supply - demand imbalance can also be obtained according to actual situations. The present application does not make any limitations in this regard.

[0123] Step 208: Compare the comprehensive costs corresponding to the first position vector, the individual optimal position vector, and the global optimal position vector, and update the individual optimal position vector and the global optimal position vector.

[0124] In the embodiments of the present application, after obtaining the updated first position vector and calculating the comprehensive cost corresponding to the first position vector, it is necessary to continue to update the velocity and position vectors of the particle. At this time, the individual optimal position vector P in the above formula (2) best,i and the global optimal position vector G best , may change due to the updated first position vector. Therefore, before updating the velocity vector next time, it is necessary to compare the comprehensive costs corresponding to the first position vector, the individual optimal position vector, and the global optimal position vector respectively to update the individual optimal position vector and the global optimal position vector. For example, if the comprehensive cost of the first position vector corresponding to the current particle is better than (i.e., less than) the comprehensive cost of its individual optimal position vector, then P best,i can be updated to the first position vector, and the update of the global optimal position vector G best is the same.

[0125] Step 209: Return to the operation of updating the first velocity vector until the number of updates and / or the comprehensive cost meet the preset termination conditions, and determine the comprehensive cost corresponding to the individual optimal position vector of each particle as the optimal comprehensive cost corresponding to the particle.

[0126] In the embodiments of the present application, after updating the individual optimal position vector and the global optimal position vector, it is possible to return to Step 205 to repeat the operations of updating the velocity vector and position vector of the particle, calculating the comprehensive cost, and updating the optimal position vector until the preset termination conditions are met, such as reaching the maximum number of updates and / or the comprehensive cost converging to a certain accuracy, and then stop updating the velocity vector and position vector. At this time, the comprehensive cost of the individual optimal position vector corresponding to each particle can be determined as the optimal comprehensive cost corresponding to the particle.

[0127] Step 210: Compare the optimal comprehensive costs corresponding to all particles respectively, determine the global optimal position vector, and schedule the current electricity consumption parameters based on the global optimal position vector.

[0128] In the embodiment of the present application, after the iterative update of all particles in the particle swarm is completed, the position vector corresponding to the minimum optimal comprehensive cost among all particles can be determined as the global optimal position vector. At this time, the particle value corresponding to the global optimal position vector is the optimal scheduling scheme of the virtual power plant at the demand response moment. The power generation power of each distributed power source, the charge and discharge power of each energy storage system, and the power regulation amount of each controllable load user in the virtual power plant can be correspondingly regulated by the values of each component in the global optimal position vector, so as to realize the scheduling of the current moment's power consumption parameters, and the implementation strategy of demand response can be notified to the corresponding users to guide the users to adjust their power consumption behaviors.

[0129] It should be noted that demand response has a certain persistence. Therefore, in the embodiment of the present application, the corresponding comprehensive cost can be calculated at each moment within the time period during which demand response persists, and then the comprehensive costs of all moments are summed to obtain the final comprehensive cost.

[0130] It should be noted that in some possible embodiments, there may be a situation where a demand response signal is not received, but the power consumption parameters of the virtual power plant need to be optimized. In this case, the method described in the above embodiment still applies.

[0131] Optionally, it can be determined that the power consumption parameters of the virtual power plant need to be optimized when a demand response signal is not received and the difference between the current moment and the historical scheduling moment reaches a threshold. At this time, the comprehensive cost does not include the demand response cost, and the demand response cost is 0. Among them, the historical scheduling moment is the time when the power consumption parameters were last scheduled, and the difference threshold between the two scheduling moments can be determined according to the actual situation. At this time, the load control of the controllable load users can operate according to a pre-set plan.

[0132] The following combines Figure 3 to illustrate how to iteratively update each particle in the particle swarm to obtain a scheduling scheme that makes the comprehensive cost required for scheduling optimal. Figure 3 It is a schematic diagram of the calculation process of a particle swarm algorithm provided by an embodiment of the present application.

[0133] As Figure 3 shown, when a demand response signal is received, such as a load reduction or increase demand sent by a controllable load user, an optimal scheduling is performed using the particle swarm algorithm. Assume that a demand response signal is received at the current moment T, and the requirement of this demand response signal is that the virtual power plant reduces or increases a certain load within the next ΔT time period.

[0134] Then, based on the power generation of distributed power sources, the state of charge of energy storage systems, and the real-time power demand of controllable loads in the virtual power plant at the current moment T, the position of each particle in the particle swarm can be initialized. The value range of each variable in the position vector should satisfy the operation constraints of each device and the upper and lower limits of the adjustable amount of the controllable load within the time period ΔT, so that the power adjustment amount of the energy storage system and the load adjustment amount of the controllable load are equal to the actual load increase or reduction required by the demand response. Moreover, an initial velocity vector can be randomly assigned to each particle.

[0135] After that, the fitness value (i.e., the comprehensive cost) of each particle can be calculated according to the method described in the above embodiments, and the individual optimal value (i.e., the minimum comprehensive cost) corresponding to each particle and the global optimal value corresponding to the particle swarm can be updated according to the size of the comprehensive cost. Then, based on the updated individual optimal value and global optimal value, the velocity and position of the particle are updated in turn. Each time the velocity and position of the particle are updated, the current iteration number needs to be incremented by 1, that is, the update number in the above embodiments, and the current iteration number is compared with the preset maximum iteration number.

[0136] When the current iteration number does not reach the maximum iteration number, return to the operation of calculating the fitness value using the updated particle position until the iteration number reaches the maximum iteration number. The global optimal value can be output as the current optimal solution, that is, the position corresponding to the global optimal value is the optimal scheduling scheme of the virtual power plant at the demand response moment.

