Virtual power plant control method, device and equipment based on enhanced snake optimizer

Abstract

This application discloses a virtual power plant control method, apparatus, and electronic device based on an enhanced snake optimizer. The method includes: constructing a power planning model for the virtual power plant based on the operational constraints of distributed equipment connected to a target distribution network and the physical constraints of the target distribution network. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints constrain the operating state of the distributed equipment, and the physical constraints constrain the current, voltage, and topology of the target distribution network. The power planning model is solved using a target algorithm to obtain a target scheduling scheme for the virtual power plant. The operation of the virtual power plant is controlled based on the target scheduling scheme. This application can achieve safe and economical operation of the virtual power plant.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a virtual power plant control method, apparatus and equipment based on an enhanced snake optimizer. Background Technology

[0002] With the large-scale integration of distributed energy resources such as photovoltaic power generation, wind power generation, energy storage systems, and diesel generators into distribution networks, the operational complexity of power systems has increased significantly. Virtual power plants, as an effective means of aggregating and managing these distributed devices, can improve the economy and reliability of power grid operation by coordinating and scheduling various resources. However, existing virtual power plant scheduling methods typically focus only on power balance and operating cost optimization. This approach may result in a scheduling scheme that is theoretically economically optimal but infeasible in actual distribution networks, even leading to equipment damage or power outages. Summary of the Invention

[0003] This application provides a virtual power plant control method, apparatus, and equipment based on an enhanced snake optimizer, aiming to ensure the safe and economical operation of the virtual power plant.

[0004] In a first aspect, embodiments of this application provide a virtual power plant control method based on an enhanced snake optimizer, comprising: Based on the operational constraints of the distributed devices connected to the target distribution network and the physical constraints of the target distribution network, a power planning model for a virtual power plant is constructed. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints are used to constrain the operating state of the distributed devices, and the physical constraints are used to constrain the current, voltage, and topology of the target distribution network. The target scheduling scheme for the virtual power plant is obtained by solving the power planning model using a target algorithm. The virtual power plant is controlled based on the target scheduling scheme.

[0005] Secondly, embodiments of this application provide a virtual power plant control device based on an enhanced snake optimizer, comprising: A construction module is used to construct a power planning model for a virtual power plant based on the operational constraints of the distributed devices connected to the target distribution network and the physical constraints of the target distribution network. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints are used to constrain the operating state of the distributed devices, and the physical constraints are used to constrain the current, voltage, and topology of the target distribution network. The solution module is used to solve the power planning model using the objective algorithm to obtain the target scheduling scheme of the virtual power plant; The control module is used to control the operation of the virtual power plant based on the target scheduling scheme.

[0006] Thirdly, embodiments of this application provide an electronic device, including: at least one processor; at least one memory for storing at least one program; and when at least one of the programs is executed by at least one of the processors, implementing the virtual power plant control method based on the enhanced snake optimizer as described in the first aspect.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the virtual power plant control method based on the enhanced snake optimizer as described in the first aspect.

[0008] Fifthly, embodiments of this application provide a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a communication device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the communication device to perform the virtual power plant control method based on the enhanced snake optimizer as described in the first aspect.

[0009] In this embodiment, a power planning model for a virtual power plant is constructed based on the operational constraints of distributed devices connected to the target distribution network and the physical constraints of the target distribution network. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints constrain the operating state of the distributed devices, and the physical constraints constrain the current, voltage, and topology of the target distribution network. A target algorithm is used to solve the power planning model to obtain a target scheduling scheme for the virtual power plant. The operation of the virtual power plant is then controlled based on this target scheduling scheme. In this way, while optimizing the operating cost of the virtual power plant, the scheduling scheme can be ensured to meet the physical constraints of the distribution network, thereby achieving safe and economical operation of the virtual power plant. Attached Figure Description

[0010] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0011] Figure 1 This is a flowchart illustrating the virtual power plant control method based on an enhanced snake optimizer provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] It should be understood that in the description of the embodiments of this application, the use of terms such as "first" and "second" is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order of the technical features indicated. "At least one" refers to one or more, and "more" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any group of these items, including any group of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0014] To facilitate understanding of the solutions in the embodiments of this application, some contents involved in the embodiments of this application are described below: Among related technologies, the Virtual Power Plant (VPP), as an emerging power system coordination and management technology, aggregates distributed energy resources (DERs) into a unified operating entity, enabling functions such as participating in the electricity market, optimizing resource allocation, and improving the utilization rate of renewable energy. However, traditional VPP optimization methods typically treat the distribution network as a simple power balance node, ignoring the physical constraints of the distribution network. This can lead to problems such as voltage exceeding limits, current overload, or topology infeasibility in actual distribution networks, affecting the safe and stable operation of the power grid.

[0015] Based on this, this application provides a virtual power plant control method based on an enhanced snake optimizer. By combining the operating constraints of distributed devices with the physical constraints of the distribution network to construct a power planning model, and using an objective algorithm to solve it, a target scheduling scheme that simultaneously satisfies the economic optimization objective and the physical constraints of the power grid is obtained, thereby realizing the safe and economical operation of the virtual power plant.

[0016] Specifically, such as Figure 1 As shown, the virtual power plant control method based on the enhanced snake optimizer includes: S101. Based on the operational constraints of the distributed equipment connected to the target distribution network and the physical constraints of the target distribution network, a power planning model for the virtual power plant is constructed, wherein the power planning model aims to minimize the operating cost of the virtual power plant.

[0017] In this embodiment, the target distribution network refers to the distribution network to which the virtual power plant is connected, and it can be a distribution network with any topology. For example, the target distribution network can be an IEEE standard test system, which includes 33 nodes and 32 branches.

