A hierarchical collaborative control method of optical storage station for safe operation of power distribution network

Abstract

The application discloses a layered cooperative control method for photovoltaic and energy storage station facing power distribution network safe operation, and relates to the technical field of power distribution network operation control. The method comprises the following steps: constructing a layered control architecture; in the partition cooperative decision layer, abstracting each regional photovoltaic and energy storage station as an agent, generating active and reactive regulation actions by using a multi-agent reinforcement learning algorithm, and mapping the actions into station-level total active and total reactive power instructions; in the station power distribution layer, according to photovoltaic available output, energy storage adjustable margin and inverter residual reactive power, performing active coordinated distribution and reactive utilization consistency distribution on the station-level power instructions, and generating power reference values of each photovoltaic unit, energy storage unit and inverter; in the inverter execution layer, adopting a closed-loop PQ control mode to track the active and reactive reference instructions, and feeding back the execution results to the next control cycle. The method improves the coordinated response capability of the multi-regional photovoltaic and energy storage station to the voltage safety target of the power distribution network.

Description

Technical Field

[0001] This invention relates to the fields of power distribution network operation control, distributed photovoltaic energy storage grid connection control, and multi-agent collaborative control technology, and in particular to a hierarchical collaborative control method for photovoltaic energy storage power plants for safe operation of power distribution networks. Background Technology

[0002] As the proportion of distributed photovoltaic (PV) and energy storage systems integrated into distribution networks continues to increase, the operation mode of distribution networks is gradually shifting from the traditional single-source, unidirectional power supply mode to a mode where multiple sources are connected, bidirectional power flow occurs, and active regulation coexists. PV power generation is characterized by its cleanliness, local consumption, and flexible integration, which can alleviate the power supply pressure caused by load growth. Energy storage systems can participate in active power regulation through charging and discharging, and provide reactive power support through grid-connected inverters. Therefore, the coordinated operation of PV and energy storage has become an important means to improve the flexible regulation capability of distribution networks and the level of new energy consumption.

[0003] However, in scenarios with a high proportion of photovoltaic (PV) and energy storage (ESS) integration, the operational uncertainty of the distribution network increases significantly. PV output is affected by sunlight, weather, and diurnal variations, exhibiting randomness, intermittency, and volatility, which can easily cause localized node voltage increases, changes in power flow direction, and fluctuations in feeder power distribution. While ESS systems can participate in power regulation and voltage support, their adjustability is affected by state of charge, rated charge / discharge power, capacity constraints, and inverter apparent capacity limitations, and changes dynamically with operation. When multiple PV and ESS sites are distributed across different regions, differences exist in voltage state, load levels, available PV output, and ESS regulation margins. Furthermore, these regions may interact through power flow changes, increasing the difficulty of controlling the safe and stable operation of the distribution network.

[0004] Existing distribution network coordinated control methods typically determine active and reactive power regulation tasks at the regional level, improving node voltage and power flow distribution by adjusting photovoltaic (PV), energy storage, or reactive power compensation equipment. While these methods can improve distribution network operational safety to some extent, their control results are mostly expressed as regional or substation-level power commands, making it difficult to directly implement them at the substation level across multiple PV units, energy storage units, and grid-connected inverters. For actual PV-energy storage substations, the available output of different PV units, the state of charge and charging / discharging capabilities of different energy storage units, and the remaining reactive power capacity of different inverters may all vary. If substation-level power commands are executed using average allocation, fixed-ratio allocation, or relying solely on local rules, it can easily lead to problems such as some energy storage units prematurely approaching their state of charge boundaries, insufficient utilization of inverter capacity margins, uneven distribution of reactive power support capabilities, and difficulty in stably tracking the higher-level power target for the total substation output.

[0005] Therefore, it is necessary to provide a hierarchical collaborative control method for photovoltaic and energy storage power plants for the safe and stable operation of distribution networks. This method enables regional collaborative decision-making, power allocation at power plants, and inverter execution control to be linked together. Under the premise of meeting the constraints of distribution network operation and photovoltaic and energy storage equipment, it can improve the coordinated response capability of multiple photovoltaic and energy storage power plants to node voltage safety targets and enhance the safe and stable operation level of distribution networks under high-proportion photovoltaic and energy storage access conditions. Summary of the Invention