[0137] As can be seen from the above embodiments, the virtual power plant optimal scheduling method based on demand response proposed in this application can determine the comprehensive cost in combination with demand response, while reducing the comprehensive cost of the virtual power plant, ensuring that the operation efficiency of the virtual power plant system meets the production requirements.

[0138] To implement the above embodiments, this application also proposes a virtual power plant optimal scheduling device based on demand response, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement the virtual power plant optimal scheduling method described in the above embodiments.

[0139] To implement the above embodiments, this application also proposes a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps in each of the above method embodiments.

[0140] To implement the above embodiments, this application also proposes a computer program product. When the computer program product runs on a data storage device, it enables the data storage device to implement the steps in each of the above method embodiments when executed.

[0141] In this application, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the user's personal information all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0142] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and signing an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0143] This application is expected to provide an implementation plan for users to selectively block the use or access of personal information data. That is, this disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0144] In the description of the foregoing embodiments, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0145] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0146] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of this application includes additional implementations, where functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0147] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0148] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0149] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0150] In addition, in each of the embodiments of the present application, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0151] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A virtual power plant optimal scheduling method based on demand response, characterized in that Including the following steps: Monitoring the demand response signal in the virtual power plant; In response to receiving the demand response signal, obtaining the electricity consumption parameters in the virtual power plant at the current moment and the corresponding change range of the electricity consumption parameters; Based on the change range of the electricity consumption parameters, randomly generating a plurality of initial position vectors and constructing a particle swarm, wherein each particle in the particle swarm corresponds to an initial position vector; Calculating the optimal comprehensive cost corresponding to the particle based on the initial position vector corresponding to each particle; Comparing the optimal comprehensive costs corresponding to all the particles, determining the global optimal position vector, and scheduling the electricity consumption parameters at the current moment based on the global optimal position vector; 2. The method according to claim 1, wherein The calculating the optimal comprehensive cost corresponding to the particle based on the initial position vector corresponding to each particle includes: Randomly generating an initial velocity vector for each particle and setting the update times associated with the particle to 0, wherein the dimension of the initial velocity vector is the same as the dimension of the initial position vector corresponding to the particle; Updating the initial velocity vector based on the randomly generated inertia weight, the initial position vector, the individual optimal position vector corresponding to the current particle, and the global optimal position vector corresponding to all the particles to obtain an updated first velocity vector, and incrementing the update times by 1; Obtaining an updated first position vector based on the first velocity vector and the initial position vector; Determining the comprehensive cost corresponding to the first position vector based on the variables in the first position vector; Comparing the comprehensive costs corresponding to the first position vector, the individual optimal position vector, and the global optimal position vector respectively, and updating the individual optimal position vector and the global optimal position vector; Returning to the operation of updating the first velocity vector until the update times and / or the comprehensive cost satisfy a preset termination condition, and determining the comprehensive cost corresponding to the individual optimal position vector of each particle as the optimal comprehensive cost corresponding to the particle; 3. The method according to claim 2, characterized in that, The randomly generated inertia weight is determined based on the following formula: where w represents the inertia weight, σ is a preset fixed value, N(0,1) represents a random number of the standard normal distribution, μ represents the weight generation factor, μ min represents the minimum value of the weight generation factor, μ max represents the maximum value of the weight generation factor, and rand(0,1) represents a random number between 0 and 1.

4. The method according to any one of claims 1 to 3, characterized in that The comprehensive cost is determined by the following formula: F = C op + C dr + C grid-int + C banlance , Among them, F represents the comprehensive cost, C op represents the operating cost of the virtual power plant, C dr represents the demand response cost, C grid-int represents the grid interaction power cost of the virtual power plant, C banlance represents the power supply-demand balance cost of the virtual power plant.

5. The method according to claim 4, characterized in that, The position vector includes the power generation power of distributed power sources and the charge and discharge power of energy storage systems in the virtual power plant, and the operating cost is determined by the following formula: Among them, n represents the number of distributed power sources in the virtual power plant, c gi represents the power generation cost coefficient of distributed power source i in the virtual power plant, where i is a positive integer less than or equal to n, and P gi represents the power generation power of distributed power source i, m represents the number of energy storage systems in the virtual power plant, and c sj represents the charge-discharge loss cost coefficient of energy storage system j, and P sj represents the charge-discharge power of energy storage system j, C grid represents the price of purchasing electricity from the power grid, and P grid represents the power of purchasing electricity from the power grid by the virtual power plant.

6. The method according to claim 4, wherein The position vector includes the power regulation amount of controllable load users, and the demand response cost is determined by the following formula: Among them, r represents the total number of users in the demand response signal, c lk represents the incentive cost coefficient of user k in the demand response signal, where k is a positive integer less than or equal to r, P lk represents the power adjustment amount of user k, S lk represents the user satisfaction of user k.

7. The method according to claim 6, wherein The user satisfaction of user k is determined by the following formula: where sn represents the number of user satisfaction indicators of user k, and w a represents the weight of the a-th user satisfaction indicator of user k, and s a represents the score of the a-th user satisfaction indicator of user k.

8. The method according to claim 4, wherein The power supply and demand balance cost is determined by the following formula: C banlance = λ2 · |ΔP|, Wherein, λ2 represents the penalty coefficient for power supply and demand balance corresponding to the virtual power plant, and ΔP represents the power supply and demand imbalance amount corresponding to the virtual power plant; 9. The method according to any one of claims 1-8, characterized in that, It further includes: In the case where the demand response signal is not received and the difference between the current moment and the historical scheduling moment reaches a threshold, determining that the demand response cost is zero, wherein the historical scheduling moment is the time of the last scheduling of the electricity consumption parameters; 10. A virtual power plant optimal scheduling device based on demand response, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1-9.

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