Multi-energy load virtual energy storage intra-day optimization scheduling method and system

Abstract

The invention discloses a multi-energy load virtual energy storage intra-day optimization scheduling method and system, and the method comprises the steps: building a target function for a multi-energy load virtual energy storage intra-day optimization scheduling target with the minimum deviation between the actual output of equipment of a multi-energy load virtual energy storage system and a day-ahead scheduling plan, and building an operation constraint condition; and under the operation constraint condition, solving the objective function to obtain a multi-energy load virtual energy storage intra-day optimization scheduling strategy and executing the multi-energy load virtual energy storage intra-day optimization scheduling strategy. According to the method, the deviation between the multi-energy load virtual energy storage system and a day-ahead scheduling plan due to the uncertainty of data prediction can be reduced, so that the operation cost is reduced, meanwhile, by improving a population initialization mode and a random number generation method of a traditional algorithm, optimization model solving is carried out, and the optimization efficiency of the multi-energy load virtual energy storage system is improved. According to the method, local optimization can be avoided, the obtained optimization result is high in quality, the convergence speed is high, and the problem of multi-energy load virtual energy storage intra-day optimization scheduling is well solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy dispatching, and relates to a method and system for optimizing the intraday dispatching of multi-energy load virtual energy storage. Background Art

[0002] In the process of optimizing the scheduling of multi-energy load virtual energy storage systems, since the day-ahead scheduling at the hourly time scale is difficult to respond in time to the fluctuations of renewable energy and virtual energy storage loads at the minute time scale, the initial plan of the integrated energy system often deviates from the actual situation. Subsequently, power imbalance or large energy waste may occur in some periods during actual operation, which will bring many adverse effects on the stable power supply and economic operation of the multi-energy load virtual energy storage system.

[0003] Intraday optimization scheduling refers to feeding back the measured system data in the current state into the intraday rolling optimization model, combining the forecast data with a future time scale of 15 minutes to solve the model, and obtaining the intraday optimization scheduling plan. The goal is to eliminate the power fluctuation caused by the time scale problem during the day-ahead scheduling as much as possible, so that the power of each source in the day can track the day-ahead plan as much as possible, and reduce the economic losses caused by uncertainty. However, in the current intraday optimization problem solving process, due to the large variety of multi-energy coupling system equipment and complex solution parameters, the traditional solution algorithm is inefficient.

[0004] Therefore, how to provide a method for optimizing the intraday scheduling of multi-energy load virtual energy storage and formulate an optimal intraday scheduling strategy for multi-energy load virtual energy storage is an urgent problem to be solved. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and system for optimizing the intraday scheduling of multi-energy load virtual energy storage. The optimization goal is to minimize the deviation between the actual output of the multi-energy load virtual energy storage system equipment and the day-ahead scheduling plan, establish a system optimization objective function, and set operating constraints according to the day-ahead scheduling. Under the operating constraints, the objective function is solved to obtain and execute the intraday optimization scheduling strategy of the power system. The deviation between the multi-energy load virtual energy storage system and the day-ahead scheduling plan caused by the uncertainty of data prediction can be reduced, thereby reducing the operating cost. In addition, a high-efficiency solution algorithm is adopted, and the efficiency of solving the intraday optimization scheduling model is high.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a method for optimizing the scheduling of multi-energy load virtual energy storage within a day, comprising:

[0008] Step 1: Establish an objective function with the minimum deviation between the actual output of the multi-energy load virtual energy storage system and the day-ahead dispatch plan as the multi-energy load virtual energy storage intraday optimization dispatch target;

[0009] Step 2: Establishing operation constraints for the multi-energy load virtual energy storage system;

[0010] Step 3: Under the operating constraints, solve the objective function, obtain the multi-energy load virtual energy storage daily optimization scheduling strategy and execute it.

[0011] Preferably, the objective function established in step 1 is:

[0012]

[0013] Where: k is the initial time of optimization;

[0014] λ i is the weight coefficient, i∈[1,2,…,q];

[0015] q = number of virtual energy storage devices p+1;

[0016] When i is 1 to p, represents the real-time output power of virtual energy storage devices 1 to p at time k;

[0017] When i takes q, represents the real-time interaction power between the virtual energy storage system and the power grid at time k;

[0018] When i is 1 to p, represents the reference power of virtual energy storage devices 1 to p at time k;

[0019] When i takes q, Represents the reference interaction power between the virtual energy storage system and the grid at time k.