[0018] The aforementioned distributed devices include various power generation devices, energy storage devices, and power consumption devices connected to the target distribution network. These distributed devices may include generators, battery energy storage systems, photovoltaic power generation devices, wind power generation devices, and electrical loads. For example, in one application example of this application, the target distribution network is an IEEE power grid, which includes 33 nodes and 32 branches. Specifically, a diesel generator can be connected to nodes 10, 16, 20, 26, and 30 of the IEEE power grid; a photovoltaic power station can be connected to nodes 18 and 28; a battery energy storage system can be connected to nodes 10, 14, and 29; and a wind power generation device can be connected to node 17. Electrical loads are connected to each node, and the load value of each node is determined according to standard IEEE 33-node system data.

[0019] Specifically, the electricity load can be synthesized into a daily load curve based on the electricity consumption patterns of three types of users: residential users, commercial users, and industrial users. The normalized electricity consumption patterns of the three types of users can be amplified according to the actual electricity consumption and energy ratio based on formula (1) to obtain a load curve with actual power values.

[0020] Formula (1) is:

[0021] Where k represents different load categories, Indicates the time step. This represents the power of the k-th type of load at time t. To normalize the shape of the load curve, Indicates the scaling factor. Indicates the energy percentage coefficient. This indicates the total electricity consumption for the whole day. This indicates three types of users: residential, commercial, and industrial.

[0022] After obtaining the power consumption of the three types of users at each time, the power of the three types of loads at the same time can be added together by formula (2) to obtain the total power demand at that time.

[0023]

[0024] in, This represents the power of the k-th type of load at time t. This represents the total power at time t.

[0025] Based on the above formulas (1) and (2), a 24-hour total load curve can be synthesized, and then this total load curve can be distributed to each node of the IEEE power grid according to the peak load ratio of each node.

[0026] Furthermore, for photovoltaic power plants on the target distribution network, the Haurwitz clear-sky irradiance formula can be used for photovoltaic power generation modeling. Clear-sky global horizontal irradiance (GHI) is calculated solely from the solar zenith angle. Specifically, the Haurwitz relation is formula (3):

[0027] in, This indicates the angle between the sunlight and the normal to the ground. This represents the cosine value of the solar zenith angle. This represents the global horizontal irradiance under clear skies. Using the above formula (3), the intensity of solar radiation received by a horizontal surface under clear, cloudless conditions can be estimated based on the solar altitude angle.

[0028] After calculating the solar radiation intensity, the horizontal irradiance (GHI) can be converted to the array plane (POA) irradiance on the tilted panel using formula (4). Formula (4) is:

[0029] in, This represents the total irradiance received on the tilted panel. Indicates the direct irradiance on a horizontal plane. Represents the diffuse irradiance on a horizontal plane. This indicates the ratio of direct light from an inclined surface to that from a horizontal surface. Indicates the angle between the tilted panel and the horizontal plane. This represents the ground albedo. Therefore, using formula (4), sunlight on the horizontal plane can be converted to light on the inclined panel.

[0030] Since the above calculations are based on a clear-sky baseline, it is also necessary to simulate the real-time fluctuations caused by cloud cover in actual weather through random cloud attenuation, thereby generating output power data of the photovoltaic power station that conforms to real-world light variations. The random cloud attenuation formula is shown in formula (5):

[0031] in, This represents the cloud transmittance at time t. For persistence coefficient, This is a random perturbation, and the output of the above formula (5) is... The output of formula (4) can be adjusted. The corrected effective irradiance is obtained by making corrections. .

[0032] After correcting the total irradiance received on the tilted panel, the photovoltaic cells in the photovoltaic power station can be modeled, and the actual operating temperature of the photovoltaic cells in the photovoltaic power station can be calculated based on the corrected irradiance. The calculation formula is shown in the following formula (6):

[0033] in, This indicates the actual operating temperature of the photovoltaic cell. Indicates ambient temperature. This indicates the rated operating temperature of the photovoltaic cell. This represents the effective irradiance of the tilted panel. Using the above formula (6), the battery temperature can be estimated based on the ambient temperature and incident irradiance.

[0034] After determining the actual operating temperature of the photovoltaic cells and the corrected effective irradiance, the output power of the photovoltaic power station can be calculated using formula (7):

[0035] in, This represents the output power of the photovoltaic power station at time t. This indicates the rated maximum output power. Indicates reference efficiency. Indicates the effective irradiance of the tilted panel at time t. This indicates the total area of ​​the photovoltaic panel. Indicates the temperature coefficient. The value t represents the actual operating temperature of the photovoltaic cell at time t.

[0036] For wind power generation equipment on the target distribution network, the output power of the wind power generation equipment can also be modeled and calculated. First, the base wind speed distribution can be calculated, specifically using a two-parameter Weibull distribution model, as shown in formula (8):

[0037] in, Indicates wind speed. Indicates shape parameters, This represents a scale parameter, which is related to the average wind speed. Let u be the probability density of the output wind speed. The above formula (8) can describe the long-term statistical pattern of wind speed in a region and determine the probability density of wind speeds at different speeds.

[0038] After calculating the long-term statistical regularity of wind speed, a wind speed sequence with diurnal variation and time correlation can be generated using formula (9):

[0039] in, The scale parameter representing time t, i.e., the average wind speed level. The reference scale parameter is set according to the output of formula (8). Indicates the diurnal modulation amplitude. This represents the number of hours corresponding to time t. Indicates phase shift, This represents the wind speed at time t. Represents the moving average coefficient. Represents white noise. To represent the deterministic mean component, you can directly take... The value of . Using the above formula (9), a wind speed sequence that conforms to the diurnal variation pattern and time correlation can be generated.

