Park micro-grid cluster multi-source collaborative hierarchical power control system and control method

Abstract

The invention relates to the technical field of power distribution networks, in particular to a park micro-grid cluster multi-source collaborative hierarchical power control system and control method, and the control system comprises a micro-grid cluster energy management system which is used for managing a micro-grid cluster; the micro-grid cluster energy management system comprises a plurality of sub-micro-grid energy management systems for managing sub-micro-grids; the micro-grid cluster is of a star-shaped network topological structure comprising a plurality of levels and comprises a plurality of sub-micro-grids, and each sub-micro-grid comprises a photovoltaic cluster, a fan cluster, an energy storage cluster and a flexible load cluster; the sub-microgrid energy management system obtains operation data of each sub-microgrid; the micro-grid cluster energy management system formulates a partition power distribution strategy based on the operation data of each sub-micro-grid; and the sub-microgrid energy management system carries out power distribution on each level of the corresponding sub-microgrid based on a partition power distribution strategy. Dependence on the power grid is reduced, and safe and stable operation of the frequency voltage of the park microgrid cluster is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of power distribution network technology, and specifically to a hierarchical power control system and control method for multi-source collaboration in a park microgrid cluster. Background Art

[0002] The existing park power supply and utilization systems generally face the following technical bottlenecks:

[0003] Distributed power generation (DG) consumption is a significant challenge. Distributed power sources such as photovoltaic and wind power within the park are limited by their intermittent and fluctuating characteristics, resulting in spatiotemporal mismatches between power generation and load curves. This leads to frequent wind and solar curtailment, and the traditional "source follows load" model struggles to adapt to the demand for a high proportion of renewable energy access. Extreme seasonal power consumption is a significant constraint. High air conditioning loads account for a large proportion in summer and winter, coupled with peak industrial production, leading to widening peak-to-valley variations in the park's distribution network and a surge in the risk of transformer overload. A single electricity pricing mechanism fails to effectively guide load-side resources to participate in peak load regulation. Power supply reliability for critical loads is insufficient. Traditional single-source power supply architectures lack backup support. During grid failures or fluctuations in DG output, sensitive loads are susceptible to downtime due to voltage sags or frequency excursions, resulting in production losses. System retrofits are economically viable and compatible. Existing power supply and utilization systems have short equipment lifecycles and low digitalization levels. Large-scale retrofits face challenges such as high investment costs, significant downtime losses, and incompatible protocols for heterogeneous equipment, hindering the feasibility of traditional technology approaches. The contradiction between the electricity price mechanism and energy consumption costs has deepened. Under the time-of-use electricity price policy, the industrial park has to bear the dual pressure of high electricity prices during peak hours and capacity electricity charges. However, the existing energy management system lacks the ability to respond to electricity price signals in real time, resulting in high energy consumption costs.

[0004] To address these challenges, the industry urgently needs to explore a lightweight, collaborative, and intelligent campus energy upgrade solution. By building a multi-link collaborative mechanism encompassing "source, grid, load, and storage," we can achieve source-to-source complementarity, source-grid coordination, grid-load interaction, grid-storage interaction, and source-load interaction. The urgent challenge is to establish a campus microgrid operating system while minimizing disruption to the existing system, improving the dynamic balance of power system power, ensuring safe and stable frequency and voltage operation of the campus microgrid, and providing cost-effective technical solutions for the green transformation of the energy structure. Summary of the Invention

[0005] The purpose of the present invention is to solve at least one of the above technical problems and provide a hierarchical power control system and control method for multi-source collaboration in a campus microgrid cluster, which is applied to campus microgrids and source-grid-load-storage integrated projects.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0007] A multi-source coordinated hierarchical power control system for a campus microgrid cluster includes: a microgrid cluster energy management system for managing the microgrid cluster; the microgrid cluster energy management system includes multiple sub-microgrid energy management systems for managing sub-microgrids;

[0008] The microgrid cluster is a star network topology structure including multiple levels, and the microgrid cluster includes multiple sub-microgrids, each of which includes: a photovoltaic cluster, a wind turbine cluster, an energy storage cluster, and a flexible load cluster; wherein each photovoltaic cluster includes multiple photovoltaic power stations, each wind turbine cluster includes multiple wind power stations, the energy storage cluster includes multiple energy storage power stations, and each flexible load cluster includes multiple load devices;

[0009] The sub-microgrid energy management system obtains the operating data of each sub-microgrid and transmits it to the microgrid cluster energy management system; the microgrid cluster energy management system formulates a partition power allocation strategy based on the operating data of each sub-microgrid; and the sub-microgrid energy management system allocates power to each level of its corresponding sub-microgrid based on the partition power allocation strategy.

[0010] Furthermore, the microgrid cluster hierarchy includes:

[0011] First level: microgrid cluster layer;

[0012] Second level: multiple sub-microgrids;

[0013] The third level: photovoltaic clusters, wind turbine clusters, energy storage clusters, and flexible load clusters of each sub-microgrid;

[0014] The fourth level: photovoltaic power stations of photovoltaic clusters, wind power stations of wind turbine clusters, energy storage power stations of energy storage clusters, and load equipment of flexible load clusters.

[0015] A hierarchical power control method for multi-source collaboration in a campus microgrid cluster adopts any of the above-described hierarchical power control systems for multi-source collaboration in a campus microgrid cluster, the method comprising the following steps:

[0016] Obtain the active power of the microgrid cluster and the grid connection point as the microgrid cluster power target value;

[0017] Based on the real-time power of each sub-microgrid connection point and the load power of each load device in each sub-microgrid, the required load of each sub-microgrid is obtained;

[0018] Based on the real-time power generation power, maximum increaseable power, and maximum decreaseable power of each power station in each sub-microgrid, the real-time power information of each sub-microgrid is obtained; the real-time power information of the sub-microgrid includes the real-time power generation power, maximum increaseable power, and maximum decreaseable power of the sub-microgrid;

[0019] The microgrid cluster energy management system calculates the remaining increaseable power and the remaining decreaseable power of each sub-microgrid, as well as the remaining increaseable power of the microgrid cluster, based on the real-time power information of each sub-microgrid and the average SOC of the energy storage cluster.

[0020] The microgrid cluster energy management system formulates a partitioned power allocation strategy with each sub-microgrid as a sub-region based on the microgrid cluster power target value and the remaining increaseable power and the remaining decreaseable power of each sub-microgrid;

[0021] Based on the partitioned power allocation strategy, hierarchical power allocation is performed on each sub-microgrid.

[0022] Furthermore, the telemetry data is read through the secondary equipment to obtain the power data and load data of the microgrid cluster.