[0006] Based on the above, the purpose of this invention is to provide a hierarchical collaborative control method for photovoltaic (PV) and energy storage (ESS) power plants aimed at ensuring the safe operation of distribution networks. This method addresses problems arising from the integration of high-proportion PV and ESS into the distribution network, such as node voltage exceeding limits, regional power coupling, difficulties in coordinating multiple devices within the power plant, and unreliable execution of power commands. By acquiring information such as the distribution network operating status, available PV output, ESS state of charge, and inverter capacity margin, multi-agent reinforcement learning is used to determine the active and reactive power regulation tasks for each PV and ESS power plant area. Furthermore, considering the operational constraints of PV, ESS, and inverters within the power plant, active and reactive power commands are coordinated and allocated at the power plant level, and reference command tracking is achieved through constant power control of the inverters. Thus, while meeting operational constraints, the coordinated response capability of multiple PV and ESS power plants to the distribution network voltage safety target is improved.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A hierarchical collaborative control method for photovoltaic and energy storage power plants aimed at ensuring the safe operation of power distribution networks includes the following steps:

[0009] Step S1: Distribution network status acquisition and hierarchical control architecture construction; acquire distribution network operating status, photovoltaic available output, energy storage charge status, load power, node voltage and inverter capacity information, divide the distribution network into multiple zones according to the access location of photovoltaic and energy storage stations, and construct a hierarchical control architecture including a zone collaborative decision-making layer, a station power allocation layer and an inverter execution layer;

[0010] Specific methods include:

[0011] Step S11: Obtain information on distribution network node voltage, load power, line power flow, available photovoltaic output, energy storage status of charge, energy storage charging and discharging power limits, and inverter rated capacity.

[0012] Step S12: Based on the distribution network topology and the access locations of photovoltaic and energy storage stations, divide the distribution network into multiple operating areas, and determine the node set and photovoltaic and energy storage station set corresponding to each operating area;

[0013] Step S13: Based on the node voltage, load power, available photovoltaic output, energy storage state of charge, and adjacent area operation information of each operating area, form the operating status input required for regional collaborative decision-making;

[0014] Step S14: Based on the node voltage over-limit situation, voltage deviation degree, power response status of the station and equipment operation constraints, generate operation evaluation information for subsequent collaborative decision-making and feedback updates.

[0015] Step S2: Regional collaborative decision-making and station-level power command generation; In the regional collaborative decision-making layer, each photovoltaic and energy storage station in each region is abstracted as an intelligent agent, and the power flow calculation process of the distribution network is regarded as the environment. Based on the local observation state and the global shared reward signal, the continuous adjustment actions of each photovoltaic and energy storage station are generated through a multi-agent reinforcement learning algorithm, and the continuous adjustment actions are mapped into station-level total active power command and total reactive power command that satisfy the constraints of photovoltaic available output, energy storage state of charge and station capacity.

[0016] Specific methods include:

[0017] Step S21: Take each operating area as the collaborative decision-making object, abstract the photovoltaic and energy storage power station in each operating area as the regional collaborative decision-making intelligent agent, and take the operating status of the distribution network and the operating status of the photovoltaic and energy storage power station as the state observation of the regional collaborative decision-making intelligent agent.

[0018] Step S22: Determine the active and reactive power regulation requirements of each operating area based on the node voltage, load power, available photovoltaic output, energy storage status of charge, and operating information of adjacent areas.

[0019] Step S23: Based on the active and reactive power regulation requirements, generate the active power regulation and reactive power regulation quantities for each photovoltaic and energy storage station;

[0020] Step S24: Perform constraint processing on the active power regulation and reactive power regulation to form a regional coordinated regulation result that satisfies the distribution network operation constraints and the constraints of photovoltaic and energy storage equipment.

[0021] The multi-agent reinforcement learning algorithm is a multi-agent dual-delay deep deterministic policy gradient algorithm; the feedback information includes one or more of the following: node voltage over-limit penalty term, voltage deviation penalty term, and energy storage operation constraint penalty term, which are used to guide the photovoltaic and energy storage stations in each region to coordinate their response to the distribution network voltage safety target.

[0022] Step S3: Coordinated allocation of active power within the power station; at the power allocation layer of the power station, active power reference values ​​for photovoltaic units and energy storage units are generated based on the total active power command at the power station level, the available output of each photovoltaic unit, and the charge and discharge adjustable margin of each energy storage unit; wherein, the available output of photovoltaic units is used as the basic active power output of the power station, and the energy storage units compensate for the total active power deviation of the power station according to the adjustable margin;

[0023] Specifically, it includes:

[0024] Step S31: Determine the total active power command for the photovoltaic-storage power station based on the regional coordinated regulation results, available photovoltaic output, energy storage state of charge, and energy storage charging and discharging power limits;

[0025] Step S32: Determine the total reactive power command of the photovoltaic and energy storage power station based on the regional coordinated regulation results and the apparent capacity constraints of the photovoltaic inverter and energy storage inverter.