[0040] After obtaining the wind speed sequence, the power output curve of the wind power generation equipment can be further determined based on the wind speed sequence using formula (10):

[0041] in, The actual wind speed can be determined based on the wind speed sequence output by formula (9). This refers to the cut-in wind speed, which is the minimum wind speed at which the wind turbine generator can begin generating electricity. Indicates the rated wind speed. This indicates the cut-off wind speed, which is the wind speed at which the wind power generation equipment is shut down for protection. This represents the rated power of the wind power generation equipment. The output power of the wind power generation equipment can be determined using the above formula (10).

[0042] After calculating the output power of the wind power generation equipment, the output power of the photovoltaic power station, and the electricity load, the net load can be calculated based on formula (11). The net load is the load that needs to be met by the generator, battery storage system, and external power grid. Formula (11) is:

[0043] in, This represents the net load at time t. This represents the output power of the photovoltaic power station at time t. This represents the output power of the wind turbine at time t. This represents the total electrical load at time t.

[0044] As an optional embodiment, the target distribution network is a node system, and the distributed devices are installed on each node of the target distribution network. The distributed devices include generators, battery energy storage systems, photovoltaic power generation equipment, wind power generation equipment, and electrical loads.

[0045] In this embodiment, the target power distribution network adopts a radial topology, that is, it extends outward in a tree-like form starting from the root node, with each line having a clear power supply path and no loops.

[0046] The distributed devices are connected to different nodes of the target distribution network, collectively forming the scheduling objects of the virtual power plant. Generators can include diesel generators, which provide power support as controllable power sources when photovoltaic and wind power are insufficient; battery energy storage systems, as bidirectionally adjustable energy storage devices, can discharge during peak loads and charge during off-peak loads, smoothing out renewable energy fluctuations; photovoltaic and wind power generation equipment, as renewable energy sources, have intermittent and uncertain output power, but their marginal cost is zero, therefore, photovoltaic and wind power generation equipment are prioritized for scheduling; electricity load represents the user's electricity demand and is the basic object that the virtual power plant needs to satisfy. The distributed devices are interconnected through the distribution network lines to form a complete power network. The virtual power plant's scheduling scheme needs to coordinate the operating states of all types of equipment, minimizing operating costs while meeting load demands, and ensuring that the voltage of each node and the current of each line are within safe ranges.

[0047] The operational constraints of the aforementioned distributed devices refer to the physical limitations and security conditions that these distributed devices need to meet during operation.

[0048] As an optional embodiment, the operating constraints of the distributed devices include at least one of the following: generator power constraints, battery energy storage system state constraints, and power exchange constraints between the target distribution network and the external power grid.

[0049] The power constraint of the aforementioned generator is used to limit the generator's output power within its rated power range, and also to limit the generator's power change rate from exceeding a preset ramp rate. For example, for a diesel generator in the target distribution network, its power constraint can be expressed as: 0.1MW ≤ P_ DG (t) ≤ 0.6MW, and its climbing rate constraint is |P_ DG (t) - P_ DG (t-1)| ≤ 0.15MW / Δt, where P_ DG(t) represents the output power of the generator at time t, and Δt represents the time step.

[0050] The state constraints of the battery energy storage system described above are used to limit the charging and discharging power and state of charge (SOC) of the battery energy storage system. The SOC constraint can be a SOC boundary constraint, as shown in formula (12): ) in, This indicates the state of charge limit of battery energy storage system b. This indicates the upper limit of the state of charge of battery b. This indicates the maximum capacity of battery b. This represents the remaining energy of battery b at time t. Formula (12) above ensures that the remaining energy of the battery storage system at any given time must be within acceptable limits.

[0051] For example, for a battery energy storage system in a target distribution network, its state constraints may include: a charging / discharging power constraint of -0.2MW ≤ P_ BESS (t) ≤ 0.2MW (positive value indicates discharge, negative value indicates charging); the constraint for the state of charge is 0.2 ≤ SOC_ BESS (t) ≤ 0.8, where P_ BESS (t) represents the charging or discharging power of the battery energy storage system at time t, SOC_ BESS (t) represents the state of charge of the battery energy storage system at time t.

[0052] After determining the rated power range of the generator, the maximum discharge power of the battery energy storage system, and the net load at each time of day, the power margin of the target distribution network can be checked using formula (13):

[0053] in, Used to represent the maximum net load for the entire day. Used to represent the sum of the rated power of all generators. This represents the sum of the maximum discharge power of all battery energy storage systems. If the maximum net load is less than or equal to the sum of the maximum discharge power of the generator and battery energy storage systems, the scheme is considered feasible; otherwise, it is considered infeasible.

[0054] In addition, after determining the ramp rate of the generator, it is necessary to further check whether the generator in the target distribution network meets the ramp constraint using formula (14), which is:

[0055] in, This represents the change in net load at time t. This represents the total ramp rate of all generators. Indicates the time step. This represents the sum of the maximum discharge power of the battery energy storage system.

[0056] If the above formula (14) is not met, it means that the scheme does not meet the ramping constraint, which means that when the net load suddenly increases, the generator's ramping ability is limited, and this gap exceeds the maximum discharge capacity of the battery energy storage system. Therefore, the scheme is not feasible.