[0020] Preferably, in step 2, the following operating constraints are established for the multi-energy load virtual energy storage system:

[0021] Operation power balance constraints of multi-energy load virtual energy storage system;

[0022] Virtual energy storage constraints for multi-energy load virtual energy storage system operation:

[0023]

[0024] Where: P ves , They are respectively the virtual charging and discharging power of the virtual energy storage device, the minimum value of the virtual charging and discharging power, and the maximum value of the virtual charging and discharging power;

[0025] SOC ves , They are respectively the virtual state of charge of the virtual energy storage device, the minimum value of the virtual state of charge, and the maximum value of the virtual state of charge;

[0026] C ves , They are the virtual capacity, minimum virtual capacity, and maximum virtual capacity of the virtual energy storage device respectively;

[0027] P ves,up / dn , They are respectively the virtual upward / downward climbing rate of the virtual energy storage device, the minimum value of the virtual upward / downward climbing rate, and the maximum value of the virtual upward / downward climbing rate.

[0028] Preferably, in step 3, under the operating constraints, solving the objective function, obtaining and executing the multi-energy load virtual energy storage intraday optimization scheduling strategy, specifically includes:

[0029] Step 3.1: Initialize population parameters based on the objective function and operating constraints, including population size N, variable dimension M, maximum number of iterations T, and initial position of individuals;

[0030] Step 3.2: If the number of iterations Then perform individual location update;

[0031] Step 3.3: If Then the position of the defense against natural enemies stage is updated;

[0032] Step 3.4: If n ≥ N, then update the position of the escape from natural enemies stage;

[0033] Step 3.5: If the maximum number of iterations T is reached, stop the iteration and get the optimal solution; otherwise, jump to step 3.2 and increase the number of iterations n by 1.

[0034] Preferably, the population initialization algorithm in step 3.1 is:

[0035] Step 3.1.1: Set the population size N. Each individual is a set of solutions to optimize the objective function. There are M-dimensional variables in the solution. These variables are the variables to be optimized in the objective function and constraints. The variable value of each individual can calculate an objective function value through the objective function.

[0036] Step 3.1.2: Set the maximum number of iterations T;

[0037] Step 3.1.3: Establish the initial positions of the forward and reverse individuals to obtain the forward and reverse initial populations. The algorithm is as follows:

[0038]

[0039] in, are the initial positions of the forward and reverse individuals, n=1,2,…,N, m=1,2,…,M;

[0040] r ais a random number in the range of 0-1;

[0041] Represents the mth decision variable x n,m The upper and lower bounds of

[0042] Step 3.1.4: Initialize individual x n,m :

[0043] In the forward and reverse initial populations, N individuals with smaller objective function values ​​under corresponding operating constraints are selected to form the initial population.

[0044] Preferably, the individual location update phase of step 3.2 is specifically as follows:

[0045] Step 3.2.1: Identify dominant individuals

[0046] Determine the dominant individual according to the objective function value under the operating constraint conditions corresponding to the current individual value. The dominant individual is the individual with the lowest objective function value under the corresponding operating constraint conditions.

[0047] Step 3.2.2: When When determining male individuals Female individuals

[0048]

[0049] in, is a random vector between 0 and 1;

[0050] r 5 、r 6 、r 7 is a random number between 0 and 1;

[0051] r b 、r b-1 It is a random number between 0 and 1 generated for the b and b-1 times;

[0052] q 1 ,q 2 is a random integer between 0 and 1;

[0053] I 1 ,I 2 is a random integer between 1 and 2;

[0054] is the average of some randomly selected individuals;

[0055] h 1 、h 2 Randomly select from the five scenarios in formula (5);

[0056] Step 3.2.3: Update the positions of male and female individuals respectively. The algorithm is as follows:

[0057]

[0058] Where f() represents the objective function value.