[0026] Step S33: Perform boundary constraint processing on the total active power command and the total reactive power command to ensure that they meet the photovoltaic output constraints, energy storage power constraints, energy storage state of charge constraints, and inverter capacity constraints.

[0027] Step S34: Use the constrained total active power command and total reactive power command at the station level as inputs for power allocation within the station.

[0028] When the total active power deviation of the power station is greater than zero, the energy storage unit undertakes the active power compensation task according to the active power coordination coefficient corresponding to the discharge adjustable margin; when the total active power deviation of the power station is less than zero, the energy storage unit undertakes the power absorption task according to the active power coordination coefficient corresponding to the charging adjustable margin; the active power coordination coefficient is obtained by normalizing the current adjustable margin of each energy storage unit.

[0029] Step S4: Consistent allocation of reactive power within the power station; calculate the remaining reactive power capacity based on the current active power reference value and apparent capacity of each inverter, construct the target reactive power utilization rate based on the total reactive power command at the power station level, and update the reactive power utilization rate of each inverter using the leader consensus algorithm, so that each inverter generates the corresponding reactive power reference value according to its own remaining reactive power capacity.

[0030] Specifically, it includes:

[0031] Step S41: Obtain the available output of each photovoltaic unit, the state of charge of each energy storage unit, the charging and discharging power limit, and the active power adjustable margin within the power station.

[0032] Step S42: Determine the active power output of the power station foundation based on the available photovoltaic power output, and determine the adjustability margin of each energy storage unit based on the energy storage state of charge and charging / discharging capacity;

[0033] Step S43: Based on the deviation between the total active power command at the power station level and the active power output of the photovoltaic base, the active power adjustment task is allocated to each energy storage unit to obtain the active power reference command for the photovoltaic unit and the energy storage unit.

[0034] Step S44: Perform feasibility verification and correction on the active power reference instructions for each unit to ensure that they meet the constraints of available photovoltaic output, energy storage charging and discharging power, and state of charge.

[0035] The leader consensus algorithm updates the reactive power utilization rate based on the communication connection relationship between each inverter and the connection relationship between each inverter and the target leader node, so that each inverter can collaboratively undertake the total reactive power command at the plant level according to its own remaining reactive power capacity; the reactive power reference value of each inverter satisfies the inverter's remaining reactive power capacity constraint, and the sum of the reactive power reference values ​​of each inverter within the plant satisfies the total reactive power command at the plant level.

[0036] Step S5: Inverter constant power control and feedback update; In the inverter execution layer, the active power reference value and reactive power reference value are input into the closed-loop PQ controller of the photovoltaic inverter and the energy storage inverter, so that each inverter tracks the corresponding reference power, and the node voltage, station output power, energy storage state of charge and inverter operating status after execution are fed back to the next control cycle.

[0037] Specifically, it includes:

[0038] Step S51: Calculate the remaining reactive power of each inverter based on the current active power output and rated apparent capacity of each photovoltaic inverter and energy storage inverter.

[0039] Step S52: Determine the coordinated allocation relationship of each inverter participating in reactive power support based on the remaining reactive power capacity of each inverter and the total reactive power command at the plant level.

[0040] Step S53: According to the coordination and allocation relationship, the total reactive power command at the site level is allocated to each photovoltaic inverter and energy storage inverter to obtain the reactive power reference command for each inverter;

[0041] Step S54: Perform capacity constraint processing on the reactive power reference command to ensure that the reactive power output of each inverter does not exceed its remaining reactive power capacity, and enable multiple inverters to work together to undertake the reactive power support task of the power station.