[0023] Furthermore, the load required by the sub-microgrid is the sum of the real-time power of the sub-microgrid connection point and the load power of each load device in the sub-microgrid;

[0024] The real-time power generation of a sub-microgrid is the sum of the real-time power generation of each cluster in the sub-microgrid;

[0025] The maximum increaseable power of a sub-microgrid is the sum of the maximum increaseable power of each cluster in the sub-microgrid;

[0026] The maximum power that can be reduced in a sub-microgrid is the sum of the maximum power that can be reduced in each cluster in the sub-microgrid;

[0027] The average SOC of the energy storage cluster is calculated using the following formula:

[0028]

[0029] Among them, SOC n is the average SOC of the energy storage cluster of the nth sub-microgrid; is the maximum increaseable power of the n2-th energy storage power station in the n-th sub-microgrid; is the SOC of the n2th energy storage power station in the nth sub-microgrid; n2 is the number of energy storage power stations in the energy storage cluster;

[0030] The remaining increaseable power of the sub-microgrid is the difference between the maximum increaseable power of the sub-microgrid and the real-time power generation power of the sub-microgrid.

[0031] Furthermore, the following rules should be followed when formulating a partition power allocation strategy:

[0032] The power allocation priorities from high to low are: meeting the overall load demand and maintaining the dynamic balance of the microgrid cluster power as the goal; maximizing the system's green power consumption as the desired goal; and minimizing energy transfer between sub-microgrids and reducing energy loss while achieving the power target.

[0033] Furthermore, based on the partition power allocation strategy, hierarchical power allocation is performed on each sub-microgrid, including: under the condition of meeting the safety constraints,

[0034] The microgrid cluster energy management system distributes the microgrid cluster power target value to each target sub-microgrid of the second level as the sub-microgrid power target value;

[0035] The target sub-microgrid allocates its corresponding sub-microgrid power target value to its subordinate third-level photovoltaic cluster, wind turbine cluster, and energy storage cluster in the order of wind and solar power first, energy storage second, and flexible load last, as the power target value of each cluster;

[0036] Each cluster distributes its corresponding cluster power target value to each subordinate power station at the fourth level as the power target value of each power station.

[0037] Furthermore, the microgrid cluster energy management system distributes the microgrid cluster power target value to each target sub-microgrid of the second level as the sub-microgrid power target value, including:

[0038] When the remaining increaseable power P of the microgrid cluster max Greater than 0 and the microgrid cluster power target value P set When it is greater than 0, the sub-microgrids whose remaining increaseable power is greater than 0 are obtained as target sub-microgrids, and the sub-microgrid power target value is allocated to each target sub-microgrid according to the following formula:

[0039]

[0040] in, is the target power value of the nth microgrid; is the remaining increaseable power of the nth microgrid.

[0041] Furthermore, the corresponding sub-microgrid power target value is allocated to the third-level photovoltaic cluster, wind turbine cluster, and energy storage cluster under it as the power target value of each cluster, including:

[0042] If the sub-microgrid power target value is greater than 0 and the remaining increaseable power of the sub-microgrid is greater than 0, determine whether the maximum increaseable power of the photovoltaic cluster is greater than the real-time power of the photovoltaic cluster; if so, use the difference between the maximum increaseable power of the photovoltaic cluster and the real-time power of the photovoltaic cluster as the photovoltaic cluster power target value;

[0043] If the sub-microgrid power target value is greater than the photovoltaic cluster power target value, and the wind power cluster's maximum increaseable power is greater than the wind power cluster's real-time power, the difference between the wind power cluster's maximum increaseable power and the wind power cluster's real-time power is used as the wind power cluster's power target value;

[0044] If the sub-microgrid power target value is greater than the sum of the photovoltaic cluster power target value and the wind power cluster power target value, the energy storage cluster SOC average value meets the SOC safety threshold, and the energy storage cluster's maximum increaseable power is greater than the energy storage cluster's real-time power, the difference between the energy storage cluster's maximum increaseable power and the energy storage cluster's real-time power is used as the energy storage cluster's power target value.

[0045] Furthermore, each cluster distributes its corresponding cluster power target value to each power station at the fourth level under it as the power target value of each power station, including:

[0046] The target cluster selects the power stations with the maximum increaseable power greater than 0 among its subordinate power stations as target power stations and assigns power target values to the target power stations using the following formula:

[0047]

[0048] in, is the power target value of the n1th PV power station in the nth microgrid, P nPVset is the target power value of the photovoltaic cluster of the nth microgrid, is the maximum increaseable power of the n1th PV power station in the nth microgrid, is the real-time power of the n1th photovoltaic power station in the nth microgrid, P nPVmax is the maximum power that can be increased by the photovoltaic cluster of the nth microgrid, P nPV is the real-time power of the PV cluster of the nth sub-microgrid, and n1 is the number of PV power stations in the PV cluster;

[0049] is the power target value of the n3th wind power station in the nth microgrid, P nPWset is the wind power cluster power target value of the nth microgrid, is the maximum increaseable power of the n3-th wind power station in the n-th microgrid, is the real-time power of the n3th wind power station in the nth microgrid, P nPWmax is the maximum increaseable power of the wind power cluster of the nth microgrid, P nPV is the real-time power of the wind power cluster of the nth sub-microgrid, and n3 is the number of wind power stations in the wind power cluster;

[0050] is the power target value of the n2th energy storage power station in the nth microgrid, P nPCSset is the target power value of the energy storage cluster of the nth microgrid, is the maximum increaseable power of the n2-th energy storage power station in the n-th microgrid, is the real-time power of the n2th energy storage power station in the nth microgrid, P nPCSmaxis the maximum increaseable power of the energy storage cluster of the nth microgrid, P nPCS is the real-time power of the energy storage cluster of the nth sub-microgrid, and n2 is the number of energy storage power stations in the energy storage cluster.

[0051] The beneficial effects of the present invention are:

[0052] The present invention realizes multi-source collaborative optimization control of the park microgrid cluster by constructing a hierarchical power control system with a four-layer star network architecture. Its technical advantages are as follows: 1) In terms of system-level collaboration, through the four-level control architecture of "microgrid cluster-sub-microgrid-cluster-power station", combined with the dynamic weight allocation algorithm and safety constraint rolling optimization, the power allocation response speed is improved to the millisecond level, and the system equivalent load fluctuation suppression rate is improved; 2) In terms of new energy consumption, the three-stage source-load matching strategy (photovoltaic priority → wind turbine regulation → energy storage compensation) reduces the rate of abandoned solar and wind power, and cooperates with the weighted SOC evaluation model of the energy storage cluster to extend the battery cycle life; 3) At the economic operation level, the allocation mechanism based on the remaining increaseable power is adopted to reduce the network loss rate and the power interaction between sub-microgrids. Combined with the millisecond-level response capability of the flexible load, the comprehensive energy consumption cost of the park is reduced.