[0057] In addition, the candidate scheduling scheme also needs to meet the discharge energy demand check and the charging energy demand check. The discharge energy demand check can be characterized by formula (15), and the charging energy demand check can be characterized by formula (16):

[0058]

[0059] in, This indicates the change in net load. This represents the total ramp rate of all generators. Indicates the time step. This indicates the available energy of the battery storage system. This indicates the battery discharge efficiency. This indicates the battery charging efficiency.

[0060] If the above formula (15) is satisfied, it means that the capacity of the battery energy storage system can support the ramp-up demand throughout the day. If the above formula (16) is satisfied, it means that the energy of the battery energy storage system can absorb the excess energy throughout the day.

[0061] The aforementioned power exchange constraint between the target distribution network and the external power grid is used to limit the power exchange between the target distribution network and the external power grid through the point of common coupling (PCC). For example, assuming that node 1 of the target distribution network is a PCC node that exchanges power with the external power grid, the power exchange constraint can be expressed as formula (17): (17) in, This represents the exchange power between the target distribution network and the external power grid at time t. This indicates the maximum exchange power between the target distribution network and the external power grid. It can be 5MW. A positive value for this exchange power indicates that electricity is purchased from an external power grid, while a negative value indicates that electricity is sold to an external power grid.

[0062] All candidate scheduling schemes must satisfy all the constraints of the above formulas (12) to (17).

[0063] The physical constraints of the target distribution network mentioned above refer to the electrical and physical laws and safety constraints that the target distribution network needs to meet during operation.

[0064] As an optional embodiment, the physical constraints of the target distribution network may include at least one of voltage constraints, current constraints, and topological constraints.

[0065] In this embodiment, the voltage constraint and the current constraint can be expressed as formula (18):

[0066] in, This indicates the lower voltage limit of the node. Indicates the upper limit of the node's voltage. This represents the voltage at node i at time t. This represents the current in line l at time t. This indicates the maximum allowable current for line l.

[0067] The voltage constraints described above are used to limit the voltage amplitude at each node of the target distribution network within the safe operating range. For example, in an IEEE 33 network, the voltage constraint can be expressed as: 0.95pu ≤ ≤ 1.05pu.

[0068] The aforementioned current constraint conditions are used to limit the current amplitude of each branch of the target distribution network from exceeding the thermal stability limit of the line, in order to prevent the line from overheating and being damaged.

[0069] The aforementioned topology constraints are used to restrict the topology of the target distribution network to meet the requirements for radial operation. For example, topology constraints can include connectivity constraints, loop-free constraints, and branch number constraints. The connectivity constraint requires that all nodes in the target distribution network are connected to the point of origin (PCC node), and there are no isolated nodes or subnetworks. The loop-free constraint requires that there are no electrical loops in the target distribution network, i.e., there is one and only one electrical path between any two nodes. The branch number constraint requires that for a radial distribution network with N nodes, the number of closed branches during normal operation should be N-1. For example, for an IEEE 33 network, the topology constraint can be expressed as: 32 branches should remain closed during normal operation, and there should be no loops in the system. When network reconfiguration is required, the topology can be changed by disconnecting some branches and closing tie switches, but the radial structure must still be maintained.

[0070] In this embodiment, by introducing voltage constraints, current constraints, and topology constraints into the power planning model, it can be ensured that the optimized target scheduling scheme is feasible in the actual distribution network, avoiding problems such as voltage overruns, current overloads, or topology infeasibility caused by ignoring the physical constraints of the distribution network.

[0071] The above power planning model is a mathematical optimization model with the goal of minimizing the operating cost of virtual power plants.

[0072] As an optional embodiment, the power planning model is an objective function, which includes a first function term, a second function term, a third function term, and a fourth function term; wherein, the first function term is used to characterize the power exchange cost between the target distribution network and the external power grid; the second function term is used to characterize the fuel consumption cost of the generators in the target distribution network; the third function term is used to characterize the degradation cost of the battery energy storage system in the target distribution network; and the fourth function term is used to characterize a first penalty value, which is used to constrain the remaining energy of the battery energy storage system.

[0073] In this embodiment, the objective function can be formula (19): (19) The first function term described above characterizes the power exchange cost between the target distribution network and the external power grid. The first function term can be expressed as:

[0074] Where Δt represents the time step, and t represents any given moment. This represents the price of electricity purchased from the external power grid at time t. This represents the power purchased from the external power grid at time t. This represents the price at which electricity is sold to the external power grid at time t. Let represent the power sold to the external grid at time t, and T represent the set of all times. Therefore, the first term above can characterize the power exchange cost between the target distribution network and the external grid at all times.

[0075] The second function term described above characterizes the fuel consumption cost of generators in the target distribution network. The second function term can be expressed as:

[0076] In this embodiment, the generator is a diesel generator, g represents any diesel generator at any node, and G represents the set of all diesel generators. This represents the output power of the g-th diesel generator at time t. This represents the fuel cost per unit output power of the g-th diesel generator. Therefore, the second term above can characterize the total fuel consumption cost of all generators at all times.

[0077] The third function term described above characterizes the degradation cost of the battery energy storage system in the target distribution network. The third function term can be expressed as:

[0078] Where b represents any battery energy storage system at any node, and B represents the set of all battery energy storage systems. This represents the degradation cost coefficient of battery energy storage system b per kilowatt-hour of charge / discharge. This represents the charging power of battery energy storage system b at time t. Let represent the discharge power of battery energy storage system b at time t. Therefore, the third function term is used to characterize the degradation cost of battery energy storage system in the target distribution network.