[0059] Preferably, the step 3.3 of defending against natural enemies is specifically as follows:

[0060] Step 3.3.1: Use the k-means clustering algorithm to form m clusters, and the centroid of each cluster is the enemy m Location;

[0061] Step 3.3.2: When When determining the position of the individual relative to the natural enemy The algorithm is as follows:

[0062]

[0063] in, is the random vector of levy motion;

[0064] e is a uniformly distributed random number between 2 and 4;

[0065] c is a uniformly distributed random number between 1 and 1.5;

[0066] d is a uniformly distributed random number between 2 and 3;

[0067] g is a random number uniformly distributed between -1 and 1;

[0068] is a random vector of dimension 1×M;

[0069] is the distance between the nth individual and its natural enemy, and the algorithm is as follows:

[0070]

[0071] Step 3.3.3: Position update in the defense against natural enemies phase. The algorithm is as follows:

[0072]

[0073] Preferably, the step 3.4 of escaping from natural enemies is specifically as follows:

[0074] Step 3.4.1: Find a safe location The algorithm is as follows:

[0075]

[0076] Among them, r v 、r v-1 It is a random number between 0 and 1 generated at the vth and v-1th times;

[0077] s 1 Randomly select from three scenarios, the algorithm is as follows:

[0078]

[0079] in, is a random vector between 0 and 1;

[0080] r 12 is a normally distributed random number;

[0081] r 13 is a random number between 0 and 1.

[0082] Step 3.4.2: Position update during the escape from natural enemies phase. The algorithm is as follows:

[0083]

[0084] A second aspect of the present invention provides a multi-energy load virtual energy storage intraday optimization scheduling system, comprising:

[0085] An objective function building module is used to establish an objective function with the multi-energy load virtual energy storage system's actual equipment output and the day-ahead dispatch plan as the multi-energy load virtual energy storage intra-day optimization dispatch target;

[0086] An operation constraint condition building module, used to establish operation constraint conditions for the multi-energy load virtual energy storage system;

[0087] The optimization scheduling module is used to solve the objective function under the operating constraints, obtain the multi-energy load virtual energy storage daily optimization scheduling strategy and execute it.

[0088] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0089] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0090] Compared with the prior art, the beneficial effects of the present invention include at least:

[0091] (1) The present invention takes into account the actual output of various virtual energy storage devices in the system, the difference between the real-time interactive power of the system and the power grid and the reference value of the day-ahead, and establishes an objective function with the minimum deviation between the actual output of the multi-energy load virtual energy storage system and the day-ahead scheduling plan as the intra-day optimization scheduling goal of the multi-energy load virtual energy storage. It also introduces relevant parameter constraints of virtual energy storage to achieve the unification of multi-energy virtual energy storage parameters, making the optimization model more realistic and able to effectively achieve the goal of minimizing the deviation between the actual output of the equipment and the day-ahead scheduling plan.

[0092] (2) The present invention improves the algorithm when solving the objective function. The population initialization method used can accelerate the convergence efficiency of the algorithm and improve the reliability of the solution; the individual position update stage can determine a more uniform and random position of male individuals, so that the algorithm escapes the local optimal solution, thereby maintaining population diversity and improving global search capabilities; the defense against natural enemies stage uses k-means clustering to search the entire solution space to find the location where the best natural enemy is generated, which improves the local optimization effect and makes the algorithm have better convergence; the safe position search algorithm used in the escape from natural enemies stage makes the safe position distribution more random and ergodic, thereby increasing the search space range and preventing local optimal solutions. The present invention solves the optimization model by improving the population initialization method and random number generation method of the traditional algorithm, has a higher exploration ability, and can avoid local optimality. The optimization results obtained by this method are of high quality and fast convergence speed, and better solve the problem of intraday optimization scheduling of virtual energy storage of multi-energy loads.

[0093] (3) The present invention can help the multi-energy load virtual energy storage system to achieve effective output of the intra-day optimization scheduling strategy, and improve the flexibility and reliability of the coordinated interaction between the system and the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 A flow chart of the intraday optimization scheduling method for multi-energy load virtual energy storage provided by the present invention;

[0095] Figure 2 Flowchart of the algorithm for solving the problem. DETAILED DESCRIPTION

[0096] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0097] Embodiment 1 of the present invention provides a method for optimizing the scheduling of multi-energy load virtual energy storage in a day. In a multi-energy system coupled with multiple energy sources such as electricity, heat, and gas, in order to obtain the intraday optimization scheduling strategy of the energy system, a multi-energy load virtual energy storage system intraday optimization scheduling model is established, and the intraday optimization scheduling strategy is obtained by solving the solution, so as to realize the coordinated interaction and unified regulation of the system and the power grid, and achieve the optimization scheduling goal with the minimum deviation from the day-ahead scheduling plan, such as Figure 1 As shown, the method comprises the following steps:

