A virtual power plant optimization scheduling method based on improved dung beetle optimization algorithm

Abstract

The invention discloses a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, comprising: taking the operation economic cost and environmental protection cost of the virtual power plant as the target, constructing a virtual power plant hybrid environmental economic multi-objective optimization scheduling model; setting multi-dimensional dynamic constraint conditions according to the power generation power of the power generation subject and the charge state of the energy storage system; optimizing and solving the virtual power plant hybrid environmental economic multi-objective optimization scheduling model through the improved dung beetle optimization algorithm, calculating the crowding distance of the dung beetle individuals after non-dominated sorting, and adjusting the dung beetle population distribution according to the crowding distance; adjusting the search radius and disturbance intensity of the dung beetle individuals according to the number of iterations; updating the dung beetle individuals by using a boundary control mechanism; iteratively screening out the optimal power scheduling plan by using the improved dung beetle optimization algorithm; the invention uses the improved dung beetle optimization algorithm to improve scheduling efficiency, reduce operation economic costs, and enhance support for environmental protection.

Description

Technical Field

[0001] The invention belongs to the technical field of virtual power plants, and in particular relates to a virtual power plant optimization scheduling method by improving a dung beetle optimization algorithm. Background Art

[0002] With the development of renewable energy, Virtual Power Plant (VPP) has attracted wide attention as a solution for distributed energy integration and dispatch. Virtual power plants manage fluctuating energy sources such as wind power generation and photovoltaic power generation in a unified manner to reduce dependence on traditional fossil energy, and promote the development of power systems in the direction of clean, efficient, safe and economical.

[0003] However, due to the instability of wind power and photovoltaic power generation, how to achieve multi-objective optimization scheduling has become a challenge. Early optimization scheduling often takes the economic cost of operation as the goal, lacks consideration of environmental pollution or fails to reasonably set the weight of the two when weighing economic and environmental goals. A single optimization method with economic goals as the core is difficult to adapt to the comprehensive requirements of modern energy management for environmental protection and operation efficiency. At the same time, the economic optimization scheduling of virtual power plants is mostly a multi-constrained, nonlinear optimization problem. Applying traditional optimization algorithms in the optimization field will not only fall into the problems of local optimality and low scheduling accuracy, but also cause frequent power imbalances and high adjustment costs due to the lack of dynamic multi-dimensional constraints. Although existing research has gradually introduced intelligent optimization algorithms, such as particle swarm optimization (PSO) and genetic algorithms (GA), these algorithms still have certain limitations when facing multi-objective and dynamic weight scheduling, and will also fall into the problem of local optimality. Summary of the invention

[0004] The present invention provides a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, aiming to improve scheduling efficiency, reduce operating economic costs, and enhance support for environmental protection by utilizing the improved dung beetle optimization algorithm.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A first aspect of the present invention provides a method for optimizing the scheduling of a virtual power plant based on an improved dung beetle optimization algorithm, comprising:

[0007] Determine the power generation entities and energy storage systems that participate in the scheduling of the virtual power plant, and build a hybrid environmental economic multi-objective optimization scheduling model for the virtual power plant with the operating economic cost and environmental protection cost of the virtual power plant as the target; set the ramp rate constraint, power balance constraint and energy storage system state of charge constraint for the hybrid environmental economic multi-objective optimization scheduling model of the virtual power plant according to the power generation of the power generation entity and the state of charge of the energy storage system;

[0008] The hybrid environment-economic multi-objective optimization scheduling model of virtual power plant is solved by the improved dung beetle optimization algorithm, including:

[0009] The power dispatching plan of the virtual power plant is used as the dung beetle individuals to generate the dung beetle population; the crowding distance of the dung beetle individuals is calculated after non-dominated sorting, and the dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance, and the dung beetle population distribution is adjusted; the search radius and disturbance intensity of the dung beetle individuals are adjusted according to the number of iterations; the dung beetle individuals are updated using the boundary control mechanism, and the inferior dung beetle individuals are replaced by elite dung beetle individuals; the optimal power dispatching plan of the virtual power plant hybrid environmental economic multi-objective optimization dispatching model is iteratively screened out through the improved dung beetle optimization algorithm.