[0079] The fourth function term described above characterizes the first penalty value, which is used to constrain the remaining energy of the battery energy storage system. The fourth function term can be expressed as:

[0080] in, This represents the penalty coefficient, and the scheduling period refers to the time range within which the virtual power plant performs optimized scheduling. This indicates the target remaining energy of the battery at the end of the scheduling cycle. This indicates the actual remaining energy of the battery at the end of the scheduling cycle. Therefore, the fourth term above can be interpreted as applying a penalty for the shortfall if the energy level in the battery storage system is lower than the target remaining energy at the end of the cycle.

[0081] Adding the first, second, third, and fourth function terms together yields J, which represents the total operating cost of the virtual power plant during the scheduling cycle.

[0082] In this embodiment, by constructing an objective function that includes the above four items, the various costs of operating the virtual power plant can be comprehensively considered, including the exchange cost with the external power grid, the fuel cost of the generator, the degradation cost of the battery energy storage system, and the terminal energy penalty, thereby achieving economic optimization of the operation of the virtual power plant.

[0083] S102. Solve the power planning model using the objective algorithm to obtain the objective scheduling scheme for the virtual power plant.

[0084] In this embodiment, the target algorithm can be any optimization algorithm capable of solving the power planning model. Specifically, the target algorithm can include metaheuristic optimization algorithms such as Particle Swarm Optimization (PSO) or Enhanced Snake Optimizer (ESO).

[0085] The above-mentioned solution of the power planning model by the objective algorithm to obtain the target scheduling scheme of the virtual power plant may include: determining at least one candidate scheduling scheme of the power planning model by the objective algorithm, and then determining the target scheduling scheme from the at least one candidate scheduling scheme.

[0086] Specifically, each candidate scheduling scheme may include the output power of the generator at each time, the charging and discharging power of the battery energy storage system, the exchange power between the target distribution network and the external power grid, and the switching status of the target distribution network. Each candidate scheduling scheme meets the operating constraints of the distributed equipment.

[0087] The aforementioned candidate scheduling schemes refer to the potential solutions generated during the optimization process that satisfy the operating constraints of distributed equipment in the power planning model. At least one decision variable differs between any two candidate scheduling schemes. These decision variables include: the switching state of the target distribution network; the generating power of generators in the target distribution network; the charging and discharging power of the battery energy storage system in the target distribution network; and the exchange power between the target distribution network and the external power grid.

[0088] As an optional embodiment, the target algorithm includes Particle Swarm Optimization (PSO) or Enhanced Snake Optimizer (ESO).

[0089] In this embodiment, the objective algorithm is used to solve the power planning model. The objective algorithm first randomly initializes multiple candidate scheduling schemes, each corresponding to a set of decision variables, including the switching state of the distribution network, the power output of diesel generators, the charging and discharging power of the battery energy storage system, and the power exchanged with the main grid. In each iteration, the algorithm updates the population based on the objective function values ​​of each candidate scheduling scheme, guiding the schemes to move towards better regions. Simultaneously, during the iteration process, a forward-backward scanning algorithm is used to verify the power flow of each candidate scheme, calculating voltage and current out-of-bounds limits, and incorporating these out-of-bounds limits as penalties into the objective function. This allows the algorithm to automatically avoid schemes that do not meet grid security constraints while searching for the optimal economic scheme. After multiple iterations, the algorithm finally converges to the objective scheduling scheme that satisfies all constraints and has the lowest operating cost.

[0090] In this embodiment, by employing metaheuristic optimization algorithms such as PSO or ESO, complex virtual power plant power planning models can be effectively solved to obtain optimal or near-optimal scheduling schemes that satisfy various constraints.

[0091] S103. Control the operation of the virtual power plant based on the target scheduling scheme.

[0092] In this step, after obtaining the target scheduling scheme of the virtual power plant, the control equipment can distribute the target scheduling scheme to each distributed device in the virtual power plant and control each distributed device to operate according to the parameters in the target scheduling scheme. For example, it can control each generator to generate electricity according to the output power curve in the target scheduling scheme, control each battery energy storage system to perform charging and discharging operations according to the charging and discharging power curve in the target scheduling scheme, and realize the topology in the target scheduling scheme by controlling the switch states in the target distribution network.

[0093] In this embodiment, a power planning model for a virtual power plant is constructed based on the operational constraints of distributed devices connected to the target distribution network and the physical constraints of the target distribution network. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints constrain the operating state of the distributed devices, and the physical constraints constrain the current, voltage, and topology of the target distribution network. A target algorithm is used to solve the power planning model to obtain a target scheduling scheme for the virtual power plant. The operation of the virtual power plant is then controlled based on this target scheduling scheme. In this way, while optimizing the operating cost of the virtual power plant, the scheduling scheme can be ensured to meet the physical constraints of the distribution network, thereby achieving safe and economical operation of the virtual power plant.

[0094] As an optional embodiment, the distributed devices in the target distribution network include generators and battery energy storage systems. The step of solving the power planning model using a target algorithm to obtain the target dispatch scheme for the virtual power plant includes: At least one candidate scheduling scheme for the power planning model is determined by the target algorithm. Each candidate scheduling scheme includes the output power of the generator at each time, the charging and discharging power of the battery energy storage system, the exchange power between the target distribution network and the external power grid, and the switching state of the target distribution network. Each candidate scheduling scheme meets the operating constraints of the distributed equipment. The candidate scheduling scheme with the smallest objective function value among the at least one candidate scheduling schemes is determined as the target scheduling scheme.

[0095] In this embodiment, each of the above candidate scheduling schemes is a potential solution generated during the optimization process, and each candidate scheduling scheme contains a complete set of values ​​for decision variables.