[0053] This invention is applicable to scenarios where power sources, grids, loads and storage are integrated. Through equivalent load modeling and global optimization control, it increases the penetration rate of new energy and reduces carbon emission intensity while ensuring power supply reliability, thus providing industrial parks with a comprehensive energy solution that is safe, economical and low-carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the structure of a multi-source coordinated hierarchical power control system for a campus microgrid cluster according to an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of a hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to an embodiment of the present invention;

[0056] Figure 3 This is a flow chart of a method for allocating target power values to sub-microgrids according to an embodiment of the present invention;

[0057] Figure 4 This is a flow chart of a cluster power target value allocation method according to an embodiment of the present invention;

[0058] Figure 5 The figure is a flow chart of a method for allocating power target values to each power station according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only intended to enable those skilled in the art to better understand and implement the present invention, rather than to imply any limitation on the scope of the present invention.

[0060] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0061] Example 1

[0062] Figure 1 This is a schematic diagram of a hierarchical power control system structure for a multi-source collaborative microgrid cluster in a park according to an embodiment of the present invention. Figure 1 As shown, according to one embodiment of the present invention, a multi-source coordinated hierarchical power control system for a campus microgrid cluster includes: a microgrid cluster energy management system for managing the microgrid cluster; the microgrid cluster energy management system includes multiple sub-microgrid energy management systems for managing sub-microgrids;

[0063] The microgrid cluster is a star-shaped network topology with multiple levels. The microgrid cluster includes multiple sub-microgrids, each of which includes: a photovoltaic cluster, a wind turbine cluster, an energy storage cluster, and a flexible load cluster. Among them, each photovoltaic cluster includes multiple photovoltaic power stations, each wind turbine cluster includes multiple wind power stations, the energy storage cluster includes multiple energy storage power stations, and each flexible load cluster includes multiple load devices.

[0064] The sub-microgrid energy management system obtains the operating data of each sub-microgrid and transmits it to the microgrid cluster energy management system; the microgrid cluster energy management system formulates a partition power allocation strategy based on the operating data of each sub-microgrid; the sub-microgrid energy management system allocates power to each level of its corresponding sub-microgrid based on the partition power allocation strategy.

[0065] Preferably, the hierarchy of the microgrid cluster includes:

[0066] First level: microgrid cluster layer;

[0067] Second level: multiple sub-microgrids;

[0068] The third level: photovoltaic clusters, wind turbine clusters, energy storage clusters, and flexible load clusters of each sub-microgrid;

[0069] The fourth level: photovoltaic power stations of photovoltaic clusters, wind power stations of wind turbine clusters, energy storage power stations of energy storage clusters, and load equipment of flexible load clusters.

[0070] In this implementation, a multi-source collaborative hierarchical power control system for a campus microgrid cluster based on a star network topology is proposed. This system achieves multi-energy collaborative optimization and refined power regulation through a multi-layer architecture. This system, with a hierarchical energy management system connected by physical links as its core, constructs a vertical control channel from the cluster layer to the device layer, specifically including:

[0071] At the physical architecture level, the entire microgrid cluster exhibits a star network topology, with the top-level microgrid cluster energy management system serving as the central control node, interconnected by multiple sub-microgrid energy management systems (EMSs) at the second level. Each sub-microgrid EMS is embedded within the microgrid cluster EMS framework as a virtual subsystem, interacting with each other through standardized data transmission channels. Each sub-microgrid (sub-microgrid 1, sub-microgrid 2, ..., sub-microgrid n) employs a hybrid networking approach. The photovoltaic cluster comprises multiple PV plants equipped with intelligent measurement and control devices, implementing DC-to-AC conversion and maximum power point tracking (MPPT) through PV inverter arrays (Inverter 1, Inverter 2, ..., Inverter n). The wind turbine cluster integrates the pitch control systems of multiple wind farms, optimizing wind energy capture efficiency through the wind turbine energy management system (Energy Management 1, Energy Management 2, ..., Energy Management n). The energy storage cluster utilizes a modular design, with multiple energy storage plants forming an energy buffer pool through energy storage converters (PCS1, PCS2, ..., PCSn). The flexible load cluster integrates various devices, such as interruptible loads and temperature-controlled loads, to form a demand response resource pool.

[0072] At the control logic level, the system implements a four-level collaborative control mechanism: the cluster layer (first level) formulates a global power balancing strategy, performs multi-objective optimization based on the operating data of each sub-microgrid (including power generation forecasts, load characteristics, energy storage status, etc.), and generates partitioned power allocation instructions; the sub-microgrid layer (second level) serves as an intermediate coordination layer, decomposing cluster instructions into photovoltaic / wind turbine output plans, energy storage charging and discharging strategies, and load control targets; the functional cluster layer (third level) performs specific control actions, the photovoltaic inverter array maintains voltage stability through dynamic reactive power compensation, the wind turbine pitch system adjusts the pitch angle to cope with wind speed fluctuations, the energy storage PCS implements millisecond-level power response, and the flexible load cluster realizes load curve reshaping through price / incentive mechanisms; the equipment layer (fourth level) is composed of each power station controller that executes the underlying control instructions and realizes equipment-level collaboration through the field bus.

[0073] In terms of technical implementation, the system uses a communication protocol based on the IEC 61850 standard to build a physical link. High-speed data exchange is achieved between the microgrid cluster energy management system and the sub-microgrid energy management system via a redundant fiber optic ring network, ensuring the high-speed transmission of control commands. A multi-source collaborative optimization algorithm integrates model predictive control (MPC) and deep reinforcement learning (DRL), performing real-time simulation on a digital twin platform. Taking into account multi-dimensional constraints such as electricity price fluctuations, weather forecasts, and load characteristics, it generates a power allocation solution that is optimal in terms of economy, reliability, and environmental protection. The energy storage system uses a virtual synchronous generator (VSG) control strategy to simulate the inertial response characteristics of traditional synchronous machines and enhance system frequency stability. Flexible loads achieve local autonomous decision-making through edge computing terminals, participating in demand response while ensuring user experience.

[0074] The system of the present invention effectively solves the problem of coordinated control of multi-source heterogeneous equipment in the park microgrid cluster through hierarchical decoupling and centralized-distributed hybrid control architecture, improves the renewable energy absorption rate, reduces load response delay and power distribution deviation, and significantly improves the comprehensive energy utilization efficiency and power supply reliability of the park.