[0042] This invention provides a hierarchical collaborative control method for photovoltaic and energy storage power plants for safe operation of distribution networks, which combines regional collaborative decision-making, power allocation of power plants, and inverter execution control. This method utilizes multi-agent reinforcement learning to determine the active and reactive power regulation tasks of photovoltaic and energy storage power plants in each region based on distribution network node voltage, load power, photovoltaic output, and energy storage status, enabling coordinated responses to distribution network voltage security targets across multiple regions. Through action mapping, the regional collaborative decision-making results are converted into total active and reactive power commands at the plant level that satisfy photovoltaic available output, energy storage state of charge, charge / discharge power boundaries, and inverter capacity constraints, avoiding the problems of high action dimensionality and difficulty in guaranteeing constraints caused by directly outputting device-level power commands. By combining active power allocation methods based on photovoltaic available output, energy storage adjustability, and capacity margin, the plant-level active power commands are decomposed into active power reference commands executable by photovoltaic and energy storage units, reducing the risk of energy storage units prematurely approaching the state of charge boundary. Finally, by using a reactive power allocation method with inverter reactive power utilization rate as a consistency variable, reactive power commands are coordinated and allocated according to the remaining reactive power capacity of each inverter, improving the coordination of reactive power resource utilization within the plant. Compared with methods that only perform regional-level coordinated control, this invention can reliably push regional power commands to reference commands for multiple devices within the power station; compared with power station control methods that rely on average distribution, fixed ratio distribution, or only local rules, this invention can take into account both the energy storage state of charge and inverter capacity constraints, thereby improving the photovoltaic-storage power station's ability to support the distribution network voltage safety target and the stability of power station output tracking. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0044] Figure 1 This is a flowchart of the hierarchical collaborative control method for photovoltaic energy storage power stations according to an embodiment of the present invention;

[0045] Figure 2 This is a diagram of the hierarchical collaborative control structure of a photovoltaic energy storage power station according to an embodiment of the present invention;

[0046] Figure 3 This is a comparison chart of node voltage changes when the method of the present invention is used and when the cooperative control method is not used in the embodiment;

[0047] Figure 4 This is a diagram showing the reactive power coordination and allocation results within the power station in an embodiment of the present invention;

[0048] Figure 5 This is a diagram showing the active and reactive power tracking results of the photovoltaic-storage inverter in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The purpose of this invention is to provide a hierarchical collaborative control method for photovoltaic and energy storage power plants aimed at the safe operation of distribution networks. In the operating scenario where a high proportion of distributed photovoltaic and energy storage systems are connected to the distribution network, a hierarchical control input is constructed using information such as the operating status of the distribution network, the available output of photovoltaics, the state of charge of energy storage, and the capacity margin of inverters. The active and reactive power regulation tasks of photovoltaic and energy storage power plants in each region are determined through multi-agent reinforcement learning. The power plant-level power command is decomposed into equipment-level reference commands through consistent allocation of active power and reactive power utilization within the power plant. Power tracking is achieved through constant power control of the inverter, thereby improving the coordinated response capability of photovoltaic and energy storage power plants in multiple regions to the voltage safety target of the distribution network.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 As shown, this invention discloses a hierarchical collaborative control method for photovoltaic and energy storage power plants aimed at the safe operation of distribution networks. The method includes:

[0053] Step S1: Distribution Network Status Acquisition and Hierarchical Control Architecture Construction. Acquire information on the distribution network's operating status, available photovoltaic output, energy storage state of charge, load power, node voltage, and inverter capacity. Divide the distribution network into multiple zones according to the access locations of photovoltaic and energy storage power plants, and construct a hierarchical control architecture including a zone collaborative decision-making layer, a power plant power allocation layer, and an inverter execution layer, as follows: Figure 2 .

[0054] Step S2: Regional Collaborative Decision-Making and Station-Level Power Command Generation. At the regional collaborative decision-making layer, each photovoltaic-storage power station within a region is abstracted as an intelligent agent. The power flow calculation process of the distribution network is considered as the environment. Based on local observation states and globally shared reward signals, a multi-agent reinforcement learning algorithm is used to generate continuous adjustment actions for each photovoltaic-storage power station. These continuous adjustment actions are then mapped to station-level total active power commands and total reactive power commands that satisfy the constraints of available photovoltaic output, energy storage state of charge, and station capacity.

[0055] Step S3: Coordinated allocation of active power within the power station. At the power allocation layer of the power station, active power reference values ​​for photovoltaic units and energy storage units are generated based on the total active power command at the power station level, the available output of each photovoltaic unit, and the charge / discharge adjustable margin of each energy storage unit. Among these, the available output of photovoltaic units is preferentially used as the basic active power output of the power station, and the energy storage units compensate for the total active power deviation of the power station according to the adjustable margin.