[0010] Furthermore, taking economic operation cost and environmental protection cost as the target, a virtual power plant hybrid environmental economic multi-objective optimization scheduling model is constructed. The specific process is:

[0011] Dynamically set economic operation cost weights and environmental protection cost weight Finally, the weighted sum of the operating economic cost and environmental protection cost is expressed as:

[0012]

[0013]

[0014]

[0015] In the formula, Solve the problem for minimization; The comprehensive cost output of the hybrid environmental economic multi-objective optimization dispatch model for virtual power plants; The economic cost of operating a virtual power plant; Environmental protection costs for virtual power plants; and Speed ​​parameter adjusted for cost weight; is the load power of the virtual power plant; is the base load value.

[0016] Furthermore, the ramp rate constraint is set for the hybrid environment economic multi-objective optimization scheduling model of the virtual power plant according to the power generation power of the power generation entity. The specific process is:

[0017]

[0018]

[0019] In the formula, is the maximum ramp rate of the virtual power plant at time t; is the initial climbing rate; is the sensitivity coefficient; is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at time t+1; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment.

[0020] Furthermore, the state of charge constraints of the energy storage system are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model according to the power generation power of the power generation entity and the state of charge of the energy storage system, including:

[0021] Set the upper and lower limits of the state of charge of the energy storage system of the virtual power plant, and express the formula:

[0022]

[0023]

[0024] In the formula, The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the initial state of energy storage charge of the virtual power plant; and is the set adjustment weight; is the load power of the virtual power plant at time t; is the proportion of renewable energy generation in total power generation at time t;

[0025] The state of charge constraint of the energy storage system is set according to the upper and lower limits of the state of charge of the energy storage system. The expression formula is:

[0026]

[0027] In the formula, is the charge state of the energy storage system in the virtual power plant at time t.

[0028] Furthermore, according to the power generation of the power generation entity and the charge state of the energy storage system, the power balance constraints are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model, including:

[0029]

[0030]

[0031]

[0032] in, is the adjustment factor, is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment; N is the number of power generation units in the virtual power plant; The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the state of charge of the energy storage system of the virtual power plant at time t; is the output power of the energy storage system in the virtual power plant at time t; is the maximum output power of the energy storage system in the virtual power plant.

[0033] Furthermore, it also includes: when the state of charge of the energy storage system of the virtual power plant is less than the lower limit threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power and load power of each power generation entity in the virtual power plant. , the expression formula is:

[0034]

[0035] When the state of charge of the energy storage system of the virtual power plant is greater than the upper threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power of the energy storage system. , the expression formula is:

[0036]

[0037] In the formula, is the load power of the virtual power plant.

[0038] Furthermore, the crowding distance of the dung beetle individuals is calculated after non-dominated sorting, and the dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance, and the distribution of the dung beetle population is adjusted, specifically including:

[0039] Calculate the crowding distance of dung beetle individuals after non-dominated sorting of dung beetle individuals and the average crowding distance , the calculation formula is:

[0040]

[0041]

[0042] Where M is the number of objective functions in the hybrid environmental economic multi-objective optimization scheduling model of virtual power plants, is the objective function Previous The target value of dung beetle individuals, is the objective function Previous The target value for each dung beetle individual; and is the objective function in the current non-dominated frontier layer The maximum and minimum values ​​of ; K is the number of dung beetle individuals in the frontier layer; dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance;

[0043] According to crowding distance and the average crowding distance Introducing density adjustment factor , through the density adjustment factor Dynamically adjust The crowding distance of a dung beetle individual is calculated as follows:

[0044]

[0045]

[0046] in, is the density adjustment coefficient, is the crowding distance after dynamic adjustment.