[0098] Step 1: Establish an objective function with the minimum deviation between the actual output of the multi-energy load virtual energy storage system and the day-ahead dispatch plan as the multi-energy load virtual energy storage intraday optimization dispatch target, and set operation constraints according to the day-ahead dispatch;

[0099] Further preferably, the multi-energy load is a load including any multiple energy sources such as electricity, gas, cold, heat, hydrogen, etc., which provides the function of virtual energy storage in the invention, that is, the multi-energy load refers to a virtual energy storage device, constituting a multi-energy load virtual energy storage system.

[0100] Taking the minimum deviation between the actual output of the multi-energy load virtual energy storage system equipment and the day-ahead dispatch plan as the optimization goal, the objective function of the intraday optimization dispatch of the multi-energy load virtual energy storage is established as follows:

[0101]

[0102] This objective function takes into account the actual output of various virtual energy storage devices in the system, the difference between the real-time interactive power of the system and the power grid and the day-ahead reference value, and can effectively achieve the goal of minimizing the deviation between the actual output of the equipment and the day-ahead dispatch plan.

[0103] Where: k is the initial time of optimization;

[0104] λ i is the weight coefficient, i∈[1,2,…,q]; it is set according to the importance of equipment fluctuation, and the deviation between the actual output and the day-ahead plan of the equipment that needs more attention can be selected;

[0105] q = number of virtual energy storage devices p+1;

[0106] R i (k) includes the real-time output power of the virtual energy storage device at time k and the real-time interaction power with the power grid;

[0107] R ref (k) Contains the reference power of each virtual energy storage device after day-ahead scheduling and the reference interaction power of the grid.

[0108] When i is 1 to p, represents the real-time output power of virtual energy storage devices 1 to p at time k, which is monitored by the monitoring device;

[0109] When i takes q, represents the real-time interaction power between the virtual energy storage system and the power grid at time k;

[0110] When i is 1 to p, represents the reference power of virtual energy storage devices 1 to p at time k;

[0111] When i takes q, Represents the reference interaction power between the virtual energy storage system and the grid at time k.

[0112] Step 2: Establishing operation constraints for the multi-energy load virtual energy storage system, including at least:

[0113] Power balance constraints for operation of multi-energy load virtual energy storage system;

[0114] Virtual energy storage constraints for multi-energy load virtual energy storage system operation:

[0115]

[0116] Where: P ves , They are virtual charging and discharging power of virtual energy storage device, minimum value of virtual charging and discharging power, and maximum value of virtual charging and discharging power;

[0117] SOC ves , They are respectively the virtual state of charge of the virtual energy storage device, the minimum value of the virtual state of charge, and the maximum value of the virtual state of charge;

[0118] C ves , They are the virtual capacity, minimum virtual capacity, and maximum virtual capacity of the virtual energy storage device respectively;

[0119] P ves,up / dn , They are respectively the virtual upward / downward climbing rate of the virtual energy storage device, the minimum value of the virtual upward / downward climbing rate, and the maximum value of the virtual upward / downward climbing rate.

[0120] Step 3: Under the operating constraints, solve the objective function, obtain the multi-energy load virtual energy storage daily scheduling strategy and execute it.

[0121] Further preferably, according to the established multi-energy load system optimization scheduling model containing virtual energy storage equipment, the multi-energy load virtual energy storage intraday optimization objective function is solved to obtain the multi-energy load virtual energy storage intraday optimization scheduling strategy, such as Figure 2 As shown, the specific process is as follows:

[0122] Step 3.1: Population initialization; the population initialization algorithm is as follows:

[0123] Step 3.1.1: Set the population size N. Each individual is a set of solutions to optimize the objective function. There are M-dimensional variables in the solution. These variables are the variables to be optimized in the objective function and constraints. The variable value of each individual can calculate an objective function value through the objective function.

[0124] Step 3.1.2: Set the maximum number of iterations T;

[0125] Step 3.1.3: Establish the initial positions of the forward and reverse individuals to obtain the forward and reverse initial populations;

[0126]

[0127] in, are the initial individual positions in the forward and reverse directions, n=1,2,…,N, m=1,2,…,M;

[0128] r a is a random number in the range of 0-1;

[0129] Represents the mth decision variable x n,m The upper and lower bounds of

[0130] Step 3.1.4: Initialize individual x n,m Location selection:

[0131] According to the minimization problem objective function, N individuals with smaller corresponding objective function values ​​are selected from the forward and reverse initial populations to form the initial population.