[0056] Step S4: Reactive power consistency allocation within the power station. Calculate the remaining reactive power capacity based on the current active power reference value and apparent capacity of each inverter. Construct the target reactive power utilization rate based on the total reactive power command at the power station level. Update the reactive power utilization rate of each inverter using the leader consensus algorithm, so that each inverter generates the corresponding reactive power reference value according to its own remaining reactive power capacity.

[0057] Step S5: Inverter Constant Power Control and Feedback Update. In the inverter execution layer, the active power reference value and reactive power reference value are input into the closed-loop PQ controller of the photovoltaic inverter and the energy storage inverter, so that each inverter tracks the corresponding reference power, and the node voltage, station output power, energy storage state of charge and inverter operating status after execution are fed back to the next control cycle.

[0058] The following is a detailed discussion of each step:

[0059] Step S1: Obtaining the status of the power distribution network and constructing a hierarchical control architecture.

[0060] Step S11: Obtain the operating status information of the distribution network and the photovoltaic-storage power station. The information includes the distribution network node voltage, load power, line power flow, available photovoltaic output, energy storage state of charge, energy storage charging and discharging power limits, and inverter rated capacity information.

[0061] Step S12: Based on the distribution network topology, the access location of the photovoltaic and energy storage stations, and the relationship between each station and node, the distribution network is divided into multiple operating areas, and the node set and photovoltaic and energy storage station set corresponding to each operating area are determined.

[0062] Step S13: Based on the node voltage, load power, available photovoltaic output, energy storage state of charge, and adjacent area operation information of each operating area, form the operating status input required for regional collaborative decision-making.

[0063] Step S14: Based on the node voltage over-limit situation, voltage deviation degree, power response status of the station and equipment operation constraints, form operation evaluation information for subsequent collaborative decision-making and feedback updates.

[0064] Step S1 transforms the power distribution network operation status, photovoltaic and energy storage site access information, and equipment adjustability into the input information required for subsequent regional collaborative decision-making, site power allocation, and inverter execution.

[0065] Step S2: Regional collaborative decision-making and station-level power command generation.

[0066] Step S21: Take each operating area as the collaborative decision-making object, abstract the photovoltaic and energy storage power station in each operating area as the regional collaborative decision-making intelligent agent, and take the operating status of the distribution network and the operating status of the photovoltaic and energy storage power station as the state observation of the regional collaborative decision-making intelligent agent.

[0067] Step S22: Based on the node voltage, load power, available photovoltaic output, energy storage state of charge, and station capacity information of each operating area, determine the active power regulation requirements and reactive power regulation requirements of the corresponding photovoltaic-energy storage station.

[0068] Step S23: Based on the active power regulation requirements and reactive power regulation requirements, generate the total active power command and the total reactive power command at the site level for each photovoltaic and energy storage power station.

[0069] Step S24: Based on the available photovoltaic output, energy storage state of charge, energy storage charging and discharging power limits, and inverter capacity, constrain the total active power command and the total reactive power command at the power station level, and use the constrained total active power command and the total reactive power command at the power station level as inputs for power allocation within the power station.

[0070] Step S2 enables the determination of the active and reactive power regulation tasks that each region's photovoltaic and energy storage power station should undertake based on the overall operating status of the distribution network, and transforms the regional collaborative decision-making results into power station-level commands that meet operational constraints.

[0071] To verify the effect of regional collaborative decision-making on the regulation of distribution network node voltage, under the same operating conditions, the node voltage changes when using the method of this invention and when not using the collaborative control method were compared. The results are as follows: Figure 3 As shown in the figure, after adopting the method of the present invention, the voltage of each node remains within the set voltage safety range during operation; without coordinated control, some nodes experience voltage over-limit phenomena during certain periods. This indicates that the regional coordinated decision-making method of the present invention can coordinate and adjust the active and reactive power of each photovoltaic and energy storage station according to the operating status of the distribution network, thereby improving the safe operation level of the distribution network node voltage.

[0072] Step S3: Coordinated allocation of active power within the power station.

[0073] Step S31: Obtain the available power output of each photovoltaic unit, the state of charge of each energy storage unit, the energy storage charge / discharge power limits, and the active power margin within the power station. Assume the i-th photovoltaic-energy storage power station contains... Each photovoltaic unit and If there are one energy storage unit, the active power distribution of the power station should meet the following requirements: In the formula, This is the reference value for the active power of the j-th photovoltaic unit; This is the reference value for the active power of the k-th energy storage unit.