[0047] Furthermore, the search radius and disturbance intensity of the dung beetle individual are adjusted according to the number of iterations, including:

[0048]

[0049]

[0050] in, and is the initial search radius and maximum disturbance intensity of the dung beetle individual, is the maximum number of iterations, s is the current number of iterations, is the decay rate control parameter, is the search radius of the dung beetle individual in the sth iteration; is the disturbance intensity of the dung beetle individual in the sth iteration; is a natural exponential function.

[0051] Furthermore, the boundary control mechanism is used to update the dung beetle individuals, including:

[0052] When the position of a dung beetle individual is less than the lower boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the lower boundary; when the position of a dung beetle individual is greater than the upper boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the upper boundary; the expression formula is:

[0053]

[0054] In the formula, is the location of the individual dung beetle; It is the lower boundary of dung beetle population distribution; It is the upper boundary of dung beetle population distribution; is a random number uniformly distributed in the interval [0,1].

[0055] Furthermore, the elite dung beetle individuals are used to replace the inferior dung beetle individuals, specifically including:

[0056]

[0057] in, is the location of the current elite dung beetle individual, is the small perturbation coefficient, is a random number uniformly distributed in the interval [0,1].

[0058] A second aspect of the present invention provides an electronic device comprising a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the method described in the first aspect of the present invention.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The improved dung beetle optimization algorithm provided in the present invention introduces the crowding distance calculation of dynamic adaptive density, dynamically adjusts the crowding distance between individuals through the density adjustment coefficient, and preferentially retains individuals with larger crowding distances, thereby ensuring the diversity of individual distribution in the solution space, thereby improving the comprehensiveness of the global search and avoiding the problem of falling into the local optimum; at the same time, dynamic constraint adjustment is performed through the boundary control mechanism, and the elite replacement mechanism is used to enhance the population quality, ensuring the continuous optimization of the population, thereby accelerating the convergence speed of the algorithm. Therefore, the improved algorithm can achieve the effect of global optimization, improved convergence speed, and higher scheduling accuracy.

[0061] The present invention dynamically adjusts multi-dimensional constraints through a real-time response mechanism, and the dual optimization weights of the virtual power plant hybrid environmental economic multi-objective optimization scheduling model are dynamically adjusted with load changes and the proportion of renewable energy power generation, which greatly improves the scheduling flexibility of the virtual power plant and enables the scheduling plan to flexibly respond to different operating scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm provided in Example 1 of the present invention;

[0063] Figure 2 It is a curve diagram showing the dynamic change of the weights of the economic operation cost and the environmental protection cost ratio under the dynamic multi-objective setting provided in Example 1 of the present invention;

[0064] Figure 3 is a flow chart of an improved dung beetle optimization algorithm provided in Example 1 of the present invention;

[0065] Figure 4 is a balance relationship diagram between operating economic cost and environmental protection cost of different feasible solutions of the improved dung beetle optimization algorithm provided in Example 1 of the present invention when there is no weight bias;

[0066] Figure 5 It is a diagram of the participation of various entities in the scheduling of the virtual power plant under the condition of the lowest operating economic cost provided by Example 1 of the present invention;

[0067] Figure 6 This is a diagram of the participation of various entities in the scheduling of a virtual power plant under the condition of the lowest environmental protection cost provided by Example 1 of the present invention;

[0068] Figure 7 This is a diagram showing the participation of various entities in the scheduling of a virtual power plant under the condition of the lowest comprehensive total cost provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0070] Example 1

[0071] like Figure 1 As shown, this implementation provides a virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, including:

[0072] Determine the power generation entities and energy storage systems participating in the scheduling of the virtual power plant. In this implementation, the power generation entities include diesel generators, gas turbines, wind power generation and photovoltaic power generation; the energy storage system includes batteries; the virtual power plant also has a power trading part with the main grid.