[0132] This population initialization method can accelerate the convergence efficiency of the algorithm and improve the reliability of the solution.

[0133] Step 3.2: Individual location update phase, specifically:

[0134] Step 3.2.1: Identify dominant individuals Location:

[0135] The dominant individual is determined according to the objective function value corresponding to the current individual value. The dominant individual in the minimization problem is the individual with the lowest objective function value under the corresponding operating constraints.

[0136] Step 3.2.2: When When determining male individuals Female individuals Position, the algorithm is as follows:

[0137]

[0138] in, is a random vector between 0 and 1;

[0139] r 5 、r 6 、r 7 is a random number between 0 and 1;

[0140] r b 、r b-1 is a random number between 0 and 1 generated for the b and b-1 times; it can improve uniformity and randomness, allowing the algorithm to escape from the local optimal solution, thereby maintaining population diversity and improving global search capabilities;

[0141] q 1 ,q 2 is a random integer between 0 and 1;

[0142] I 1 ,I 2 is a random integer between 1 and 2;

[0143] is the average of some randomly selected individuals;

[0144] h 1 、h 2 Randomly select from the five scenarios in formula (5).

[0145] Step 3.2.3: Update the positions of male and female individuals. The algorithm is as follows:

[0146]

[0147] Where: f() represents the objective function value.

[0148] Step 3.3: Defense against natural enemies, specifically:

[0149] Step 3.3.1: Use the k-means clustering algorithm to form m clusters, and the centroid of each cluster is the enemy m Location; Use k-means clustering to search the entire solution space to find the location where the best natural enemy is produced, which can improve the local optimization effect and make the algorithm have better convergence;

[0150] Step 3.3.2: When When determining the position of the individual relative to the natural enemy The algorithm is as follows:

[0151]

[0152] in, is the random vector of levy motion;

[0153] e is a uniformly distributed random number between 2 and 4;

[0154] c is a uniformly distributed random number between 1 and 1.5;

[0155] d is a uniformly distributed random number between 2 and 3;

[0156] g is a random number uniformly distributed between -1 and 1;

[0157] is a random vector of dimension 1×M.

[0158] is the distance between the nth individual and its natural enemy, and the algorithm is as follows:

[0159]

[0160] Step 3.3.3: Position update in the defense against natural enemies phase. The algorithm is as follows:

[0161]

[0162] Step 3.4: Escape from the natural enemy stage, specifically:

[0163] Step 3.4.1: Find a safe location The algorithm is as follows:

[0164]

[0165] Among them, r v 、r v-1 is a random number between 0 and 1 generated by the vth and v-1th nodes, which makes the distribution of safe locations more random and ergodic, thereby increasing the scope of the search space and preventing local optimal solutions;

[0166] s 1 Randomly select from three scenarios, the algorithm is as follows:

[0167]

[0168] in, is a random vector between 0 and 1;

[0169] r 12 is a normally distributed random number;

[0170] r 13 is a random number between 0 and 1.

[0171] Step 3.4.2: Position update during the escape from natural enemies phase. The algorithm is as follows:

[0172]

[0173] Step 3.5: If the termination condition is met (the maximum number of iterations T is reached), stop and obtain the optimal solution; otherwise, jump to step 3.2 and increase the number of iterations n by 1.

[0174] Embodiment 2 of the present invention provides a multi-energy load virtual energy storage intraday optimization scheduling system, including:

[0175] An objective function building module is used to establish an objective function with the multi-energy load virtual energy storage system's actual equipment output and the day-ahead dispatch plan as the multi-energy load virtual energy storage intra-day optimization dispatch target;

[0176] An operation constraint condition building module, used to establish operation constraint conditions for the multi-energy load virtual energy storage system;

[0177] The optimization scheduling module is used to solve the objective function under the operating constraints, obtain the multi-energy load virtual energy storage daily optimization scheduling strategy and execute it.