[0075] Example 2

[0076] Figure 2 This is a flow chart of a hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to an embodiment of the present invention. Figure 2 As shown, according to one embodiment of the present invention, a hierarchical power control method for multi-source collaboration of a campus microgrid cluster adopts the hierarchical power control system for multi-source collaboration of a campus microgrid cluster of the present invention, and the method includes the following steps:

[0077] Step S102: Obtain the active power P of the microgrid cluster and the grid connection point point As the microgrid cluster power target value P set ;

[0078] Step S104: Based on the real-time power of each microgrid connection point The load power of each load device in each sub-microgrid is used to obtain the load required by each sub-microgrid

[0079] Step S106: Based on the real-time power of each microgrid connection point Maximum power that can be increased, maximum power that can be decreased, and real-time power information of each sub-microgrid are obtained; the real-time power information of the sub-microgrid includes the real-time power generation power of the sub-microgrid Maximum power that can be increased and maximum power that can be decreased;

[0080] Step S108: The microgrid cluster energy management system is based on the real-time power information of each microgrid and the average SOC (SOC n), calculate the remaining increaseable power of each microgrid The remaining power that can be reduced and the remaining power that can be increased P of the microgrid cluster max ;

[0081] Step S110: The microgrid cluster energy management system is based on the microgrid cluster power target value P set and the remaining increaseable power of each microgrid For the remaining power that can be reduced, a zone power allocation strategy is formulated with each microgrid as a sub-region;

[0082] Step S112: performing hierarchical power allocation on each sub-microgrid based on the partition power allocation strategy.

[0083] Preferably, the telemetry data is read by secondary equipment to obtain various power data and load data in the microgrid cluster.

[0084] In this implementation, a multi-source collaborative hierarchical power control method for a campus microgrid cluster is proposed, which realizes refined power regulation through a four-layer architecture. First, the system reads telemetry data through secondary equipment to accurately obtain various power data and load data in the microgrid cluster, providing a reliable basis for subsequent control. point As the microgrid cluster power target value P set , combined with the power of each microgrid's high-voltage side busbar grid connection point and the power of internal load equipment, dynamically calculate the microgrid's load demand. Then, through the real-time collection of the microgrid's real-time power generation power Maximum power increase / decrease and average SOC of energy storage cluster (SOC n ) to evaluate the remaining regulation capacity of the sub-microgrid. Based on the overall cluster goal and the regulation capacity of the sub-microgrid, a multi-objective optimization algorithm is used to generate a partitioned power allocation strategy, giving priority to sub-microgrids with small fluctuations in renewable energy output and moderate energy storage SOC to undertake regulation tasks. Ultimately, instructions are sent to the sub-microgrid energy management system through a hierarchical control mechanism, which coordinates the photovoltaic inverter, wind turbine converter, energy storage PCS and flexible load terminals to achieve precise power distribution from the cluster layer to the device layer. The remaining power is calculated by weighting the energy storage SOC to avoid overcharging and over-discharging; at the same time, millisecond-level data exchange between sub-microgrids is achieved through the IEC 61850 standard communication protocol to ensure strong consistency between control instructions and actual operating conditions.

[0085] The present invention significantly improves the energy utilization efficiency and operational reliability of the campus microgrid through hierarchical collaborative control: on the one hand, the dynamic power allocation strategy based on energy storage SOC extends battery life, and combined with the demand response mechanism of flexible loads, the renewable energy absorption rate is improved; on the other hand, the collaborative control of multi-source equipment reduces power distribution deviation, improves power supply reliability, and realizes clean, efficient and economical campus energy supply.

[0086] According to one embodiment of the present invention, the load required by the sub-microgrid Real-time power of the microgrid connection point The sum of the load power of each load device in the sub-microgrid;

[0087] Real-time power generation of microgrid is the sum of the real-time power generation of each cluster in the sub-microgrid;

[0088] The maximum increaseable power of a sub-microgrid is the sum of the maximum increaseable power of each cluster in the sub-microgrid;

[0089] The maximum power that can be reduced in a sub-microgrid is the sum of the maximum power that can be reduced in each cluster in the sub-microgrid;

[0090] The average SOC of the energy storage cluster is calculated using the following formula:

[0091]

[0092] Among them, SOC n is the average SOC of the energy storage cluster of the nth sub-microgrid; is the maximum increaseable power of the n2-th energy storage power station in the n-th sub-microgrid; is the SOC of the n2th energy storage power station in the nth sub-microgrid; n2 is the number of energy storage power stations in the energy storage cluster;

[0093] Remaining power that can be increased in the microgrid The difference between the maximum power that can be increased by the sub-microgrid and the real-time power generation of the sub-microgrid

[0094] In this implementation, multi-source collaborative optimization control of the campus microgrid cluster is achieved through refined power modeling and energy storage status assessment. Specifically, the load required by the sub-microgrid is calculated by real-time collection of telemetry data on grid connection point power and internal load equipment power, using a weighted summation method to ensure the real-time and accuracy of load demand assessment. The real-time power generation of the sub-microgrid is obtained by aggregating the real-time output data of the photovoltaic cluster, wind turbine cluster, and energy storage cluster. The power generation of the photovoltaic and wind turbine clusters is optimized by the maximum power point tracking (MPPT) algorithm, and the output of the energy storage cluster is precisely controlled by the bidirectional converter (PCS) to achieve charging and discharging power. In terms of power regulation capability assessment, the maximum increase / decrease power of the sub-microgrid is determined by aggregating the technical output limits of each energy cluster: the photovoltaic cluster is limited by irradiation intensity and inverter capacity, the wind turbine cluster is constrained by the wind speed-power curve, and the energy storage cluster is calculated based on the battery charge and discharge rate and PCS power level. The calculation of the average SOC of the energy storage cluster introduces a weighted algorithm, taking the maximum increaseable power of each energy storage power station as the weight coefficient and performing a weighted average of the SOC value. This design makes the SOC assessment closer to the actual regulation capacity - the weight coefficient is positively correlated with the regulation capacity, avoiding the misjudgment of the regulation potential that may be caused by the traditional arithmetic average method. Through the maximum increase in power and real-time power generation This indicator directly reflects the upward regulation margin of the microgrid after meeting its own load. The microgrid cluster energy management system uses a hierarchical optimization algorithm to generate a power allocation strategy based on the remaining power margin and energy storage SOC status of each microgrid.

[0095] Through refined power modeling and weighted evaluation of energy storage status, the present invention realizes multi-source collaborative optimization control of the campus microgrid cluster, significantly improves the power distribution accuracy and system regulation capability, and ensures the high efficiency and stability of microgrid operation.