[0073] Taking the operation economic cost and environmental protection cost of virtual power plants as the targets, a hybrid environmental economic multi-objective optimization scheduling model of virtual power plants is constructed. The specific process is as follows:

[0074] Dynamically set operating economic cost weights and environmental protection cost weight Finally, the weighted sum of the operating economic cost and environmental protection cost is expressed as:

[0075]

[0076]

[0077]

[0078] In the formula, Solve the problem for minimization; The comprehensive cost output of the hybrid environmental economic multi-objective optimization dispatch model for virtual power plants; The economic cost of operating a virtual power plant; Environmental protection costs for virtual power plants; and Speed ​​parameter adjusted for cost weight; is the load power of the virtual power plant; is the base load value.

[0079] By adjusting the weight of the operating economic cost and the weight of the environmental protection cost, the optimal scheduling scheme can be obtained, such as Figure 5 As shown, under the condition of lowest operating economic cost, =1, = 0, non-renewable energy sources such as gas turbines and diesel generators generate more electricity, while renewable energy sources generate less electricity; Figure 6 As shown in Figure 2, under the condition of lowest environmental protection cost, =0, =1, non-renewable energy sources such as gas turbines and diesel generators generate less electricity, while renewable energy sources generate more electricity; Figure 7 As shown, when the comprehensive total cost is the lowest, =0.5, =0.5, non-renewable energy generation and renewable energy generation are both dispatched in a relatively balanced manner; the cost weight coefficient is adjusted in real time according to different work requirements, which greatly improves the dispatch flexibility of the virtual power plant and enables the dispatch plan to flexibly respond to different operating scenarios.

[0080] According to the power generation power of the power generation entity, the ramp rate constraint is set for the virtual power plant hybrid environment economic multi-objective optimization scheduling model. The specific process is:

[0081]

[0082]

[0083] In the formula, is the maximum ramp rate of the virtual power plant at time t; is the initial climbing rate; is the sensitivity coefficient; is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at time t+1; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment.

[0084] According to the power generation of the power generation entity and the charge state of the energy storage system, the charge state constraints of the energy storage system are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model, including:

[0085] Set the upper and lower limits of the state of charge of the energy storage system of the virtual power plant, and express the formula:

[0086]

[0087]

[0088] In the formula, The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the initial state of energy storage charge of the virtual power plant; and is the set adjustment weight; is the load power of the virtual power plant at time t; is the proportion of renewable energy generation in total power generation at time t;

[0089] The state of charge constraint of the energy storage system is set according to the upper and lower limits of the state of charge of the energy storage system. The expression formula is:

[0090]

[0091] In the formula, is the charge state of the energy storage system in the virtual power plant at time t.

[0092] According to the power generation of the power generation entity and the charge state of the energy storage system, the power balance constraints are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model, including:

[0093]

[0094]

[0095]

[0096] in, is the adjustment factor, is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment; N is the number of power generation units in the virtual power plant; The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the state of charge of the energy storage system of the virtual power plant at time t; is the output power of the energy storage system in the virtual power plant at time t; is the maximum output power of the energy storage system in the virtual power plant.

[0097] When the state of charge of the energy storage system of the virtual power plant is less than the lower limit threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power and load power of the virtual power plant. , the expression formula is:

[0098]

[0099] When the state of charge of the energy storage system of the virtual power plant is greater than the upper threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power of the energy storage system. , the expression formula is:

[0100]

[0101] In the formula, is the load power of the virtual power plant; in this embodiment, the main grid interactive power The initial value of is set to 0; when the state of charge of the energy storage system of the virtual power plant is less than the lower limit threshold, the output power of the energy storage system is reduced. , increase the main network interaction power ; When the state of charge of the energy storage system of the virtual power plant is greater than the upper threshold, the energy storage system is used for power supply first to reduce the main grid interaction power Dynamically adjusting power distribution according to the charge state of the energy storage system can make more rational use of power resources and improve energy efficiency; it helps to maintain a balance between power supply and demand and ensure stable operation of the power grid.