[0178] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0179] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0180] Compared with the prior art, the beneficial effects of the present invention include at least:

[0181] (1) The present invention takes into account the actual output of various virtual energy storage devices in the system, the difference between the real-time interactive power of the system and the power grid and the reference value of the day-ahead, and establishes an objective function with the minimum deviation between the actual output of the multi-energy load virtual energy storage system and the day-ahead scheduling plan as the intra-day optimization scheduling goal of the multi-energy load virtual energy storage. It also introduces relevant parameter constraints of virtual energy storage to achieve the unification of multi-energy virtual energy storage parameters, making the optimization model more realistic and able to effectively achieve the goal of minimizing the deviation between the actual output of the equipment and the day-ahead scheduling plan.

[0182] (2) The present invention improves the algorithm when solving the objective function. The population initialization method used can accelerate the convergence efficiency of the algorithm and improve the reliability of the solution; the individual position update stage can determine a more uniform and random position of male individuals, so that the algorithm escapes the local optimal solution, thereby maintaining population diversity and improving global search capabilities; the defense against natural enemies stage uses k-means clustering to search the entire solution space to find the location where the best natural enemy is generated, which improves the local optimization effect and makes the algorithm have better convergence; the safe position search algorithm used in the escape from natural enemies stage makes the safe position distribution more random and ergodic, thereby increasing the search space range and preventing local optimal solutions. The present invention solves the optimization model by improving the population initialization method and random number generation method of the traditional algorithm, has a higher exploration ability, and can avoid local optimality. The optimization results obtained by this method are of high quality and fast convergence speed, and better solve the problem of intraday optimization scheduling of virtual energy storage of multi-energy loads.

[0183] (3) The present invention can help the multi-energy load virtual energy storage system to achieve effective output of the intra-day optimization scheduling strategy, and improve the flexibility and reliability of the coordinated interaction between the system and the power grid.

[0184] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0185] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0186] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0187] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-energy load virtual energy storage intraday optimization scheduling method, characterized in that: The method comprises: Step 1: Establish an objective function with the minimum deviation between the actual output of the multi-energy load virtual energy storage system and the day-ahead dispatch plan as the multi-energy load virtual energy storage intraday optimization dispatch target; Step 2: Establishing operation constraints for the multi-energy load virtual energy storage system; Step 3: Under the operating constraints, solve the objective function, obtain the multi-energy load virtual energy storage daily optimization scheduling strategy and execute it.

2. A multi-energy load virtual energy storage intraday optimization scheduling method according to claim 1, characterized in that: The objective function established in step 1 is: Where: k is the initial time of optimization; λ i is the weight coefficient, i∈[1,2,…,q]; q = number of virtual energy storage devices p+1; When i is 1 to p, represents the real-time output power of virtual energy storage devices 1 to p at time k; When i takes q, represents the real-time interaction power between the virtual energy storage system and the power grid at time k; When i is 1 to p, represents the reference power of virtual energy storage devices 1 to p at time k; When i takes q, Represents the reference interaction power between the virtual energy storage system and the grid at time k.

3. A multi-energy load virtual energy storage intraday optimization scheduling method according to claim 1, characterized in that: In step 2, the operation constraints established for the multi-energy load virtual energy storage system include the multi-energy load virtual energy storage system operation power balance constraint and the multi-energy load virtual energy storage system operation virtual energy storage constraint; wherein the multi-energy load virtual energy storage system operation virtual energy storage constraint is: Where: P ves , They are respectively the virtual charging and discharging power of the virtual energy storage device, the minimum value of the virtual charging and discharging power, and the maximum value of the virtual charging and discharging power; SOC ves , They are respectively the virtual state of charge of the virtual energy storage device, the minimum value of the virtual state of charge, and the maximum value of the virtual state of charge; C ves , They are the virtual capacity, minimum virtual capacity, and maximum virtual capacity of the virtual energy storage device respectively; P ves,up / dn , They are respectively the virtual upward / downward climbing rate of the virtual energy storage device, the minimum value of the virtual upward / downward climbing rate, and the maximum value of the virtual upward / downward climbing rate.