[0096] According to an embodiment of the present invention, in step S110, the partition power allocation strategy is formulated in accordance with the following rules:

[0097] The power allocation priorities from high to low are: meeting the overall load demand and maintaining the dynamic balance of the microgrid cluster power as the goal; maximizing the system's green power consumption as the desired goal; and minimizing energy transfer between sub-microgrids and reducing energy loss while achieving the power target.

[0098] In this implementation, the partitioned power allocation strategy adopts a three-level optimization control architecture. At the system level, a dynamic balance model with the overall load demand satisfaction rate as the primary constraint is established. By real-time monitoring of the power flow of each sub-microgrid grid connection point, the model predictive control (MPC) algorithm is used to rollingly optimize the matching degree of total power generation power and load demand. When there is a shortage or redundancy in the system power, the second-level optimization goal - the green power consumption maximization mechanism is triggered. This mechanism uses the power curtailment rate prediction model of the photovoltaic / wind turbine cluster and the charging and discharging window period evaluation of the energy storage system to give priority to the use of renewable energy generation resources for power regulation. In the third-level optimization, a power transmission topology optimization algorithm based on graph theory is designed. The microgrid cluster is abstracted into a weighted directed graph. The transmission loss coefficient and the line thermal stability limit are used as the edge weights. The optimal power transmission path is searched through the Dijkstra algorithm to minimize the energy interaction between each sub-microgrid while meeting the power target. In terms of specific implementation, each sub-microgrid follows the principle of "internal autonomy and external coordination" and maintains the net power of the grid connection point close to zero through the local controller. When a power shortage is detected, it gives priority to calling the rapid adjustment resources of the adjacent sub-microgrid (such as the energy storage cluster), and then gradually expands to the flexible resource pool of the entire system, forming a three-level response system of "regional autonomy-regional mutual assistance-global optimization".

[0099] Through a multi-level collaborative optimization mechanism, the present invention can improve the green electricity consumption rate and reduce network losses while ensuring power supply reliability, thereby significantly enhancing the economy and environmental protection of the microgrid cluster.

[0100] Figure 3 This is a flow chart of a method for allocating target power values to sub-microgrids according to an embodiment of the present invention; Figure 4 This is a flow chart of a cluster power target value allocation method according to an embodiment of the present invention; Figure 5 This is a flow chart of a method for allocating target power values to each power station according to an embodiment of the present invention. Figure 3-5 As shown, according to an embodiment of the present invention, step S112 includes: under the condition that the safety constraint is satisfied,

[0101] Step S1121: The microgrid cluster energy management system sets the microgrid cluster power target value P set Allocate to each target sub-microgrid at the second level as the sub-microgrid power target value

[0102] Step S1122: The target sub-microgrid sets its corresponding sub-microgrid power target value in the order of wind and solar power first, energy storage second, and flexible load last. The power target values of the third-level photovoltaic clusters, wind turbine clusters, and energy storage clusters allocated to them are used as the power target values of each cluster.

[0103] Step S1123: Each cluster sets its corresponding cluster power target value Allocate to the power stations at the fourth level below it as the power target value of each power station

[0104] Preferably, step S1121 includes: when the remaining increaseable power P of the microgrid cluster max Greater than 0 and the microgrid cluster power target value P set When it is greater than 0, the sub-microgrids whose remaining increaseable power is greater than 0 are obtained as target sub-microgrids, and the sub-microgrid power target value is allocated to each target sub-microgrid according to the following formula:

[0105]

[0106] in, is the target power value of the nth microgrid; P set is the power target value of the microgrid cluster; the remaining power that can be increased by the microgrid cluster P max ; is the remaining increaseable power of the nth microgrid.

[0107] Step S1122 includes: if the power target value of the microgrid Greater than 0 and the remaining increaseable power of the sub-microgrid Greater than 0, judging the maximum power that can be increased by the photovoltaic cluster Is it greater than the real-time power of the photovoltaic cluster? If so, increase the maximum power that can be added to the photovoltaic cluster Real-time power of photovoltaic cluster The difference is used as the target power value of the photovoltaic cluster

[0108] If the power target value of the microgrid Greater than the target power value of the photovoltaic cluster And the maximum power that can be increased by the wind power cluster Greater than the real-time power of the wind power cluster The maximum increase in power of the wind power cluster Real-time power of wind power cluster The difference between the two values is taken as the wind power cluster power target value

[0109] If the power target value of the microgrid Greater than the photovoltaic cluster power target value and wind power cluster power target value The sum of the energy storage cluster SOC average SOC n Meet the SOC safety threshold and the maximum power increase of the energy storage cluster Greater than the real-time power of the energy storage cluster The maximum increase in power of the energy storage cluster Real-time power of energy storage cluster The difference is used as the target power value of the energy storage cluster

[0110] Step S1123 includes: the target cluster selects the power station with the maximum increaseable power greater than 0 among its subordinate power stations as the target power station, and allocates a power target value to the target power station using the following formula:

[0111]

[0112] in, is the power target value of the n1th PV power station in the nth microgrid, P nPVset is the target power value of the photovoltaic cluster of the nth microgrid, is the maximum increaseable power of the n1th PV power station in the nth microgrid, is the real-time power of the n1th photovoltaic power station in the nth microgrid, P nPVmax is the maximum power that can be increased by the photovoltaic cluster of the nth microgrid, P nPV is the real-time power of the PV cluster of the nth sub-microgrid, and n1 is the number of PV power stations in the PV cluster;

[0113] is the power target value of the n3th wind power station in the nth microgrid, P nPWset is the wind power cluster power target value of the nth microgrid, is the maximum increaseable power of the n3-th wind power station in the n-th microgrid, is the real-time power of the n3th wind power station in the nth microgrid, P nPWmax is the maximum increaseable power of the wind power cluster of the nth microgrid, P nPV is the real-time power of the wind power cluster of the nth sub-microgrid, and n3 is the number of wind power stations in the wind power cluster;

[0114] is the power target value of the n2th energy storage power station in the nth microgrid, P nPCSset is the target power value of the energy storage cluster of the nth microgrid, is the maximum increaseable power of the n2-th energy storage power station in the n-th microgrid, is the real-time power of the n2th energy storage power station in the nth microgrid, P nPCSmax is the maximum increaseable power of the energy storage cluster of the nth microgrid, P nPCS is the real-time power of the energy storage cluster of the nth sub-microgrid, and n2 is the number of energy storage power stations in the energy storage cluster.