[0102] like Figure 2 As shown, based on the changes in the weights of these factors, this embodiment can more intelligently adjust the economic and environmental weights to reflect the following situations: during high-load periods (such as morning peaks and evening peaks), the scheduling target is more focused on economic costs, and low-cost power generation entities such as gas turbines are prioritized to reduce power supply costs; during high renewable energy power generation periods (such as photovoltaic power generation peaks during the day), more emphasis is placed on environmental protection costs, and wind power or photovoltaic clean energy is prioritized.

[0103] like Figure 3 As shown in the figure, the virtual power plant hybrid environmental economic multi-objective optimization scheduling model is solved by the improved dung beetle optimization algorithm, including:

[0104] The power dispatching plan of the virtual power plant is used as the dung beetle individual to generate the dung beetle population; the dung beetle individuals are non-dominated sorted and the crowding distance of the dung beetle individuals is calculated; the dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance, and the distribution of the dung beetle population is adjusted, including:

[0105] After performing non-dominated sorting on dung beetle individuals, perform dynamic non-dominated sorting on dung beetle individuals and calculate the crowding distance of dung beetle individuals. and the average crowding distance , the calculation formula is:

[0106]

[0107]

[0108] Where M is the number of objective functions in the hybrid environmental economic multi-objective optimization scheduling model of virtual power plants, is the objective function Previous The target value of dung beetle individuals, is the objective function Previous The target value for each dung beetle individual; and is the objective function in the current non-dominated frontier layer The maximum and minimum values ​​of ; K is the number of dung beetle individuals in the frontier layer; dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance; that is, dung beetle individuals with larger crowding distance have better distribution characteristics in the solution space and are more likely to lead to finding the global optimal solution, so it is more likely to become an elite solution.

[0109] According to crowding distance and the average crowding distance Introducing density adjustment factor , through the density adjustment factor Dynamically adjust The crowding distance of a dung beetle individual is calculated as follows:

[0110]

[0111]

[0112] in, is the density adjustment coefficient, is the crowding distance after dynamic adjustment; the above formula can realize that when the crowding distance of dung beetle individuals is less than the average distance, it will be weighted and the individuals with crowding distance greater than the average distance will be retained, thereby achieving a balanced diversity distribution in space.

[0113] Adjust the search radius and disturbance intensity of the dung beetle individuals according to the number of iterations, including:

[0114]

[0115]

[0116] in, and is the initial search radius and maximum disturbance intensity of the dung beetle individual, is the maximum number of iterations, s is the current number of iterations, is the decay rate control parameter, is the search radius of the dung beetle individual in the sth iteration; is the disturbance intensity of the dung beetle individual in the sth iteration; is a natural exponential function.

[0117] According to the above formula, the search radius is large and the disturbance intensity is high in the early stage, so the global search can be performed; in the later stage, the radius is reduced, and the disturbance intensity also decreases with the number of iterations, and a detailed exploration of the local area helps the algorithm gradually converge to the global optimal solution. And through the elite information propagation mechanism, the information of the elite dung beetle individuals is spread to other individuals for the dung beetle to follow the behavior, that is, to move closer to the optimal solution.

[0118] Update dung beetle individuals using boundary control mechanisms, including:

[0119] When the position of a dung beetle individual is less than the lower boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the lower boundary; when the position of a dung beetle individual is greater than the upper boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the upper boundary; the expression formula is:

[0120]

[0121] In the formula, is the location of the individual dung beetle; It is the lower boundary of dung beetle population distribution; It is the upper boundary of dung beetle population distribution; is a random number uniformly distributed in the interval [0,1].

[0122] Use elite dung beetle individuals to replace inferior dung beetle individuals, including:

[0123]

[0124] in, is the location of the current elite dung beetle individual, is the small perturbation coefficient, is a random number uniformly distributed in the interval [0,1]. This is the replaced dung beetle individual.