4. The method for optimizing the scheduling of multi-energy load virtual energy storage within a day according to claim 1, characterized in that: In step 3, under the operating constraints, the objective function is solved to obtain and execute the multi-energy load virtual energy storage intraday optimization scheduling strategy, which specifically includes: Step 3.1: Initialize population parameters based on the objective function and operating constraints, including population size N, variable dimension M, maximum number of iterations T, and initial position of individuals; Step 3.2: If the number of iterations Then perform individual location update; Step 3.3: If Then the position of the defense against natural enemies stage is updated; Step 3.4: If n ≥ N, then update the position of the escape from natural enemies stage; Step 3.5: If the maximum number of iterations T is reached, stop the iteration and get the optimal solution; otherwise, jump to step 3.2 and increase the number of iterations n by 1.

5. A method for optimizing the scheduling of virtual energy storage for multiple energy loads during the day according to claim 4, characterized in that: The step 3.1 population initialization algorithm: Step 3.1.1: Set the population size N. Each individual is a set of solutions to optimize the objective function. There are M-dimensional variables in the solution. These variables are the variables to be optimized in the objective function and constraints. The variable value of each individual can calculate an objective function value through the objective function. Step 3.1.2: Set the maximum number of iterations T; Step 3.1.3: Establish the initial positions of the forward and reverse individuals to obtain the forward and reverse initial populations. The algorithm is as follows: in, are the initial positions of the forward and reverse individuals, n=1,2,…,N, m=1,2,…,M; r a is a random number in the range of 0-1; Represents the mth decision variable x n,m The upper and lower bounds of Step 3.1.4: Initialize individual x n,m : In the forward and reverse initial populations, N individuals with smaller objective function values ​​under corresponding operating constraints are selected to form the initial population.

6. A multi-energy load virtual energy storage intraday optimization scheduling method according to claim 4, characterized in that: The individual location update phase of step 3.2 is specifically as follows: Step 3.2.1: Identify dominant individuals Determine the dominant individual according to the objective function value under the operating constraint conditions corresponding to the current individual value. The dominant individual is the individual with the lowest objective function value under the corresponding operating constraint conditions. Step 3.2.2: When When determining male individuals Female individuals in, is a random vector between 0 and 1; r5, r6, r7 are random numbers between 0 and 1; r b 、r b-1 It is a random number between 0 and 1 generated for the b and b-1 times; q1, q2 are random integers between 0 and 1; I1 and I2 are random integers between 1 and 2; is the average of some randomly selected individuals; h1 and h2 are randomly selected from the five scenarios in formula (5); Step 3.2.3: Update the positions of male and female individuals respectively. The algorithm is as follows: Where f() represents the objective function value.

7. A multi-energy load virtual energy storage intraday optimization scheduling method according to claim 4, characterized in that: The step 3.3 of the defense against natural enemies stage is specifically as follows: Step 3.3.1: Use the k-means clustering algorithm to form m clusters, and the centroid of each cluster is the enemy m Location; Step 3.3.2: When When determining the position of the individual relative to the natural enemy The algorithm is as follows: in, is the random vector of levy motion; e is a uniformly distributed random number between 2 and 4; c is a uniformly distributed random number between 1 and 1.5; d is a uniformly distributed random number between 2 and 3; g is a random number uniformly distributed between -1 and 1; is a random vector of dimension 1×M; is the distance between the nth individual and its natural enemy, and the algorithm is as follows: Step 3.3.3: Position update in the defense against natural enemies phase. The algorithm is as follows:

8. A multi-energy load virtual energy storage intraday optimization scheduling method according to claim 4, characterized in that: The step 3.4 of escaping from natural enemies is specifically as follows: Step 3.4.1: Find a safe location The algorithm is as follows: Among them, r v 、r v-1 It is a random number between 0 and 1 generated at the vth and v-1th times; S1 is randomly selected from three scenarios, and the algorithm is as follows: in, is a random vector between 0 and 1; r 12 is a normally distributed random number; r 13 is a random number between 0 and 1. Step 3.4.2: Position update during the escape from natural enemies phase. The algorithm is as follows:

9. A multi-energy load virtual energy storage intraday optimization scheduling system, using the method described in any one of claims 1 to 8, characterized in that: The system comprises: An objective function building module is used to establish an objective function with the multi-energy load virtual energy storage system's actual equipment output and the day-ahead dispatch plan as the multi-energy load virtual energy storage intra-day optimization dispatch target; An operation constraint condition building module, used to establish operation constraint conditions for the multi-energy load virtual energy storage system; The optimization scheduling module is used to solve the objective function under the operating constraints, obtain the multi-energy load virtual energy storage daily optimization scheduling strategy and execute it.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images