[0096] Specifically, a candidate scheduling scheme can be represented as: X = [P_ DG , P_ BESS , P_ PCC , S_ switch ] Among them, P_ DG P_ represents the output power matrix of each generator at each time point. BESS P_ represents the charge and discharge power matrix of each battery energy storage system at each time point. PCC S_ represents the power exchange vector between the target distribution network and the external power grid at each time point. switch This represents the state matrix of each switch at each time point.

[0097] After generating candidate scheduling schemes, it is necessary to check whether the schemes meet the operational constraints of distributed devices. Specifically, constraint checks may include: power constraint checks for generators, ramp rate constraint checks for generators, power constraint checks for battery energy storage systems, SOC constraint checks for battery energy storage systems, and exchange power constraint checks for the target distribution network.

[0098] The constraint check for generator power can be performed as follows: for each generator i and at each time t, check whether P_ DG i min ≤ P_ DG i(t) ≤ P_ DG i max Among them, P_ DG i(t) represents the output power of generator i at time t, P_ DG i min P_ represents the minimum output power of generator i. DG i max This represents the maximum output power of generator i.

[0099] The constraint check for the generator ramp rate can be performed as follows: for each generator i and at each time t (t>1), check whether the following conditions are met: |P_ DG i(t) - P_ DG i(t-1)| ≤ R_ DG i·Δt Among them, R_ DG i represents the maximum ramp rate of generator i, and Δt represents the time step. That is, the difference in power output of the generator between two adjacent moments cannot exceed the maximum ramp rate.

[0100] Power constraints on battery energy storage systems are used to limit their charging and discharging power to within the rated power range.

[0101] The SOC constraint check for the battery energy storage system is as follows: For each battery energy storage system b and each time t, check whether the following formula (20) is satisfied: (20) in, This indicates the state of charge (SBC) limit of the battery energy storage system, which can be 20%. This indicates the upper limit of the state of charge (SOC) of the battery energy storage system, which can be 80%. This indicates the maximum capacity of battery energy storage system b, which can be 0.8MWh or 1.0MWh. This represents the remaining energy of battery energy storage system b at time t. The SOC constraint check described above ensures that the remaining energy of the battery energy storage system at any given time must be between the minimum and maximum allowable values.

[0102] The power exchange constraint check is as follows: check whether the formula (21) is satisfied at each time t: (twenty one) The above formula is used to define the switching power constraint at the point of common coupling in the target distribution network. Wherein, This represents the power exchanged between the virtual power plant and the external power grid at time t. This indicates the maximum allowed switching power at the common coupling point.

[0103] A scheduling scheme is considered a candidate scheduling scheme and its objective function value is calculated only if it satisfies all the above constraints. During the optimization process, the objective algorithm generates multiple candidate scheduling schemes and calculates the objective function value for each feasible candidate scheme. Finally, the candidate scheduling scheme with the smallest objective function value is determined as the target scheduling scheme.

[0104] In this embodiment, by systematically generating candidate scheduling schemes and checking constraints, it can be ensured that the final target scheduling scheme meets the operating constraints of all distributed devices, thereby guaranteeing the safe and stable operation of the virtual power plant.

[0105] As an optional embodiment, before determining the candidate scheduling scheme with the smallest objective function value among the at least one candidate scheduling schemes as the target scheduling scheme, the method further includes: Determine the current distribution state and voltage distribution state in the target distribution network under each of the candidate scheduling schemes; Based on the current distribution state and the voltage distribution state, a second penalty value for each candidate scheduling scheme on the target distribution network is determined. The second penalty value is used to characterize the degree to which the candidate scheduling scheme satisfies the voltage constraint condition and the current constraint condition. The objective function value of each candidate scheduling scheme is determined based on the power exchange cost, fuel consumption cost, battery energy storage system degradation cost, first penalty value, and second penalty value corresponding to each candidate scheduling scheme.

[0106] In this embodiment, the current distribution state refers to the set of current values ​​flowing through each line in the target distribution network at a specific time. The voltage distribution state refers to the set of voltage values ​​at each node in the target distribution network at a specific time.

[0107] Specifically, for each candidate scheduling scheme, the injected power of each node in the target distribution network is determined based on the generator's power output, the battery storage system's charging and discharging power, the power exchanged with the external power grid, and the output power of the photovoltaic and wind power generation equipment in that candidate scheduling scheme. Then, the target distribution network can be calculated to obtain the voltage distribution state of each node and the current distribution state of each line.

[0108] As an optional embodiment, determining the current distribution state and voltage distribution state in the target distribution network under each of the candidate scheduling schemes includes: The target distribution network is subjected to power flow calculation using a forward and backward scanning algorithm to obtain the current distribution state and voltage distribution state in the target distribution network.

[0109] The forward-backward scan algorithm is a power flow calculation method suitable for radial distribution networks, consisting of two steps: forward scan and backward scan. The forward scan starts from the root node of the distribution network and calculates the voltage of each node segment by segment along the feeder direction based on line impedance and node injected power. The backward scan starts from the terminal node and calculates the current of each line segment by segment along the feeder direction based on voltage and impedance. Through multiple iterations, the algorithm eventually converges to obtain the voltage distribution state of each node and the current distribution state of each line in the distribution network.

[0110] In this embodiment, for each candidate scheduling scheme, the injected power of each node in the target distribution network is first determined based on the power generation of the diesel generator, the charging and discharging power of the battery energy storage system, the power exchanged with the external power grid, and the output power of the photovoltaic power generation equipment and the wind power generation equipment in the candidate scheduling scheme. Then, the power flow calculation of the target distribution network is performed using a forward and backward scanning algorithm to obtain the current distribution state and voltage distribution state in the target distribution network.