[0115] In this implementation, the refined scheduling of power flow is achieved through the four-level coordinated control of “system-sub-microgrid-cluster-power station”. At the system level (first level), the energy management system is based on the power target value P of the microgrid cluster. set , based on the proportional allocation principle of the remaining increaseable power, the sub-microgrid with regulation capability is activated first to achieve the sub-microgrid power target value The power target value of each sub-microgrid is positively correlated with the sub-microgrid regulation capacity to ensure the fairness and efficiency of power distribution. The sub-microgrid level (second layer) follows the source-load matching principle of "clean priority, energy storage regulation" and constructs a three-stage decision tree: First, the available regulation capacity of the photovoltaic cluster is fully utilized. Determine whether the available regulation capacity of the PV cluster fully covers the power target value of the sub-microgrid If yes, set the PV cluster power target value Set as the sub-microgrid power target value Otherwise, the PV cluster power target value Set to Secondly, activate the backup power of the wind turbine cluster. The setting method of the wind turbine cluster power target value is the same as that of the photovoltaic cluster power target value. First, fully call the available adjustment capacity of the wind turbine cluster. Determine whether the available regulation capacity of the wind turbine cluster fully covers the power target value of the sub-microgrid to be allocated If so, set the wind turbine cluster power target value Set to Otherwise, set the wind turbine cluster power target value Set to Finally, when the energy storage SOC meets the safety threshold, the energy storage charge and discharge adjustment is started. This strategy effectively balances the consumption of renewable energy and equipment life management. At the cluster level (third layer), an improved equal incremental rate criterion is adopted. By distributing the cluster-level target value to each power generation unit according to the maximum increaseable power ratio of the power station, it can avoid local equipment overload and ensure the cluster output tracking accuracy. At the power station level (fourth layer), the closed-loop execution of power instructions is realized through the local controller, forming a two-way interactive mechanism of "global optimization-local autonomy". By constructing a multi-objective optimization model that includes safety constraints (line thermal stability limit, SOC safety threshold, converter capacity limit), the model predictive control (MPC) framework is used to roll the power allocation strategy, and the synergy of 15-minute system optimization and minute-level local control is achieved to effectively cope with the output fluctuations and load uncertainties of renewable energy.

[0116] The present invention uses a four-layer collaborative power distribution architecture to achieve precise regulation of the power flow of microgrid clusters, thereby increasing the renewable energy absorption rate, reducing system operating losses, improving equipment utilization, and significantly enhancing the flexibility and economy of complex microgrids.

[0117] Example 3

[0118] like Figure 2-5 As shown, according to one embodiment of the present invention, a hierarchical power control method for multi-source collaboration of a campus microgrid cluster adopts any of the hierarchical power control systems for multi-source collaboration of a campus microgrid cluster of the present invention, and the method includes the following steps:

[0119] Step S201: The microgrid cluster energy management system obtains the active power P of the grid connection point through the high-voltage side line measurement and control device. point , use this power as the shortfall load of the entire microgrid cluster;

[0120] Among them, obtaining the microgrid cluster shortage load includes:

[0121] The microgrid cluster energy management system obtains the active power P of the grid connection point from the line measurement and control device. point , this active power is used as the microgrid cluster shortfall load and is also the microgrid cluster power target value P set ;

[0122] In step S202, the energy management system of each sub-microgrid obtains the power of each sub-microgrid and the high-voltage side bus grid connection point (i.e., the real-time power of the sub-microgrid grid connection point) through the secondary equipment such as measurement and control and electric meters in the sub-microgrid. ), the load power of each load device in each sub-microgrid, and the overall algebraic sum as the load required by each sub-microgrid Each sub-microgrid transmits the required load to the microgrid cluster energy management system;

[0123] The power and load data of each microgrid and the high-voltage busbar connection point are obtained, including:

[0124] Each microgrid energy management system reads telemetry data from secondary equipment such as measurement and control, electricity meters, etc., including the real-time power of the grid connection point. Current real-time load of sub-microgrid load line 1 Load line l Current real-time load Calculate the load required by the microgrid for: Then calculate the load P required by the microgrid cluster load for:

[0125] In step S203, the energy management system of each sub-microgrid accesses the primary and secondary devices within the sub-microgrid, such as the inverter, wind turbine energy management system, energy storage PCS, BMS, and flexible loads, to obtain the real-time generated power, maximum increaseable power, and maximum decreaseable power of each device within the sub-microgrid. The overall algebraic sum is used as the real-time generated power, maximum increaseable power, and maximum decreaseable power of the current sub-microgrid. Each sub-microgrid transmits the current real-time generated power, maximum increaseable power, maximum decreaseable power, and the overall SOC of the energy storage cluster to the microgrid cluster energy management system.

[0126] The current real-time power, maximum increaseable power, and maximum decreaseable power of each microgrid power generation unit are obtained, including:

[0127] Each microgrid energy management system reads telemetry and rated parameter data from inverters, wind turbine energy management devices, etc., including the real-time power of the photovoltaic cluster. Maximum power increase of photovoltaic cluster Maximum power reduction of photovoltaic cluster Real-time power of wind power cluster Maximum increase in power of wind power cluster Maximum power reduction of wind power cluster Real-time power of energy storage cluster Maximum increase in power of energy storage cluster Maximum power reduction of the energy storage cluster Energy storage cluster average SOC (SOC n );

[0128] The maximum increaseable power of the photovoltaic cluster is calculated as:

[0129]

[0130] Among them, P nPVmax is the maximum power that can be increased by the photovoltaic cluster of the nth sub-microgrid; is the maximum increaseable power of the n1th PV power station in the nth sub-microgrid; n1 is the number of PV power stations in the PV cluster;

[0131] The calculation method for wind power clusters and energy storage clusters is the same as that for photovoltaic clusters;

[0132] Calculate the average SOC of the energy storage cluster as:

[0133]

[0134] Among them, SOC n is the average SOC of the energy storage cluster of the nth sub-microgrid; is the maximum increaseable power of the n2-th energy storage power station in the n-th sub-microgrid; is the SOC of the n2th energy storage power station in the nth sub-microgrid; n2 is the number of energy storage power stations in the energy storage cluster;

[0135] Calculate the current power generation of the microgrid for:

[0136]

[0137] Load required by the microgrid Current power generation Plus the real-time power of the grid connection point

[0138]

[0139] Calculate the remaining power that can be increased in the microgrid for:

[0140]

[0141] Thus, the remaining increaseable power P of the microgrid cluster is calculated max for:

[0142]

[0143] The current power generation power P of the microgrid cluster curent for:

[0144]

[0145] Microgrid cluster power target value P set for:

[0146] P set =P load -P curent =P point

[0147] In step S204, the microgrid cluster energy management system abstracts each sub-microgrid into independent sub-regions and calculates the remaining increaseable power (maximum increaseable power minus real-time generated power) and the decreaseable power of each sub-region;

[0148] Step S205: formulate a partition power allocation strategy between partitions, and the power allocation priorities from high to low are: meeting the overall load demand and maintaining the dynamic balance of the microgrid cluster power as the goal; maximizing the system's green power consumption as the desired goal. Under the premise of achieving the power target, try to reduce the energy transfer between each sub-microgrid and reduce energy loss; the strategy is to give priority to zero power at the sub-microgrid grid connection point and to coordinate and assist multiple partitions. Priority is given to ensuring power balance within the sub-microgrid (the power generated by the sub-microgrid is used first, and the power at the grid connection point is the minimum), and the shortfall in load in other partitions is supplemented by areas with sufficient power generation capacity;

[0149] Step S206: The microgrid cluster energy management system allocates power to the microgrid cluster according to the partition power allocation strategy. Specifically, the step includes:

[0150] Step S2061: The microgrid cluster energy management system distributes the shortfall load to each sub-microgrid of the second level in equal proportion based on the shortfall load of the high-voltage side grid connection point and subject to safety constraints.