[0125] The improved dung beetle optimization algorithm is used to iteratively screen the power dispatching schemes and output the optimal power dispatching scheme. The improved dung beetle optimization algorithm provided in this embodiment introduces a crowding distance calculation that dynamically adapts to the density, dynamically adjusts the crowding distance between dung beetle individuals through the density adjustment coefficient, and gives priority to retaining individuals with larger crowding distances, thereby ensuring the diversity of individual distribution in the solution space, thereby improving the comprehensiveness of the global search and avoiding the problem of falling into the local optimum; at the same time, dynamic constraint adjustment is performed through the boundary control mechanism, and the elite replacement mechanism is used to enhance the quality of the population, ensuring that the population is continuously optimized, thereby accelerating the convergence speed of the algorithm. Therefore, the improved algorithm can achieve the effect of global optimization, improved convergence speed, and higher dispatching accuracy.

[0126] like Figure 4 As shown, in this embodiment, the maximum number of iterations is set to 100, the population size is 100, and the improved dung beetle optimization algorithm has no weight bias in the objective function (i.e. =0.5, =0.5), the balance between the economic cost of operation and the environmental protection cost of different feasible solutions. If a specific weight is set, for example, the economic cost weight accounts for a higher proportion, the feasible solutions will be more concentrated in areas with lower economic cost of operation.

[0127] Example 2

[0128] This embodiment provides an electronic device, including a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the virtual power plant optimization scheduling method described in Example 1.

[0129] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A virtual power plant optimization scheduling method based on an improved dung beetle optimization algorithm, characterized in that: include: Determine the power generation entities and energy storage systems involved in the scheduling of the virtual power plant, take the operating economic cost and environmental protection cost of the virtual power plant as the target, and build a hybrid environmental economic multi-objective optimization scheduling model for the virtual power plant; the specific process: Dynamically set economic operation cost weights and environmental protection cost weight Finally, the weighted sum of economic operation cost and environmental protection cost is calculated, and the expression formula is: ； ； ； In the formula, Solve the problem for minimization; The comprehensive cost output of the hybrid environmental economic multi-objective optimization dispatch model for virtual power plants; The economic cost of operating a virtual power plant; Environmental protection costs for virtual power plants; and Speed ​​parameter adjusted for cost weight; is the load power of the virtual power plant; is the reference load value; According to the power generation power of the power generation entity, the ramp rate constraint is set for the virtual power plant hybrid environment economic multi-objective optimization scheduling model. The specific process is: ； ； In the formula, is the maximum ramp rate of the virtual power plant at time t; is the initial climbing rate; is the sensitivity coefficient; is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at time t+1; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment; According to the power generation of the power generation entity and the charge state of the energy storage system, the charge state constraints of the energy storage system are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model, including: Set the upper and lower limits of the state of charge of the energy storage system of the virtual power plant, and express the formula: ； ； In the formula, The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the initial state of energy storage charge of the virtual power plant; and is the set adjustment weight; is the load power of the virtual power plant at time t; is the proportion of renewable energy generation in total power generation at time t; The state of charge constraint of the energy storage system is set according to the upper and lower limits of the state of charge of the energy storage system. The expression formula is: ； In the formula, is the state of charge of the energy storage system in the virtual power plant at time t; According to the power generation of the power generation entity and the charge state of the energy storage system, the power balance constraints are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model, including: ； ； ； in, is the adjustment factor, is the load power of the virtual power plant at time t; is the output power of the i-th power generation unit in the virtual power plant at the t-th moment; N is the number of power generation units in the virtual power plant; The upper limit of the state of charge of the energy storage system of the virtual power plant; The lower limit of the charge state of the energy storage system of the virtual power plant; is the state of charge of the energy storage system of the virtual power plant at time t; is the output power of the energy storage system in the virtual power plant at time t; is the maximum output power of the energy storage system in the virtual power plant; According to the power generation of the power generation entity and the charge state of the energy storage system, the ramp rate constraint, power balance constraint and charge state constraint of the energy storage system are set for the virtual power plant hybrid environmental economic multi-objective optimization scheduling model; The hybrid environment-economic multi-objective optimization scheduling model of virtual power plant is solved by the improved dung beetle optimization algorithm, including: The power dispatching plan of the virtual power plant is used as the dung beetle individuals to generate the dung beetle population; the crowding distance of the dung beetle individuals is calculated after non-dominated sorting, and the dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance, and the dung beetle population distribution is adjusted; the search radius and disturbance intensity of the dung beetle individuals are adjusted according to the number of iterations; the dung beetle individuals are updated using the boundary control mechanism, and the inferior dung beetle individuals are replaced by elite dung beetle individuals; the optimal power dispatching plan of the virtual power plant hybrid environmental economic multi-objective optimization dispatching model is iteratively screened out through the improved dung beetle optimization algorithm.