[0111] The current distribution status includes the current values ​​flowing through each line in the target distribution network at different times, and the voltage distribution status includes the voltage values ​​of each node in the target distribution network at different times. By comparing the current distribution status and voltage distribution status with preset current and voltage limits, it can be determined whether the candidate scheduling scheme meets the current and voltage constraints, and thus the corresponding second penalty value can be determined.

[0112] For example, in the following formula (22): (twenty two) in, This represents the current distribution vector of each line at time t. This represents the voltage distribution vector at each node at time t. This represents the injected power vector of each node at time t. This represents the voltage overshoot of each line at time t. ( ) indicates the forward-backward scanning algorithm.

[0113] Then, based on the obtained current distribution state and the voltage distribution state, a second penalty value for each candidate scheduling scheme on the target distribution network can be determined. This second penalty value is used to characterize the degree to which the candidate scheduling scheme satisfies the voltage and current constraints.

[0114] For example, the voltage excess can be determined based on the voltage distribution state, the current excess can be determined based on the current distribution state, and the voltage excess and the current excess can be weighted and summed to obtain the second penalty value. When both the voltage and current in the candidate scheduling scheme are within the allowable range, the second penalty value is zero; when there is a voltage or current excess, the second penalty value is positive, and the more severe the excess, the larger the penalty value.

[0115] Finally, the objective function value of each candidate scheduling scheme is determined based on the power exchange cost, fuel consumption cost, battery energy storage system degradation cost, first penalty value, and second penalty value corresponding to each candidate scheduling scheme. The first penalty value is the terminal state-of-charge penalty, used to constrain the remaining energy of the battery energy storage system at the end of the scheduling cycle; the second penalty value is the network violation penalty, used to constrain voltage and current within safe ranges. The objective function value of each candidate scheduling scheme is obtained by weighted summing of the above items.

[0116] The candidate scheduling scheme with the minimum objective function value is the optimal scheme, which minimizes the operating cost while satisfying all operational and physical constraints.

[0117] In this embodiment, by introducing a second penalty value on the basis of power flow calculation, the physical constraints of the distribution network can be effectively incorporated into the optimization process, ensuring that the final target scheduling scheme is feasible in the actual distribution network.

[0118] As an optional embodiment, at least one decision variable differs between any two candidate scheduling schemes, and the decision variable includes: The switch status in the target distribution network; The generating capacity of the generators in the target distribution network; The charging and discharging power of the battery energy storage system in the target power distribution network; The exchange power between the target distribution network and the external power grid.

[0119] In this embodiment, to ensure the diversity of candidate scheduling schemes and enable the optimization algorithm to fully search the solution space, at least one decision variable must differ between any two candidate scheduling schemes. These decision variables include: the switching states in the target distribution network; the power generation of generators in the target distribution network; the charging and discharging power of the battery energy storage system in the target distribution network; and the exchange power between the target distribution network and the external power grid.

[0120] In this embodiment, the optimization algorithm generates multiple candidate scheduling schemes iteratively, each uniquely determined by a set of decision variables. In each iteration, the algorithm generates a new candidate scheduling scheme by adjusting at least one of the aforementioned decision variables. For example, it can change the power generation of a diesel generator at a certain moment, or change the charging and discharging strategy of the battery energy storage system, or adjust the switching state of the distribution network to change the topology, or change the power exchange with the external power grid.

[0121] In this way, the algorithm can explore different operating strategies and gradually converge to the globally optimal target scheduling scheme during the iteration process.

[0122] As an optional embodiment, the switching state of the target distribution network can be adjusted between any two candidate scheduling schemes through a single-step branch switching operation, wherein the single-step branch switching operation is to disconnect an existing line and close a tie switch.

[0123] In this embodiment, between any two candidate scheduling schemes, the switching state of the target distribution network is adjusted through a single-step branch switching operation to change the topology of the distribution network. The single-step branch switching operation is as follows: disconnecting an existing line and simultaneously closing a tie switch.

[0124] In this embodiment, a single-step branch switching operation is used to generate different topologies. This ensures that the distribution network maintains a radial topology after adjustment, preventing the formation of loops and meeting the operational requirements of the distribution network. Furthermore, the single-step operation avoids the computational complexity and operational risks associated with large-scale topology changes.

[0125] This adjustment method can generate multiple candidate scheduling schemes with different switching states.

[0126] The virtual power plant control method based on the enhanced snake optimizer provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0127] This application also provides an electronic device, such as... Figure 2 As shown, the electronic device 1400 includes: One or more processors 1410; The memory 1420 stores one or more programs that, when executed by one or more processors 1410, enable the one or more processors 1410 to implement the virtual power plant control method based on the enhanced snake optimizer described in any of the above embodiments.

[0128] Memory 1420, as a non-transitory network system, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory 1420 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 1420 may optionally include remotely located memories 1420 relative to processor 1410, which can be connected to processor 1410 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The memory 1420 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1420 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1420 and is called and executed by the processor 1410.

[0130] The processor 1410 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0131] In some embodiments, the electronic device further includes: Input / output interfaces are used to implement information input and output; The communication interface is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). The bus transmits information between various components of the device (e.g., processor 1410, memory 1420, input / output interfaces, and communication interfaces); The processor 1410, memory 1420, input / output interface, and communication interface can communicate with each other within the device via a bus.

[0132] One embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions for performing the virtual power plant control method based on an enhanced snake optimizer as provided in any embodiment of this application.

[0133] An embodiment of this application also provides a computer program product, including a computer program or computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the virtual power plant control method based on an enhanced snake optimizer as provided in any embodiment of this application.