[0151] Among them, power is distributed among the sub-microgrids according to the established power allocation strategy, including:

[0152] The target power value of the microgrid cluster is P set , the power to be allocated for each sub-microgrid is: ...,

[0153] When the power target value P set and the remaining increaseable power P max When both are greater than 0, obtain the remaining increaseable power of each sub-microgrid A list greater than 0, with a total of k sub-microgrids with remaining power that can be increased greater than 0;

[0154] The power target value P set The power is distributed to the second-level k sub-microgrids in equal proportion, and the power generation unit of each microgrid increases the power generation to obtain the sub-microgrid power target value. for:

[0155]

[0156] Step S2062: After receiving the shortfall load assigned by the microgrid cluster energy management system, the single sub-microgrid allocates the shortfall load to the third-level photovoltaic cluster, wind turbine cluster, energy storage cluster, and flexible load cluster in the order of wind and solar power first, energy storage second, and flexible load last.

[0157] After receiving the power target value, the sub-microgrid performs the third-level power allocation according to the established power allocation strategy, including:

[0158] PV cluster power target value for:

[0159]

[0160] Wind power cluster power target value for:

[0161]

[0162] Energy storage cluster power target value for:

[0163]

[0164] Step S2063: After the PV cluster receives the shortfall load allocated by the sub-microgrid, the system creates a virtual PV power control system. The PV power control system distributes the shortfall load to each PV inverter at the fourth level in equal proportion.

[0165] After receiving the target power value, the PV cluster performs the fourth level of power allocation according to the established power allocation strategy, including:

[0166] The target power value of the photovoltaic cluster is The system creates a virtual photovoltaic power control system. The power control system first obtains the photovoltaic arrays with remaining increaseable power greater than 0, and then distributes the power to each photovoltaic array in equal proportion. Each photovoltaic array increases the generated power, and the photovoltaic array power target value is obtained as follows:

[0167]

[0168] in, is the power target value of the n1th PV power station in the nth microgrid, P nPVset is the target power value of the photovoltaic cluster of the nth microgrid, is the maximum increaseable power of the n1th PV power station in the nth microgrid, is the real-time power of the n1th photovoltaic power station in the nth microgrid, P nPVmax is the maximum power that can be increased by the photovoltaic cluster of the nth microgrid, P nPV is the real-time power of the photovoltaic cluster of the nth sub-microgrid;

[0169] The power control method of wind turbine clusters and energy storage clusters is the same as that of photovoltaic clusters, and power is distributed according to the same distribution method.

[0170] At this point, after monitoring the shortfall load, the microgrid cluster energy management system completes power distribution according to the multi-source collaborative hierarchical power control method, and finally achieves dynamic power balance of the microgrid cluster.

[0171] The present invention adopts a hierarchical power control method of multi-source collaboration of the park microgrid cluster, which can maximize the dynamic power balance of the park microgrid cluster and maximize the green electricity consumption through hierarchical power control.

[0172] This invention is applicable to park microgrid cluster projects and integrated source-grid-load-storage projects. Based on the power of the microgrid cluster's grid connection point and the equivalent microgrid cluster's shortfall load, the entire microgrid cluster is packaged into a system for unified control, enabling rapid response to load changes and maintaining dynamic power balance. Multi-source collaborative hierarchical power control within the microgrid cluster can maximize the power generation capacity of renewable energy within the microgrid while meeting rapid load changes, maximizing the utilization of renewable energy power generation and reducing park carbon emissions, thereby ensuring that the park microgrid cluster meets the requirements of safety, efficiency, and economy at the same time.

[0173] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

[0174] It should be understood that the size of the serial numbers of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Claims

1. A hierarchical power control system for multi-source collaboration in a campus microgrid cluster, characterized by: include: A microgrid cluster energy management system for managing a microgrid cluster; The microgrid cluster energy management system includes a plurality of sub-microgrid energy management systems for managing sub-microgrids; The microgrid cluster is a star network topology structure including multiple levels, and the microgrid cluster includes multiple sub-microgrids, each of which includes: a photovoltaic cluster, a wind turbine cluster, an energy storage cluster, and a flexible load cluster; wherein each photovoltaic cluster includes multiple photovoltaic power stations, each wind turbine cluster includes multiple wind power stations, the energy storage cluster includes multiple energy storage power stations, and each flexible load cluster includes multiple load devices; The sub-microgrid energy management system obtains the operating data of each sub-microgrid and transmits it to the microgrid cluster energy management system; the microgrid cluster energy management system formulates a partition power allocation strategy based on the operating data of each sub-microgrid; and the sub-microgrid energy management system allocates power to each level of its corresponding sub-microgrid based on the partition power allocation strategy.

2. The hierarchical power control system for multi-source collaboration of a campus microgrid cluster according to claim 1 is characterized in that: The microgrid cluster hierarchy includes: First level: microgrid cluster layer; Second level: multiple sub-microgrids; The third level: photovoltaic clusters, wind turbine clusters, energy storage clusters, and flexible load clusters of each sub-microgrid; The fourth level: photovoltaic power stations of photovoltaic clusters, wind power stations of wind turbine clusters, energy storage power stations of energy storage clusters, and load equipment of flexible load clusters.