[0074] Step S32: Prioritize the use of available photovoltaic power output as the active power output of the power station foundation, and calculate the deviation between the total active power command at the power station level and the photovoltaic foundation output: In the formula, The total active power deviation of the i-th photovoltaic energy storage station; Let be the available active power output of the j-th photovoltaic unit. When When this occurs, it indicates that the energy storage unit needs to discharge for compensation; when When this occurs, it indicates that the energy storage unit needs to absorb power or reduce the active power deviation of the power station.

[0075] Step S33: Determine the current adjustable margin of each energy storage unit based on its state of charge, rated charge / discharge power, and capacity boundary, and construct the active power allocation weight accordingly. In the formula, Let be the discharge coordination coefficient of the k-th energy storage unit; Let be the charging coordination coefficient of the k-th energy storage unit.

[0076] Step S34: Based on the active power allocation weight, distribute the active power deviation of the power station to each energy storage unit to obtain the active power reference value of each energy storage unit: .

[0077] The system generates active power reference values ​​for each photovoltaic unit based on its available output. Then, it performs a feasibility check on the active power reference values ​​for each photovoltaic unit and energy storage unit to ensure they meet the constraints of available photovoltaic output, energy storage charging and discharging power, and energy storage state of charge. If any reference values ​​exceed the feasible range, they are corrected based on the equipment's adjustability margin.

[0078] Step S4: Consistent allocation of reactive power within the power station.

[0079] Step S41: Calculate the remaining reactive power of each inverter based on its current active power reference value and rated apparent capacity. For the m-th inverter, its remaining reactive power is expressed as: In the formula, The remaining reactive power of the m-th inverter; Let m be the rated apparent capacity of the m-th inverter; This is the reference value for the active power of the m-th inverter.

[0080] Step S42: Based on the remaining reactive power of each inverter and the total reactive power command at the plant level, determine the coordinated allocation relationship for each inverter's participation in reactive power support. Let the first... The total reactive power command for each photovoltaic and energy storage station is: Then the reactive power reference values ​​of each inverter inside the power station satisfy: In the formula, The number of inverters participating in reactive power regulation; For the first Reference value for reactive power of each inverter.

[0081] Step S43: The degree to which each inverter participates in reactive power support is characterized by the reactive power utilization rate, which is expressed as: In the formula, For the first The reactive power utilization rate of each inverter is determined based on the total reactive power command at the plant level and the remaining reactive power capacity of each inverter, and the corresponding reactive power reference value is generated. .

[0082] In this way, each inverter participates in reactive power support according to its own remaining reactive power capacity, avoiding inverters with smaller remaining reactive power capacity from undertaking excessive reactive power tasks.

[0083] Step S44: Perform capacity constraint processing on the reactive power reference value to ensure that the reactive power output of each inverter does not exceed its remaining reactive power capacity. .

[0084] If there is a reactive power reference value that exceeds the capacity constraint, the allocation result is re-corrected according to the remaining reactive power of each inverter, so that multiple photovoltaic inverters and energy storage inverters can work together to undertake the reactive power support task of the site.

[0085] The changes in reactive power utilization and consistency error of each inverter within the power station are as follows: Figure 4 As shown, the reactive power utilization rate of each inverter gradually tends to be consistent after the total reactive power command at the plant level changes; when the power command changes, the consistency error will fluctuate briefly, then decrease again and tend to stabilize. This indicates that the reactive power consistency allocation method can coordinate and generate reactive power reference values ​​based on the remaining reactive power capacity of each inverter.

[0086] Step S5: Inverter constant power control and feedback update.

[0087] Step S51: Input the active power reference value obtained in step S3 and the reactive power reference value obtained in step S4 into the corresponding photovoltaic inverter and energy storage inverter respectively, so that each inverter can obtain the corresponding active and reactive power control targets.

[0088] Step S52: Each photovoltaic inverter and energy storage inverter performs power tracking control based on the active power reference value and reactive power reference value, so that the actual output power of the inverter tracks the corresponding reference power and outputs the corresponding active power and reactive power to the distribution network.

[0089] Step S53: After the control cycle ends, collect information on the actual output power of each inverter, the change in the state of charge of the energy storage, the power of the power station's grid connection point, the voltage of the distribution network nodes, and the operating status of the equipment.

[0090] Step S54: Update the operating status of the photovoltaic-storage power station based on the actual output power, energy storage state of charge, power at the grid connection point of the power station, and node voltage changes, and feed the updated operating status back to the next control cycle for subsequent regional collaborative decision-making and power allocation of the power station.