[0134] The system architecture and application scenarios described in this application are intended to more clearly illustrate the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. Those skilled in the art will understand that as system architectures evolve and new application scenarios emerge, the technical solutions provided in this application are also applicable to similar technical problems.

[0135] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0136] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0137] The above description, with reference to the accompanying drawings, illustrates some embodiments of this application, but does not limit the scope of this application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of this application shall be within the scope of this application.

Claims

1. A virtual power plant control method based on an enhanced snake optimizer, comprising: Based on the operational constraints of the distributed devices connected to the target distribution network and the physical constraints of the target distribution network, a power planning model for a virtual power plant is constructed. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints are used to constrain the operating state of the distributed devices, and the physical constraints are used to constrain the current, voltage, and topology of the target distribution network. The target scheduling scheme for the virtual power plant is obtained by solving the power planning model using a target algorithm. The virtual power plant is controlled based on the target scheduling scheme.

2. The virtual power plant control method based on an enhanced snake optimizer according to claim 1, characterized in that, The physical constraints of the target distribution network include at least one of voltage constraints, current constraints, and topological constraints.

3. The virtual power plant control method based on enhanced snake optimizer according to claim 1, characterized in that, The power planning model is an objective function, which includes a first function term, a second function term, a third function term, and a fourth function term. The first function term characterizes the power exchange cost between the target distribution network and the external power grid; the second function term characterizes the fuel consumption cost of generators in the target distribution network; the third function term characterizes the degradation cost of the battery energy storage system in the target distribution network; and the fourth function term characterizes a first penalty value, which constrains the remaining energy of the battery energy storage system.

4. The virtual power plant control method based on an enhanced snake optimizer according to claim 1, characterized in that, The distributed equipment in the target distribution network includes generators and battery energy storage systems. Solving the power planning model using a target algorithm to obtain the target dispatch scheme for the virtual power plant includes: At least one candidate scheduling scheme for the power planning model is determined by the target algorithm. Each candidate scheduling scheme includes the output power of the generator at each time, the charging and discharging power of the battery energy storage system, the exchange power between the target distribution network and the external power grid, and the switching state of the target distribution network. Each candidate scheduling scheme meets the operating constraints of the distributed equipment. The candidate scheduling scheme with the smallest objective function value among the at least one candidate scheduling schemes is determined as the target scheduling scheme.

5. The virtual power plant control method based on enhanced snake optimizer according to claim 4, characterized in that, Before determining the candidate scheduling scheme with the smallest objective function value among the at least one candidate scheduling schemes as the target scheduling scheme, the method further includes: Determine the current distribution state and voltage distribution state in the target distribution network under each of the candidate scheduling schemes; Based on the current distribution state and the voltage distribution state, a second penalty value for each candidate scheduling scheme on the target distribution network is determined. The second penalty value is used to characterize the degree to which the candidate scheduling scheme satisfies the voltage constraint condition and the current constraint condition. The objective function value of each candidate scheduling scheme is determined based on the power exchange cost, fuel consumption cost, battery energy storage system degradation cost, first penalty value, and second penalty value corresponding to each candidate scheduling scheme.

6. The virtual power plant control method based on an enhanced snake optimizer according to claim 5, characterized in that, The step of determining the current distribution state and voltage distribution state in the target distribution network under each of the candidate scheduling schemes includes: The target distribution network is subjected to power flow calculation using a forward and backward scanning algorithm to obtain the current distribution state and voltage distribution state in the target distribution network.

7. The virtual power plant control method based on enhanced snake optimizer according to claim 4, characterized in that, At least one decision variable differs between any two candidate scheduling schemes, and the decision variables include: The switch status in the target distribution network; The generating capacity of the generators in the target distribution network; The charging and discharging power of the battery energy storage system in the target power distribution network; The exchange power between the target distribution network and the external power grid.

8. The virtual power plant control method based on an enhanced snake optimizer according to claim 7, characterized in that, The method further includes: adjusting the switching state of the target distribution network through a single-step branch switching operation between any two candidate scheduling schemes, wherein the single-step branch switching operation is to disconnect an existing line and close a tie switch.

9. The virtual power plant control method based on enhanced snake optimizer according to claim 1, wherein, The target algorithm includes either particle swarm optimization or enhanced snake optimizer algorithm.

10. The virtual power plant control method based on enhanced snake optimizer according to claim 1, wherein, The target distribution network is a node system, and the distributed equipment is installed at each node of the target distribution network. The distributed equipment includes generators, battery energy storage systems, photovoltaic power generation equipment, wind power generation equipment, and electrical loads.

11. The virtual power plant control method based on an enhanced snake optimizer according to claim 1, characterized in that, The operational constraints of the distributed equipment include at least one of the following: generator power constraints, battery energy storage system state constraints, and power exchange constraints between the target distribution network and the external power grid.

12. A virtual power plant control device based on an enhanced snake optimizer, comprising: A construction module is used to construct a power planning model for a virtual power plant based on the operational constraints of the distributed devices connected to the target distribution network and the physical constraints of the target distribution network. The power planning model aims to minimize the operating cost of the virtual power plant. The operational constraints are used to constrain the operating state of the distributed devices, and the physical constraints are used to constrain the current, voltage, and topology of the target distribution network. The solution module is used to solve the power planning model using the objective algorithm to obtain the target scheduling scheme of the virtual power plant; The control module is used to control the operation of the virtual power plant based on the target scheduling scheme.

13. An electronic device, comprising: One or more processors; A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement the virtual power plant control method based on an enhanced snake optimizer as described in any one of claims 1 to 11.

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