3. A hierarchical power control method for multi-source collaboration of a campus microgrid cluster, using the hierarchical power control system for multi-source collaboration of a campus microgrid cluster as described in any one of claims 1-2, characterized in that: The method comprises the following steps: Obtain the active power of the microgrid cluster and the grid connection point as the microgrid cluster power target value; Based on the real-time power of each sub-microgrid connection point and the load power of each load device in each sub-microgrid, the required load of each sub-microgrid is obtained; Based on the real-time power generation power, maximum increaseable power, and maximum decreaseable power of each power station in each sub-microgrid, the real-time power information of each sub-microgrid is obtained; the real-time power information of the sub-microgrid includes the real-time power generation power, maximum increaseable power, and maximum decreaseable power of the sub-microgrid; The microgrid cluster energy management system calculates the remaining increaseable power and the remaining decreaseable power of each sub-microgrid, as well as the remaining increaseable power of the microgrid cluster, based on the real-time power information of each sub-microgrid and the average SOC of the energy storage cluster. The microgrid cluster energy management system formulates a partitioned power allocation strategy with each sub-microgrid as a sub-region based on the microgrid cluster power target value and the remaining increaseable power and the remaining decreaseable power of each sub-microgrid; Based on the partitioned power allocation strategy, hierarchical power allocation is performed on each sub-microgrid.

4. The hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to claim 3 is characterized by: Read telemetry data through secondary equipment to obtain various power data and load data in the microgrid cluster.

5. The hierarchical power control method for multi-source coordination of a campus microgrid cluster according to claim 3 is characterized by: The load required by the sub-microgrid is the sum of the real-time power of the sub-microgrid connection point and the load power of each load device in the sub-microgrid; The real-time power generation of a sub-microgrid is the sum of the real-time power generation of each cluster in the sub-microgrid; The maximum increaseable power of a sub-microgrid is the sum of the maximum increaseable power of each cluster in the sub-microgrid; The maximum power that can be reduced in a sub-microgrid is the sum of the maximum power that can be reduced in each cluster in the sub-microgrid; The average SOC of the energy storage cluster is calculated using the following formula: Among them, SOC n is the average SOC of the energy storage cluster of the nth sub-microgrid; is the maximum increaseable power of the n2-th energy storage power station in the n-th sub-microgrid; is the SOC of the n2th energy storage power station in the nth sub-microgrid; n2 is the number of energy storage power stations in the energy storage cluster; The remaining increaseable power of the sub-microgrid is the difference between the maximum increaseable power of the sub-microgrid and the real-time power generation power of the sub-microgrid.

6. The hierarchical power control method for multi-source collaboration of a campus microgrid cluster according to claim 3 is characterized in that: The following rules should be followed when formulating a partition power allocation strategy: The power allocation priorities from high to low are: meeting the overall load demand and maintaining the dynamic balance of the microgrid cluster power as the goal; maximizing the system's green power consumption as the desired goal; and minimizing energy transfer between sub-microgrids and reducing energy loss while achieving the power target.

7. The hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to claim 6 is characterized in that: Based on the partition power allocation strategy, hierarchical power allocation is performed on each sub-microgrid, including: under the condition of meeting the safety constraints, The microgrid cluster energy management system distributes the microgrid cluster power target value to each target sub-microgrid of the second level as the sub-microgrid power target value; The target sub-microgrid allocates its corresponding sub-microgrid power target value to its subordinate third-level photovoltaic cluster, wind turbine cluster, and energy storage cluster in the order of wind and solar power first, energy storage second, and flexible load last, as the power target value of each cluster; Each cluster distributes its corresponding cluster power target value to each subordinate power station at the fourth level as the power target value of each power station.

8. The hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to claim 7 is characterized in that: The microgrid cluster energy management system distributes the microgrid cluster power target value to each target sub-microgrid of the second level as the sub-microgrid power target value, including: When the remaining increaseable power P of the microgrid cluster max Greater than 0 and the microgrid cluster power target value P set When it is greater than 0, the sub-microgrids whose remaining increaseable power is greater than 0 are obtained as target sub-microgrids, and the sub-microgrid power target value is allocated to each target sub-microgrid according to the following formula: in, is the target power value of the nth microgrid; is the remaining increaseable power of the nth microgrid.

9. The hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to claim 7 is characterized in that: The corresponding sub-microgrid power target value is allocated to the third-level photovoltaic cluster, wind turbine cluster, and energy storage cluster under it as the power target value of each cluster, including: If the sub-microgrid power target value is greater than 0 and the remaining increaseable power of the sub-microgrid is greater than 0, determine whether the maximum increaseable power of the photovoltaic cluster is greater than the real-time power of the photovoltaic cluster; if so, use the difference between the maximum increaseable power of the photovoltaic cluster and the real-time power of the photovoltaic cluster as the photovoltaic cluster power target value; If the sub-microgrid power target value is greater than the photovoltaic cluster power target value, and the wind power cluster's maximum increaseable power is greater than the wind power cluster's real-time power, the difference between the wind power cluster's maximum increaseable power and the wind power cluster's real-time power is used as the wind power cluster's power target value; If the sub-microgrid power target value is greater than the sum of the photovoltaic cluster power target value and the wind power cluster power target value, the energy storage cluster SOC average value meets the SOC safety threshold, and the energy storage cluster's maximum increaseable power is greater than the energy storage cluster's real-time power, the difference between the energy storage cluster's maximum increaseable power and the energy storage cluster's real-time power is used as the energy storage cluster's power target value.

10. The hierarchical power control method for multi-source collaboration in a campus microgrid cluster according to claim 7, characterized in that: Each cluster distributes its corresponding cluster power target value to each power station in the fourth level under it as the power target value of each power station, including: The target cluster selects the power stations with the maximum increaseable power greater than 0 among its subordinate power stations as target power stations and assigns power target values to the target power stations using the following formula: in, is the power target value of the n1th PV power station in the nth microgrid, P nPVset is the target power value of the photovoltaic cluster of the nth microgrid, is the maximum increaseable power of the n1th PV power station in the nth microgrid, is the real-time power of the n1th photovoltaic power station in the nth microgrid, P nPVmax is the maximum power that can be increased by the photovoltaic cluster of the nth microgrid, P nPV is the real-time power of the PV cluster of the nth sub-microgrid, and n1 is the number of PV power stations in the PV cluster; is the power target value of the n3th wind power station in the nth microgrid, P nPWset is the wind power cluster power target value of the nth microgrid, is the maximum increaseable power of the n3-th wind power station in the n-th microgrid, is the real-time power of the n3th wind power station in the nth microgrid, P nPWmax is the maximum increaseable power of the wind power cluster of the nth microgrid, P nPV is the real-time power of the wind power cluster of the nth sub-microgrid, and n3 is the number of wind power stations in the wind power cluster; is the power target value of the n2th energy storage power station in the nth microgrid, P nPCSset is the target power value of the energy storage cluster of the nth microgrid, is the maximum increaseable power of the n2-th energy storage power station in the n-th microgrid, is the real-time power of the n2th energy storage power station in the nth microgrid, P nPCSmax is the maximum increaseable power of the energy storage cluster of the nth microgrid, P nPCS is the real-time power of the energy storage cluster of the nth sub-microgrid, and n2 is the number of energy storage power stations in the energy storage cluster.

Images