[0091] The active and reactive power tracking results of photovoltaic inverters and energy storage inverters are as follows: Figure 5 As shown, the actual active and reactive power of each inverter can change with the corresponding reference value, and after a step change in the power command, it briefly adjusts to re-track the target value. This indicates that the closed-loop PQ control can convert the equipment-level power reference value generated by the power distribution layer of the power plant into the actual power output of the inverter.

[0092] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0093] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A hierarchical collaborative control method for photovoltaic and energy storage power plants for safe operation of distribution networks, characterized in that, The method includes the following steps: Step S1: Distribution network status acquisition and hierarchical control architecture construction; acquire distribution network operating status, photovoltaic available output, energy storage charge status, load power, node voltage and inverter capacity information, divide the distribution network into multiple zones according to the access location of photovoltaic and energy storage stations, and construct a hierarchical control architecture including a zone collaborative decision-making layer, a station power allocation layer and an inverter execution layer; Step S2: Regional collaborative decision-making and station-level power command generation; In the regional collaborative decision-making layer, each photovoltaic and energy storage station in each region is abstracted as an intelligent agent, and the power flow calculation process of the distribution network is regarded as the environment. Based on the local observation state and the global shared reward signal, the continuous adjustment actions of each photovoltaic and energy storage station are generated through a multi-agent reinforcement learning algorithm, and the continuous adjustment actions are mapped into station-level total active power command and total reactive power command that satisfy the constraints of photovoltaic available output, energy storage state of charge and station capacity. Step S3: Coordinated allocation of active power within the power station; at the power allocation layer of the power station, active power reference values ​​for photovoltaic units and energy storage units are generated based on the total active power command at the power station level, the available output of each photovoltaic unit, and the charge and discharge adjustable margin of each energy storage unit; wherein, the available output of photovoltaic units is used as the basic active power output of the power station, and the energy storage units compensate for the total active power deviation of the power station according to the adjustable margin; Step S4: Consistent allocation of reactive power within the power station; calculate the remaining reactive power capacity based on the current active power reference value and apparent capacity of each inverter, construct the target reactive power utilization rate based on the total reactive power command at the power station level, and update the reactive power utilization rate of each inverter using the leader consensus algorithm, so that each inverter generates the corresponding reactive power reference value according to its own remaining reactive power capacity. Step S5: Inverter constant power control and feedback update; In the inverter execution layer, the active power reference value and reactive power reference value are input into the closed-loop PQ controller of the photovoltaic inverter and the energy storage inverter, so that each inverter tracks the corresponding reference power, and the node voltage, station output power, energy storage state of charge and inverter operating status after execution are fed back to the next control cycle.

2. The hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, The specific methods for step S1, obtaining the distribution network status and constructing the hierarchical control architecture, include: Step S11: Obtain information on distribution network node voltage, load power, line power flow, available photovoltaic output, energy storage status of charge, energy storage charging and discharging power limits, and inverter rated capacity. Step S12: Based on the distribution network topology and the access locations of photovoltaic and energy storage stations, divide the distribution network into multiple operating areas, and determine the node set and photovoltaic and energy storage station set corresponding to each operating area; Step S13: Based on the node voltage, load power, available photovoltaic output, energy storage state of charge, and adjacent area operation information of each operating area, form the operating status input required for regional collaborative decision-making; Step S14: Based on the node voltage over-limit situation, voltage deviation degree, power response status of the station and equipment operation constraints, generate operation evaluation information for subsequent collaborative decision-making and feedback updates.

3. The hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, The regional collaborative decision-making and station-level power command generation method described in step S2 includes: Step S21: Take each operating area as the collaborative decision-making object, abstract the photovoltaic and energy storage power station in each operating area as the regional collaborative decision-making intelligent agent, and take the operating status of the distribution network and the operating status of the photovoltaic and energy storage power station as the state observation of the regional collaborative decision-making intelligent agent. Step S22: Determine the active and reactive power regulation requirements of each operating area based on the node voltage, load power, available photovoltaic output, energy storage status of charge, and operating information of adjacent areas. Step S23: Based on the active and reactive power regulation requirements, generate the active power regulation and reactive power regulation quantities for each photovoltaic and energy storage station; Step S24: Perform constraint processing on the active power regulation and reactive power regulation to form a regional coordinated regulation result that satisfies the distribution network operation constraints and the constraints of photovoltaic and energy storage equipment.