2. The virtual power plant optimization scheduling method according to claim 1, characterized in that: Also includes: When the state of charge of the energy storage system of the virtual power plant is less than the lower limit threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power and load power of each power generation entity in the virtual power plant. , the expression formula is: ； When the state of charge of the energy storage system of the virtual power plant is greater than the upper threshold, the main grid interactive power in the virtual power plant is adjusted according to the output power of the energy storage system. , the expression formula is: ； In the formula, is the load power of the virtual power plant.

3. The virtual power plant optimization scheduling method according to claim 1, characterized in that: After non-dominated sorting of dung beetle individuals, the crowding distance of dung beetle individuals is calculated. According to the crowding distance, dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals, and the distribution of dung beetle population is adjusted, including: Calculate the crowding distance of dung beetle individuals after non-dominated sorting and the average crowding distance , the calculation formula is: ； ； Where M is the number of objective functions in the hybrid environmental economic multi-objective optimization scheduling model of virtual power plants, is the objective function Previous The target value of dung beetle individuals, is the objective function Previous The target value for each dung beetle individual; and is the objective function in the current non-dominated frontier layer The maximum and minimum values ​​of ; K is the number of dung beetle individuals in the frontier layer; dung beetle individuals are divided into elite dung beetle individuals and inferior dung beetle individuals according to the crowding distance; According to crowding distance and the average crowding distance Introducing density adjustment factor , through the density adjustment factor Dynamically adjust The crowding distance of a dung beetle individual is calculated as follows: ； ； in, is the density adjustment coefficient, is the crowding distance after dynamic adjustment.

4. The virtual power plant optimization scheduling method according to claim 1, characterized in that: Adjust the search radius and disturbance intensity of the dung beetle individuals according to the number of iterations, including: ； ； in, and is the initial search radius and maximum disturbance intensity of the dung beetle individual, is the maximum number of iterations, s is the current number of iterations, is the decay rate control parameter, is the search radius of the dung beetle individual in the sth iteration; is the disturbance intensity of the dung beetle individual in the sth iteration; is a natural exponential function.

5. The virtual power plant optimization scheduling method according to claim 1, characterized in that: Update dung beetle individuals using boundary control mechanisms, including: When the position of a dung beetle individual is less than the lower boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the lower boundary; when the position of a dung beetle individual is greater than the upper boundary of the dung beetle population distribution, the position of the dung beetle individual is reset to a random value within the upper boundary; the expression formula is: ； In the formula, is the location of the individual dung beetle; It is the lower boundary of dung beetle population distribution; It is the upper boundary of dung beetle population distribution; is a random number uniformly distributed in the interval [0,1].

6. An electronic device comprising a storage medium and a processor; the storage medium is used to store instructions; characterized in that: The processor is used to operate according to the instructions to execute the virtual power plant optimization scheduling method described in any one of claims 1 to 5.

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