4. The hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 3, characterized in that, The state observations of the regional collaborative decision-making agent include one or more of the following: node voltage information, load active power, load reactive power, photovoltaic available output, energy storage charge status, and adjacent area or historical operation information; the active and reactive power adjustment actions output by the regional collaborative decision-making agent are not equipment-level power commands, but intermediate adjustment quantities used to generate station-level total active power commands and total reactive power commands.

5. A hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, The multi-agent reinforcement learning algorithm is a multi-agent dual-delay deep deterministic policy gradient algorithm; the feedback information includes one or more of the following: node voltage over-limit penalty term, voltage deviation penalty term, and energy storage operation constraint penalty term, which are used to guide the photovoltaic and energy storage stations in each region to coordinate their response to the distribution network voltage safety target.

6. The hierarchical collaborative control method for photovoltaic and energy storage power plants for safe operation of distribution networks according to claim 1, characterized in that, Step S3, which involves the coordinated allocation of active power within the power station, specifically includes: Step S31: Determine the total active power command for the photovoltaic-storage power station based on the regional coordinated regulation results, available photovoltaic output, energy storage state of charge, and energy storage charging and discharging power limits; Step S32: Determine the total reactive power command of the photovoltaic and energy storage power station based on the regional coordinated regulation results and the apparent capacity constraints of the photovoltaic inverter and energy storage inverter. Step S33: Perform boundary constraint processing on the total active power command and the total reactive power command to ensure that they meet the photovoltaic output constraints, energy storage power constraints, energy storage state of charge constraints, and inverter capacity constraints. Step S34: Use the constrained total active power command and total reactive power command at the station level as inputs for power allocation within the station.

7. A hierarchical collaborative control method for photovoltaic and energy storage power plants for safe operation of distribution networks according to claim 6, characterized in that, When the total active power deviation of the power station is greater than zero, the energy storage unit undertakes the active power compensation task according to the active power coordination coefficient corresponding to the discharge adjustable margin; when the total active power deviation of the power station is less than zero, the energy storage unit undertakes the power absorption task according to the active power coordination coefficient corresponding to the charging adjustable margin; the active power coordination coefficient is obtained by normalizing the current adjustable margin of each energy storage unit.

8. A hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, Step S4, the reactive power consistency allocation within the power station, specifically includes: Step S41: Obtain the available output of each photovoltaic unit, the state of charge of each energy storage unit, the charging and discharging power limit, and the active power adjustable margin within the power station. Step S42: Determine the active power output of the power station foundation based on the available photovoltaic power output, and determine the adjustability margin of each energy storage unit based on the energy storage state of charge and charging / discharging capacity; Step S43: Based on the deviation between the total active power command at the power station level and the active power output of the photovoltaic base, the active power adjustment task is allocated to each energy storage unit to obtain the active power reference command for the photovoltaic unit and the energy storage unit. Step S44: Perform feasibility verification and correction on the active power reference command for each unit to ensure that it meets the constraints of photovoltaic available output, energy storage charging and discharging power and state of charge.

9. A hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, The leader consensus algorithm updates the reactive power utilization rate based on the communication connection relationship between each inverter and the connection relationship between each inverter and the target leader node, so that each inverter can collaboratively undertake the total reactive power command at the plant level according to its own remaining reactive power capacity; the reactive power reference value of each inverter satisfies the inverter's remaining reactive power capacity constraint, and the sum of the reactive power reference values ​​of each inverter within the plant satisfies the total reactive power command at the plant level.

10. A hierarchical collaborative control method for photovoltaic and energy storage power stations for safe operation of distribution networks according to claim 1, characterized in that, Step S5, the inverter constant power control and feedback update, specifically includes: Step S51: Calculate the remaining reactive power of each inverter based on the current active power output and rated apparent capacity of each photovoltaic inverter and energy storage inverter. Step S52: Determine the coordinated allocation relationship of each inverter participating in reactive power support based on the remaining reactive power capacity of each inverter and the total reactive power command at the plant level. Step S53: According to the coordination and allocation relationship, the total reactive power command at the site level is allocated to each photovoltaic inverter and energy storage inverter to obtain the reactive power reference command for each inverter; Step S54: Perform capacity constraint processing on the reactive power reference command to ensure that the reactive power output of each inverter does not exceed its remaining reactive power capacity, and enable multiple inverters to work together to undertake the reactive power support task of the power station.

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